diff --git a/README.md b/README.md index 1c2fcef..c4b18e3 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ ### About -Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) and [Qwen Image](https://github.com/QwenLM/Qwen-Image) models locally on your Mac! +Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux), [Qwen Image](https://github.com/QwenLM/Qwen-Image) and [FIBO](https://huggingface.co/briaai/FIBO) models locally on your Mac! ### Table of contents @@ -23,6 +23,7 @@ Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) and [Qwen Image](h - [🦙 Qwen Models](#-qwen-models) * [🖼️ Qwen Image](#%EF%B8%8F-qwen-image) * [✏️ Qwen Image Edit](#%EF%B8%8F-qwen-image-edit) +- [🌀 FIBO](#-fibo) - [🔌 LoRA](#-lora) - [🎭 In-Context Generation](#-in-context-generation) * [📸 Kontext](#-kontext) @@ -50,7 +51,7 @@ Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) and [Qwen Image](h ### Philosophy -MFLUX is a line-by-line port of the FLUX and Qwen implementations in the [Huggingface Diffusers](https://github.com/huggingface/diffusers) and [Huggingface Transformers](https://github.com/huggingface/transformers) libraries to [Apple MLX](https://github.com/ml-explore/mlx). +MFLUX is a line-by-line port of the FLUX, Qwen and Bria models implementations in the [Huggingface Diffusers](https://github.com/huggingface/diffusers) and [Huggingface Transformers](https://github.com/huggingface/transformers) libraries to [Apple MLX](https://github.com/ml-explore/mlx). MFLUX is purposefully kept minimal and explicit - Network architectures are hardcoded and no config files are used except for the tokenizers. The aim is to have a tiny codebase with the single purpose of expressing these models (thereby avoiding too many abstractions). While MFLUX priorities readability over generality and performance, [it can still be quite fast](#%EF%B8%8F-image-generation-speed-updated), [and even faster quantized](#%EF%B8%8F-quantization). @@ -1087,6 +1088,344 @@ mflux-generate-qwen-edit \ --- +### 🌀 FIBO + +MFLUX supports [FIBO](https://huggingface.co/briaai/FIBO) from [Bria.ai](https://bria.ai), the first open-source JSON-native text-to-image model trained on long structured captions. FIBO delivers high image quality, strong prompt adherence, and professional-grade control—trained exclusively on licensed data. ([Technical Paper](https://arxiv.org/abs/2511.06876)) + +![FIBO Example](src/mflux/assets/fibo_example.jpg) + +FIBO is an 8B-parameter DiT-based, flow-matching model using **SmolLM3-3B** as the text encoder with a novel **DimFusion** conditioning architecture for efficient long-caption training, and **Wan 2.2** as the VAE. The VLM-assisted prompting uses a fine-tuned **Qwen3-VL** to expand short user intents, fill in missing details, and extract/edit structured prompts from images. + +Most text-to-image models excel at imagination—but not control. FIBO is trained on structured JSON captions up to 1,000+ words, enabling precise, reproducible control over lighting, composition, color, and camera settings. The structured captions foster native disentanglement, allowing targeted, iterative refinement without prompt drift. + + +#### Key Features + +- **VLM-guided JSON-native prompting**: Transform short prompts into structured schemas with 1,000+ words (lighting, camera, composition, DoF) +- **Disentangled control**: Tweak a single attribute (e.g., camera angle) without breaking the scene +- **Strong prompt adherence**: High alignment on PRISM-style evaluations +- **Enterprise-grade**: 100% licensed data with governance, repeatability, and legal clarity + +#### The three modes: ✨ Generate, 🔧 Refine, and 💡 Inspire + +**✨ Generate**: While the actual prompt input to FIBO is a structured JSON file, the generate command provides an interface to input pure text prompts. These are then expanded into structured JSON prompts using FIBO's Vision-Language Model (VLM) before being passed to the diffusion model for image generation. For example, the following prompt produces one of the images above: + +```sh +mflux-generate-fibo \ + --prompt "Three cartoon animal chefs in a colorful bakery kitchen, Pixar style: a bunny with floppy ears wearing a tall white chef hat and pink apron holding a chocolate cake on the left, a raccoon with a striped tail wearing blue oven mitts and a yellow bandana frosting cupcakes in the center, a penguin wearing a red bowtie and checkered apron carrying a tray of golden croissants on the right, warm kitchen lighting with flour dust in air" \ + --width 1200 \ + --height 540 \ + --steps 20 \ + --guidance 4.0 \ + --seed 42 \ + --output animal_bakers.png +``` + +This command will output both the generated image (`animal_bakers.png`) and a JSON prompt file (`animal_bakers.json`) containing the expanded structured prompt used for generation. +When the input prompt is pure text, it will be processed through FIBO's VLM to create the structured JSON prompt automatically. +Conversely, if a JSON prompt file is provided, it will be used directly for image generation, thus bypassing the VLM step and giving you full control over the prompt structure. +Another way to call the model directly is to provide a JSON prompt file as input: + +```sh +mflux-generate-fibo \ + --prompt-file animal_bakers.json \ + --width 1200 \ + --height 540 \ + --steps 20 \ + --guidance 4.0 \ + --seed 42 \ + --output animal_bakers.png +``` + +A point worth emphasizing is that when working with a JSON prompt file, the user can use whatever tool they prefer to edit it and is not forced to use the built in FIBO-VLM. Other good alternatives are [coding](https://cursor.com/agents) [agents](https://www.claude.com/product/claude-code), other [LLMs](https://github.com/ml-explore/mlx-lm)/[VLMs](https://github.com/Blaizzy/mlx-vlm) etc. + +
+Click to expand the JSON prompt file used (animal_bakers.json) + +```json +{ + "short_description": "Three cartoon animal chefs are in a bakery kitchen, each holding a culinary creation. A bunny chef on the left presents a chocolate cake, a raccoon chef in the center is frosting cupcakes, and a penguin chef on the right carries a tray of croissants. The kitchen is brightly lit with warm tones, and flour dusts the air, creating a lively and cheerful baking atmosphere.", + "objects": [ + { + "description": "A cartoon bunny wearing a white chef's hat and a pink apron, holding a chocolate cake with white frosting and cherries.", + "location": "left foreground", + "relationship": "The bunny chef is presenting the chocolate cake.", + "relative_size": "medium", + "shape_and_color": "Rounded bunny shape, white hat, pink apron, brown cake, white frosting, red cherries.", + "texture": "smooth", + "appearance_details": "Floppy ears, rosy cheeks, smiling expression.", + "pose": "Standing upright, holding the cake with both hands.", + "expression": "Joyful and proud.", + "clothing": "White chef's hat, pink apron.", + "action": "Holding and presenting a cake.", + "gender": "female", + "skin_tone_and_texture": "White fur, smooth texture.", + "orientation": "Upright, facing forward." + }, + { + "description": "A cartoon raccoon wearing blue oven mitts and a yellow bandana, actively frosting cupcakes with pink and yellow frosting.", + "location": "center midground", + "relationship": "The raccoon chef is in the process of frosting cupcakes.", + "relative_size": "medium", + "shape_and_color": "Distinct raccoon shape, blue mitts, yellow bandana, pink and yellow frosting, brown cupcakes.", + "texture": "smooth", + "appearance_details": "Striped tail, bushy fur, focused expression.", + "pose": "Leaning forward slightly, hands busy frosting.", + "expression": "Concentrated and happy.", + "clothing": "Blue oven mitts, yellow bandana.", + "action": "Frosting cupcakes.", + "gender": "male", + "skin_tone_and_texture": "Brown and black fur, smooth texture.", + "orientation": "Upright, slightly angled." + }, + { + "description": "A cartoon penguin wearing a red bowtie and a checkered apron, carrying a tray of golden croissants.", + "location": "right foreground", + "relationship": "The penguin chef is carrying a tray of freshly baked croissants.", + "relative_size": "medium", + "shape_and_color": "Classic penguin shape, red bowtie, red and white apron, golden croissants.", + "texture": "smooth", + "appearance_details": "Black and white body, orange beak and feet, smiling expression.", + "pose": "Standing upright, holding the tray with both hands.", + "expression": "Cheerful and friendly.", + "clothing": "Red bowtie, checkered apron.", + "action": "Carrying a tray of croissants.", + "gender": "male", + "skin_tone_and_texture": "Feathered texture, smooth appearance.", + "orientation": "Upright, facing forward." + }, + { + "description": "A chocolate cake with white frosting and two red cherries on top.", + "location": "left foreground", + "relationship": "Held by the bunny chef.", + "relative_size": "medium", + "shape_and_color": "Round cake, dark brown, white frosting, red cherries.", + "texture": "smooth frosting, slightly textured cake", + "appearance_details": "Decorated with cherries.", + "number_of_objects": 1, + "orientation": "Horizontal" + }, + { + "description": "A tray filled with golden-brown croissants.", + "location": "right foreground", + "relationship": "Carried by the penguin chef.", + "relative_size": "medium", + "shape_and_color": "Elongated, crescent-shaped croissants, golden brown.", + "texture": "flaky, slightly crisp exterior", + "appearance_details": "Arranged neatly on a metal tray.", + "number_of_objects": 1, + "orientation": "Horizontal" + } + ], + "background_setting": "A brightly lit bakery kitchen with wooden countertops, shelves stocked with baking ingredients and equipment, and a window in the background. Flour is lightly dusted in the air.", + "lighting": { + "conditions": "warm indoor lighting", + "direction": "front-lit and side-lit", + "shadows": "soft, diffused shadows" + }, + "aesthetics": { + "composition": "centered composition with the three chefs forming a horizontal line", + "color_scheme": "warm and cheerful, with dominant browns, yellows, pinks, and whites", + "mood_atmosphere": "joyful, friendly, and inviting", + "aesthetic_score": "very high", + "preference_score": "very high" + }, + "photographic_characteristics": { + "depth_of_field": "shallow", + "focus": "sharp focus on the chefs and their creations", + "camera_angle": "eye-level", + "lens_focal_length": "standard lens" + }, + "style_medium": "digital illustration", + "text_render": [], + "context": "This image is a charming illustration, likely for a children's book, educational material, or a bakery-themed advertisement, designed to evoke feelings of happiness and the joy of baking.", + "artistic_style": "Pixar-style animation" +} +``` +Note: This JSON prompt was generated using FIBO's "Generate" mode from a short text description. Note the strong alignment between the JSON prompt and the image! +
+ +**🔧 Refine**: While the JSON prompt can be edited manually, it can be quite complex and inconvenient to modify directly. The refinement mode helps to solve this issue by also expanding a simple user instruction in order to tweak specific attributes. The VLM processes these instructions and updates the JSON prompt accordingly before generating new images. + +![FIBO Refine Example](src/mflux/assets/fibo_refine_example.jpg) + + +Assuming we already have a previous prompt file, like `owl_brown.json`, we can refine this prompt to change the owl's color and add some accessories: + +```sh +mflux-refine-fibo \ + --prompt-file owl_brown.json \ + --instructions "Make the owl white instead of brown, and add round glasses and a black scarf. Keep everything else exactly the same - the same forest background, moonlight lighting, composition, and overall whimsical atmosphere." \ + --output owl_white.json +``` + +
+Click to expand the refined JSON prompt file (owl_white.json) + +```json +{ + "short_description": "A hyper-detailed, ultra-fluffy owl sitting in the trees at night, looking directly at the camera with wide, adorable, expressive eyes. Its feathers are soft and voluminous, catching the cool moonlight with subtle silver highlights. The owl's gaze is curious and full of charm, giving it a whimsical, storybook-like personality. It is wearing round glasses and a black scarf.", + "objects": [ + { + "description": "An adorable, fluffy owl with large, expressive eyes and soft, voluminous feathers. Its plumage is white, with subtle silver highlights from the moonlight. It is wearing round glasses and a black scarf.", + "location": "center", + "relationship": "The owl is the sole subject, perched comfortably within its environment.", + "relative_size": "large within frame", + "shape_and_color": "Round head, large eyes, bulky body, predominantly white with silver accents.", + "texture": "Extremely soft, fluffy, and detailed feathers, giving a plush toy-like appearance.", + "appearance_details": "The eyes are wide, dark, and reflective, conveying a sense of wonder and curiosity. The beak is small and light-colored, almost hidden by the feathers. Subtle silver highlights catch the moonlight on its feathers. It has round glasses on its nose and a black scarf around its neck.", + "orientation": "upright, facing forward" + } + ], + "background_setting": "A dark, nocturnal forest setting with blurred trees and foliage, illuminated by a soft, cool moonlight. The background is out of focus, emphasizing the owl.", + "lighting": { + "conditions": "moonlight", + "direction": "backlit and side-lit from the left", + "shadows": "soft, diffused shadows on the right side of the owl and within the background foliage, indicating a single light source." + }, + "aesthetics": { + "composition": "centered, portrait composition", + "color_scheme": "cool blues and silvers from the moonlight contrasting with white of the owl and forest.", + "mood_atmosphere": "mysterious, enchanting, whimsical, and serene.", + "aesthetic_score": "very high", + "preference_score": "very high" + }, + "photographic_characteristics": { + "depth_of_field": "shallow", + "focus": "sharp focus on the owl's face and eyes, with a soft blur in the background.", + "camera_angle": "eye-level", + "lens_focal_length": "portrait lens (e.g., 50mm-85mm)" + }, + "style_medium": "digital illustration", + "text_render": [], + "context": "A whimsical character illustration, possibly for a children's book, animated film, or fantasy art collection.", + "artistic_style": "fantasy, illustrative, detailed" +} +``` +Note: This JSON prompt was refined from the original `owl_brown.json` by changing the owl's color to white and adding round glasses and a black scarf, while preserving the forest background, moonlight lighting, and whimsical atmosphere. +
+ +Finally, generate the refined white owl image using the updated JSON prompt: + +```sh +mflux-generate-fibo \ + --prompt-file owl_white.json \ + --width 1024 \ + --height 560 \ + --steps 20 \ + --guidance 4.0 \ + --seed 42 \ + --quantize 4 \ + --output owl_white.png +``` + +The refine command reads the existing JSON prompt, applies the refinement instructions to change the owl's color and add accessories, and outputs a new refined JSON file. This refined prompt is then used to generate a new image that reflects the requested changes while maintaining the overall scene and composition. + +It is worth noting that refine does not work the same way as other editing techniques like Flux Kontext or Qwen Image Edit. Instead of modifying an existing image, it modifies the underlying **structured prompt** to produce a new image that reflects the requested changes while maintaining the overall scene and composition. + +**💡 Inspire**: Provide an image instead of text. FIBO's vision-language model extracts a detailed, structured prompt, blends it with your creative intent, and produces related images—ideal for inspiration without overreliance on the original. + +![FIBO Inspire Example](src/mflux/assets/fibo_inspire_example.jpg) + +Starting from an image, you can extract a structured JSON prompt that captures its visual characteristics. For example, using a [blue and brown bird on brown tree trunk](https://unsplash.com/photos/blue-and-brown-bird-on-brown-tree-trunk-DPXytK8Z59Y), we can extract a detailed prompt: + +```sh +mflux-inspire-fibo \ + --image-path bird.jpg \ + --prompt "blue and brown bird on brown tree trunk" \ + --output bird_inspired.json \ + --seed 42 +``` + +This command analyzes the image and generates a structured JSON prompt file (`bird_inspired.json`) that describes the visual elements, composition, lighting, and style. You can then use this JSON prompt to generate new images with similar characteristics: + +```sh +mflux-generate-fibo \ + --prompt-file bird_inspired.json \ + --width 1024 \ + --height 672 \ + --steps 20 \ + --guidance 4.0 \ + --seed 42 \ + -q 8 \ + --output bird_inspired.png +``` + + +
+Click to expand the JSON prompt file used (bird_inspired.json) + +```json +{ + "short_description": "A vibrant blue and brown kingfisher is perched on a weathered tree trunk, facing left. The bird's iridescent plumage is detailed, with striking orange and white markings on its chest and throat. Its long, sharp black beak is prominent. The background is a soft, out-of-focus gradient of warm yellow and green, creating a natural and serene environment. The lighting highlights the textures of the bird's feathers and the rough bark of the trunk.", + "objects": [ + { + "description": "A male kingfisher with striking iridescent blue plumage on its back and wings, a rich orange and white chest, and a black beak. It has a small, dark eye and a red-orange patch on its face.", + "location": "center", + "relationship": "perched on the tree trunk", + "relative_size": "medium within frame", + "shape_and_color": "Bird shape, predominantly blue, orange, and white.", + "texture": "Feathers appear smooth and slightly glossy.", + "appearance_details": "The beak is long, thin, and black. The orange patch on its face is distinct.", + "number_of_objects": 1, + "pose": "Standing upright on its legs, head turned to the left.", + "expression": "Alert and focused.", + "action": "Perched, observing its surroundings.", + "gender": "male", + "orientation": "Facing left, upright" + }, + { + "description": "A section of a weathered, rough tree trunk, providing a perch for the kingfisher. It has a natural, organic shape with visible bark texture.", + "location": "bottom-center foreground", + "relationship": "supports the kingfisher", + "relative_size": "medium", + "shape_and_color": "Irregular cylindrical shape, brown and grey tones.", + "texture": "Rough, gnarled bark texture.", + "appearance_details": "Some smaller branches or knots are visible on the trunk.", + "number_of_objects": 1, + "orientation": "Horizontal, lying on its side" + } + ], + "background_setting": "A soft, blurred background with a warm gradient transitioning from a light yellow at the top to a muted green at the bottom. This creates a natural, out-of-focus environment, likely foliage or sky.", + "lighting": { + "conditions": "natural daylight", + "direction": "side-lit from the right", + "shadows": "soft shadows, particularly on the left side of the bird and the trunk" + }, + "aesthetics": { + "composition": "rule of thirds, with the bird positioned slightly off-center", + "color_scheme": "vibrant blues and oranges contrasting with the soft yellow and green background", + "mood_atmosphere": "serene, natural, captivating", + "aesthetic_score": "very high", + "preference_score": "very high" + }, + "photographic_characteristics": { + "depth_of_field": "shallow, with a strong bokeh effect in the background", + "focus": "sharp focus on the kingfisher", + "camera_angle": "eye-level", + "lens_focal_length": "telephoto lens" + }, + "style_medium": "photograph", + "text_render": [], + "context": "This image is a wildlife photograph, likely intended for nature magazines, educational materials, or as a decorative print for nature enthusiasts.", + "artistic_style": "photorealistic, detailed" +} +``` +Note: This JSON prompt was extracted from the input image using FIBO's VLM, capturing the visual characteristics of a kingfisher bird perched on a tree trunk with a bokeh background. +
+ +The inspire command is particularly useful when you want to: +- Extract the visual style and composition from a reference image +- Create variations of an existing image while maintaining its core characteristics +- Understand how FIBO interprets and structures visual information +- Blend your creative intent (via the optional `--prompt` parameter) with the visual content of the image + +Note: The optional `--prompt` parameter allows you to guide the VLM's interpretation of the image. For example, you might use `--prompt "futuristic cityscape"` to influence how the image is analyzed and structured. + +⚠️ *Note: FIBO requires downloading the `briaai/FIBO` model weights (~24GB) and the `briaai/FIBO-vlm` vision-language model (~8GB), totaling ~32GB for the full model, or use quantization for smaller sizes.* + +--- + ### 🔌 LoRA MFLUX support loading trained [LoRA](https://huggingface.co/docs/diffusers/en/training/lora) adapters (actual training support is coming). diff --git a/pyproject.toml b/pyproject.toml index 085c60f..899b033 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,7 @@ source-exclude = [ [project] name = "mflux" -version = "0.11.1" +version = "0.12.0.dev0" description = "A MLX port of FLUX based on the Huggingface Diffusers implementation." readme = "README.md" keywords = ["diffusers", "flux", "mlx"] @@ -50,7 +50,7 @@ dependencies = [ "torch>=2.3.1,<3.0; python_version<'3.13'", "torch>=2.8.0,<3.0; python_version>='3.13'", "tqdm>=4.66.5,<5.0", - "transformers>=4.55.0,<5.0", + "transformers>=4.57,<5.0", "twine>=6.1.0,<7.0", ] classifiers = [ @@ -86,6 +86,9 @@ mflux-generate-redux = "mflux.generate_redux:main" mflux-generate-kontext = "mflux.generate_kontext:main" mflux-generate-qwen = "mflux.generate_qwen:main" mflux-generate-qwen-edit = "mflux.generate_qwen_edit:main" +mflux-generate-fibo = "mflux.generate_fibo:main" +mflux-refine-fibo = "mflux.refine_fibo:main" +mflux-inspire-fibo = "mflux.inspire_fibo:main" mflux-concept = "mflux.concept:main" mflux-concept-from-image = "mflux.concept_from_image:main" mflux-save = "mflux.save:main" diff --git a/src/mflux/__init__.py b/src/mflux/__init__.py new file mode 100644 index 0000000..c72ee76 --- /dev/null +++ b/src/mflux/__init__.py @@ -0,0 +1,6 @@ +import os + +# Set TOKENIZERS_PARALLELISM to avoid fork warning +# This must be set before any tokenizers are imported/used +if "TOKENIZERS_PARALLELISM" not in os.environ: + os.environ["TOKENIZERS_PARALLELISM"] = "false" diff --git a/src/mflux/assets/fibo_example.jpg b/src/mflux/assets/fibo_example.jpg new file mode 100644 index 0000000..30453fc Binary files /dev/null and b/src/mflux/assets/fibo_example.jpg differ diff --git a/src/mflux/assets/fibo_inspire_example.jpg b/src/mflux/assets/fibo_inspire_example.jpg new file mode 100644 index 0000000..6fa642f Binary files /dev/null and b/src/mflux/assets/fibo_inspire_example.jpg differ diff --git a/src/mflux/assets/fibo_refine_example.jpg b/src/mflux/assets/fibo_refine_example.jpg new file mode 100644 index 0000000..cce1d02 Binary files /dev/null and b/src/mflux/assets/fibo_refine_example.jpg differ diff --git a/src/mflux/callbacks/instances/battery_saver.py b/src/mflux/callbacks/instances/battery_saver.py index 267ccfc..c06d26c 100644 --- a/src/mflux/callbacks/instances/battery_saver.py +++ b/src/mflux/callbacks/instances/battery_saver.py @@ -4,7 +4,7 @@ import re import subprocess from mflux.callbacks.callback import BeforeLoopCallback -from mflux.error.exceptions import StopImageGenerationException +from mflux.utils.exceptions import StopImageGenerationException PMSET_AC_POWER_STATUS = "Now drawing from 'AC Power'" PMSET_BATT_STATUS_PATTERN = r"InternalBattery-.+?(\d+)%" diff --git a/src/mflux/callbacks/instances/canny_saver.py b/src/mflux/callbacks/instances/canny_saver.py index 5b9e364..2f2c232 100644 --- a/src/mflux/callbacks/instances/canny_saver.py +++ b/src/mflux/callbacks/instances/canny_saver.py @@ -6,7 +6,7 @@ import PIL.Image from mflux.callbacks.callback import BeforeLoopCallback from mflux.config.runtime_config import RuntimeConfig -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.image_util import ImageUtil class CannyImageSaver(BeforeLoopCallback): diff --git a/src/mflux/callbacks/instances/depth_saver.py b/src/mflux/callbacks/instances/depth_saver.py index e07bd9d..6d1f938 100644 --- a/src/mflux/callbacks/instances/depth_saver.py +++ b/src/mflux/callbacks/instances/depth_saver.py @@ -6,7 +6,7 @@ import PIL.Image from mflux.callbacks.callback import BeforeLoopCallback from mflux.config.runtime_config import RuntimeConfig -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.image_util import ImageUtil class DepthImageSaver(BeforeLoopCallback): diff --git a/src/mflux/callbacks/instances/stepwise_handler.py b/src/mflux/callbacks/instances/stepwise_handler.py index 8a6902e..fd14cc9 100644 --- a/src/mflux/callbacks/instances/stepwise_handler.py +++ b/src/mflux/callbacks/instances/stepwise_handler.py @@ -6,8 +6,8 @@ import tqdm from mflux.callbacks.callback import BeforeLoopCallback, InLoopCallback, InterruptCallback from mflux.config.runtime_config import RuntimeConfig -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.image_util import ImageUtil class StepwiseHandler(BeforeLoopCallback, InLoopCallback, InterruptCallback): diff --git a/src/mflux/concept.py b/src/mflux/concept.py index 360acee..55c4863 100644 --- a/src/mflux/concept.py +++ b/src/mflux/concept.py @@ -1,11 +1,11 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.concept_attention.flux_concept import Flux1Concept from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -41,7 +41,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), concept=args.concept, heatmap_timesteps=args.heatmap_timesteps, heatmap_layer_indices=args.heatmap_layer_indices, diff --git a/src/mflux/concept_from_image.py b/src/mflux/concept_from_image.py index fa2699b..c45ec1f 100644 --- a/src/mflux/concept_from_image.py +++ b/src/mflux/concept_from_image.py @@ -1,11 +1,11 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.concept_attention.flux_concept_from_image import Flux1ConceptFromImage from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -41,7 +41,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), concept=args.concept, image_path=str(args.input_image_path), heatmap_timesteps=args.heatmap_timesteps, diff --git a/src/mflux/config/model_config.py b/src/mflux/config/model_config.py index 1d9e51d..c51d316 100644 --- a/src/mflux/config/model_config.py +++ b/src/mflux/config/model_config.py @@ -1,7 +1,7 @@ from functools import lru_cache from typing import Literal -from mflux.error.error import InvalidBaseModel, ModelConfigError +from mflux.utils.exceptions import InvalidBaseModel, ModelConfigError class ModelConfig: @@ -94,6 +94,11 @@ class ModelConfig: def qwen_image_edit() -> "ModelConfig": return AVAILABLE_MODELS["qwen-image-edit"] + @staticmethod + @lru_cache + def fibo() -> "ModelConfig": + return AVAILABLE_MODELS["fibo"] + def x_embedder_input_dim(self) -> int: if "Fill" in self.model_name: return 384 @@ -319,4 +324,16 @@ AVAILABLE_MODELS = { requires_sigma_shift=None, priority=12, ), + "fibo": ModelConfig( + aliases=["fibo"], + model_name="briaai/FIBO", + base_model=None, + controlnet_model=None, + custom_transformer_model=None, + num_train_steps=1000, + max_sequence_length=512, + supports_guidance=True, + requires_sigma_shift=False, + priority=13, + ), } diff --git a/src/mflux/config/runtime_config.py b/src/mflux/config/runtime_config.py index 6fa4e62..7d99a5b 100644 --- a/src/mflux/config/runtime_config.py +++ b/src/mflux/config/runtime_config.py @@ -5,8 +5,8 @@ import mlx.core as mx from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.schedulers import SCHEDULER_REGISTRY, try_import_external_scheduler -from mflux.schedulers.linear_scheduler import LinearScheduler +from mflux.models.common.schedulers import SCHEDULER_REGISTRY, try_import_external_scheduler +from mflux.models.common.schedulers.linear_scheduler import LinearScheduler logger = logging.getLogger(__name__) diff --git a/src/mflux/error/error.py b/src/mflux/error/error.py deleted file mode 100644 index 886a2b5..0000000 --- a/src/mflux/error/error.py +++ /dev/null @@ -1,6 +0,0 @@ -class ModelConfigError(ValueError): - """User error in model config.""" - - -class InvalidBaseModel(ModelConfigError): - """Invalid base model, cannot infer model properties.""" diff --git a/src/mflux/generate.py b/src/mflux/generate.py index 944f009..820a10a 100644 --- a/src/mflux/generate.py +++ b/src/mflux/generate.py @@ -1,11 +1,11 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.txt2img.flux import Flux1 from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_negative_prompt, get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -40,8 +40,8 @@ def main(): # 3. Generate an image for each seed value image = model.generate_image( seed=seed, - prompt=get_effective_prompt(args), - negative_prompt=get_effective_negative_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), + negative_prompt=PromptUtils.get_effective_negative_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/generate_controlnet.py b/src/mflux/generate_controlnet.py index a2336f1..20148b5 100644 --- a/src/mflux/generate_controlnet.py +++ b/src/mflux/generate_controlnet.py @@ -1,11 +1,11 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.controlnet.flux_controlnet import Flux1Controlnet from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -40,7 +40,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), controlnet_image_path=args.controlnet_image_path, config=Config( num_inference_steps=args.steps, diff --git a/src/mflux/generate_depth.py b/src/mflux/generate_depth.py index c477ceb..bef1ac6 100644 --- a/src/mflux/generate_depth.py +++ b/src/mflux/generate_depth.py @@ -1,10 +1,10 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.depth.flux_depth import Flux1Depth from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -38,7 +38,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/generate_fibo.py b/src/mflux/generate_fibo.py new file mode 100644 index 0000000..ac1696b --- /dev/null +++ b/src/mflux/generate_fibo.py @@ -0,0 +1,87 @@ +import gc +import json + +import mlx.core as mx + +from mflux.callbacks.callback_manager import CallbackManager +from mflux.config.config import Config +from mflux.config.model_config import ModelConfig +from mflux.models.fibo.variants.txt2img.fibo import FIBO +from mflux.models.fibo_vlm.model.fibo_vlm import FiboVLM +from mflux.ui import defaults as ui_defaults +from mflux.ui.cli.parsers import CommandLineParser +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException + + +def main(): + # 0. Parse command line arguments + parser = CommandLineParser(description="Generate an image using FIBO model.") + parser.add_general_arguments() + parser.add_model_arguments(require_model_arg=False) + parser.add_lora_arguments() + parser.add_image_generator_arguments(supports_metadata_config=True) + parser.add_image_to_image_arguments(required=False) + parser.add_output_arguments() + args = parser.parse_args() + + # 0. Set default guidance value if not provided by user + if args.guidance is None: + args.guidance = ui_defaults.GUIDANCE_SCALE + + json_prompt = _get_json_prompt(args) + + # 1. Load the FIBO model + fibo = FIBO( + model_config=ModelConfig.fibo(), + quantize=args.quantize, + local_path=args.path, + ) + + # 2. Register callbacks + memory_saver = CallbackManager.register_callbacks(args=args, model=fibo) + + try: + for seed in args.seed: + # 3. Generate an image for each seed value + image = fibo.generate_image( + seed=seed, + prompt=json_prompt, + negative_prompt=PromptUtils.get_effective_negative_prompt(args), + config=Config( + num_inference_steps=args.steps, + height=args.height, + width=args.width, + guidance=args.guidance, + image_path=args.image_path, + image_strength=args.image_strength, + scheduler="flow_match_euler_discrete", + ), + ) + + # 4. Save the image + image.save(path=args.output.format(seed=seed), export_json_metadata=args.metadata) + except (StopImageGenerationException, PromptFileReadError) as exc: + print(exc) + finally: + if memory_saver: + print(memory_saver.memory_stats()) + + +def _get_json_prompt(args): + prompt = PromptUtils.get_effective_prompt(args) + + try: + json.loads(prompt) + json_prompt = prompt + except json.JSONDecodeError: + vlm = FiboVLM() + json_prompt = vlm.generate(prompt=prompt, seed=42) + del vlm + gc.collect() + mx.clear_cache() + return json_prompt + + +if __name__ == "__main__": + main() diff --git a/src/mflux/generate_fill.py b/src/mflux/generate_fill.py index 6fbc052..0e27ea9 100644 --- a/src/mflux/generate_fill.py +++ b/src/mflux/generate_fill.py @@ -1,10 +1,10 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.fill.flux_fill import Flux1Fill from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -38,7 +38,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/generate_in_context_catvton.py b/src/mflux/generate_in_context_catvton.py index 6f8f8a8..b80fd7b 100644 --- a/src/mflux/generate_in_context_catvton.py +++ b/src/mflux/generate_in_context_catvton.py @@ -3,11 +3,11 @@ from pathlib import Path from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.in_context.flux_in_context_fill import Flux1InContextFill from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -51,7 +51,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), left_image_path=args.garment_image, right_image_path=args.person_image, config=Config( diff --git a/src/mflux/generate_in_context_dev.py b/src/mflux/generate_in_context_dev.py index 6b4d8ed..42dcca6 100644 --- a/src/mflux/generate_in_context_dev.py +++ b/src/mflux/generate_in_context_dev.py @@ -3,12 +3,12 @@ from pathlib import Path from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.in_context.flux_in_context_dev import Flux1InContextDev from mflux.models.flux.variants.in_context.utils.in_context_loras import LORA_REPO_ID, get_lora_filename from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -49,7 +49,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/generate_in_context_edit.py b/src/mflux/generate_in_context_edit.py index 23c9391..cd1daa4 100644 --- a/src/mflux/generate_in_context_edit.py +++ b/src/mflux/generate_in_context_edit.py @@ -5,12 +5,12 @@ from PIL import Image from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.in_context.flux_in_context_fill import Flux1InContextFill from mflux.models.flux.variants.in_context.utils.in_context_loras import prepare_ic_edit_loras from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -82,7 +82,7 @@ def _get_effective_ic_edit_prompt(args): if hasattr(args, "instruction") and args.instruction: return f"A diptych with two side-by-side images of the same scene. On the right, the scene is exactly the same as on the left but {args.instruction}" else: - return get_effective_prompt(args) + return PromptUtils.get_effective_prompt(args) def _resize_for_ic_edit_optimal_width(args): diff --git a/src/mflux/generate_kontext.py b/src/mflux/generate_kontext.py index f56aaeb..6f89a29 100644 --- a/src/mflux/generate_kontext.py +++ b/src/mflux/generate_kontext.py @@ -2,11 +2,11 @@ from pathlib import Path from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.kontext.flux_kontext import Flux1Kontext from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -40,7 +40,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/generate_qwen.py b/src/mflux/generate_qwen.py index b2aff90..623e5f5 100644 --- a/src/mflux/generate_qwen.py +++ b/src/mflux/generate_qwen.py @@ -1,11 +1,11 @@ from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_negative_prompt, get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -40,8 +40,8 @@ def main(): # 3. Generate an image for each seed value image = qwen.generate_image( seed=seed, - prompt=get_effective_prompt(args), - negative_prompt=get_effective_negative_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), + negative_prompt=PromptUtils.get_effective_negative_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/generate_qwen_edit.py b/src/mflux/generate_qwen_edit.py index 1b72fbe..a4c22fd 100644 --- a/src/mflux/generate_qwen_edit.py +++ b/src/mflux/generate_qwen_edit.py @@ -2,11 +2,11 @@ from pathlib import Path from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.qwen.variants.edit.qwen_image_edit import QwenImageEdit from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_negative_prompt, get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -53,7 +53,7 @@ def main(): # 4. Generate an image for each seed value image = qwen.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, @@ -61,7 +61,7 @@ def main(): guidance=args.guidance, image_path=config_image_path, ), - negative_prompt=get_effective_negative_prompt(args), + negative_prompt=PromptUtils.get_effective_negative_prompt(args), image_paths=image_paths, ) diff --git a/src/mflux/generate_redux.py b/src/mflux/generate_redux.py index c1a322c..e198259 100644 --- a/src/mflux/generate_redux.py +++ b/src/mflux/generate_redux.py @@ -3,11 +3,11 @@ from pathlib import Path from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.redux.flux_redux import Flux1Redux from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -48,7 +48,7 @@ def main(): # 3. Generate an image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), config=Config( num_inference_steps=args.steps, height=args.height, diff --git a/src/mflux/info.py b/src/mflux/info.py index 591a291..c74cc4a 100644 --- a/src/mflux/info.py +++ b/src/mflux/info.py @@ -4,8 +4,8 @@ import sys from datetime import datetime from pathlib import Path -from mflux.post_processing.metadata_reader import MetadataReader from mflux.ui.cli.parsers import CommandLineParser +from mflux.utils.metadata_reader import MetadataReader def format_metadata(metadata: dict) -> str: @@ -121,4 +121,3 @@ def main(): if __name__ == "__main__": main() - diff --git a/src/mflux/inspire_fibo.py b/src/mflux/inspire_fibo.py new file mode 100644 index 0000000..a3ae008 --- /dev/null +++ b/src/mflux/inspire_fibo.py @@ -0,0 +1,68 @@ +import json +from pathlib import Path + +from PIL import Image + +from mflux.models.fibo_vlm.model.fibo_vlm import FiboVLM +from mflux.ui.cli.parsers import CommandLineParser +from mflux.utils.exceptions import PromptFileReadError + + +def main(): + # 0. Parse command line arguments + parser = CommandLineParser(description="Generate FIBO JSON prompts from images using VLM.") + # fmt: off + parser.add_argument("--image-path", type=Path, required=True, help="Path to image file to inspire from") + parser.add_argument("--prompt", type=str, default=None, help="Optional text prompt to blend with the image (e.g., 'Make futuristic')") + parser.add_argument("--output", type=Path, default=Path("inspired.json"), help="Output path for generated JSON prompt (default: inspired.json)") + parser.add_argument("--path", type=str, default=None, help="Local path for loading the VLM model from disk") + parser.add_argument("--top-p", type=float, default=0.9, help="Top-p sampling for VLM (default: 0.9)") + parser.add_argument("--temperature", type=float, default=0.2, help="Temperature for VLM (default: 0.2)") + parser.add_argument("--max-tokens", type=int, default=4096, help="Max tokens for VLM generation (default: 4096)") + parser.add_argument("--seed", type=int, default=None, help="Seed for VLM generation") + # fmt: on + args = parser.parse_args() + + try: + # 1. Load the image + image = _load_image(args.image_path) + + # 2. Generate JSON prompt from image + vlm = FiboVLM(local_path=args.path) + inspired_json = vlm.inspire( + image=image, + prompt=args.prompt, + top_p=args.top_p, + temperature=args.temperature, + max_tokens=args.max_tokens, + seed=args.seed, + ) + + # 3. Parse and save generated JSON prompt + _save_prompt(args, inspired_json) + except (PromptFileReadError, ValueError) as exc: + print(exc) + + +def _save_prompt(args, inspired_json): + try: + inspired_json_parsed = json.loads(inspired_json) + except json.JSONDecodeError as e: + raise ValueError(f"VLM did not return valid JSON: {e}") + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(args.output, "w") as f: + json.dump(inspired_json_parsed, f, indent=2, ensure_ascii=False) + + +def _load_image(image_path: Path): + if not image_path.exists(): + raise PromptFileReadError(f"Image file does not exist: {image_path}") + try: + image = Image.open(image_path).convert("RGB") + except (OSError, IOError, ValueError) as e: + raise PromptFileReadError(f"Failed to load image: {e}") + return image + + +if __name__ == "__main__": + main() diff --git a/src/mflux/latent_creator/latent_creator.py b/src/mflux/latent_creator/latent_creator.py deleted file mode 100644 index 9c8672a..0000000 --- a/src/mflux/latent_creator/latent_creator.py +++ /dev/null @@ -1,85 +0,0 @@ -from pathlib import Path - -import mlx.core as mx -from mlx import nn - -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import ImageUtil - - -class Img2Img: - def __init__( - self, - vae: nn.Module, - sigmas: mx.array, - init_time_step: int, - image_path: str | Path | None, - ): - self.vae = vae - self.sigmas = sigmas - self.init_time_step = init_time_step - self.image_path = image_path - - -class LatentCreator: - @staticmethod - def create( - seed: int, - height: int, - width: int, - ) -> mx.array: - return mx.random.normal( - shape=[1, (height // 16) * (width // 16), 64], - key=mx.random.key(seed), - ) - - @staticmethod - def create_for_txt2img_or_img2img( - seed: int, - height: int, - width: int, - img2img: Img2Img, - ) -> mx.array: - # 0. Determine type of image generation - if img2img.image_path is None: - # 1. Create the pure noise - return LatentCreator.create( - seed=seed, - height=height, - width=width, - ) - else: - # 1. Create the pure noise - pure_noise = LatentCreator.create( - seed=seed, - height=height, - width=width, - ) - - # 2. Encode the image - encoded = LatentCreator.encode_image(vae=img2img.vae, image_path=img2img.image_path, height=height, width=width) # fmt: off - latents = ArrayUtil.pack_latents(latents=encoded, height=height, width=width) - - # 3. Find the appropriate sigma value - sigma = img2img.sigmas[img2img.init_time_step] - - # 4. Blend the appropriate amount of noise based on linear interpolation - return LatentCreator.add_noise_by_interpolation( - clean=latents, - noise=pure_noise, - sigma=sigma, - ) - - @staticmethod - def encode_image(vae: nn.Module, image_path: str | Path, height: int, width: int): - scaled_user_image = ImageUtil.scale_to_dimensions( - image=ImageUtil.load_image(image_path).convert("RGB"), - target_width=width, - target_height=height, - ) - encoded = vae.encode(ImageUtil.to_array(scaled_user_image)) - return encoded - - @staticmethod - def add_noise_by_interpolation(clean: mx.array, noise: mx.array, sigma: float) -> mx.array: - return (1 - sigma) * clean + sigma * noise diff --git a/src/mflux/lora_library.py b/src/mflux/lora_library.py index 81e8ee6..ca650f2 100644 --- a/src/mflux/lora_library.py +++ b/src/mflux/lora_library.py @@ -7,7 +7,7 @@ import sys from collections import defaultdict from pathlib import Path -from mflux.utils.lora_library import _discover_lora_files +from mflux.models.common.lora.download.lora_library import _discover_lora_files def list_loras(paths: list[str] | None = None) -> int: diff --git a/src/mflux/error/__init__.py b/src/mflux/models/common/latent_creator/__init__.py similarity index 100% rename from src/mflux/error/__init__.py rename to src/mflux/models/common/latent_creator/__init__.py diff --git a/src/mflux/models/common/latent_creator/latent_creator.py b/src/mflux/models/common/latent_creator/latent_creator.py new file mode 100644 index 0000000..1696741 --- /dev/null +++ b/src/mflux/models/common/latent_creator/latent_creator.py @@ -0,0 +1,63 @@ +from pathlib import Path +from typing import TYPE_CHECKING + +import mlx.core as mx +from mlx import nn + +from mflux.utils.image_util import ImageUtil + +if TYPE_CHECKING: + from mflux.models.fibo.latent_creator.fibo_latent_creator import FiboLatentCreator + from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator + from mflux.models.qwen.latent_creator.qwen_latent_creator import QwenLatentCreator + + +class Img2Img: + def __init__( + self, + vae: nn.Module, + latent_creator: type["FiboLatentCreator"] | type["FluxLatentCreator"] | type["QwenLatentCreator"], + sigmas: mx.array, + init_time_step: int, + image_path: str | Path | None, + ): + self.vae = vae + self.sigmas = sigmas + self.init_time_step = init_time_step + self.image_path = image_path + self.latent_creator = latent_creator + + +class LatentCreator: + @staticmethod + def create_for_txt2img_or_img2img( + seed: int, + height: int, + width: int, + img2img: Img2Img, + ) -> mx.array: + latent_creator = img2img.latent_creator + + if img2img.image_path is None: + # txt2img: just create noise + return latent_creator.create_noise(seed, height, width) + else: + # img2img: blend encoded image with noise + pure_noise = latent_creator.create_noise(seed, height, width) + encoded = LatentCreator.encode_image(vae=img2img.vae, image_path=img2img.image_path, height=height, width=width) # fmt: off + latents = latent_creator.pack_latents(encoded, height, width) + sigma = img2img.sigmas[img2img.init_time_step] + return LatentCreator.add_noise_by_interpolation(clean=latents, noise=pure_noise, sigma=sigma) + + @staticmethod + def encode_image(vae: nn.Module, image_path: str | Path, height: int, width: int) -> mx.array: + scaled_user_image = ImageUtil.scale_to_dimensions( + image=ImageUtil.load_image(image_path).convert("RGB"), + target_width=width, + target_height=height, + ) + return vae.encode(ImageUtil.to_array(scaled_user_image)) + + @staticmethod + def add_noise_by_interpolation(clean: mx.array, noise: mx.array, sigma: float) -> mx.array: + return (1 - sigma) * clean + sigma * noise diff --git a/src/mflux/utils/lora_library.py b/src/mflux/models/common/lora/download/lora_library.py similarity index 100% rename from src/mflux/utils/lora_library.py rename to src/mflux/models/common/lora/download/lora_library.py diff --git a/src/mflux/latent_creator/__init__.py b/src/mflux/models/common/quantization/__init__.py similarity index 100% rename from src/mflux/latent_creator/__init__.py rename to src/mflux/models/common/quantization/__init__.py diff --git a/src/mflux/utils/quantization_util.py b/src/mflux/models/common/quantization/quantization_util.py similarity index 78% rename from src/mflux/utils/quantization_util.py rename to src/mflux/models/common/quantization/quantization_util.py index a94c758..9cb93a4 100644 --- a/src/mflux/utils/quantization_util.py +++ b/src/mflux/models/common/quantization/quantization_util.py @@ -3,6 +3,7 @@ from typing import TYPE_CHECKING import mlx.nn as nn if TYPE_CHECKING: + from mflux.models.fibo.weights.fibo_weight_handler import FIBOWeightHandler from mflux.models.flux.variants.controlnet.weight_handler_controlnet import WeightHandlerControlnet from mflux.models.flux.weights.weight_handler import WeightHandler from mflux.models.qwen.weights.qwen_weight_handler import QwenWeightHandler @@ -19,9 +20,6 @@ class QuantizationUtil: weights: "WeightHandler", ) -> None: q_level = weights.meta_data.quantization_level - - # mx.save_tensors saves metadata dict kv 'quantization_level': 'None' as a str: str mapping - # we coerce both configs to NoneType to help users use non-quantized saved model files if q_level == "None": q_level = None if quantize == "None": @@ -92,3 +90,23 @@ class QuantizationUtil: nn.quantize(vae, bits=bits) nn.quantize(transformer, bits=bits) # nn.quantize(text_encoder, bits=bits) # Quantization of text encoder causes significant semantic degradation + + @staticmethod + def quantize_fibo_models( + vae: nn.Module, + transformer: nn.Module, + text_encoder: nn.Module | None, + quantize: int, + weights: "FIBOWeightHandler", + ) -> None: + q_level = weights.meta_data.quantization_level + if q_level == "None": + q_level = None + if quantize == "None": + quantize = None + + if quantize is not None or q_level is not None: + bits = int(q_level) if q_level is not None else quantize + nn.quantize(vae, class_predicate=QuantizationUtil.quantization_predicate, bits=bits) + nn.quantize(transformer, class_predicate=QuantizationUtil.quantization_predicate, bits=bits) + nn.quantize(text_encoder, class_predicate=QuantizationUtil.quantization_predicate, bits=bits) diff --git a/src/mflux/schedulers/__init__.py b/src/mflux/models/common/schedulers/__init__.py similarity index 96% rename from src/mflux/schedulers/__init__.py rename to src/mflux/models/common/schedulers/__init__.py index bb20416..dc10515 100644 --- a/src/mflux/schedulers/__init__.py +++ b/src/mflux/models/common/schedulers/__init__.py @@ -66,7 +66,7 @@ def try_import_external_scheduler(scheduler_object_path: str): if not issubclass(SchedulerClass, BaseScheduler): raise InvalidSchedulerType( f"{scheduler_object_path!r} does not inherit from BaseScheduler. " - f"All schedulers must inherit from mflux.schedulers.BaseScheduler." + f"All schedulers must inherit from mflux.models.common.schedulers.BaseScheduler." ) return SchedulerClass diff --git a/src/mflux/schedulers/base_scheduler.py b/src/mflux/models/common/schedulers/base_scheduler.py similarity index 100% rename from src/mflux/schedulers/base_scheduler.py rename to src/mflux/models/common/schedulers/base_scheduler.py diff --git a/src/mflux/schedulers/flow_match_euler_discrete_scheduler.py b/src/mflux/models/common/schedulers/flow_match_euler_discrete_scheduler.py similarity index 97% rename from src/mflux/schedulers/flow_match_euler_discrete_scheduler.py rename to src/mflux/models/common/schedulers/flow_match_euler_discrete_scheduler.py index 065c42f..09b7fe4 100644 --- a/src/mflux/schedulers/flow_match_euler_discrete_scheduler.py +++ b/src/mflux/models/common/schedulers/flow_match_euler_discrete_scheduler.py @@ -6,7 +6,7 @@ import mlx.core as mx if TYPE_CHECKING: from mflux.config.runtime_config import RuntimeConfig -from mflux.schedulers.base_scheduler import BaseScheduler +from mflux.models.common.schedulers.base_scheduler import BaseScheduler class FlowMatchEulerDiscreteScheduler(BaseScheduler): diff --git a/src/mflux/schedulers/linear_scheduler.py b/src/mflux/models/common/schedulers/linear_scheduler.py similarity index 96% rename from src/mflux/schedulers/linear_scheduler.py rename to src/mflux/models/common/schedulers/linear_scheduler.py index 66a42a7..277f606 100644 --- a/src/mflux/schedulers/linear_scheduler.py +++ b/src/mflux/models/common/schedulers/linear_scheduler.py @@ -5,7 +5,7 @@ import mlx.core as mx if TYPE_CHECKING: from mflux.config.runtime_config import RuntimeConfig -from mflux.schedulers.base_scheduler import BaseScheduler +from mflux.models.common.schedulers.base_scheduler import BaseScheduler class LinearScheduler(BaseScheduler): diff --git a/src/mflux/models/common/weights/__init__.py b/src/mflux/models/common/weights/__init__.py index 88528ca..d602926 100644 --- a/src/mflux/models/common/weights/__init__.py +++ b/src/mflux/models/common/weights/__init__.py @@ -1 +1,5 @@ """Common weight mapping utilities.""" + +from mflux.models.common.weights.model_saver import ModelSaver + +__all__ = ["ModelSaver"] diff --git a/src/mflux/models/common/weights/mapping/weight_mapper.py b/src/mflux/models/common/weights/mapping/weight_mapper.py index ca71143..02a7160 100644 --- a/src/mflux/models/common/weights/mapping/weight_mapper.py +++ b/src/mflux/models/common/weights/mapping/weight_mapper.py @@ -133,8 +133,11 @@ class WeightMapper: flat[concrete_hf] = (concrete_mlx, target.transform) elif has_block: # Expand {block} only (for transformer blocks or visual blocks) + # Check if target has max_blocks override + if target.max_blocks is not None: + max_blocks = target.max_blocks # Check if this is for visual blocks (32 blocks) or transformer blocks - if "visual.blocks" in hf_pattern or "visual.blocks" in target.mlx_path: + elif "visual.blocks" in hf_pattern or "visual.blocks" in target.mlx_path: max_blocks = 32 # Visual blocks are always 32 else: max_blocks = num_blocks if num_blocks > 0 else 4 # Default 4 for up_blocks diff --git a/src/mflux/models/common/weights/mapping/weight_mapping.py b/src/mflux/models/common/weights/mapping/weight_mapping.py index 906b29e..501c37a 100644 --- a/src/mflux/models/common/weights/mapping/weight_mapping.py +++ b/src/mflux/models/common/weights/mapping/weight_mapping.py @@ -22,6 +22,7 @@ class WeightTarget: hf_patterns: List[str] # HuggingFace naming patterns, e.g., ["transformer_blocks.{block}.attn.to_q.weight"] transform: Optional[Callable[[mx.array], mx.array]] = None # Optional transform (reshape, transpose, etc.) required: bool = True # If False, weight is optional (may not exist in all models) + max_blocks: Optional[int] = None # Override num_blocks for this target (e.g., for fixed-size lists) class WeightMapping(Protocol): diff --git a/src/mflux/models/common/weights/model_saver.py b/src/mflux/models/common/weights/model_saver.py new file mode 100644 index 0000000..44ee146 --- /dev/null +++ b/src/mflux/models/common/weights/model_saver.py @@ -0,0 +1,114 @@ +from pathlib import Path +from typing import Any + +import mlx.core as mx +from mlx import nn +from mlx.utils import tree_flatten +from transformers import PreTrainedTokenizer + +from mflux.utils.version_util import VersionUtil + + +class ModelSaver: + @staticmethod + def save_model( + model: Any, + bits: int, + base_path: str, + tokenizers: list[tuple[str, str]] | None = None, + components: list[tuple[str, str]] | None = None, + ) -> None: + # Default tokenizers: try common patterns + if tokenizers is None: + tokenizers = ModelSaver._detect_tokenizers(model) + + # Default components: try common patterns + if components is None: + components = ModelSaver._detect_components(model) + + # Save tokenizers + for attr_path, subdir in tokenizers: + tokenizer = ModelSaver._get_nested_attr(model, attr_path) + if tokenizer is not None: + ModelSaver._save_tokenizer(base_path, tokenizer, subdir) + + # Save model components + for attr_name, subdir in components: + component = getattr(model, attr_name, None) + if component is not None: + ModelSaver._save_weights(base_path, bits, component, subdir) + + @staticmethod + def _detect_tokenizers(model: Any) -> list[tuple[str, str]]: + tokenizers = [] + if hasattr(model, "clip_tokenizer") and hasattr(model.clip_tokenizer, "tokenizer"): + tokenizers.append(("clip_tokenizer.tokenizer", "tokenizer")) + if hasattr(model, "t5_tokenizer") and hasattr(model.t5_tokenizer, "tokenizer"): + tokenizers.append(("t5_tokenizer.tokenizer", "tokenizer_2")) + if hasattr(model, "qwen_tokenizer") and hasattr(model.qwen_tokenizer, "tokenizer"): + tokenizers.append(("qwen_tokenizer.tokenizer", "tokenizer")) + if hasattr(model, "fibo_tokenizer") and hasattr(model.fibo_tokenizer, "tokenizer"): + tokenizers.append(("fibo_tokenizer.tokenizer", "tokenizer")) + return tokenizers + + @staticmethod + def _detect_components(model: Any) -> list[tuple[str, str]]: + components = [] + if hasattr(model, "vae"): + components.append(("vae", "vae")) + if hasattr(model, "transformer"): + components.append(("transformer", "transformer")) + if hasattr(model, "clip_text_encoder"): + components.append(("clip_text_encoder", "text_encoder")) + if hasattr(model, "t5_text_encoder"): + components.append(("t5_text_encoder", "text_encoder_2")) + if hasattr(model, "text_encoder"): + # Only add if we haven't already added clip_text_encoder or t5_text_encoder + if not any(c[1] == "text_encoder" for c in components): + components.append(("text_encoder", "text_encoder")) + return components + + @staticmethod + def _get_nested_attr(obj: Any, attr_path: str) -> Any: + attrs = attr_path.split(".") + result = obj + for attr in attrs: + if not hasattr(result, attr): + return None + result = getattr(result, attr) + return result + + @staticmethod + def _save_tokenizer(base_path: str, tokenizer: PreTrainedTokenizer, subdir: str) -> None: + path = Path(base_path) / subdir + path.mkdir(parents=True, exist_ok=True) + tokenizer.save_pretrained(path) + + @staticmethod + def _save_weights(base_path: str, bits: int, model: nn.Module, subdir: str) -> None: + path = Path(base_path) / subdir + path.mkdir(parents=True, exist_ok=True) + weights = ModelSaver._split_weights(base_path, dict(tree_flatten(model.parameters()))) + for i, weight in enumerate(weights): + mx.save_safetensors( + str(path / f"{i}.safetensors"), + weight, + { + "quantization_level": str(bits), + "mflux_version": VersionUtil.get_mflux_version(), + }, + ) + + @staticmethod + def _split_weights(base_path: str, weights: dict, max_file_size_gb: int = 2) -> list: + max_file_size_bytes = max_file_size_gb << 30 + shards = [] + shard, shard_size = {}, 0 + for k, v in weights.items(): + if shard_size + v.nbytes > max_file_size_bytes: + shards.append(shard) + shard, shard_size = {}, 0 + shard[k] = v + shard_size += v.nbytes + shards.append(shard) + return shards diff --git a/src/mflux/models/depth_pro/depth_pro.py b/src/mflux/models/depth_pro/depth_pro.py index bb3f2bf..139310c 100644 --- a/src/mflux/models/depth_pro/depth_pro.py +++ b/src/mflux/models/depth_pro/depth_pro.py @@ -9,7 +9,7 @@ from PIL import Image from mflux.models.depth_pro.depth_pro_initializer import DepthProInitializer from mflux.models.depth_pro.model.depth_pro_model import DepthProModel from mflux.models.depth_pro.model.depth_pro_util import DepthProUtil -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.image_util import ImageUtil @dataclass diff --git a/src/mflux/post_processing/__init__.py b/src/mflux/models/fibo/__init__.py similarity index 100% rename from src/mflux/post_processing/__init__.py rename to src/mflux/models/fibo/__init__.py diff --git a/src/mflux/models/fibo/fibo_initializer.py b/src/mflux/models/fibo/fibo_initializer.py new file mode 100644 index 0000000..b41f526 --- /dev/null +++ b/src/mflux/models/fibo/fibo_initializer.py @@ -0,0 +1,47 @@ +from mflux.config.model_config import ModelConfig +from mflux.models.fibo.model.fibo_text_encoder import SmolLM3_3B_TextEncoder +from mflux.models.fibo.model.fibo_transformer import FiboTransformer +from mflux.models.fibo.model.fibo_vae.wan_2_2_vae import Wan2_2_VAE +from mflux.models.fibo.tokenizer import FiboTokenizerHandler +from mflux.models.fibo.weights.fibo_weight_handler import FIBOWeightHandler +from mflux.models.fibo.weights.fibo_weight_util import FIBOWeightUtil + + +class FIBOInitializer: + @staticmethod + def init( + fibo_model, + model_config: ModelConfig | None = None, + quantize: int | None = None, + local_path: str | None = None, + ) -> None: + # 1. Load VAE weights + weights = FIBOWeightHandler.load_regular_weights( + repo_id=model_config.model_name, + local_path=local_path, + ) + + # 2. Initialize tokenizers + tokenizer_handler = FiboTokenizerHandler( + repo_id=model_config.model_name, + local_path=local_path, + ) + fibo_model.fibo_tokenizer = tokenizer_handler.fibo + + # 3. Initialize all models + fibo_model.vae = Wan2_2_VAE() + fibo_model.text_encoder = SmolLM3_3B_TextEncoder() + fibo_model.transformer = FiboTransformer( + in_channels=48, + num_layers=8, + num_single_layers=38, + ) + + # 4. Apply weights and quantize VAE, transformer, and text encoder + fibo_model.bits = FIBOWeightUtil.set_weights_and_quantize( + quantize_arg=quantize, + weights=weights, + vae=fibo_model.vae, + transformer=fibo_model.transformer, + text_encoder=fibo_model.text_encoder, + ) diff --git a/src/mflux/models/fibo/latent_creator/__init__.py b/src/mflux/models/fibo/latent_creator/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mflux/models/fibo/latent_creator/fibo_latent_creator.py b/src/mflux/models/fibo/latent_creator/fibo_latent_creator.py new file mode 100644 index 0000000..d07f9f2 --- /dev/null +++ b/src/mflux/models/fibo/latent_creator/fibo_latent_creator.py @@ -0,0 +1,19 @@ +import mlx.core as mx + + +class FiboLatentCreator: + @staticmethod + def create_noise(seed: int, height: int, width: int) -> mx.array: + latents = mx.random.normal( + shape=(1, 48, (height // 16), (width // 16)), + key=mx.random.key(seed), + ) + return FiboLatentCreator.pack_latents(latents, height, width) + + @staticmethod + def pack_latents(latents: mx.array, height: int, width: int) -> mx.array: + if latents.ndim == 5: + latents = latents[:, :, 0, :, :] + batch_size, channels, latent_height, latent_width = latents.shape + latents = mx.transpose(latents, (0, 2, 3, 1)) + return mx.reshape(latents, (batch_size, latent_height * latent_width, channels)) diff --git a/src/mflux/models/fibo/model/__init__.py b/src/mflux/models/fibo/model/__init__.py new file mode 100644 index 0000000..55a5bd5 --- /dev/null +++ b/src/mflux/models/fibo/model/__init__.py @@ -0,0 +1 @@ +"""FIBO model components.""" diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/__init__.py b/src/mflux/models/fibo/model/fibo_text_encoder/__init__.py new file mode 100644 index 0000000..2fba2b1 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/__init__.py @@ -0,0 +1,7 @@ +from .prompt_encoder import PromptEncoder +from .smol_lm3_3b_text_encoder import SmolLM3_3B_TextEncoder + +__all__ = [ + "PromptEncoder", + "SmolLM3_3B_TextEncoder", +] diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/prompt_encoder.py b/src/mflux/models/fibo/model/fibo_text_encoder/prompt_encoder.py new file mode 100644 index 0000000..b61d7a9 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/prompt_encoder.py @@ -0,0 +1,166 @@ +import json +from typing import List, Union + +import mlx.core as mx + +from mflux.models.fibo.model.fibo_text_encoder.smol_lm3_3b_text_encoder import SmolLM3_3B_TextEncoder +from mflux.models.fibo.tokenizer.fibo_tokenizer import TokenizerFibo + + +class PromptEncoder: + @staticmethod + def encode_prompt( + prompt: str, + negative_prompt: str | None, + tokenizer: TokenizerFibo, + text_encoder: SmolLM3_3B_TextEncoder, + ) -> tuple[str, mx.array, List[mx.array]]: + # 0. Set default negative prompt if not provided + if negative_prompt is None or negative_prompt == "": + negative_prompt = "ugly, blurry, low quality" + + # 1. Convert prompt to JSON format + json.loads(prompt) + json_prompt = prompt + + # 2. Get prompt embeddings for positive and negative prompt + prompt_embeds, prompt_layers, prompt_attention_mask = PromptEncoder._get_prompt_embeds( + prompt=json_prompt, + tokenizer=tokenizer, + text_encoder=text_encoder, + num_images_per_prompt=1, + max_sequence_length=2048, + tokenization_prefix="positive", + ) + neg_prompt_embeds, neg_prompt_layers, neg_prompt_attention_mask = PromptEncoder._get_prompt_embeds( + prompt=negative_prompt, + tokenizer=tokenizer, + text_encoder=text_encoder, + num_images_per_prompt=1, + max_sequence_length=2048, + tokenization_prefix="negative", + ) + encoder_hidden_states, max_tokens = PromptEncoder._get_encoder_hidden_states( + prompt_embeds=prompt_embeds, + neg_prompt_embeds=neg_prompt_embeds, + prompt_attention_mask=prompt_attention_mask, + neg_prompt_attention_mask=neg_prompt_attention_mask, + ) + + prompt_layers = PromptEncoder._get_prompt_layers( + max_tokens=max_tokens, + prompt_layers=prompt_layers, + neg_prompt_layers=neg_prompt_layers, + ) + return json_prompt, encoder_hidden_states, prompt_layers + + @staticmethod + def _get_encoder_hidden_states(neg_prompt_attention_mask, neg_prompt_embeds, prompt_attention_mask, prompt_embeds): + max_tokens = max(neg_prompt_embeds.shape[1], prompt_embeds.shape[1]) + prompt_embeds, prompt_attention_mask = PromptEncoder._pad_embedding( + max_tokens=max_tokens, + prompt_embeds=prompt_embeds, + attention_mask=prompt_attention_mask, + ) + neg_prompt_embeds, neg_prompt_attention_mask = PromptEncoder._pad_embedding( + max_tokens=max_tokens, + prompt_embeds=neg_prompt_embeds, + attention_mask=neg_prompt_attention_mask, + ) + encoder_hidden_states = mx.concatenate([neg_prompt_embeds, prompt_embeds], axis=0) + return encoder_hidden_states, max_tokens + + @staticmethod + def _get_prompt_layers(max_tokens, neg_prompt_layers, prompt_layers): + prompt_layers = [PromptEncoder._pad_embedding(layer, max_tokens)[0] for layer in prompt_layers] + neg_prompt_layers = [PromptEncoder._pad_embedding(layer, max_tokens)[0] for layer in neg_prompt_layers] + prompt_layers = [mx.concatenate([neg_prompt_layers[i], prompt_layers[i]], axis=0) for i in range(len(prompt_layers))] # fmt: off + + total_num_layers_transformer = 46 # FIBO transformer has 8 joint + 38 single blocks = 46 total layers + if len(prompt_layers) >= total_num_layers_transformer: + prompt_layers = prompt_layers[len(prompt_layers) - total_num_layers_transformer :] + else: + prompt_layers = prompt_layers + [prompt_layers[-1]] * (total_num_layers_transformer - len(prompt_layers)) + return prompt_layers + + @staticmethod + def _get_prompt_embeds( + prompt: Union[str, List[str]], + text_encoder: SmolLM3_3B_TextEncoder, + tokenizer: TokenizerFibo, + num_images_per_prompt: int = 1, + max_sequence_length: int = 2048, + tokenization_prefix: str | None = None, + ) -> tuple[mx.array, List[mx.array], mx.array]: + prompts = [prompt] if isinstance(prompt, str) else list(prompt) + if not prompts: + raise ValueError("`prompt` must be a non-empty string or list of strings.") + + # 1) Tokenize and convert to MX + input_ids_mx, attention_mask_mx = tokenizer.tokenize( + prompts=prompts, + max_length=max_sequence_length, + padding="longest", + truncation=True, + add_special_tokens=True, + ) + + # 2) Run MLX text encoder and collect hidden states + hidden_states_list = text_encoder( + input_ids=input_ids_mx, + attention_mask=attention_mask_mx, + output_hidden_states=True, + ) + + # 3) Build prompt_embeds from last two layers + last_hidden = hidden_states_list[-1] + second_last_hidden = hidden_states_list[-2] + prompt_embeds = mx.concatenate( + [last_hidden, second_last_hidden], + axis=-1, + ) + + # 4) Repeat along batch dimension for num_images_per_prompt + prompt_embeds = PromptEncoder._repeat_interleave_batch(prompt_embeds, num_images_per_prompt) + attention_mask = PromptEncoder._repeat_interleave_batch(attention_mask_mx, num_images_per_prompt) + prompt_layers = [PromptEncoder._repeat_interleave_batch(layer, num_images_per_prompt) for layer in hidden_states_list] # fmt: off + return prompt_embeds, prompt_layers, attention_mask + + @staticmethod + def _pad_embedding( + prompt_embeds: mx.array, + max_tokens: int, + attention_mask: mx.array | None = None, + ) -> tuple[mx.array, mx.array]: + batch_size, seq_len, dim = prompt_embeds.shape + + if attention_mask is None: + attention_mask = mx.ones((batch_size, seq_len), dtype=prompt_embeds.dtype) + else: + attention_mask = attention_mask.astype(prompt_embeds.dtype) + + if max_tokens < seq_len: + raise ValueError("`max_tokens` must be >= current sequence length.") + + if max_tokens > seq_len: + pad_length = max_tokens - seq_len + padding = mx.zeros((batch_size, pad_length, dim), dtype=prompt_embeds.dtype) + prompt_embeds = mx.concatenate([prompt_embeds, padding], axis=1) + + mask_padding = mx.zeros((batch_size, pad_length), dtype=attention_mask.dtype) + attention_mask = mx.concatenate([attention_mask, mask_padding], axis=1) + + return prompt_embeds, attention_mask + + @staticmethod + def _repeat_interleave_batch( + tensor: mx.array, + num_images_per_prompt: int, + ) -> mx.array: + if num_images_per_prompt == 1: + return tensor + batch, *rest = tensor.shape + tensor = mx.expand_dims(tensor, axis=1) + tensor = mx.broadcast_to(tensor, (batch, num_images_per_prompt, *rest)) + new_shape = (batch * num_images_per_prompt, *rest) + return mx.reshape(tensor, new_shape) diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_attention.py b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_attention.py new file mode 100644 index 0000000..5e779dd --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_attention.py @@ -0,0 +1,133 @@ +import math +from typing import Tuple + +import mlx.core as mx +from mlx import nn +from mlx.core.fast import scaled_dot_product_attention + +from .smol_lm3_3b_rope import SmolLM3_3B_RotaryEmbedding + + +class SmolLM3_3B_SelfAttention(nn.Module): + def __init__( + self, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + max_position_embeddings: int = 65_536, + rope_theta: float = 5_000_000.0, + attention_dropout: float = 0.0, + ): + super().__init__() + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.head_dim = hidden_size // num_attention_heads + self.num_key_value_groups = num_attention_heads // num_key_value_heads + self.scale = 1.0 / math.sqrt(self.head_dim) + self.attention_dropout = attention_dropout + + # Projections – no bias, matching SmolLM3 config (attention_bias = False) + self.q_proj = nn.Linear(hidden_size, num_attention_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(hidden_size, num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(hidden_size, num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(num_attention_heads * self.head_dim, hidden_size, bias=False) + + self.rotary_emb = SmolLM3_3B_RotaryEmbedding( + dim=self.head_dim, + max_position_embeddings=max_position_embeddings, + base=rope_theta, + ) + + def __call__( + self, + hidden_states: mx.array, + attention_mask: mx.array | None = None, + cos_sin: Tuple[mx.array, mx.array] | None = None, + ) -> mx.array: + batch_size, seq_len, _ = hidden_states.shape + + # Projections: Use nn.Linear directly (like Qwen/Flux models) + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + # Cast back to input dtype if Linear output is float32 (for consistency) + if q.dtype != hidden_states.dtype: + q = q.astype(hidden_states.dtype) + k = k.astype(hidden_states.dtype) + v = v.astype(hidden_states.dtype) + + # Force evaluation to ensure computation is complete + mx.eval(q, k, v) + + # Reshape to (batch, heads, seq, head_dim) + q = q.reshape(batch_size, seq_len, self.num_attention_heads, self.head_dim).transpose(0, 2, 1, 3) + k = k.reshape(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(0, 2, 1, 3) + v = v.reshape(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(0, 2, 1, 3) + + # Rotary embeddings + if cos_sin is None: + cos_sin = self.rotary_emb(seq_len) + cos, sin = cos_sin + q, k = SmolLM3_3B_SelfAttention._apply_rope(q, k, cos, sin) + + # Grouped-query attention: repeat kv heads if needed + if self.num_key_value_heads != self.num_attention_heads: + k = SmolLM3_3B_SelfAttention._repeat_kv(k, self.num_key_value_groups) + v = SmolLM3_3B_SelfAttention._repeat_kv(v, self.num_key_value_groups) + + # Prepare mask for scaled_dot_product_attention + attn_mask = None + if attention_mask is not None: + seq_len_k = k.shape[2] + causal_mask = attention_mask[:, :, :, :seq_len_k] + if causal_mask.shape[1] == 1: + causal_mask = mx.broadcast_to(causal_mask, (batch_size, self.num_attention_heads, seq_len, seq_len_k)) + attn_mask = causal_mask.astype(q.dtype) + + # Use MLX fast SDPA + attn_output = scaled_dot_product_attention( + q, + k, + v, + scale=self.scale, + mask=attn_mask, + ) + + attn_output = attn_output.transpose(0, 2, 1, 3).reshape(batch_size, seq_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + + return attn_output + + @staticmethod + def _rotate_half(x: mx.array) -> mx.array: + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return mx.concatenate([-x2, x1], axis=-1) + + @staticmethod + def _apply_rope( + q: mx.array, + k: mx.array, + cos: mx.array, + sin: mx.array, + ) -> Tuple[mx.array, mx.array]: + q_dtype = q.dtype + k_dtype = k.dtype + q = q.astype(mx.float32) + k = k.astype(mx.float32) + cos = cos.astype(mx.float32) + sin = sin.astype(mx.float32) + + q_embed = (q * cos) + (SmolLM3_3B_SelfAttention._rotate_half(q) * sin) + k_embed = (k * cos) + (SmolLM3_3B_SelfAttention._rotate_half(k) * sin) + + return q_embed.astype(q_dtype), k_embed.astype(k_dtype) + + @staticmethod + def _repeat_kv(hidden_states: mx.array, n_rep: int) -> mx.array: + batch, num_kv_heads, seq_len, head_dim = hidden_states.shape + hidden_states = mx.expand_dims(hidden_states, axis=2) + hidden_states = mx.broadcast_to(hidden_states, (batch, num_kv_heads, n_rep, seq_len, head_dim)) + return hidden_states.reshape(batch, num_kv_heads * n_rep, seq_len, head_dim) diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_encoder_layer.py b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_encoder_layer.py new file mode 100644 index 0000000..146473d --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_encoder_layer.py @@ -0,0 +1,64 @@ +from typing import Tuple + +import mlx.core as mx +from mlx import nn + +from .smol_lm3_3b_attention import SmolLM3_3B_SelfAttention +from .smol_lm3_3b_mlp import SmolLM3_3B_MLP +from .smol_lm3_3b_rms_norm import SmolLM3_3B_RMSNorm + + +class SmolLM3_3B_EncoderLayer(nn.Module): + def __init__( + self, + hidden_size: int = 2048, + num_attention_heads: int = 16, + num_key_value_heads: int = 4, + intermediate_size: int = 11_008, + rms_norm_eps: float = 1e-6, + max_position_embeddings: int = 65_536, + rope_theta: float = 5_000_000.0, + attention_dropout: float = 0.0, + hidden_act: str = "silu", + ): + super().__init__() + self.input_layernorm = SmolLM3_3B_RMSNorm(hidden_size, eps=rms_norm_eps) + self.self_attn = SmolLM3_3B_SelfAttention( + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + num_key_value_heads=num_key_value_heads, + max_position_embeddings=max_position_embeddings, + rope_theta=rope_theta, + attention_dropout=attention_dropout, + ) + self.post_attention_layernorm = SmolLM3_3B_RMSNorm(hidden_size, eps=rms_norm_eps) + self.mlp = SmolLM3_3B_MLP( + hidden_size=hidden_size, + intermediate_size=intermediate_size, + hidden_act=hidden_act, + ) + + def __call__( + self, + hidden_states: mx.array, + attention_mask: mx.array | None, + cos_sin: Tuple[mx.array, mx.array], + layer_idx: int | None = None, + ) -> mx.array: + # Self-attention block + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + hidden_states = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + cos_sin=cos_sin, + ) + hidden_states = residual + hidden_states + + # Feed-forward block + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_mlp.py b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_mlp.py new file mode 100644 index 0000000..eec92f5 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_mlp.py @@ -0,0 +1,30 @@ +import mlx.core as mx +from mlx import nn + + +class SmolLM3_3B_MLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str = "silu", + ): + super().__init__() + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + + def _activation(self, x: mx.array) -> mx.array: + if self.hidden_act == "silu": + return x * mx.sigmoid(x) + return x * mx.sigmoid(x) + + def __call__(self, hidden_states: mx.array) -> mx.array: + gate = self._activation(self.gate_proj(hidden_states)) + up = self.up_proj(hidden_states) + hidden = gate * up + hidden = self.down_proj(hidden) + return hidden diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_rms_norm.py b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_rms_norm.py new file mode 100644 index 0000000..cdcc949 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_rms_norm.py @@ -0,0 +1,17 @@ +import mlx.core as mx +from mlx import nn + + +class SmolLM3_3B_RMSNorm(nn.Module): + def __init__(self, hidden_size: int, eps: float = 1e-6): + super().__init__() + self.weight = mx.ones((hidden_size,)) + self.eps = eps + + def __call__(self, hidden_states: mx.array) -> mx.array: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.astype(mx.float32) + variance = mx.mean(mx.square(hidden_states), axis=-1, keepdims=True) + hidden_states = hidden_states * mx.rsqrt(variance + self.eps) + result = self.weight.astype(mx.float32) * hidden_states + return result.astype(input_dtype) diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_rope.py b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_rope.py new file mode 100644 index 0000000..124a676 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_rope.py @@ -0,0 +1,31 @@ +from typing import Tuple + +import mlx.core as mx +from mlx import nn + + +class SmolLM3_3B_RotaryEmbedding(nn.Module): + def __init__( + self, + dim: int, + max_position_embeddings: int = 65_536, + base: float = 5_000_000.0, + ): + super().__init__() + self.inv_freq = 1.0 / (base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)) + self.max_position_embeddings = max_position_embeddings + + def __call__(self, seq_len: int) -> Tuple[mx.array, mx.array]: + cos, sin = SmolLM3_3B_RotaryEmbedding._build_cos_sin(self.inv_freq, seq_len) + cos = mx.expand_dims(mx.expand_dims(cos, axis=0), axis=0) + sin = mx.expand_dims(mx.expand_dims(sin, axis=0), axis=0) + return cos, sin + + @staticmethod + def _build_cos_sin(inv_freq, seq_len: int) -> Tuple[mx.array, mx.array]: + positions = mx.arange(seq_len, dtype=mx.float32) + freqs = mx.outer(positions, inv_freq) + emb = mx.concatenate([freqs, freqs], axis=-1) + cos = mx.cos(emb) + sin = mx.sin(emb) + return cos, sin diff --git a/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_text_encoder.py b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_text_encoder.py new file mode 100644 index 0000000..03d6827 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_text_encoder/smol_lm3_3b_text_encoder.py @@ -0,0 +1,105 @@ +from typing import List + +import mlx.core as mx +from mlx import nn + +from .smol_lm3_3b_encoder_layer import SmolLM3_3B_EncoderLayer +from .smol_lm3_3b_rms_norm import SmolLM3_3B_RMSNorm +from .smol_lm3_3b_rope import SmolLM3_3B_RotaryEmbedding + + +class SmolLM3_3B_TextEncoder(nn.Module): + def __init__( + self, + vocab_size: int = 128_256, + hidden_size: int = 2048, + intermediate_size: int = 11_008, + num_hidden_layers: int = 36, + num_attention_heads: int = 16, + num_key_value_heads: int = 4, + max_position_embeddings: int = 65_536, + rope_theta: float = 5_000_000.0, + rms_norm_eps: float = 1e-6, + hidden_act: str = "silu", + ): + super().__init__() + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.max_position_embeddings = max_position_embeddings + self.rope_theta = rope_theta + self.rms_norm_eps = rms_norm_eps + self.hidden_act = hidden_act + self.embed_tokens = nn.Embedding(vocab_size, hidden_size) + self.layers: List[SmolLM3_3B_EncoderLayer] = [ + SmolLM3_3B_EncoderLayer( + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + num_key_value_heads=num_key_value_heads, + intermediate_size=intermediate_size, + rms_norm_eps=rms_norm_eps, + max_position_embeddings=max_position_embeddings, + rope_theta=rope_theta, + hidden_act=hidden_act, + ) + for _ in range(num_hidden_layers) + ] + self.norm = SmolLM3_3B_RMSNorm(hidden_size, eps=rms_norm_eps) + self.rotary_emb = SmolLM3_3B_RotaryEmbedding( + dim=hidden_size // num_attention_heads, + max_position_embeddings=max_position_embeddings, + base=rope_theta, + ) + + def __call__( + self, + input_ids: mx.array, + attention_mask: mx.array, + output_hidden_states: bool = True, + ) -> List[mx.array] | mx.array: + batch_size, seq_len = input_ids.shape + hidden_states = self.embed_tokens(input_ids) + attention_mask_4d = SmolLM3_3B_TextEncoder._build_attention_mask(attention_mask) + cos, sin = self.rotary_emb(seq_len) + hidden_states_list: List[mx.array] = [hidden_states] + for layer_idx, layer in enumerate(self.layers): + hidden_states = layer( + hidden_states=hidden_states, + attention_mask=attention_mask_4d, + cos_sin=(cos, sin), + layer_idx=layer_idx, + ) + if output_hidden_states: + hidden_states_list.append(hidden_states) + + hidden_states = self.norm(hidden_states) + if output_hidden_states: + hidden_states_list[-1] = hidden_states + return hidden_states_list + return hidden_states + + @staticmethod + def _build_attention_mask(attention_mask: mx.array) -> mx.array: + batch_size, seq_len = attention_mask.shape + mask_dtype = mx.float32 + min_dtype_value = mx.finfo(mask_dtype).min + padding_mask = mx.where( + attention_mask == 1, + mx.zeros_like(attention_mask).astype(mask_dtype), + mx.ones_like(attention_mask).astype(mask_dtype) * min_dtype_value, + ) + padding_mask = mx.expand_dims(mx.expand_dims(padding_mask, axis=1), axis=1) + idx = mx.arange(seq_len, dtype=mx.int32) + j = mx.expand_dims(idx, axis=0) + i = mx.expand_dims(idx, axis=1) + tri_bool = j > i + zeros_2d = mx.zeros((seq_len, seq_len), dtype=mask_dtype) + minval_2d = mx.ones((seq_len, seq_len), dtype=mask_dtype) * min_dtype_value + causal_tri_mask = mx.where(tri_bool, minval_2d, zeros_2d) + causal_tri_mask = mx.expand_dims(mx.expand_dims(causal_tri_mask, axis=0), axis=0) + causal_tri_mask = mx.broadcast_to(causal_tri_mask, (batch_size, 1, seq_len, seq_len)) + attention_mask_4d = causal_tri_mask + padding_mask + return attention_mask_4d diff --git a/src/mflux/models/fibo/model/fibo_transformer/__init__.py b/src/mflux/models/fibo/model/fibo_transformer/__init__.py new file mode 100644 index 0000000..0c1efff --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/__init__.py @@ -0,0 +1,3 @@ +from .transformer import FiboTransformer + +__all__ = ["FiboTransformer"] diff --git a/src/mflux/models/fibo/model/fibo_transformer/bria_fibo_timesteps.py b/src/mflux/models/fibo/model/fibo_transformer/bria_fibo_timesteps.py new file mode 100644 index 0000000..2eba42f --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/bria_fibo_timesteps.py @@ -0,0 +1,38 @@ +import math + +import mlx.core as mx +from mlx import nn + + +class BriaFiboTimesteps(nn.Module): + def __init__( + self, + num_channels: int, + flip_sin_to_cos: bool, + downscale_freq_shift: float, + scale: float = 1.0, + time_theta: int = 10000, + ): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + self.scale = scale + self.time_theta = time_theta + + def __call__(self, timesteps: mx.array) -> mx.array: + half_dim = self.num_channels // 2 + exponent = -math.log(self.time_theta) * mx.arange(0, half_dim, dtype=mx.float32) + exponent = exponent / (half_dim - self.downscale_freq_shift) + emb = mx.exp(exponent) + emb = mx.expand_dims(timesteps.astype(mx.float32), axis=-1) * mx.expand_dims(emb, axis=0) + emb = self.scale * emb + sin = mx.sin(emb) + cos = mx.cos(emb) + emb = mx.concatenate([sin, cos], axis=-1) + if self.flip_sin_to_cos: + emb = mx.concatenate([emb[:, half_dim:], emb[:, :half_dim]], axis=-1) + if self.num_channels % 2 == 1: + pad = mx.zeros((emb.shape[0], 1), dtype=emb.dtype) + emb = mx.concatenate([emb, pad], axis=-1) + return emb diff --git a/src/mflux/models/fibo/model/fibo_transformer/feed_forward.py b/src/mflux/models/fibo/model/fibo_transformer/feed_forward.py new file mode 100644 index 0000000..ca98a53 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/feed_forward.py @@ -0,0 +1,28 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_transformer.fibo_gelu import FiboGELU + + +class FiboFeedForward(nn.Module): + def __init__( + self, + dim: int, + dim_out: int | None = None, + mult: int = 4, + activation_fn: str = "gelu-approximate", + dropout: float = 0.0, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim if dim_out is None else dim_out + self.net: list[nn.Module] = [ + FiboGELU(dim_in=dim, dim_out=inner_dim, approximate="tanh", bias=True), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out, bias=True), + ] + + def __call__(self, hidden_states: mx.array) -> mx.array: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states diff --git a/src/mflux/models/fibo/model/fibo_transformer/fibo_ada_layer_norm_zero.py b/src/mflux/models/fibo/model/fibo_transformer/fibo_ada_layer_norm_zero.py new file mode 100644 index 0000000..65ee289 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/fibo_ada_layer_norm_zero.py @@ -0,0 +1,31 @@ +import mlx.core as mx +from mlx import nn + + +class FiboAdaLayerNormZero(nn.Module): + def __init__(self, embedding_dim: int, eps: float = 1e-6): + super().__init__() + self.embedding_dim = embedding_dim + self.eps = eps + self.linear = nn.Linear(embedding_dim, 6 * embedding_dim) + + def __call__(self, hidden_states: mx.array, text_embeddings: mx.array): + emb = self.linear(nn.silu(text_embeddings)) + chunk = self.embedding_dim + shift_msa = emb[:, 0 * chunk : 1 * chunk] + scale_msa = emb[:, 1 * chunk : 2 * chunk] + gate_msa = emb[:, 2 * chunk : 3 * chunk] + shift_mlp = emb[:, 3 * chunk : 4 * chunk] + scale_mlp = emb[:, 4 * chunk : 5 * chunk] + gate_mlp = emb[:, 5 * chunk : 6 * chunk] + norm_hidden_states = FiboAdaLayerNormZero._layer_norm(self.eps, hidden_states) + hidden_states = norm_hidden_states * (1 + scale_msa[:, None, :]) + shift_msa[:, None, :] + return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp + + @staticmethod + def _layer_norm(eps, x: mx.array) -> mx.array: + x_f32 = x.astype(mx.float32) + mean = mx.mean(x_f32, axis=-1, keepdims=True) + var = mx.mean((x_f32 - mean) ** 2, axis=-1, keepdims=True) + y = (x_f32 - mean) / mx.sqrt(var + eps) + return y.astype(x.dtype) diff --git a/src/mflux/models/fibo/model/fibo_transformer/fibo_embed_nd.py b/src/mflux/models/fibo/model/fibo_transformer/fibo_embed_nd.py new file mode 100644 index 0000000..2d0fc6e --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/fibo_embed_nd.py @@ -0,0 +1,50 @@ +import mlx.core as mx +from mlx import nn + + +class FiboEmbedND(nn.Module): + def __init__(self, theta: int = 10000, axes_dim: list[int] | None = None): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim or [16, 56, 56] + + def __call__(self, ids: mx.array) -> tuple[mx.array, mx.array]: + if ids.ndim == 3 and ids.shape[0] == 1: + ids = ids[0] + + n_axes = ids.shape[-1] + pos = ids.astype(mx.float32) + + cos_out: list[mx.array] = [] + sin_out: list[mx.array] = [] + + for i in range(n_axes): + axis_dim = self.axes_dim[i] + cos_axis, sin_axis = FiboEmbedND._get_1d_rotary_pos_embed( + dim=axis_dim, + pos=pos[:, i], + theta=self.theta, + ) + cos_out.append(cos_axis) + sin_out.append(sin_axis) + + freqs_cos = mx.concatenate(cos_out, axis=-1) + freqs_sin = mx.concatenate(sin_out, axis=-1) + return freqs_cos, freqs_sin + + @staticmethod + def _get_1d_rotary_pos_embed( + dim: int, + pos: mx.array, + theta: float = 10000.0, + ) -> tuple[mx.array, mx.array]: + if pos.ndim != 1: + pos = mx.reshape(pos, (-1,)) + pos = pos.astype(mx.float32) + freqs = 1.0 / (theta ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)) + angles = pos[:, None] * freqs[None, :] + cos_base = mx.cos(angles) + sin_base = mx.sin(angles) + cos = mx.reshape(mx.stack([cos_base, cos_base], axis=-1), (pos.shape[0], -1)) + sin = mx.reshape(mx.stack([sin_base, sin_base], axis=-1), (pos.shape[0], -1)) + return cos, sin diff --git a/src/mflux/models/fibo/model/fibo_transformer/fibo_gelu.py b/src/mflux/models/fibo/model/fibo_transformer/fibo_gelu.py new file mode 100644 index 0000000..5772cc3 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/fibo_gelu.py @@ -0,0 +1,14 @@ +import mlx.core as mx +from mlx import nn + + +class FiboGELU(nn.Module): + def __init__(self, dim_in: int, dim_out: int, approximate: str = "tanh", bias: bool = True): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out, bias=bias) + self.approximate = approximate + + def __call__(self, hidden_states: mx.array) -> mx.array: + hidden_states = self.proj(hidden_states) + hidden_states = nn.gelu_approx(hidden_states) + return hidden_states diff --git a/src/mflux/models/fibo/model/fibo_transformer/fibo_joint_attention.py b/src/mflux/models/fibo/model/fibo_transformer/fibo_joint_attention.py new file mode 100644 index 0000000..44d7eef --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/fibo_joint_attention.py @@ -0,0 +1,115 @@ +import mlx.core as mx +from mlx import nn +from mlx.core.fast import scaled_dot_product_attention + +from mflux.models.fibo.model.fibo_transformer.fibo_single_attention import FiboSingleAttention + + +class FiboJointAttention(nn.Module): + def __init__(self, dim: int, num_attention_heads: int, attention_head_dim: int, eps: float = 1e-6): + super().__init__() + self.head_dim = attention_head_dim + self.num_heads = num_attention_heads + self.inner_dim = dim + + self.to_q = nn.Linear(dim, self.inner_dim) + self.to_k = nn.Linear(dim, self.inner_dim) + self.to_v = nn.Linear(dim, self.inner_dim) + + self.norm_q = nn.RMSNorm(self.head_dim, eps=eps) + self.norm_k = nn.RMSNorm(self.head_dim, eps=eps) + + # Added KV projections for encoder_hidden_states + self.add_q_proj = nn.Linear(dim, self.inner_dim) + self.add_k_proj = nn.Linear(dim, self.inner_dim) + self.add_v_proj = nn.Linear(dim, self.inner_dim) + + self.norm_added_q = nn.RMSNorm(self.head_dim, eps=eps) + self.norm_added_k = nn.RMSNorm(self.head_dim, eps=eps) + + # Output projections + self.to_out = [nn.Linear(self.inner_dim, dim)] + self.to_add_out = nn.Linear(self.inner_dim, dim) + + def __call__( + self, + hidden_states: mx.array, + encoder_hidden_states: mx.array, + image_rotary_emb: tuple[mx.array, mx.array], + attention_mask: mx.array | None = None, + ) -> tuple[mx.array, mx.array]: + batch_size, seq_img, dim = hidden_states.shape + _, seq_ctx, _ = encoder_hidden_states.shape + + cos, sin = image_rotary_emb + + # QKV for image stream: [B, S_img, inner_dim] + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + + # QKV for context stream (added_kv): [B, S_ctx, inner_dim] + enc_query = self.add_q_proj(encoder_hidden_states) + enc_key = self.add_k_proj(encoder_hidden_states) + enc_value = self.add_v_proj(encoder_hidden_states) + + # Reshape to [B, S, H, D] + query = mx.reshape(query, (batch_size, seq_img, self.num_heads, self.head_dim)) + key = mx.reshape(key, (batch_size, seq_img, self.num_heads, self.head_dim)) + value = mx.reshape(value, (batch_size, seq_img, self.num_heads, self.head_dim)) + + enc_query = mx.reshape(enc_query, (batch_size, seq_ctx, self.num_heads, self.head_dim)) + enc_key = mx.reshape(enc_key, (batch_size, seq_ctx, self.num_heads, self.head_dim)) + enc_value = mx.reshape(enc_value, (batch_size, seq_ctx, self.num_heads, self.head_dim)) + + # RMSNorm over last dim: produce normalized Q/K matching BriaFiboAttention semantics. + query = self.norm_q(query.astype(mx.float32)).astype(query.dtype) + key = self.norm_k(key.astype(mx.float32)).astype(key.dtype) + + enc_query = self.norm_added_q(enc_query.astype(mx.float32)).astype(enc_query.dtype) + enc_key = self.norm_added_k(enc_key.astype(mx.float32)).astype(enc_key.dtype) + + # Concatenate encoder + image along sequence dim (matches BriaFiboAttnProcessor) + query = mx.concatenate([enc_query, query], axis=1) + key = mx.concatenate([enc_key, key], axis=1) + value = mx.concatenate([enc_value, value], axis=1) + + seq_total = seq_ctx + seq_img + + # Apply RoPE to Q,K (layout [B, S_total, H, D]) + query = FiboSingleAttention.apply_rotary_emb(query, cos, sin) + key = FiboSingleAttention.apply_rotary_emb(key, cos, sin) + + # Convert to [B, H, S, D] for fast SDPA + query_bhsd = mx.transpose(query, (0, 2, 1, 3)) + key_bhsd = mx.transpose(key, (0, 2, 1, 3)) + value_bhsd = mx.transpose(value, (0, 2, 1, 3)) + + # Prepare mask for scaled_dot_product_attention + attn_mask = None + if attention_mask is not None: + attn_mask = mx.broadcast_to(attention_mask, (batch_size, self.num_heads, seq_total, seq_total)) + attn_mask = attn_mask.astype(query_bhsd.dtype) + + scale = 1.0 / mx.sqrt(mx.array(self.head_dim, dtype=query_bhsd.dtype)) + attn_output_bhsd = scaled_dot_product_attention( + query_bhsd, + key_bhsd, + value_bhsd, + scale=scale, + mask=attn_mask, + ) + + # Back to [B, S_total, H, D] then flatten to [B, S_total, inner_dim] + attn_output = mx.transpose(attn_output_bhsd, (0, 2, 1, 3)) + attn_output = mx.reshape(attn_output, (batch_size, seq_total, self.inner_dim)) + + # Split back into context and image streams + context_attn_output = attn_output[:, :seq_ctx, :] + hidden_attn_output = attn_output[:, seq_ctx:, :] + + # Output projections + hidden_attn_output = self.to_out[0](hidden_attn_output) + context_attn_output = self.to_add_out(context_attn_output) + + return hidden_attn_output, context_attn_output diff --git a/src/mflux/models/fibo/model/fibo_transformer/fibo_single_attention.py b/src/mflux/models/fibo/model/fibo_transformer/fibo_single_attention.py new file mode 100644 index 0000000..1ccf9df --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/fibo_single_attention.py @@ -0,0 +1,80 @@ +import mlx.core as mx +from mlx import nn +from mlx.core.fast import scaled_dot_product_attention + + +class FiboSingleAttention(nn.Module): + def __init__(self, dim: int, num_attention_heads: int, attention_head_dim: int, eps: float = 1e-6): + super().__init__() + self.head_dim = attention_head_dim + self.num_heads = num_attention_heads + self.inner_dim = dim + + self.to_q = nn.Linear(dim, self.inner_dim) + self.to_k = nn.Linear(dim, self.inner_dim) + self.to_v = nn.Linear(dim, self.inner_dim) + + self.norm_q = nn.RMSNorm(self.head_dim, eps=eps) + self.norm_k = nn.RMSNorm(self.head_dim, eps=eps) + + def __call__( + self, + hidden_states: mx.array, + image_rotary_emb: tuple[mx.array, mx.array], + attention_mask: mx.array | None = None, + ) -> mx.array: + batch_size, seq_len, _ = hidden_states.shape + cos, sin = image_rotary_emb + + # [B, S, inner_dim] + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + + query = mx.reshape(query, (batch_size, seq_len, self.num_heads, self.head_dim)) + key = mx.reshape(key, (batch_size, seq_len, self.num_heads, self.head_dim)) + value = mx.reshape(value, (batch_size, seq_len, self.num_heads, self.head_dim)) + + # RMSNorm + query = self.norm_q(query.astype(mx.float32)).astype(query.dtype) + key = self.norm_k(key.astype(mx.float32)).astype(key.dtype) + + # RoPE + query = FiboSingleAttention.apply_rotary_emb(query, cos, sin) + key = FiboSingleAttention.apply_rotary_emb(key, cos, sin) + + # [B, H, S, D] + query_bhsd = mx.transpose(query, (0, 2, 1, 3)) + key_bhsd = mx.transpose(key, (0, 2, 1, 3)) + value_bhsd = mx.transpose(value, (0, 2, 1, 3)) + + scale = 1.0 / mx.sqrt(mx.array(self.head_dim, dtype=query_bhsd.dtype)) + attn_output = scaled_dot_product_attention( + query_bhsd, + key_bhsd, + value_bhsd, + scale=scale, + mask=attention_mask, + ) + + attn_output = mx.transpose(attn_output, (0, 2, 1, 3)) + attn_output = mx.reshape(attn_output, (batch_size, seq_len, self.inner_dim)) + return attn_output + + @staticmethod + def apply_rotary_emb( + x: mx.array, + freqs_cos: mx.array, + freqs_sin: mx.array, + ) -> mx.array: + bsz, seq_len, num_heads, head_dim = x.shape + cos = mx.expand_dims(mx.expand_dims(freqs_cos, axis=0), axis=2) + sin = mx.expand_dims(mx.expand_dims(freqs_sin, axis=0), axis=2) + x2 = x.reshape(bsz, seq_len, num_heads, -1, 2) + x_real = x2[..., 0] + x_imag = x2[..., 1] + x_rotated_real = -x_imag + x_rotated_imag = x_real + x_rotated = mx.stack([x_rotated_real, x_rotated_imag], axis=-1).reshape(bsz, seq_len, num_heads, head_dim) + out = (x.astype(mx.float32) * cos + x_rotated.astype(mx.float32) * sin).astype(x.dtype) + return out diff --git a/src/mflux/models/fibo/model/fibo_transformer/joint_transformer_block.py b/src/mflux/models/fibo/model/fibo_transformer/joint_transformer_block.py new file mode 100644 index 0000000..e03863b --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/joint_transformer_block.py @@ -0,0 +1,76 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_transformer.feed_forward import FiboFeedForward +from mflux.models.fibo.model.fibo_transformer.fibo_ada_layer_norm_zero import FiboAdaLayerNormZero +from mflux.models.fibo.model.fibo_transformer.fibo_joint_attention import FiboJointAttention + + +class FiboJointTransformerBlock(nn.Module): + def __init__( + self, + layer: int, + dim: int = 3072, + num_attention_heads: int = 24, + attention_head_dim: int = 128, + eps: float = 1e-6, + ): + super().__init__() + self.layer = layer + self.norm1 = FiboAdaLayerNormZero(embedding_dim=dim) + self.norm1_context = FiboAdaLayerNormZero(embedding_dim=dim) + self.attn = FiboJointAttention(dim=dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim) # fmt: off + self.norm2 = nn.LayerNorm(dims=dim, eps=eps, affine=False) + self.ff = FiboFeedForward(dim=dim, dim_out=dim, mult=4, activation_fn="gelu-approximate") + self.norm2_context = nn.LayerNorm(dims=dim, eps=eps, affine=False) + self.ff_context = FiboFeedForward(dim=dim, dim_out=dim, mult=4, activation_fn="gelu-approximate") + + def __call__( + self, + temb: mx.array, + hidden_states: mx.array, + encoder_hidden_states: mx.array, + image_rotary_emb: mx.array, + attention_mask: mx.array | None = None, + ) -> tuple[mx.array, mx.array]: + # 1. AdaLayerNormZero for both streams + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states=hidden_states, + text_embeddings=temb, + ) + + norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context( + hidden_states=encoder_hidden_states, + text_embeddings=temb, + ) + + # 2. Joint attention over image + context streams. + attn_output, context_attn_output = self.attn( + hidden_states=norm_hidden_states, + encoder_hidden_states=norm_encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + ) + + # 3a. Process attention outputs for the image stream. + attn_output = mx.expand_dims(gate_msa, axis=1) * attn_output + hidden_states = hidden_states + attn_output + + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + ff_output = self.ff(norm_hidden_states) + ff_output = mx.expand_dims(gate_mlp, axis=1) * ff_output + + hidden_states = hidden_states + ff_output + + # 3b. Process attention outputs for the context stream. + context_attn_output = mx.expand_dims(c_gate_msa, axis=1) * context_attn_output + encoder_hidden_states = encoder_hidden_states + context_attn_output + + norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states) + norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + + context_ff_output = self.ff_context(norm_encoder_hidden_states) + encoder_hidden_states = encoder_hidden_states + mx.expand_dims(c_gate_mlp, axis=1) * context_ff_output + return encoder_hidden_states, hidden_states diff --git a/src/mflux/models/fibo/model/fibo_transformer/single_transformer_block.py b/src/mflux/models/fibo/model/fibo_transformer/single_transformer_block.py new file mode 100644 index 0000000..2cdcbcc --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/single_transformer_block.py @@ -0,0 +1,55 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_transformer.fibo_single_attention import FiboSingleAttention +from mflux.models.flux.model.flux_transformer.ada_layer_norm_zero_single import AdaLayerNormZeroSingle + + +class FiboSingleTransformerBlock(nn.Module): + def __init__( + self, + layer: int, + dim: int = 3072, + num_attention_heads: int = 24, + attention_head_dim: int = 128, + mlp_ratio: float = 4.0, + ): + super().__init__() + self.layer = layer + self.mlp_hidden_dim = int(dim * mlp_ratio) + self.norm = AdaLayerNormZeroSingle() + self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim) + self.act_mlp = nn.gelu_approx + self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim) + self.attn = FiboSingleAttention(dim=dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim) # fmt: off + + def __call__( + self, + temb: mx.array, + hidden_states: mx.array, + image_rotary_emb: tuple[mx.array, mx.array], + attention_mask: mx.array | None = None, + ) -> mx.array: + # 0. Residual connection + residual = hidden_states + + # 1. AdaLayerNormZeroSingle + norm_hidden_states, gate = self.norm( + hidden_states=hidden_states, + text_embeddings=temb, + ) + + # 2. Attention + attn_output = self.attn( + hidden_states=norm_hidden_states, + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + ) + + # 3. MLP + projection + mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states)) + hidden_states = mx.concatenate([attn_output, mlp_hidden_states], axis=2) + gate = mx.expand_dims(gate, axis=1) + hidden_states = gate * self.proj_out(hidden_states) + hidden_states = residual + hidden_states + return hidden_states diff --git a/src/mflux/models/fibo/model/fibo_transformer/text_projection.py b/src/mflux/models/fibo/model/fibo_transformer/text_projection.py new file mode 100644 index 0000000..2070c33 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/text_projection.py @@ -0,0 +1,10 @@ +from mlx import nn + + +class BriaFiboTextProjection(nn.Module): + def __init__(self, in_features: int = 2048, hidden_size: int = 1536): + super().__init__() + self.linear = nn.Linear(in_features, hidden_size, bias=False) + + def __call__(self, caption): + return self.linear(caption) diff --git a/src/mflux/models/fibo/model/fibo_transformer/time_embed.py b/src/mflux/models/fibo/model/fibo_transformer/time_embed.py new file mode 100644 index 0000000..1878cd1 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/time_embed.py @@ -0,0 +1,17 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_transformer.bria_fibo_timesteps import BriaFiboTimesteps +from mflux.models.flux.model.flux_transformer.timestep_embedder import TimestepEmbedder + + +class BriaFiboTimestepProjEmbeddings(nn.Module): + def __init__(self, embedding_dim: int = 3072, time_theta: int = 10000): + super().__init__() + self.time_proj = BriaFiboTimesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, time_theta=time_theta, scale=1.0) # fmt: off + self.timestep_embedder = TimestepEmbedder() + + def __call__(self, timestep: mx.array, dtype) -> mx.array: + timesteps_proj = self.time_proj(timestep) + timesteps_emb = self.timestep_embedder(timesteps_proj.astype(dtype)) + return timesteps_emb diff --git a/src/mflux/models/fibo/model/fibo_transformer/transformer.py b/src/mflux/models/fibo/model/fibo_transformer/transformer.py new file mode 100644 index 0000000..17ba14a --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_transformer/transformer.py @@ -0,0 +1,225 @@ +import mlx.core as mx +from mlx import nn + +from mflux.config.runtime_config import RuntimeConfig +from mflux.models.fibo.model.fibo_transformer.fibo_embed_nd import FiboEmbedND +from mflux.models.fibo.model.fibo_transformer.joint_transformer_block import FiboJointTransformerBlock +from mflux.models.fibo.model.fibo_transformer.single_transformer_block import FiboSingleTransformerBlock +from mflux.models.fibo.model.fibo_transformer.text_projection import BriaFiboTextProjection +from mflux.models.fibo.model.fibo_transformer.time_embed import BriaFiboTimestepProjEmbeddings +from mflux.models.flux.model.flux_transformer.ada_layer_norm_continuous import AdaLayerNormContinuous + + +class FiboTransformer(nn.Module): + def __init__( + self, + in_channels: int = 48, + num_layers: int = 8, + num_single_layers: int = 38, + ): + super().__init__() + self.pos_embed = FiboEmbedND() + self.x_embedder = nn.Linear(in_channels, 3072) + self.time_embed = BriaFiboTimestepProjEmbeddings() + self.context_embedder = nn.Linear(4096, 3072) + self.transformer_blocks = [FiboJointTransformerBlock(i) for i in range(num_layers)] + self.single_transformer_blocks = [FiboSingleTransformerBlock(i) for i in range(num_single_layers)] + self.norm_out = AdaLayerNormContinuous(3072, 3072) + self.proj_out = nn.Linear(3072, in_channels) + self.caption_projection = [BriaFiboTextProjection() for _ in range(num_layers + num_single_layers)] + + def __call__( + self, + t: int, + config: RuntimeConfig, + hidden_states: mx.array, + encoder_hidden_states: mx.array, + text_encoder_layers: list[mx.array], + ) -> mx.array: + # 1. Create embeddings + hidden_states = FiboTransformer._handle_classifier_free_guidance(hidden_states, encoder_hidden_states) + hidden_states = self.x_embedder(hidden_states) + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + time_embeddings = FiboTransformer._compute_time_embeddings(t, config, hidden_states.shape[0], hidden_states.dtype, self.time_embed) # fmt: off + image_rotary_emb = FiboTransformer._compute_rotary_embeddings(encoder_hidden_states, self.pos_embed, config, hidden_states.dtype) # fmt: off + + # 2. Compute attention mask + attention_mask = FiboTransformer._compute_attention_mask( + config=config, + batch_size=hidden_states.shape[0], + encoder_hidden_states=encoder_hidden_states, + max_tokens=encoder_hidden_states.shape[1], + ) + + # 3. Project the fibo-specific text encoder layers + text_encoder_layers = [ + self.caption_projection[i](text_layer) + for i, text_layer in enumerate(text_encoder_layers) + ] # fmt: off + + # 4. Run the joint transformer blocks + block_id = 0 + for _, block in enumerate(self.transformer_blocks): + encoder_hidden_states, hidden_states = FiboTransformer._apply_joint_transformer_block( + block=block, + time_embeddings=time_embeddings, + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + text_encoder_layer=text_encoder_layers[block_id], + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + ) + block_id += 1 + + # 5. Run the single transformer blocks + for _, block in enumerate(self.single_transformer_blocks): + encoder_hidden_states, hidden_states = FiboTransformer._apply_single_transformer_block( + block=block, + time_embeddings=time_embeddings, + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + text_encoder_layer=text_encoder_layers[block_id], + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + ) + block_id += 1 + + # 6. Project the final output + hidden_states = self.norm_out(hidden_states, time_embeddings) + hidden_states = self.proj_out(hidden_states) + return hidden_states + + @staticmethod + def _apply_joint_transformer_block( + time_embeddings: mx.array, + block: FiboJointTransformerBlock, + hidden_states: mx.array, + encoder_hidden_states: mx.array, + text_encoder_layer: mx.array, + image_rotary_emb: mx.array, + attention_mask: mx.array, + ) -> tuple[mx.array, mx.array]: + encoder_hidden_states_half = encoder_hidden_states[:, :, :1536] + encoder_hidden_states = mx.concatenate([encoder_hidden_states_half, text_encoder_layer], axis=-1) + + encoder_hidden_states, hidden_states = block( + temb=time_embeddings, + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + ) + + return encoder_hidden_states, hidden_states + + @staticmethod + def _apply_single_transformer_block( + time_embeddings: mx.array, + block: FiboSingleTransformerBlock, + hidden_states: mx.array, + encoder_hidden_states: mx.array, + text_encoder_layer: mx.array, + image_rotary_emb: mx.array, + attention_mask: mx.array, + ) -> tuple[mx.array, mx.array]: + encoder_hidden_states_half = encoder_hidden_states[:, :, :1536] + encoder_hidden_states = mx.concatenate([encoder_hidden_states_half, text_encoder_layer], axis=-1) + combined = mx.concatenate([encoder_hidden_states, hidden_states], axis=1) + + combined = block( + temb=time_embeddings, + hidden_states=combined, + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + ) + + encoder_len = encoder_hidden_states.shape[1] + encoder_hidden_states = combined[:, :encoder_len, ...] + hidden_states = combined[:, encoder_len:, ...] + + return encoder_hidden_states, hidden_states + + @staticmethod + def _handle_classifier_free_guidance(hidden_states: mx.array, encoder_hidden_states: mx.array) -> mx.array: + batch_size = hidden_states.shape[0] + encoder_batch_size = encoder_hidden_states.shape[0] + if encoder_batch_size == batch_size * 2: + hidden_states = mx.concatenate([hidden_states, hidden_states], axis=0) + return hidden_states + + @staticmethod + def _compute_time_embeddings( + t: int, + config: RuntimeConfig, + batch_size: int, + dtype: mx.Dtype, + time_embed: BriaFiboTimestepProjEmbeddings, + ) -> mx.array: + timestep_value = config.scheduler.timesteps[t] + timestep = mx.full((batch_size,), timestep_value, dtype=dtype) + return time_embed(timestep, dtype=dtype) + + @staticmethod + def _compute_rotary_embeddings( + encoder_hidden_states: mx.array, + pos_embed: FiboEmbedND, + config: RuntimeConfig, + dtype: mx.Dtype, + ) -> mx.array: + max_tokens = encoder_hidden_states.shape[1] + txt_ids = mx.zeros((max_tokens, 3), dtype=dtype) + img_ids = FiboTransformer._prepare_latent_image_ids(height=config.height, width=config.width, dtype=dtype) + + if txt_ids.ndim == 3 and txt_ids.shape[0] == 1: + txt_ids = txt_ids[0] + if img_ids.ndim == 3 and img_ids.shape[0] == 1: + img_ids = img_ids[0] + + ids = mx.concatenate((txt_ids, img_ids), axis=0) + ids = mx.expand_dims(ids, axis=0) + return pos_embed(ids) + + @staticmethod + def _prepare_latent_image_ids(height: int, width: int, dtype=mx.float32) -> mx.array: + vae_scale_factor = 16 + latent_height = height // vae_scale_factor + latent_width = width // vae_scale_factor + row_indices = mx.arange(0, latent_height, dtype=dtype)[:, None] + row_indices = mx.broadcast_to(row_indices, (latent_height, latent_width)) + col_indices = mx.arange(0, latent_width, dtype=dtype)[None, :] + col_indices = mx.broadcast_to(col_indices, (latent_height, latent_width)) + zeros_channel = mx.zeros((latent_height, latent_width), dtype=dtype) + latent_image_ids = mx.stack([zeros_channel, row_indices, col_indices], axis=-1) + latent_image_ids = mx.reshape(latent_image_ids, (latent_height * latent_width, 3)) + return latent_image_ids + + @staticmethod + def _prepare_attention_mask(attention_mask_2d: mx.array) -> mx.array: + attention_matrix = mx.einsum("bi,bj->bij", attention_mask_2d, attention_mask_2d) + mask_dtype = attention_mask_2d.dtype + min_dtype_value = mx.finfo(mask_dtype).min + attention_matrix = mx.where( + attention_matrix == 1, + mx.zeros_like(attention_matrix).astype(mask_dtype), + (mx.ones_like(attention_matrix) * min_dtype_value).astype(mask_dtype), + ) + attention_matrix = mx.expand_dims(attention_matrix, axis=1) + return attention_matrix + + @staticmethod + def _compute_attention_mask( + batch_size: int, + config: RuntimeConfig, + encoder_hidden_states: mx.array, + max_tokens: int, + ) -> mx.array: + vae_scale_factor = 16 + latent_height = config.height // vae_scale_factor + latent_width = config.width // vae_scale_factor + latent_seq_len = latent_height * latent_width + prompt_attention_mask = mx.ones((batch_size, max_tokens), dtype=mx.float32) + latent_attention_mask = mx.ones((batch_size, latent_seq_len), dtype=mx.float32) + attention_mask_2d = mx.concatenate([prompt_attention_mask, latent_attention_mask], axis=1) + attention_mask = FiboTransformer._prepare_attention_mask(attention_mask_2d) + attention_mask = attention_mask.astype(encoder_hidden_states.dtype) + return attention_mask diff --git a/src/mflux/models/fibo/model/fibo_vae/__init__.py b/src/mflux/models/fibo/model/fibo_vae/__init__.py new file mode 100644 index 0000000..2136841 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/__init__.py @@ -0,0 +1 @@ +"""FIBO VAE components.""" diff --git a/src/mflux/models/fibo/model/fibo_vae/common/__init__.py b/src/mflux/models/fibo/model/fibo_vae/common/__init__.py new file mode 100644 index 0000000..0683107 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/__init__.py @@ -0,0 +1,17 @@ +"""FIBO VAE common/shared components.""" + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_attention_block import Wan2_2_AttentionBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_causal_conv_3d import Wan2_2_CausalConv3d +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_mid_block import Wan2_2_MidBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_resample import Wan2_2_Resample +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_residual_block import Wan2_2_ResidualBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_rms_norm import Wan2_2_RMSNorm + +__all__ = [ + "Wan2_2_AttentionBlock", + "Wan2_2_CausalConv3d", + "Wan2_2_MidBlock", + "Wan2_2_ResidualBlock", + "Wan2_2_Resample", + "Wan2_2_RMSNorm", +] diff --git a/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_attention_block.py b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_attention_block.py new file mode 100644 index 0000000..7cf6a09 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_attention_block.py @@ -0,0 +1,48 @@ +import mlx.core as mx +from mlx import nn +from mlx.core.fast import scaled_dot_product_attention + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_rms_norm import Wan2_2_RMSNorm + + +class Wan2_2_AttentionBlock(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.dim = dim + self.norm = Wan2_2_RMSNorm(dim, images=True) + self.to_qkv = nn.Conv2d(dim, dim * 3, kernel_size=1) + self.proj = nn.Conv2d(dim, dim, kernel_size=1) + + def __call__(self, x: mx.array) -> mx.array: + identity = x + batch_size, channels, time, height, width = x.shape + x = mx.transpose(x, (0, 2, 1, 3, 4)) + x = mx.reshape(x, (batch_size * time, channels, height, width)) + + x = self.norm(x) + x = mx.transpose(x, (0, 2, 3, 1)) + + qkv = self.to_qkv(x) + qkv = mx.transpose(qkv, (0, 3, 1, 2)) + qkv = mx.reshape(qkv, (batch_size * time, 1, channels * 3, height * width)) + qkv = mx.transpose(qkv, (0, 1, 3, 2)) + q, k, v = mx.split(qkv, 3, axis=3) + + scale = 1.0 / (channels**0.5) + x = scaled_dot_product_attention( + q, + k, + v, + scale=scale, + mask=None, + ) + x = mx.reshape(x, (batch_size * time, height * width, channels)) + x = mx.transpose(x, (0, 2, 1)) + x = mx.reshape(x, (batch_size * time, channels, height, width)) + x = mx.transpose(x, (0, 2, 3, 1)) + + x = self.proj(x) + x = mx.transpose(x, (0, 3, 1, 2)) + x = mx.reshape(x, (batch_size, time, channels, height, width)) + x = mx.transpose(x, (0, 2, 1, 3, 4)) + return x + identity diff --git a/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_causal_conv_3d.py b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_causal_conv_3d.py new file mode 100644 index 0000000..f32043a --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_causal_conv_3d.py @@ -0,0 +1,42 @@ +import mlx.core as mx +from mlx import nn + + +class Wan2_2_CausalConv3d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + padding: int = 1, + name: str | None = None, + ): + super().__init__() + self.conv3d = nn.Conv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=0, + ) + self.padding = padding + self.stride = stride + self.kernel_size = kernel_size + self.name = name or f"conv3d_{in_channels}to{out_channels}" + + def __call__(self, x: mx.array) -> mx.array: + pad_t = pad_h = pad_w = self.padding + if pad_t > 0 or pad_h > 0 or pad_w > 0: + pad_spec = [ + (0, 0), + (0, 0), + (2 * pad_t, 0), + (pad_h, pad_h), + (pad_w, pad_w), + ] + x = mx.pad(x, pad_spec) + x = mx.transpose(x, (0, 2, 3, 4, 1)) + x = self.conv3d(x) + x = mx.transpose(x, (0, 4, 1, 2, 3)) + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_mid_block.py b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_mid_block.py new file mode 100644 index 0000000..968f3b6 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_mid_block.py @@ -0,0 +1,22 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_attention_block import Wan2_2_AttentionBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_residual_block import Wan2_2_ResidualBlock + + +class Wan2_2_MidBlock(nn.Module): + def __init__(self, dim: int, non_linearity: str = "silu", num_layers: int = 1): + super().__init__() + self.resnets = [Wan2_2_ResidualBlock(dim, dim, non_linearity)] + self.attentions = [] + for _ in range(num_layers): + self.attentions.append(Wan2_2_AttentionBlock(dim)) + self.resnets.append(Wan2_2_ResidualBlock(dim, dim, non_linearity)) + + def __call__(self, x: mx.array) -> mx.array: + x = self.resnets[0](x) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + x = attn(x) + x = resnet(x) + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_resample.py b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_resample.py new file mode 100644 index 0000000..ed6af87 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_resample.py @@ -0,0 +1,64 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_causal_conv_3d import Wan2_2_CausalConv3d + + +class Wan2_2_Resample(nn.Module): + def __init__(self, dim: int, mode: str, upsample_out_dim: int = None): + super().__init__() + self.dim = dim + self.mode = mode + + if upsample_out_dim is None: + upsample_out_dim = dim // 2 + + if mode == "upsample3d": + self.time_conv = Wan2_2_CausalConv3d(dim, dim * 2, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) + self.resample_conv = nn.Conv2d(dim, upsample_out_dim, kernel_size=3, stride=1, padding=1) + elif mode == "upsample2d": + self.resample_conv = nn.Conv2d(dim, upsample_out_dim, kernel_size=3, stride=1, padding=1) + self.time_conv = None + elif mode == "downsample2d": + self.resample_conv = nn.Conv2d(dim, dim, kernel_size=3, stride=2, padding=0) + self.time_conv = None + elif mode == "downsample3d": + self.resample_conv = nn.Conv2d(dim, dim, kernel_size=3, stride=2, padding=0) + self.time_conv = None + else: + raise ValueError(f"Unsupported resample mode: {mode}") + + def __call__(self, x: mx.array, block_idx: int | None = None) -> mx.array: + b, c, t, h, w = x.shape + if self.mode in ("upsample2d", "upsample3d"): + if self.mode == "upsample3d" and self.time_conv is not None: + x = self.time_conv(x) + x = mx.reshape(x, (b, 2, c, t, h, w)) + x = mx.transpose(x, (0, 2, 3, 1, 4, 5)) + x = mx.reshape(x, (b, c, t * 2, h, w)) + t = t * 2 + x = mx.transpose(x, (0, 2, 1, 3, 4)) + x = mx.reshape(x, (b * t, c, h, w)) + x = mx.transpose(x, (0, 2, 3, 1)) + x = mx.repeat(x, 2, axis=1) + x = mx.repeat(x, 2, axis=2) + x = self.resample_conv(x) + x = mx.transpose(x, (0, 3, 1, 2)) + new_c = x.shape[1] + new_h, new_w = x.shape[2], x.shape[3] + x = mx.reshape(x, (b, t, new_c, new_h, new_w)) + x = mx.transpose(x, (0, 2, 1, 3, 4)) + return x + + # downsample modes + x = mx.transpose(x, (0, 2, 1, 3, 4)) + x = mx.reshape(x, (b * t, c, h, w)) + x = mx.transpose(x, (0, 2, 3, 1)) + x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)]) + x = self.resample_conv(x) + x = mx.transpose(x, (0, 3, 1, 2)) + new_c = x.shape[1] + new_h, new_w = x.shape[2], x.shape[3] + x = mx.reshape(x, (b, t, new_c, new_h, new_w)) + x = mx.transpose(x, (0, 2, 1, 3, 4)) + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_residual_block.py b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_residual_block.py new file mode 100644 index 0000000..5284e22 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_residual_block.py @@ -0,0 +1,35 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_causal_conv_3d import Wan2_2_CausalConv3d +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_rms_norm import Wan2_2_RMSNorm + + +class Wan2_2_ResidualBlock(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + non_linearity: str = "silu", + ): + super().__init__() + self.norm1 = Wan2_2_RMSNorm(in_dim, images=False) + self.conv1 = Wan2_2_CausalConv3d(in_dim, out_dim, 3, padding=1) + self.norm2 = Wan2_2_RMSNorm(out_dim, images=False) + self.conv2 = Wan2_2_CausalConv3d(out_dim, out_dim, 3, padding=1) + + if in_dim != out_dim: + self.conv_shortcut = Wan2_2_CausalConv3d(in_dim, out_dim, 1, padding=0) + else: + self.conv_shortcut = None + + def __call__(self, x: mx.array, resnet_idx: int | None = None, block_idx: int | None = None) -> mx.array: + h = self.conv_shortcut(x) if self.conv_shortcut is not None else x + x = self.norm1(x) + x = nn.silu(x) + x = self.conv1(x) + x = self.norm2(x) + x = nn.silu(x) + x = self.conv2(x) + result = x + h + return result diff --git a/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_rms_norm.py b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_rms_norm.py new file mode 100644 index 0000000..bdc7d50 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/common/wan_2_2_rms_norm.py @@ -0,0 +1,32 @@ +import mlx.core as mx +from mlx import nn + + +class Wan2_2_RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-12, images: bool = True): + super().__init__() + self.eps = eps + self.scale = float(dim) ** 0.5 + self.images = images + if images: + self.weight = mx.ones((dim, 1, 1)) + else: + self.weight = mx.ones((dim, 1, 1, 1)) + + def __call__(self, x: mx.array) -> mx.array: + sum_sq = mx.sum(x * x, axis=1, keepdims=True) + l2_norm = mx.sqrt(sum_sq) + denom = mx.maximum(l2_norm, mx.array(self.eps, dtype=l2_norm.dtype)) + x_normalized = x / denom + if x.ndim == 5 and not self.images: + weight = self.weight.reshape(1, -1, 1, 1, 1) + elif x.ndim == 4 and self.images: + weight = self.weight.reshape(1, -1, 1, 1) + else: + if x.ndim == 5: + weight = self.weight.reshape(1, -1, 1, 1, 1) + elif x.ndim == 4: + weight = self.weight.reshape(1, -1, 1, 1) + else: + weight = self.weight + return x_normalized * self.scale * weight diff --git a/src/mflux/models/fibo/model/fibo_vae/decoder/__init__.py b/src/mflux/models/fibo/model/fibo_vae/decoder/__init__.py new file mode 100644 index 0000000..76a4c21 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/decoder/__init__.py @@ -0,0 +1,5 @@ +"""FIBO VAE decoder components.""" + +from mflux.models.fibo.model.fibo_vae.decoder.wan_2_2_decoder_3d import Wan2_2_Decoder3d + +__all__ = ["Wan2_2_Decoder3d"] diff --git a/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_decoder_3d.py b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_decoder_3d.py new file mode 100644 index 0000000..dbb8c15 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_decoder_3d.py @@ -0,0 +1,58 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_causal_conv_3d import Wan2_2_CausalConv3d +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_mid_block import Wan2_2_MidBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_rms_norm import Wan2_2_RMSNorm +from mflux.models.fibo.model.fibo_vae.decoder.wan_2_2_residual_up_block import Wan2_2_ResidualUpBlock + + +class Wan2_2_Decoder3d(nn.Module): + def __init__( + self, + dim: int = 256, + z_dim: int = 48, + dim_mult: list[int] = [1, 2, 4, 4], + num_res_blocks: int = 2, + temporal_upsample: list[bool] | None = None, + non_linearity: str = "silu", + out_channels: int = 12, + ): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.temporal_upsample = temporal_upsample or [] + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + self.conv_in = Wan2_2_CausalConv3d(z_dim, dims[0], 3, padding=1, name="decoder_conv_in") + self.mid_block = Wan2_2_MidBlock(dims[0], non_linearity, num_layers=1) + self.up_blocks: list[Wan2_2_ResidualUpBlock] = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + up_flag = i != len(dim_mult) - 1 + temporal_flag = ( + bool(self.temporal_upsample and i < len(self.temporal_upsample) and self.temporal_upsample[i]) + if up_flag + else False + ) + up_block = Wan2_2_ResidualUpBlock( + in_dim=in_dim, + out_dim=out_dim, + num_res_blocks=num_res_blocks, + temporal_upsample=temporal_flag, + up_flag=up_flag, + non_linearity=non_linearity, + ) + self.up_blocks.append(up_block) + self.norm_out = Wan2_2_RMSNorm(out_dim, images=False) + self.conv_out = Wan2_2_CausalConv3d(out_dim, out_channels, 3, padding=1, name="decoder_conv_out") + + def __call__(self, x: mx.array) -> mx.array: + x = self.conv_in(x) + x = self.mid_block(x) + for i, up_block in enumerate(self.up_blocks): + x = up_block(x, block_idx=i, first_chunk=True) + x = self.norm_out(x) + x = nn.silu(x) + x = self.conv_out(x) + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_dup_up_3d.py b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_dup_up_3d.py new file mode 100644 index 0000000..78ba23c --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_dup_up_3d.py @@ -0,0 +1,53 @@ +import mlx.core as mx +from mlx import nn + + +class Wan2_2_DupUp3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + factor_t: int, + factor_s: int = 1, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor_t = factor_t + self.factor_s = factor_s + self.factor = self.factor_t * self.factor_s * self.factor_s + self.repeats = out_channels * self.factor // in_channels + + def __call__(self, x: mx.array, first_chunk: bool = False) -> mx.array: + b, c, t, h, w = x.shape + x = mx.repeat(x, self.repeats, axis=1) + x = mx.reshape( + x, + ( + b, + self.out_channels, + self.factor_t, + self.factor_s, + self.factor_s, + t, + h, + w, + ), + ) + + x = mx.transpose(x, (0, 1, 5, 2, 6, 3, 7, 4)) + x = mx.reshape( + x, + ( + b, + self.out_channels, + t * self.factor_t, + h * self.factor_s, + w * self.factor_s, + ), + ) + + if first_chunk and self.factor_t > 1: + x = x[:, :, self.factor_t - 1 :, :, :] + + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_residual_up_block.py b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_residual_up_block.py new file mode 100644 index 0000000..cfd04a9 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_residual_up_block.py @@ -0,0 +1,60 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_resample import Wan2_2_Resample +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_residual_block import Wan2_2_ResidualBlock +from mflux.models.fibo.model.fibo_vae.decoder.wan_2_2_dup_up_3d import Wan2_2_DupUp3D + + +class Wan2_2_ResidualUpBlock(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + num_res_blocks: int, + temporal_upsample: bool = False, + up_flag: bool = False, + non_linearity: str = "silu", + ): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + if up_flag: + self.avg_shortcut: Wan2_2_DupUp3D | None = Wan2_2_DupUp3D( + in_channels=in_dim, + out_channels=out_dim, + factor_t=2 if temporal_upsample else 1, + factor_s=2, + ) + else: + self.avg_shortcut = None + + resnets: list[Wan2_2_ResidualBlock] = [] + current_dim = in_dim + for _ in range(num_res_blocks + 1): + resnets.append(Wan2_2_ResidualBlock(current_dim, out_dim, non_linearity)) + current_dim = out_dim + self.resnets = resnets + + if up_flag: + upsample_mode = "upsample3d" if temporal_upsample else "upsample2d" + self.upsampler: Wan2_2_Resample | None = Wan2_2_Resample( + out_dim, mode=upsample_mode, upsample_out_dim=out_dim + ) + else: + self.upsampler = None + + def __call__(self, x: mx.array, block_idx: int | None = None, first_chunk: bool = False) -> mx.array: + x_copy = x + + for i, resnet in enumerate(self.resnets): + x = resnet(x, resnet_idx=i, block_idx=block_idx) + + if self.upsampler is not None: + x = self.upsampler(x, block_idx=block_idx) + + if self.avg_shortcut is not None: + x = x + self.avg_shortcut(x_copy, first_chunk=first_chunk) + + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_up_block.py b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_up_block.py new file mode 100644 index 0000000..7ee4d6a --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/decoder/wan_2_2_up_block.py @@ -0,0 +1,34 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_resample import Wan2_2_Resample +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_residual_block import Wan2_2_ResidualBlock + + +class Wan2_2_UpBlock(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + num_res_blocks: int, + upsample_mode: str | None = None, + non_linearity: str = "silu", + ): + super().__init__() + self.resnets: list[Wan2_2_ResidualBlock] = [] + current_dim = in_dim + for _ in range(num_res_blocks + 1): + self.resnets.append(Wan2_2_ResidualBlock(current_dim, out_dim, non_linearity)) + current_dim = out_dim + self.upsampler: Wan2_2_Resample | None = None + if upsample_mode is not None: + self.upsampler = Wan2_2_Resample(out_dim, mode=upsample_mode, upsample_out_dim=out_dim) + + def __call__(self, x: mx.array, block_idx: int | None = None) -> mx.array: + for i, resnet in enumerate(self.resnets): + x = resnet(x, resnet_idx=i, block_idx=block_idx) + + if self.upsampler is not None: + x = self.upsampler(x, block_idx=block_idx) + + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/encoder/__init__.py b/src/mflux/models/fibo/model/fibo_vae/encoder/__init__.py new file mode 100644 index 0000000..1ba10c4 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/encoder/__init__.py @@ -0,0 +1,5 @@ +"""FIBO VAE encoder components.""" + +from mflux.models.fibo.model.fibo_vae.encoder.wan_2_2_encoder_3d import Wan2_2_Encoder3d + +__all__ = ["Wan2_2_Encoder3d"] diff --git a/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_avg_down_3d.py b/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_avg_down_3d.py new file mode 100644 index 0000000..efbab79 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_avg_down_3d.py @@ -0,0 +1,75 @@ +import mlx.core as mx +from mlx import nn + + +class Wan2_2_AvgDown3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + factor_t: int, + factor_s: int = 1, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor_t = factor_t + self.factor_s = factor_s + self.factor = self.factor_t * self.factor_s * self.factor_s + + assert in_channels * self.factor % out_channels == 0 + self.group_size = in_channels * self.factor // out_channels + + def __call__(self, x: mx.array) -> mx.array: + pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t + if pad_t > 0: + x = mx.pad( + x, + [ + (0, 0), # batch + (0, 0), # channels + (pad_t, 0), # time + (0, 0), # height + (0, 0), # width + ], + ) + + b, c, t, h, w = x.shape + + x = mx.reshape( + x, + ( + b, + c, + t // self.factor_t, + self.factor_t, + h // self.factor_s, + self.factor_s, + w // self.factor_s, + self.factor_s, + ), + ) + x = mx.transpose(x, (0, 1, 3, 5, 7, 2, 4, 6)) + x = mx.reshape( + x, + ( + b, + c * self.factor, + t // self.factor_t, + h // self.factor_s, + w // self.factor_s, + ), + ) + x = mx.reshape( + x, + ( + b, + self.out_channels, + self.group_size, + t // self.factor_t, + h // self.factor_s, + w // self.factor_s, + ), + ) + x = mx.mean(x, axis=2) + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_down_block.py b/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_down_block.py new file mode 100644 index 0000000..9087cd1 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_down_block.py @@ -0,0 +1,57 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_attention_block import Wan2_2_AttentionBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_resample import Wan2_2_Resample +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_residual_block import Wan2_2_ResidualBlock +from mflux.models.fibo.model.fibo_vae.encoder.wan_2_2_avg_down_3d import Wan2_2_AvgDown3D + + +class Wan2_2_DownBlock(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + num_res_blocks: int, + attn_scales: list[float] | None = None, + scale: float = 1.0, + temporal_downsample: bool = False, + non_linearity: str = "silu", + is_last: bool = False, + ): + super().__init__() + if attn_scales is None: + attn_scales = [] + + resnets: list[nn.Module] = [] + current_dim = in_dim + for _ in range(num_res_blocks): + resnets.append(Wan2_2_ResidualBlock(current_dim, out_dim, non_linearity)) + if scale in attn_scales: + resnets.append(Wan2_2_AttentionBlock(out_dim)) + current_dim = out_dim + + self.resnets = resnets + + # Shortcut path with downsample (mirrors AvgDown3D in diffusers) + self.avg_shortcut = Wan2_2_AvgDown3D( + in_dim, + out_dim, + factor_t=2 if temporal_downsample else 1, + factor_s=2 if not is_last else 1, + ) + + # Main path downsampler + if not is_last: + mode = "downsample3d" if temporal_downsample else "downsample2d" + self.downsampler = Wan2_2_Resample(out_dim, mode=mode) + else: + self.downsampler = None + + def __call__(self, x: mx.array) -> mx.array: + x_copy = x + for layer in self.resnets: + x = layer(x) + if self.downsampler is not None: + x = self.downsampler(x) + return x + self.avg_shortcut(x_copy) diff --git a/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_encoder_3d.py b/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_encoder_3d.py new file mode 100644 index 0000000..f4354e2 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/encoder/wan_2_2_encoder_3d.py @@ -0,0 +1,75 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_causal_conv_3d import Wan2_2_CausalConv3d +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_mid_block import Wan2_2_MidBlock +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_rms_norm import Wan2_2_RMSNorm +from mflux.models.fibo.model.fibo_vae.encoder.wan_2_2_down_block import Wan2_2_DownBlock + + +class Wan2_2_Encoder3d(nn.Module): + def __init__( + self, + in_channels: int = 3, + dim: int = 128, + z_dim: int = 4, + dim_mult: list[int] | None = None, + num_res_blocks: int = 2, + attn_scales: list[float] | None = None, + temporal_downsample: list[bool] | None = None, + non_linearity: str = "silu", + is_residual: bool = False, + ): + super().__init__() + + if dim_mult is None: + dim_mult = [1, 2, 4, 4] + if attn_scales is None: + attn_scales = [] + if temporal_downsample is None: + temporal_downsample = [False, True, True] + + if is_residual: + raise NotImplementedError("Residual down blocks are not implemented for Wan2_2_Encoder3d in MLX.") + + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + dims = [dim * u for u in [1] + dim_mult] + self.temporal_downsample = temporal_downsample + + self.conv_in = Wan2_2_CausalConv3d(in_channels, dims[0], 3, padding=1) + scale = 1.0 + + self.down_blocks: list[Wan2_2_DownBlock] = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + block = Wan2_2_DownBlock( + in_dim=in_dim, + out_dim=out_dim, + num_res_blocks=num_res_blocks, + attn_scales=attn_scales, + scale=scale, + temporal_downsample=temporal_downsample[i] if i < len(temporal_downsample) else False, + non_linearity=non_linearity, + is_last=i == len(dim_mult) - 1, + ) + self.down_blocks.append(block) + if i != len(dim_mult) - 1: + scale /= 2.0 + + self.mid_block = Wan2_2_MidBlock(out_dim, non_linearity, num_layers=1) + + self.norm_out = Wan2_2_RMSNorm(out_dim, images=False) + self.conv_out = Wan2_2_CausalConv3d(out_dim, z_dim, 3, padding=1) + + def __call__(self, x: mx.array) -> mx.array: + x = self.conv_in(x) + for block in self.down_blocks: + x = block(x) + x = self.mid_block(x) + x = self.norm_out(x) + x = nn.silu(x) + x = self.conv_out(x) + return x diff --git a/src/mflux/models/fibo/model/fibo_vae/wan_2_2_vae.py b/src/mflux/models/fibo/model/fibo_vae/wan_2_2_vae.py new file mode 100644 index 0000000..d9e98c5 --- /dev/null +++ b/src/mflux/models/fibo/model/fibo_vae/wan_2_2_vae.py @@ -0,0 +1,111 @@ +import mlx.core as mx +import numpy as np +from mlx import nn + +from mflux.models.fibo.model.fibo_vae.common.wan_2_2_causal_conv_3d import Wan2_2_CausalConv3d +from mflux.models.fibo.model.fibo_vae.decoder.wan_2_2_decoder_3d import Wan2_2_Decoder3d +from mflux.models.fibo.model.fibo_vae.encoder.wan_2_2_encoder_3d import Wan2_2_Encoder3d + + +class Wan2_2_VAE(nn.Module): + Z_DIM = 48 + ENCODER_BASE_DIM = 160 + DECODER_BASE_DIM = 256 + DIM_MULT = [1, 2, 4, 4] + NUM_RES_BLOCKS = 2 + OUT_CHANNELS = 12 + + LATENTS_MEAN = np.array([-0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, -0.1382, 0.0542, 0.2813, 0.0891, 0.157, -0.0098, 0.0375, -0.1825, -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.123, -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.052, 0.3748, 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667], dtype=np.float32) # fmt: off + LATENTS_STD = np.array([0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.499, 0.4818, 0.5013, 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, 0.3971, 1.06, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744], dtype=np.float32) # fmt: off + + def __init__(self): + super().__init__() + + self.encoder = Wan2_2_Encoder3d( + in_channels=3, + dim=self.ENCODER_BASE_DIM, + z_dim=self.Z_DIM * 2, + dim_mult=self.DIM_MULT, + num_res_blocks=self.NUM_RES_BLOCKS, + attn_scales=[], + temporal_downsample=[False, True, True], + ) + self.quant_conv = Wan2_2_CausalConv3d(self.Z_DIM * 2, self.Z_DIM * 2, 1, padding=0, name="quant_conv") + + self.decoder = Wan2_2_Decoder3d( + dim=self.DECODER_BASE_DIM, + z_dim=self.Z_DIM, + dim_mult=self.DIM_MULT, + num_res_blocks=self.NUM_RES_BLOCKS, + temporal_upsample=None, + out_channels=self.OUT_CHANNELS, + ) + self.post_quant_conv = Wan2_2_CausalConv3d(self.Z_DIM, self.Z_DIM, 1, padding=0, name="post_quant_conv") + + def encode(self, images: mx.array) -> mx.array: + if images.ndim == 4: + x = images.reshape(images.shape[0], images.shape[1], 1, images.shape[2], images.shape[3]) + elif images.ndim == 5: + x = images + else: + raise ValueError(f"Expected 4D or 5D input for VAE.encode, got shape {images.shape}") + + patch_size = 2 + x = self._patchify(x, patch_size=patch_size) + h = self.encoder(x) + h = self.quant_conv(h) + mean = h[:, : self.Z_DIM, :, :, :] + latents_mean = mx.array(self.LATENTS_MEAN).reshape(1, self.Z_DIM, 1, 1, 1) + latents_std = mx.array(self.LATENTS_STD).reshape(1, self.Z_DIM, 1, 1, 1) + encoded = (mean - latents_mean) / latents_std + return encoded + + def decode(self, latents: mx.array) -> mx.array: + if latents.ndim == 4: + latents = latents.reshape(latents.shape[0], latents.shape[1], 1, latents.shape[2], latents.shape[3]) + + latents_mean = mx.array(self.LATENTS_MEAN).reshape(1, self.Z_DIM, 1, 1, 1) + latents_std = mx.array(self.LATENTS_STD).reshape(1, self.Z_DIM, 1, 1, 1) + latents = latents * latents_std + latents_mean + latents = self.post_quant_conv(latents) + decoded = self.decoder(latents) + patch_size = 2 + decoded = self._unpatchify(decoded, patch_size=patch_size) + if decoded.shape[2] == 1: + decoded = decoded[:, :, 0, :, :] + return decoded + + @staticmethod + def _patchify(x: mx.array, patch_size: int) -> mx.array: + if patch_size == 1: + return x + batch_size, channels, frames, height, width = x.shape + x = mx.reshape( + x, + ( + batch_size, + channels, + frames, + height // patch_size, + patch_size, + width // patch_size, + patch_size, + ), + ) + x = mx.transpose(x, (0, 1, 6, 4, 2, 3, 5)) + x = mx.reshape( + x, + (batch_size, channels * patch_size * patch_size, frames, height // patch_size, width // patch_size), + ) + return x + + @staticmethod + def _unpatchify(x: mx.array, patch_size: int) -> mx.array: + if patch_size == 1: + return x + batch_size, c_patches, frames, height, width = x.shape + channels = c_patches // (patch_size * patch_size) + x = mx.reshape(x, (batch_size, channels, patch_size, patch_size, frames, height, width)) + x = mx.transpose(x, (0, 1, 4, 5, 3, 6, 2)) + x = mx.reshape(x, (batch_size, channels, frames, height * patch_size, width * patch_size)) + return x diff --git a/src/mflux/models/fibo/tokenizer/__init__.py b/src/mflux/models/fibo/tokenizer/__init__.py new file mode 100644 index 0000000..8c086d5 --- /dev/null +++ b/src/mflux/models/fibo/tokenizer/__init__.py @@ -0,0 +1,7 @@ +from mflux.models.fibo.tokenizer.fibo_tokenizer import TokenizerFibo +from mflux.models.fibo.tokenizer.smol_lm3_3b_tokenizer import FiboTokenizerHandler + +__all__ = [ + "FiboTokenizerHandler", + "TokenizerFibo", +] diff --git a/src/mflux/models/fibo/tokenizer/fibo_tokenizer.py b/src/mflux/models/fibo/tokenizer/fibo_tokenizer.py new file mode 100644 index 0000000..461a49d --- /dev/null +++ b/src/mflux/models/fibo/tokenizer/fibo_tokenizer.py @@ -0,0 +1,37 @@ +import mlx.core as mx +import numpy as np +from transformers import PreTrainedTokenizer + + +class TokenizerFibo: + def __init__(self, tokenizer: PreTrainedTokenizer, bot_token_id: int = 128000): + self.tokenizer = tokenizer + self.bot_token_id = bot_token_id + + def tokenize( + self, + prompts: list[str], + max_length: int = 2048, + padding: str = "longest", + truncation: bool = True, + add_special_tokens: bool = True, + ) -> tuple[mx.array, mx.array]: + prompts = [p if p is not None else "" for p in prompts] + + if all(p == "" for p in prompts): + batch_size = len(prompts) + input_ids_mx = mx.array(np.empty((batch_size, 0), dtype=np.int32)) + attention_mask_mx = mx.array(np.empty((batch_size, 0), dtype=np.int32)) + else: + tokenized = self.tokenizer( + prompts, + padding=padding, + max_length=max_length, + truncation=truncation, + add_special_tokens=add_special_tokens, + return_tensors="mlx", + ) + input_ids_mx = tokenized["input_ids"] + attention_mask_mx = tokenized["attention_mask"] + + return input_ids_mx, attention_mask_mx diff --git a/src/mflux/models/fibo/tokenizer/qwen2vl_image_processor.py b/src/mflux/models/fibo/tokenizer/qwen2vl_image_processor.py new file mode 100644 index 0000000..38effa0 --- /dev/null +++ b/src/mflux/models/fibo/tokenizer/qwen2vl_image_processor.py @@ -0,0 +1,14 @@ +from mflux.models.qwen.tokenizer.qwen_image_processor import QwenImageProcessor + + +class Qwen2VLImageProcessor(QwenImageProcessor): + def __init__(self): + super().__init__( + min_pixels=256 * 28 * 28, + max_pixels=1024 * 28 * 28, + patch_size=16, + temporal_patch_size=2, + merge_size=2, + image_mean=[0.5, 0.5, 0.5], + image_std=[0.5, 0.5, 0.5], + ) diff --git a/src/mflux/models/fibo/tokenizer/qwen2vl_processor.py b/src/mflux/models/fibo/tokenizer/qwen2vl_processor.py new file mode 100644 index 0000000..aa03e6a --- /dev/null +++ b/src/mflux/models/fibo/tokenizer/qwen2vl_processor.py @@ -0,0 +1,205 @@ +from typing import Optional, Union + +import numpy as np +from PIL import Image + +from mflux.models.fibo.tokenizer.qwen2vl_image_processor import Qwen2VLImageProcessor + + +class Qwen2VLProcessor: + def __init__(self, tokenizer): + self.tokenizer = tokenizer + self.image_processor = Qwen2VLImageProcessor() + + def apply_chat_template( + self, + messages, + tokenize: bool = True, + add_generation_prompt: bool = False, + return_tensors: Optional[str] = None, + return_dict: bool = True, + **kwargs, + ): + formatted = self.tokenizer.apply_chat_template( + messages, + tokenize=tokenize, + add_generation_prompt=add_generation_prompt, + return_tensors=return_tensors, + return_dict=return_dict, + **kwargs, + ) + if tokenize and return_dict: + if return_tensors == "pt": + import torch + + if isinstance(formatted, dict): + result = {} + for key, value in formatted.items(): + if isinstance(value, torch.Tensor): + result[key] = value.numpy() + else: + result[key] = value + return result + elif return_tensors == "np" or return_tensors is None: + if isinstance(formatted, dict): + result = {} + for key, value in formatted.items(): + if hasattr(value, "numpy"): + result[key] = value.numpy() + elif isinstance(value, (list, tuple)): + result[key] = np.array(value) + else: + result[key] = value + return result + return formatted + + def __call__( + self, + text: Optional[Union[str, list[str]]] = None, + images: Optional[Union[Image.Image, list[Image.Image]]] = None, + padding: bool = True, + return_tensors: Optional[str] = None, + **kwargs, + ): + result = {} + + if images is not None: + pixel_values, image_grid_thw = self.image_processor.preprocess(images) + result["pixel_values"] = pixel_values + result["image_grid_thw"] = image_grid_thw + + if text is not None: + if not isinstance(text, list): + text = [text] + + text_inputs = self.tokenizer( + text, + padding=padding, + return_tensors=return_tensors or "np", + **kwargs, + ) + + # If images are provided, replace <|image_pad|> tokens with actual image tokens + # The number of image tokens is calculated as: prod(image_grid_thw) // merge_size^2 + if images is not None and "input_ids" in text_inputs: + image_token_id = 151655 # <|image_pad|> token ID + input_ids = text_inputs["input_ids"] + + # Calculate number of image tokens per image based on image_grid_thw and merge_size + # This matches transformers behavior: num_tokens = prod(image_grid_thw) // merge_size^2 + merge_size = self.image_processor.merge_size + merge_length = merge_size**2 + num_images = len(images) if isinstance(images, list) else 1 + + # Get image_grid_thw from result (already computed above) + image_grid_thw = result["image_grid_thw"] + + # Calculate tokens per image + if return_tensors == "pt": + import torch + + if isinstance(image_grid_thw, torch.Tensor): + image_grid_thw_np = image_grid_thw.cpu().numpy() + else: + image_grid_thw_np = image_grid_thw + else: + if isinstance(image_grid_thw, np.ndarray): + image_grid_thw_np = image_grid_thw + else: + image_grid_thw_np = np.array(image_grid_thw) + + # Calculate tokens for each image + num_image_tokens_per_image_list = [] + for i in range(num_images): + if i < len(image_grid_thw_np): + grid = image_grid_thw_np[i] + num_tokens = int(np.prod(grid)) // merge_length + num_image_tokens_per_image_list.append(num_tokens) + else: + num_image_tokens_per_image_list.append(256) # fallback + + # Replace image pad tokens with actual image tokens + if return_tensors == "pt": + import torch + + new_input_ids_list = [] + for seq in input_ids: + new_seq = [] + image_idx = 0 + for token_id in seq: + if token_id == image_token_id and image_idx < num_images: + # Replace with calculated number of image tokens for this image + num_tokens = ( + num_image_tokens_per_image_list[image_idx] + if image_idx < len(num_image_tokens_per_image_list) + else 256 + ) + new_seq.extend([image_token_id] * num_tokens) + image_idx += 1 + else: + new_seq.append(token_id) + new_input_ids_list.append(new_seq) + + # Pad sequences to same length + max_len = max(len(seq) for seq in new_input_ids_list) + padded_input_ids = [] + padded_attention_mask = [] + for seq in new_input_ids_list: + pad_len = max_len - len(seq) + padded_seq = ( + seq + [self.tokenizer.pad_token_id] * pad_len + if self.tokenizer.pad_token_id is not None + else seq + [0] * pad_len + ) + padded_input_ids.append(padded_seq) + padded_attention_mask.append([1] * len(seq) + [0] * pad_len) + + text_inputs["input_ids"] = torch.tensor(padded_input_ids) + if "attention_mask" in text_inputs: + text_inputs["attention_mask"] = torch.tensor(padded_attention_mask) + else: + # NumPy version + new_input_ids_list = [] + for seq in input_ids: + new_seq = [] + image_idx = 0 + for token_id in seq: + if token_id == image_token_id and image_idx < num_images: + # Replace with calculated number of image tokens for this image + num_tokens = ( + num_image_tokens_per_image_list[image_idx] + if image_idx < len(num_image_tokens_per_image_list) + else 256 + ) + new_seq.extend([image_token_id] * num_tokens) + image_idx += 1 + else: + new_seq.append(token_id) + new_input_ids_list.append(new_seq) + + # Pad sequences to same length + max_len = max(len(seq) for seq in new_input_ids_list) + padded_input_ids = [] + padded_attention_mask = [] + for seq in new_input_ids_list: + pad_len = max_len - len(seq) + padded_seq = ( + seq + + [self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else 0] * pad_len + ) + padded_input_ids.append(padded_seq) + padded_attention_mask.append([1] * len(seq) + [0] * pad_len) + + text_inputs["input_ids"] = np.array(padded_input_ids) + if "attention_mask" in text_inputs: + text_inputs["attention_mask"] = np.array(padded_attention_mask) + + if return_tensors == "pt": + result["input_ids"] = text_inputs["input_ids"] + result["attention_mask"] = text_inputs.get("attention_mask") + else: + result["input_ids"] = np.array(text_inputs["input_ids"]) + if "attention_mask" in text_inputs: + result["attention_mask"] = np.array(text_inputs["attention_mask"]) + + return result diff --git a/src/mflux/models/fibo/tokenizer/smol_lm3_3b_tokenizer.py b/src/mflux/models/fibo/tokenizer/smol_lm3_3b_tokenizer.py new file mode 100644 index 0000000..b7aa04e --- /dev/null +++ b/src/mflux/models/fibo/tokenizer/smol_lm3_3b_tokenizer.py @@ -0,0 +1,42 @@ +from pathlib import Path + +import transformers + +from mflux.models.fibo.tokenizer.fibo_tokenizer import TokenizerFibo +from mflux.utils.download import snapshot_download + + +class FiboTokenizerHandler: + def __init__( + self, + repo_id: str, + bot_token_id: int = 128000, + local_path: str | None = None, + ): + root_path = Path(local_path) if local_path else FiboTokenizerHandler._download_or_get_cached_tokenizer(repo_id) + + # Try different possible tokenizer paths + tokenizer_path = root_path / "tokenizer" + if not tokenizer_path.exists(): + tokenizer_path = root_path / "text_encoder" + if not tokenizer_path.exists(): + tokenizer_path = root_path + + # Load the raw tokenizer + self.tokenizer_raw = transformers.AutoTokenizer.from_pretrained( + pretrained_model_name_or_path=str(tokenizer_path), + local_files_only=True, + fix_mistral_regex=True, # Fix Mistral regex pattern warning + ) + + # Wrap in our TokenizerFibo class + self.fibo = TokenizerFibo(self.tokenizer_raw, bot_token_id=bot_token_id) + + @staticmethod + def _download_or_get_cached_tokenizer(repo_id: str) -> Path: + return Path( + snapshot_download( + repo_id=repo_id, + allow_patterns=["tokenizer/**", "text_encoder/**"], + ) + ) diff --git a/src/mflux/models/fibo/variants/__init__.py b/src/mflux/models/fibo/variants/__init__.py new file mode 100644 index 0000000..e27de1f --- /dev/null +++ b/src/mflux/models/fibo/variants/__init__.py @@ -0,0 +1 @@ +"""FIBO model variants.""" diff --git a/src/mflux/models/fibo/variants/txt2img/__init__.py b/src/mflux/models/fibo/variants/txt2img/__init__.py new file mode 100644 index 0000000..38b2180 --- /dev/null +++ b/src/mflux/models/fibo/variants/txt2img/__init__.py @@ -0,0 +1 @@ +"""FIBO text-to-image variant.""" diff --git a/src/mflux/models/fibo/variants/txt2img/fibo.py b/src/mflux/models/fibo/variants/txt2img/fibo.py new file mode 100644 index 0000000..b85cfdf --- /dev/null +++ b/src/mflux/models/fibo/variants/txt2img/fibo.py @@ -0,0 +1,187 @@ +import mlx.core as mx +from mlx import nn +from tqdm import tqdm + +from mflux.callbacks.callbacks import Callbacks +from mflux.config.config import Config +from mflux.config.model_config import ModelConfig +from mflux.config.runtime_config import RuntimeConfig +from mflux.models.common.latent_creator.latent_creator import Img2Img, LatentCreator +from mflux.models.common.weights.model_saver import ModelSaver +from mflux.models.fibo.fibo_initializer import FIBOInitializer +from mflux.models.fibo.latent_creator.fibo_latent_creator import FiboLatentCreator +from mflux.models.fibo.model.fibo_text_encoder.prompt_encoder import PromptEncoder +from mflux.models.fibo.model.fibo_text_encoder.smol_lm3_3b_text_encoder import SmolLM3_3B_TextEncoder +from mflux.models.fibo.model.fibo_transformer import FiboTransformer +from mflux.models.fibo.model.fibo_vae.wan_2_2_vae import Wan2_2_VAE +from mflux.models.fibo.tokenizer.fibo_tokenizer import TokenizerFibo +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil + + +class FIBO(nn.Module): + vae: Wan2_2_VAE + transformer: FiboTransformer + text_encoder: SmolLM3_3B_TextEncoder + fibo_tokenizer: TokenizerFibo + + def __init__( + self, + model_config: ModelConfig, + quantize: int | None = None, + local_path: str | None = None, + ): + super().__init__() + self.model_config = model_config + self.bits = quantize + self.local_path = local_path + + FIBOInitializer.init( + fibo_model=self, + model_config=model_config, + quantize=quantize, + local_path=local_path, + ) + + def generate_image( + self, + seed: int, + prompt: str, + config: Config, + negative_prompt: str | None = None, + ) -> GeneratedImage: + # 0. Create a new runtime config based on the model type and input parameters + runtime_config = RuntimeConfig(config, self.model_config) + time_steps = tqdm(range(runtime_config.init_time_step, runtime_config.num_inference_steps)) + + # 1. Create the initial latents + latents = LatentCreator.create_for_txt2img_or_img2img( + seed=seed, + height=runtime_config.height, + width=runtime_config.width, + img2img=Img2Img( + vae=self.vae, + latent_creator=FiboLatentCreator, + image_path=runtime_config.image_path, + sigmas=runtime_config.scheduler.sigmas, + init_time_step=runtime_config.init_time_step, + ), + ) + + # 2. Encode the prompt + json_prompt, encoder_hidden_states, text_encoder_layers = PromptEncoder.encode_prompt( + prompt=prompt, + negative_prompt=negative_prompt, + tokenizer=self.fibo_tokenizer, + text_encoder=self.text_encoder, + ) + + # (Optional) Call subscribers for beginning of loop + Callbacks.before_loop( + seed=seed, + prompt=json_prompt, + latents=latents, + config=runtime_config, + ) + + for t in time_steps: + try: + # 3.t Predict the noise + noise_pred = self.transformer( + t=t, + config=runtime_config, + hidden_states=latents, + encoder_hidden_states=encoder_hidden_states, + text_encoder_layers=text_encoder_layers, + ) + noise_pred = FIBO._apply_classifier_free_guidance(noise_pred, runtime_config.guidance) + + # 4.t Take one denoise step + latents = runtime_config.scheduler.step( + model_output=noise_pred, + timestep=t, + sample=latents, + ) + + # (Optional) Call subscribers in-loop + Callbacks.in_loop( + t=t, + seed=seed, + prompt=json_prompt, + latents=latents, + config=runtime_config, + time_steps=time_steps, + ) + + # (Optional) Evaluate to enable progress tracking + mx.eval(latents) + + except KeyboardInterrupt: # noqa: PERF203 + Callbacks.interruption( + t=t, + seed=seed, + prompt=json_prompt, + latents=latents, + config=runtime_config, + time_steps=time_steps, + ) + raise StopImageGenerationException( + f"Stopping image generation at step {t + 1}/{runtime_config.num_inference_steps}" + ) + + # (Optional) Call subscribers after loop + Callbacks.after_loop( + seed=seed, + prompt=json_prompt, + latents=latents, + config=runtime_config, + ) + + # 5. Decode the latent array and return the image + latents = FIBO._unpack_latents(latents, runtime_config.height, runtime_config.width) + decoded = self.vae.decode(latents) + return ImageUtil.to_image( + decoded_latents=decoded, + config=runtime_config, + seed=seed, + prompt=json_prompt, + quantization=self.bits, + lora_paths=None, + lora_scales=None, + image_path=runtime_config.image_path, + image_strength=runtime_config.image_strength, + generation_time=time_steps.format_dict["elapsed"], + ) + + @staticmethod + def _apply_classifier_free_guidance(noise_pred: mx.array, guidance: float) -> mx.array: + half = noise_pred.shape[0] // 2 + noise_uncond = noise_pred[:half] + noise_text = noise_pred[half:] + return noise_uncond + guidance * (noise_text - noise_uncond) + + @staticmethod + def _unpack_latents(latents: mx.array, height: int, width: int) -> mx.array: + batch_size, seq_len, channels = latents.shape + vae_scale_factor = 16 + latent_height = height // vae_scale_factor + latent_width = width // vae_scale_factor + latents = mx.reshape(latents, (batch_size, latent_height, latent_width, channels)) + latents = mx.transpose(latents, (0, 3, 1, 2)) + return latents + + def save_model(self, base_path: str) -> None: + ModelSaver.save_model( + model=self, + bits=self.bits, + base_path=base_path, + tokenizers=[ + ("fibo_tokenizer.tokenizer", "tokenizer"), + ], + components=[ + ("vae", "vae"), + ("transformer", "transformer"), + ("text_encoder", "text_encoder"), + ], + ) diff --git a/src/mflux/models/fibo/weights/__init__.py b/src/mflux/models/fibo/weights/__init__.py new file mode 100644 index 0000000..5ec85eb --- /dev/null +++ b/src/mflux/models/fibo/weights/__init__.py @@ -0,0 +1,7 @@ +"""FIBO weight handling.""" + +from mflux.models.fibo.weights.fibo_weight_handler import FIBOWeightHandler +from mflux.models.fibo.weights.fibo_weight_mapping import FIBOWeightMapping +from mflux.models.fibo.weights.fibo_weight_util import FIBOWeightUtil + +__all__ = ["FIBOWeightHandler", "FIBOWeightMapping", "FIBOWeightUtil"] diff --git a/src/mflux/models/fibo/weights/fibo_weight_handler.py b/src/mflux/models/fibo/weights/fibo_weight_handler.py new file mode 100644 index 0000000..d3b8a10 --- /dev/null +++ b/src/mflux/models/fibo/weights/fibo_weight_handler.py @@ -0,0 +1,179 @@ +from pathlib import Path + +import mlx.core as mx +import torch +from mlx.utils import tree_unflatten +from safetensors.torch import load_file as torch_load_file + +from mflux.models.common.weights.mapping.weight_mapper import WeightMapper +from mflux.models.fibo.weights.fibo_weight_mapping import FIBOWeightMapping +from mflux.models.flux.weights.weight_handler import ( + MetaData, + WeightHandler as FluxWeightHandler, +) + + +class FIBOWeightHandler: + def __init__( + self, + meta_data: MetaData, + vae: dict | None = None, + transformer: dict | None = None, + text_encoder: dict | None = None, + decoder: dict | None = None, + visual: dict | None = None, + config: dict | None = None, + ): + self.vae = vae + self.transformer = transformer + self.text_encoder = text_encoder + self.decoder = decoder + self.visual = visual + self.config = config + self.meta_data = meta_data + + @staticmethod + def load_regular_weights( + repo_id: str | None = None, + local_path: str | None = None, + ) -> "FIBOWeightHandler": + root_path: Path | None = None + if local_path: + root_path = Path(local_path) + elif repo_id: + root_path = FluxWeightHandler.download_or_get_cached_weights(repo_id) + + vae_weights = None + transformer_weights = None + text_encoder_weights = None + quantization_level: int | None = None + mflux_version: str | None = None + + if root_path is not None: + vae_weights, _, _ = FIBOWeightHandler._try_load_saved_component(root_path, "vae") + text_encoder_weights, _, _ = FIBOWeightHandler._try_load_saved_component(root_path, "text_encoder") # fmt: off + transformer_weights, quantization_level, mflux_version = FIBOWeightHandler._try_load_saved_component(root_path, "transformer") # fmt: off + + if vae_weights is None or transformer_weights is None or text_encoder_weights is None: + vae_weights = FIBOWeightHandler._load_vae_weights(repo_id, local_path) + transformer_weights = FIBOWeightHandler._load_transformer_weights(repo_id, local_path) + text_encoder_weights = FIBOWeightHandler._load_text_encoder_weights(repo_id, local_path) + quantization_level = None + mflux_version = None + + return FIBOWeightHandler( + vae=vae_weights, + transformer=transformer_weights, + text_encoder=text_encoder_weights, + meta_data=MetaData( + quantization_level=quantization_level, + scale=None, + is_lora=False, + mflux_version=mflux_version, + ), + ) + + @staticmethod + def _load_vae_weights( + repo_id: str | None = None, + local_path: str | None = None, + ) -> dict: + root_path = FIBOWeightHandler._get_root_path(repo_id, local_path) + vae_path = root_path / "vae" + raw_weights = FIBOWeightHandler._load_safetensors_shards(vae_path) + mapping = FIBOWeightMapping.get_vae_mapping() + mapped_weights = WeightMapper.apply_mapping(raw_weights, mapping, num_blocks=4) + return mapped_weights + + @staticmethod + def _load_transformer_weights( + repo_id: str | None = None, + local_path: str | None = None, + ) -> dict: + root_path = FIBOWeightHandler._get_root_path(repo_id, local_path) + transformer_path = root_path / "transformer" + if transformer_path.exists() and list(transformer_path.glob("*.safetensors")): + raw_weights = FIBOWeightHandler._load_safetensors_shards(transformer_path) + else: + raw_weights = FIBOWeightHandler._load_safetensors_shards(root_path) + mapping = FIBOWeightMapping.get_transformer_mapping() + mapped_weights = WeightMapper.apply_mapping( + raw_weights, + mapping, + num_blocks=38, + num_layers=46, + ) + return mapped_weights + + @staticmethod + def _load_text_encoder_weights( + repo_id: str | None = None, + local_path: str | None = None, + ) -> dict: + root_path = FIBOWeightHandler._get_root_path(repo_id, local_path) + text_encoder_path = root_path / "text_encoder" + raw_weights = FIBOWeightHandler._load_safetensors_shards(text_encoder_path) + mapping = FIBOWeightMapping.get_text_encoder_mapping() + mapped_weights = WeightMapper.apply_mapping( + raw_weights, + mapping, + num_blocks=36, + ) + return mapped_weights + + @staticmethod + def _get_root_path( + repo_id: str | None = None, + local_path: str | None = None, + ) -> Path: + if local_path: + return Path(local_path) + return Path(FluxWeightHandler.download_or_get_cached_weights(repo_id or "briaai/FIBO")) + + @staticmethod + def _load_safetensors_shards(path: Path) -> dict[str, mx.array]: + shard_files = sorted(f for f in path.glob("*.safetensors") if not f.name.startswith("._")) + if not shard_files: + raise FileNotFoundError(f"No safetensors files found in {path}") + + all_weights: dict[str, mx.array] = {} + for shard in shard_files: + torch_weights = torch_load_file(str(shard)) + for key, tensor in torch_weights.items(): + if tensor.dtype == torch.bfloat16: + tensor = tensor.to(torch.float16) + all_weights[key] = mx.array(tensor.numpy()) + + return all_weights + + @staticmethod + def _try_load_saved_component( + root_path: Path, + component_name: str, + ) -> tuple[dict | None, int | None, str | None]: + component_path = root_path / component_name + if not component_path.exists(): + return None, None, None + + shard_files = sorted(f for f in component_path.glob("*.safetensors") if not f.name.startswith("._")) + if not shard_files: + return None, None, None + + all_weights: dict[str, mx.array] = {} + quantization_level: int | None = None + mflux_version: str | None = None + + for idx, shard in enumerate(shard_files): + data = mx.load(str(shard), return_metadata=True) + weights_dict = data[0] + all_weights.update(dict(weights_dict.items())) + + if idx == 0 and len(data) > 1: + quantization_level = data[1].get("quantization_level") + mflux_version = data[1].get("mflux_version") + + if quantization_level is None and mflux_version is None: + return None, None, None + + unflattened = tree_unflatten(list(all_weights.items())) + return unflattened, quantization_level, mflux_version diff --git a/src/mflux/models/fibo/weights/fibo_weight_mapping.py b/src/mflux/models/fibo/weights/fibo_weight_mapping.py new file mode 100644 index 0000000..5032579 --- /dev/null +++ b/src/mflux/models/fibo/weights/fibo_weight_mapping.py @@ -0,0 +1,635 @@ +from typing import List + +from mflux.models.common.weights.mapping.weight_mapping import WeightMapping, WeightTarget +from mflux.models.qwen.weights.qwen_weight_mapping import ( + reshape_gamma_to_1d, + transpose_conv2d_weight, + transpose_conv3d_weight, +) + + +class FIBOWeightMapping(WeightMapping): + @staticmethod + def get_transformer_mapping() -> List[WeightTarget]: + return [ + # ========== Global projections ========== + WeightTarget( + mlx_path="time_embed.timestep_embedder.linear_1.weight", + hf_patterns=["time_embed.timestep_embedder.linear_1.weight"], + ), + WeightTarget( + mlx_path="time_embed.timestep_embedder.linear_1.bias", + hf_patterns=["time_embed.timestep_embedder.linear_1.bias"], + ), + WeightTarget( + mlx_path="time_embed.timestep_embedder.linear_2.weight", + hf_patterns=["time_embed.timestep_embedder.linear_2.weight"], + ), + WeightTarget( + mlx_path="time_embed.timestep_embedder.linear_2.bias", + hf_patterns=["time_embed.timestep_embedder.linear_2.bias"], + ), + WeightTarget( + mlx_path="context_embedder.weight", + hf_patterns=["context_embedder.weight"], + ), + WeightTarget( + mlx_path="context_embedder.bias", + hf_patterns=["context_embedder.bias"], + ), + WeightTarget( + mlx_path="x_embedder.weight", + hf_patterns=["x_embedder.weight"], + ), + WeightTarget( + mlx_path="x_embedder.bias", + hf_patterns=["x_embedder.bias"], + ), + # ========== Joint transformer blocks ========== + # AdaLayerNormZero (image + context streams) + WeightTarget( + mlx_path="transformer_blocks.{block}.norm1.linear.weight", + hf_patterns=["transformer_blocks.{block}.norm1.linear.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.norm1.linear.bias", + hf_patterns=["transformer_blocks.{block}.norm1.linear.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.norm1_context.linear.weight", + hf_patterns=["transformer_blocks.{block}.norm1_context.linear.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.norm1_context.linear.bias", + hf_patterns=["transformer_blocks.{block}.norm1_context.linear.bias"], + ), + # Attention weights (BriaFiboAttention) + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_q.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_q.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_k.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_k.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_q.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_q.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_q.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_q.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_k.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_k.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_k.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_k.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_v.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_v.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_v.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_v.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_out.0.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_out.0.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_out.0.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_out.0.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_added_q.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_added_q.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_added_k.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_added_k.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_q_proj.weight", + hf_patterns=["transformer_blocks.{block}.attn.add_q_proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_q_proj.bias", + hf_patterns=["transformer_blocks.{block}.attn.add_q_proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_k_proj.weight", + hf_patterns=["transformer_blocks.{block}.attn.add_k_proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_k_proj.bias", + hf_patterns=["transformer_blocks.{block}.attn.add_k_proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_v_proj.weight", + hf_patterns=["transformer_blocks.{block}.attn.add_v_proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_v_proj.bias", + hf_patterns=["transformer_blocks.{block}.attn.add_v_proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_add_out.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_add_out.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_add_out.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_add_out.bias"], + ), + # LayerNorm / FFN for image stream + WeightTarget( + mlx_path="transformer_blocks.{block}.norm2.weight", + hf_patterns=["transformer_blocks.{block}.norm2.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.norm2.bias", + hf_patterns=["transformer_blocks.{block}.norm2.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff.net.0.proj.weight", + hf_patterns=["transformer_blocks.{block}.ff.net.0.proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff.net.0.proj.bias", + hf_patterns=["transformer_blocks.{block}.ff.net.0.proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff.net.2.weight", + hf_patterns=["transformer_blocks.{block}.ff.net.2.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff.net.2.bias", + hf_patterns=["transformer_blocks.{block}.ff.net.2.bias"], + ), + # LayerNorm / FFN for context stream + WeightTarget( + mlx_path="transformer_blocks.{block}.norm2_context.weight", + hf_patterns=["transformer_blocks.{block}.norm2_context.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.norm2_context.bias", + hf_patterns=["transformer_blocks.{block}.norm2_context.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff_context.net.0.proj.weight", + hf_patterns=["transformer_blocks.{block}.ff_context.net.0.proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff_context.net.0.proj.bias", + hf_patterns=["transformer_blocks.{block}.ff_context.net.0.proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff_context.net.2.weight", + hf_patterns=["transformer_blocks.{block}.ff_context.net.2.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.ff_context.net.2.bias", + hf_patterns=["transformer_blocks.{block}.ff_context.net.2.bias"], + ), + # ========== Single transformer blocks ========== + WeightTarget( + mlx_path="single_transformer_blocks.{block}.norm.linear.weight", + hf_patterns=["single_transformer_blocks.{block}.norm.linear.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.norm.linear.bias", + hf_patterns=["single_transformer_blocks.{block}.norm.linear.bias"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.norm_q.weight", + hf_patterns=["single_transformer_blocks.{block}.attn.norm_q.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.norm_k.weight", + hf_patterns=["single_transformer_blocks.{block}.attn.norm_k.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.to_q.weight", + hf_patterns=["single_transformer_blocks.{block}.attn.to_q.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.to_q.bias", + hf_patterns=["single_transformer_blocks.{block}.attn.to_q.bias"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.to_k.weight", + hf_patterns=["single_transformer_blocks.{block}.attn.to_k.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.to_k.bias", + hf_patterns=["single_transformer_blocks.{block}.attn.to_k.bias"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.to_v.weight", + hf_patterns=["single_transformer_blocks.{block}.attn.to_v.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.attn.to_v.bias", + hf_patterns=["single_transformer_blocks.{block}.attn.to_v.bias"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.proj_mlp.weight", + hf_patterns=["single_transformer_blocks.{block}.proj_mlp.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.proj_mlp.bias", + hf_patterns=["single_transformer_blocks.{block}.proj_mlp.bias"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.proj_out.weight", + hf_patterns=["single_transformer_blocks.{block}.proj_out.weight"], + ), + WeightTarget( + mlx_path="single_transformer_blocks.{block}.proj_out.bias", + hf_patterns=["single_transformer_blocks.{block}.proj_out.bias"], + ), + # ========== Caption projection & output head ========== + WeightTarget( + mlx_path="norm_out.linear.weight", + hf_patterns=["norm_out.linear.weight"], + ), + WeightTarget( + mlx_path="norm_out.linear.bias", + hf_patterns=["norm_out.linear.bias"], + ), + WeightTarget( + mlx_path="proj_out.weight", + hf_patterns=["proj_out.weight"], + ), + WeightTarget( + mlx_path="proj_out.bias", + hf_patterns=["proj_out.bias"], + ), + # Caption_projection layers: we rely on explicit layer index to be + # expanded via num_layers; we pass num_layers explicitly from the loader. + WeightTarget( + mlx_path="caption_projection.{layer}.linear.weight", + hf_patterns=["caption_projection.{layer}.linear.weight"], + ), + ] + + @staticmethod + def get_text_encoder_mapping() -> List[WeightTarget]: + return [ + WeightTarget( + mlx_path="embed_tokens.weight", + hf_patterns=["model.embed_tokens.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.q_proj.weight", + hf_patterns=["model.layers.{block}.self_attn.q_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.k_proj.weight", + hf_patterns=["model.layers.{block}.self_attn.k_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.v_proj.weight", + hf_patterns=["model.layers.{block}.self_attn.v_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.o_proj.weight", + hf_patterns=["model.layers.{block}.self_attn.o_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.gate_proj.weight", + hf_patterns=["model.layers.{block}.mlp.gate_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.up_proj.weight", + hf_patterns=["model.layers.{block}.mlp.up_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.down_proj.weight", + hf_patterns=["model.layers.{block}.mlp.down_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.input_layernorm.weight", + hf_patterns=["model.layers.{block}.input_layernorm.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.post_attention_layernorm.weight", + hf_patterns=["model.layers.{block}.post_attention_layernorm.weight"], + ), + WeightTarget( + mlx_path="norm.weight", + hf_patterns=["model.norm.weight"], + ), + ] + + @staticmethod + def get_vae_mapping() -> List[WeightTarget]: + return [ + # ========== Encoder conv_in ========== + WeightTarget( + mlx_path="encoder.conv_in.conv3d.weight", + hf_patterns=["encoder.conv_in.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.conv_in.conv3d.bias", + hf_patterns=["encoder.conv_in.bias"], + ), + # ========== Encoder down_blocks ========== + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.norm1.weight", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.norm2.weight", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.conv_shortcut.conv3d.weight", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.conv_shortcut.weight"], + transform=transpose_conv3d_weight, + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.resnets.{res}.conv_shortcut.conv3d.bias", + hf_patterns=["encoder.down_blocks.{block}.resnets.{res}.conv_shortcut.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.downsampler.resample_conv.weight", + hf_patterns=["encoder.down_blocks.{block}.downsampler.resample.1.weight"], + transform=transpose_conv2d_weight, + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.downsampler.resample_conv.bias", + hf_patterns=["encoder.down_blocks.{block}.downsampler.resample.1.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.downsampler.time_conv.conv3d.weight", + hf_patterns=["encoder.down_blocks.{block}.downsampler.time_conv.weight"], + transform=transpose_conv3d_weight, + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.{block}.downsampler.time_conv.conv3d.bias", + hf_patterns=["encoder.down_blocks.{block}.downsampler.time_conv.bias"], + required=False, + ), + # ========== Encoder mid_block ========== + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.norm1.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv1.conv3d.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv1.conv3d.bias", + hf_patterns=["encoder.mid_block.resnets.{i}.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.norm2.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv2.conv3d.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv2.conv3d.bias", + hf_patterns=["encoder.mid_block.resnets.{i}.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.{i}.norm.weight", + hf_patterns=["encoder.mid_block.attentions.{i}.norm.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.{i}.to_qkv.weight", + hf_patterns=["encoder.mid_block.attentions.{i}.to_qkv.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.{i}.to_qkv.bias", + hf_patterns=["encoder.mid_block.attentions.{i}.to_qkv.bias"], + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.{i}.proj.weight", + hf_patterns=["encoder.mid_block.attentions.{i}.proj.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.{i}.proj.bias", + hf_patterns=["encoder.mid_block.attentions.{i}.proj.bias"], + ), + # ========== Encoder output ========== + WeightTarget( + mlx_path="encoder.norm_out.weight", + hf_patterns=["encoder.norm_out.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.conv_out.conv3d.weight", + hf_patterns=["encoder.conv_out.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.conv_out.conv3d.bias", + hf_patterns=["encoder.conv_out.bias"], + ), + # ========== Decoder conv_in ========== + WeightTarget( + mlx_path="decoder.conv_in.conv3d.weight", + hf_patterns=["decoder.conv_in.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.conv_in.conv3d.bias", + hf_patterns=["decoder.conv_in.bias"], + ), + # ========== Decoder mid_block ========== + # Mid block resnets + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.norm1.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv1.conv3d.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv1.conv3d.bias", + hf_patterns=["decoder.mid_block.resnets.{i}.conv1.bias"], + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.norm2.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv2.conv3d.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv2.conv3d.bias", + hf_patterns=["decoder.mid_block.resnets.{i}.conv2.bias"], + ), + # Mid block attention + WeightTarget( + mlx_path="decoder.mid_block.attentions.{i}.norm.weight", + hf_patterns=["decoder.mid_block.attentions.{i}.norm.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.{i}.to_qkv.weight", + hf_patterns=["decoder.mid_block.attentions.{i}.to_qkv.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.{i}.to_qkv.bias", + hf_patterns=["decoder.mid_block.attentions.{i}.to_qkv.bias"], + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.{i}.proj.weight", + hf_patterns=["decoder.mid_block.attentions.{i}.proj.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.{i}.proj.bias", + hf_patterns=["decoder.mid_block.attentions.{i}.proj.bias"], + ), + # ========== Decoder up_blocks ========== + # Up blocks resnets + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.norm1.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.conv1.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.conv1.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv1.bias"], + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.norm2.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.conv2.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.conv2.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv2.bias"], + ), + # Up blocks resnets - conv_shortcut (optional, when in_dim != out_dim) + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.conv_shortcut.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv_shortcut.weight"], + transform=transpose_conv3d_weight, + required=False, # Optional - only exists when dimensions differ + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.resnets.{res}.conv_shortcut.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv_shortcut.bias"], + required=False, # Optional - only exists when dimensions differ + ), + # Up blocks upsamplers - time_conv (blocks 0, 1 only) + WeightTarget( + mlx_path="decoder.up_blocks.{block}.upsampler.time_conv.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.upsampler.time_conv.weight"], + transform=transpose_conv3d_weight, + required=False, # Only exists for blocks 0, 1 + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.upsampler.time_conv.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.upsampler.time_conv.bias"], + required=False, # Only exists for blocks 0, 1 + ), + # Up blocks upsamplers - resample_conv (blocks 0, 1, 2) + WeightTarget( + mlx_path="decoder.up_blocks.{block}.upsampler.resample_conv.weight", + hf_patterns=["decoder.up_blocks.{block}.upsampler.resample.1.weight"], + transform=transpose_conv2d_weight, + required=False, # Only exists for blocks 0, 1, 2 + ), + WeightTarget( + mlx_path="decoder.up_blocks.{block}.upsampler.resample_conv.bias", + hf_patterns=["decoder.up_blocks.{block}.upsampler.resample.1.bias"], + required=False, # Only exists for blocks 0, 1, 2 + ), + # ========== Decoder output ========== + WeightTarget( + mlx_path="decoder.norm_out.weight", + hf_patterns=["decoder.norm_out.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.conv_out.conv3d.weight", + hf_patterns=["decoder.conv_out.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.conv_out.conv3d.bias", + hf_patterns=["decoder.conv_out.bias"], + ), + # ========== Quant conv ========== + WeightTarget( + mlx_path="quant_conv.conv3d.weight", + hf_patterns=["quant_conv.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="quant_conv.conv3d.bias", + hf_patterns=["quant_conv.bias"], + ), + # ========== Post quant conv ========== + WeightTarget( + mlx_path="post_quant_conv.conv3d.weight", + hf_patterns=["post_quant_conv.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="post_quant_conv.conv3d.bias", + hf_patterns=["post_quant_conv.bias"], + ), + ] diff --git a/src/mflux/models/fibo/weights/fibo_weight_util.py b/src/mflux/models/fibo/weights/fibo_weight_util.py new file mode 100644 index 0000000..d386ed9 --- /dev/null +++ b/src/mflux/models/fibo/weights/fibo_weight_util.py @@ -0,0 +1,47 @@ +from typing import TYPE_CHECKING + +import mlx.nn as nn + +from mflux.models.common.quantization.quantization_util import QuantizationUtil + +if TYPE_CHECKING: + from mflux.models.fibo.weights.fibo_weight_handler import FIBOWeightHandler + + +class FIBOWeightUtil: + @staticmethod + def set_weights_and_quantize( + quantize_arg: int | None, + weights: "FIBOWeightHandler", + vae: nn.Module, + transformer: nn.Module, + text_encoder: nn.Module | None = None, + ) -> int | None: + if weights.meta_data.quantization_level is None and quantize_arg is None: + FIBOWeightUtil._set_model_weights(weights, vae, transformer, text_encoder) + return None + + if weights.meta_data.quantization_level is None and quantize_arg is not None: + bits = quantize_arg + FIBOWeightUtil._set_model_weights(weights, vae, transformer, text_encoder) + QuantizationUtil.quantize_fibo_models(vae, transformer, text_encoder, bits, weights) + return bits + + if weights.meta_data.quantization_level is not None: + bits = weights.meta_data.quantization_level + QuantizationUtil.quantize_fibo_models(vae, transformer, text_encoder, bits, weights) + FIBOWeightUtil._set_model_weights(weights, vae, transformer, text_encoder) + return bits + + raise Exception("Error setting weights") + + @staticmethod + def _set_model_weights( + weights: "FIBOWeightHandler", + vae: nn.Module, + transformer: nn.Module, + text_encoder: nn.Module | None = None, + ): + vae.update(weights.vae, strict=False) + transformer.update(weights.transformer, strict=False) + text_encoder.update(weights.text_encoder, strict=False) diff --git a/src/mflux/models/fibo_vlm/__init__.py b/src/mflux/models/fibo_vlm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mflux/models/fibo_vlm/fibo_vlm_initializer.py b/src/mflux/models/fibo_vlm/fibo_vlm_initializer.py new file mode 100644 index 0000000..88c0529 --- /dev/null +++ b/src/mflux/models/fibo_vlm/fibo_vlm_initializer.py @@ -0,0 +1,88 @@ +import os +from pathlib import Path + +from transformers import Qwen2Tokenizer + +from mflux.models.fibo.tokenizer.qwen2vl_processor import Qwen2VLProcessor +from mflux.models.fibo_vlm.model.qwen3_vl_decoder import Qwen3VLDecoder +from mflux.models.fibo_vlm.model.qwen3_vl_vision_model import Qwen3VLVisionModel +from mflux.models.fibo_vlm.weights.fibo_vlm_weight_handler import FIBOVLMWeightHandler +from mflux.utils.download import snapshot_download + + +class FIBOVLMInitializer: + @staticmethod + def init( + vlm_model, + model_id: str = "briaai/FIBO-vlm", + local_path: str | None = None, + ) -> None: + # 1. Load VLM weights + weights = FIBOVLMWeightHandler.load_vlm_regular_weights( + repo_id=model_id, + local_path=local_path, + ) + + # 2. Initialize processor for tokenization + tokenizer = FIBOVLMInitializer._get_tokenizer(local_path, model_id) + vlm_model.processor = Qwen2VLProcessor(tokenizer=tokenizer) + + # 3. Initialize all models + vlm_model.decoder = Qwen3VLDecoder(visual=Qwen3VLVisionModel()) + + # 4. Apply weights to decoder and visual encoder + vlm_model.decoder.update(weights.decoder, strict=False) + vlm_model.decoder.visual.update(weights.visual, strict=False) + + # Store model ID and local path + vlm_model.model_id = model_id + vlm_model.local_path = local_path + + @staticmethod + def _get_tokenizer(local_path, model_id): + # Get the root path + if local_path: + root_path = Path(local_path) + else: + # Use snapshot_download to get cached path - download ALL files without filtering + # This ensures we get the complete snapshot directory structure + try: + root_path = Path( + snapshot_download( + repo_id=model_id, + local_files_only=True, + ) + ) + except (FileNotFoundError, OSError): + # Model not in cache, allow download if online + root_path = Path( + snapshot_download( + repo_id=model_id, + ) + ) + + # Try different possible tokenizer paths (like FiboTokenizerHandler does) + tokenizer_path = root_path / "tokenizer" + if not tokenizer_path.exists(): + tokenizer_path = root_path / "text_encoder" + if not tokenizer_path.exists(): + # Tokenizer files are in the root - this is the case for FIBO-vlm + tokenizer_path = root_path + + # Set HF_HUB_OFFLINE to force offline mode + old_offline = os.environ.get("HF_HUB_OFFLINE") + try: + os.environ["HF_HUB_OFFLINE"] = "1" + # Use Qwen2Tokenizer directly instead of AutoTokenizer to avoid config confusion + tokenizer = Qwen2Tokenizer.from_pretrained( + pretrained_model_name_or_path=str(tokenizer_path), + local_files_only=True, + ) + finally: + # Restore original value + if old_offline is None: + os.environ.pop("HF_HUB_OFFLINE", None) + else: + os.environ["HF_HUB_OFFLINE"] = old_offline + + return tokenizer diff --git a/src/mflux/models/fibo_vlm/model/__init__.py b/src/mflux/models/fibo_vlm/model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mflux/models/fibo_vlm/model/fibo_vlm.py b/src/mflux/models/fibo_vlm/model/fibo_vlm.py new file mode 100644 index 0000000..13063d0 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/fibo_vlm.py @@ -0,0 +1,234 @@ +import json +import textwrap +from typing import Any, Dict, List, Optional + +import mlx.core as mx +import numpy as np +from PIL import Image + +from mflux.models.fibo.tokenizer.qwen2vl_processor import Qwen2VLProcessor +from mflux.models.fibo_vlm.fibo_vlm_initializer import FIBOVLMInitializer +from mflux.models.fibo_vlm.model.qwen3_vl_decoder import Qwen3VLDecoder +from mflux.models.fibo_vlm.model.qwen3_vl_util import Qwen3VLUtil + + +class FiboVLM: + decoder = Qwen3VLDecoder + processor: Qwen2VLProcessor + + def __init__( + self, + model_id: str = "briaai/FIBO-vlm", + local_path: str | None = None, + ): + FIBOVLMInitializer.init( + vlm_model=self, + model_id=model_id, + local_path=local_path, + ) + + def generate( + self, + prompt: str, + top_p: float = 0.9, + temperature: float = 0.2, + max_tokens: int = 4096, + stop: List[str] | None = None, + seed: int | None = None, + ) -> str: + return self._generate_internal( + task="generate", + prompt=prompt, + top_p=top_p, + temperature=temperature, + max_tokens=max_tokens, + stop=stop, + seed=seed, + ) + + def refine( + self, + structured_prompt: str, + editing_instructions: str, + top_p: float = 0.9, + temperature: float = 0.2, + max_tokens: int = 4096, + stop: List[str] | None = None, + seed: int | None = None, + ) -> str: + return self._generate_internal( + task="refine", + structured_prompt=structured_prompt, + editing_instructions=editing_instructions, + top_p=top_p, + temperature=temperature, + max_tokens=max_tokens, + stop=stop, + seed=seed, + ) + + def inspire( + self, + image: Image.Image, + prompt: str | None = None, + top_p: float = 0.9, + temperature: float = 0.2, + max_tokens: int = 4096, + stop: List[str] | None = None, + seed: int | None = None, + ) -> str: + return self._generate_internal( + task="inspire", + image=image, + prompt=prompt, + top_p=top_p, + temperature=temperature, + max_tokens=max_tokens, + stop=stop, + seed=seed, + ) + + def _generate_internal( + self, + task: str, + *, + prompt: Optional[str] = None, + image: Optional[Image.Image] = None, + refine_image: Optional[Image.Image] = None, + structured_prompt: Optional[str] = None, + editing_instructions: Optional[str] = None, + top_p: float = 0.9, + temperature: float = 0.2, + max_tokens: int = 4096, + stop: List[str] | None = None, + seed: int | None = None, + ) -> str: + stop = stop or ["<|im_end|>", "<|end_of_text|>"] + messages = FiboVLM._build_messages( + task=task, + image=image, + refine_image=refine_image, + prompt=prompt, + structured_prompt=FiboVLM._normalize_json(structured_prompt), + editing_instructions=editing_instructions, + ) + formatted = FiboVLM._process_messages(self.processor, messages, image, refine_image) + input_ids = mx.array(formatted["input_ids"]) + attention_mask = mx.array(formatted["attention_mask"]) if formatted.get("attention_mask") is not None else None + pixel_values = mx.array(formatted["pixel_values"]) if formatted.get("pixel_values") is not None else None + image_grid_thw = mx.array(formatted["image_grid_thw"]) if formatted.get("image_grid_thw") is not None else None + stop_token_sequences = [self.processor.tokenizer.encode(s, add_special_tokens=False) for s in stop] + generated_ids = Qwen3VLUtil.generate_text( + decoder=self.decoder, + input_ids=input_ids, + attention_mask=attention_mask, + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + max_new_tokens=max_tokens, + top_p=top_p, + temperature=temperature, + stop_token_sequences=stop_token_sequences, + eos_token_id=self.processor.tokenizer.eos_token_id, + seed=seed, + ) + generated_tokens = generated_ids[:, input_ids.shape[1] :] + generated_text = self.processor.tokenizer.decode(np.array(generated_tokens[0]), skip_special_tokens=True) + return FiboVLM._format_json_output(generated_text) + + @staticmethod + def _normalize_json(structured_prompt: Optional[str]) -> Optional[str]: + if structured_prompt is None: + return None + try: + return json.dumps(json.loads(structured_prompt.strip()), separators=(",", ":"), ensure_ascii=False) + except json.JSONDecodeError: + return structured_prompt + + @staticmethod + def _process_messages( + processor: Qwen2VLProcessor, + messages: List[Dict[str, Any]], + image: Optional[Image.Image], + refine_image: Optional[Image.Image], + ) -> Dict[str, Any]: + has_images = image is not None or refine_image is not None + + if has_images: + images_list = FiboVLM._extract_images(messages) + prompt_text = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + inputs = {"text": [prompt_text], "padding": True, "return_tensors": "np"} + if images_list: + inputs["images"] = images_list + return processor(**inputs) + + return processor.apply_chat_template( + messages, tokenize=True, add_generation_prompt=True, return_tensors="np", return_dict=True + ) + + @staticmethod + def _extract_images(messages: List[Dict[str, Any]]) -> List[Image.Image]: + images = [] + for msg in messages: + content = msg.get("content", []) + if isinstance(content, list): + for item in content: + if item.get("type") == "image": + img = item.get("image") + if img is not None: + images.append(img) + return images + + @staticmethod + def _format_json_output(text: str) -> str: + try: + return f"\n{json.dumps(json.loads(text), indent=2, ensure_ascii=False)}\n" + except json.JSONDecodeError: + return text + + @staticmethod + def _build_messages( + task: str, + *, + image: Optional[Image.Image] = None, + refine_image: Optional[Image.Image] = None, + prompt: Optional[str] = None, + structured_prompt: Optional[str] = None, + editing_instructions: Optional[str] = None, + ) -> List[Dict[str, Any]]: + if task == "inspire": + inspire_text = "" + if prompt: + inspire_text = f"\n{(prompt or '').strip()}" + content = [{"type": "image", "image": image}, {"type": "text", "text": inspire_text}] + elif task == "generate": + content = [{"type": "text", "text": f"\n{(prompt or '').strip()}"}] + else: + content = FiboVLM._build_refine_content(refine_image, structured_prompt, editing_instructions) + + return [{"role": "user", "content": content}] + + @staticmethod + def _build_refine_content( + refine_image: Optional[Image.Image], + structured_prompt: Optional[str], + editing_instructions: Optional[str], + ) -> List[Dict[str, Any]]: + edits = (editing_instructions or "").strip() + + if refine_image is None: + base_prompt = (structured_prompt or "").strip() + text = textwrap.dedent( + f""" +Input: +{base_prompt} +Editing instructions: +{edits}""" + ).strip() + return [{"type": "text", "text": text}] + + text = textwrap.dedent( + f""" +Editing instructions: +{edits}""" + ).strip() + return [{"type": "image", "image": refine_image}, {"type": "text", "text": text}] diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_attention.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_attention.py new file mode 100644 index 0000000..bf535ab --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_attention.py @@ -0,0 +1,140 @@ +import math + +import mlx.core as mx +from mlx import nn +from mlx.core.fast import scaled_dot_product_attention + +from mflux.models.fibo_vlm.model.qwen3_vl_rms_norm import Qwen3VLRMSNorm + + +class Qwen3VLAttention(nn.Module): + def __init__( + self, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + head_dim: int, + max_position_embeddings: int = 262144, + rope_theta: float = 1000000.0, + mrope_section: list[int] | None = None, + attention_bias: bool = False, + rms_norm_eps: float = 1e-6, + ): + super().__init__() + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.head_dim = head_dim + self.num_key_value_groups = num_attention_heads // num_key_value_heads + self.scaling = 1.0 / math.sqrt(self.head_dim) + self.q_proj = nn.Linear(hidden_size, num_attention_heads * head_dim, bias=attention_bias) + self.k_proj = nn.Linear(hidden_size, num_key_value_heads * head_dim, bias=attention_bias) + self.v_proj = nn.Linear(hidden_size, num_key_value_heads * head_dim, bias=attention_bias) + self.o_proj = nn.Linear(num_attention_heads * head_dim, hidden_size, bias=attention_bias) + self.q_norm = Qwen3VLRMSNorm(head_dim, eps=rms_norm_eps) + self.k_norm = Qwen3VLRMSNorm(head_dim, eps=rms_norm_eps) + self.mrope_section = mrope_section or [24, 20, 20] + + def __call__( + self, + hidden_states: mx.array, + attention_mask: mx.array | None = None, + position_embeddings: tuple[mx.array, mx.array] | None = None, + past_key_value: tuple[mx.array, mx.array] | None = None, + ) -> mx.array | tuple[mx.array, tuple[mx.array, mx.array]]: + bsz, q_len, _ = hidden_states.shape + + q_proj = self.q_proj(hidden_states) + k_proj = self.k_proj(hidden_states) + v_proj = self.v_proj(hidden_states) + + query_states = q_proj.reshape(bsz, q_len, self.num_attention_heads, self.head_dim) + key_states = k_proj.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim) + value_states = v_proj.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim) + + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + query_states = query_states.transpose(0, 2, 1, 3) + key_states = key_states.transpose(0, 2, 1, 3) + value_states = value_states.transpose(0, 2, 1, 3) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = Qwen3VLAttention._apply_rotary_pos_emb( + q=query_states, + k=key_states, + cos=cos, + sin=sin, + ) + + cache_key_states = key_states + cache_value_states = value_states + + if past_key_value is not None: + past_key, past_value = past_key_value + cache_key_states = mx.concatenate([past_key, cache_key_states], axis=2) + cache_value_states = mx.concatenate([past_value, cache_value_states], axis=2) + + if self.num_key_value_heads != self.num_attention_heads: + key_states = Qwen3VLAttention._repeat_kv(cache_key_states, self.num_key_value_groups) + value_states = Qwen3VLAttention._repeat_kv(cache_value_states, self.num_key_value_groups) + else: + key_states = cache_key_states + value_states = cache_value_states + + attn_mask = None + if attention_mask is not None: + kv_len = key_states.shape[2] + attn_mask = attention_mask[:, :, :, :kv_len] + + query_states_f32 = query_states.astype(mx.float32) + key_states_f32 = key_states.astype(mx.float32) + value_states_f32 = value_states.astype(mx.float32) + + # Use fast MLX attention + attn_output = scaled_dot_product_attention( + query_states_f32, + key_states_f32, + value_states_f32, + scale=self.scaling, + mask=attn_mask, + ) + + attn_output = attn_output.astype(query_states.dtype) + attn_output = attn_output.transpose(0, 2, 1, 3).reshape(bsz, q_len, self.num_attention_heads * self.head_dim) + attn_output = self.o_proj(attn_output) + return attn_output, (cache_key_states, cache_value_states) + + @staticmethod + def _repeat_kv(hidden_states: mx.array, n_rep: int) -> mx.array: + shape = hidden_states.shape + + if len(shape) == 5: + batch, num_key_value_heads, rep, slen, head_dim = shape + return hidden_states.reshape(batch, num_key_value_heads * rep, slen, head_dim) + + batch, num_key_value_heads, slen, head_dim = shape + hidden_states = mx.expand_dims(hidden_states, axis=2) + hidden_states = mx.broadcast_to(hidden_states, (batch, num_key_value_heads, n_rep, slen, head_dim)) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + @staticmethod + def _apply_rotary_pos_emb( + q: mx.array, + k: mx.array, + cos: mx.array, + sin: mx.array, + unsqueeze_dim: int = 1, + ) -> tuple[mx.array, mx.array]: + cos = mx.expand_dims(cos, axis=unsqueeze_dim) + sin = mx.expand_dims(sin, axis=unsqueeze_dim) + q_embed = (q * cos) + (Qwen3VLAttention._rotate_half(q) * sin) + k_embed = (k * cos) + (Qwen3VLAttention._rotate_half(k) * sin) + return q_embed, k_embed + + @staticmethod + def _rotate_half(x: mx.array) -> mx.array: + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return mx.concatenate([-x2, x1], axis=-1) diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_decoder.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_decoder.py new file mode 100644 index 0000000..160e7fb --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_decoder.py @@ -0,0 +1,189 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo_vlm.model.qwen3_vl_decoder_layer import Qwen3VLDecoderLayer +from mflux.models.fibo_vlm.model.qwen3_vl_rms_norm import Qwen3VLRMSNorm +from mflux.models.fibo_vlm.model.qwen3_vl_rope import Qwen3VLRotaryEmbedding + + +class Qwen3VLDecoder(nn.Module): + def __init__( + self, + vocab_size: int = 151936, + hidden_size: int = 2560, + num_hidden_layers: int = 36, + num_attention_heads: int = 32, + num_key_value_heads: int = 8, + intermediate_size: int = 9728, + max_position_embeddings: int = 262144, + rope_theta: float = 5000000.0, + rms_norm_eps: float = 1e-6, + head_dim: int = 128, + attention_bias: bool = False, + mrope_section: list[int] | None = None, + attention_scaling: float = 1.0, + image_token_id: int = 151655, + visual=None, + ): + super().__init__() + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + if mrope_section is None: + mrope_section = [24, 20, 20] + + self.embed_tokens = nn.Embedding(vocab_size, hidden_size) + self.layers = [ + Qwen3VLDecoderLayer( + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + num_key_value_heads=num_key_value_heads, + head_dim=head_dim, + max_position_embeddings=max_position_embeddings, + rope_theta=rope_theta, + mrope_section=mrope_section, + attention_bias=attention_bias, + rms_norm_eps=rms_norm_eps, + intermediate_size=intermediate_size, + ) + for _ in range(num_hidden_layers) + ] + self.norm = Qwen3VLRMSNorm(hidden_size, eps=rms_norm_eps) + self.rotary_emb = Qwen3VLRotaryEmbedding( + dim=head_dim, + max_position_embeddings=max_position_embeddings, + base=rope_theta, + scaling_factor=attention_scaling, + mrope_section=mrope_section, + ) + self.lm_head = nn.Linear(hidden_size, vocab_size, bias=False) + self.visual = visual + self.image_token_id = image_token_id + + def __call__( + self, + input_ids: mx.array, + attention_mask: mx.array | None = None, + pixel_values: mx.array | None = None, + image_grid_thw: mx.array | None = None, + use_cache: bool = False, + past_key_values: list[tuple[mx.array, mx.array]] | None = None, + ) -> mx.array | tuple[mx.array, list[tuple[mx.array, mx.array]]]: + batch_size, seq_len = input_ids.shape + + # Get embeddings + inputs_embeds = self.embed_tokens(input_ids) + + # Handle vision inputs if provided + if pixel_values is not None and image_grid_thw is not None and self.visual is not None: + image_embeds_split = self._get_image_features(pixel_values, image_grid_thw) + image_embeds = mx.concatenate(image_embeds_split, axis=0) + + if self.image_token_id is not None: + image_positions = input_ids == self.image_token_id + n_image_tokens = mx.sum(image_positions).item() + + if n_image_tokens > 0 and image_embeds.shape[0] >= n_image_tokens: + image_positions_flat = image_positions.flatten() + inputs_embeds_flat = inputs_embeds.reshape(-1, inputs_embeds.shape[-1]) + + new_embeds_list = [] + image_idx = 0 + for i in range(len(image_positions_flat)): + if image_positions_flat[i] and image_idx < image_embeds.shape[0]: + new_embeds_list.append(image_embeds[image_idx]) + image_idx += 1 + else: + new_embeds_list.append(inputs_embeds_flat[i]) + + new_embeds = mx.stack(new_embeds_list, axis=0) + inputs_embeds = new_embeds.reshape(inputs_embeds.shape) + + # Create attention mask with causal masking (from QwenEncoder) + if attention_mask is None: + attention_mask = mx.ones((batch_size, seq_len), dtype=mx.int32) + + # Position embeddings + if use_cache and past_key_values is not None: + cached_seq_len = past_key_values[0][0].shape[2] if len(past_key_values) > 0 else 0 + cache_position = mx.arange(cached_seq_len, cached_seq_len + seq_len, dtype=mx.int32) + else: + cache_position = mx.arange(seq_len, dtype=mx.int32) + position_ids = mx.expand_dims(mx.expand_dims(cache_position, axis=0), axis=0) + position_ids = mx.broadcast_to(position_ids, (3, batch_size, seq_len)) + + # Create causal + padding mask + mask_dtype = inputs_embeds.dtype + padding_mask = mx.where( + attention_mask == 1, + mx.zeros(attention_mask.shape, dtype=mask_dtype), + mx.full(attention_mask.shape, -float("inf"), dtype=mask_dtype), + ) + padding_mask = mx.expand_dims(mx.expand_dims(padding_mask, axis=1), axis=1) + + # Optimize: when seq_len=1 (cached iterations), causal mask is trivial (all zeros) + if seq_len == 1: + causal_tri_mask = mx.zeros((batch_size, 1, 1, 1), dtype=mask_dtype) + else: + idx = mx.arange(seq_len, dtype=mx.int32) + j = mx.expand_dims(idx, axis=0) + i = mx.expand_dims(idx, axis=1) + tri_bool = j > i + zeros_2d = mx.zeros((seq_len, seq_len), dtype=mask_dtype) + neginf_2d = mx.full((seq_len, seq_len), -float("inf"), dtype=mask_dtype) + causal_tri_mask = mx.where(tri_bool, neginf_2d, zeros_2d) + causal_tri_mask = mx.expand_dims(mx.expand_dims(causal_tri_mask, axis=0), axis=0) + causal_tri_mask = mx.broadcast_to(causal_tri_mask, (batch_size, 1, seq_len, seq_len)) + attention_mask_4d = causal_tri_mask + padding_mask + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + present_key_values = [] if use_cache else None + for layer_idx, layer in enumerate(self.layers): + layer_past = None + if use_cache and past_key_values is not None: + layer_past = past_key_values[layer_idx] if layer_idx < len(past_key_values) else None + + layer_output = layer( + hidden_states, + attention_mask_4d, + position_embeddings, + past_key_value=layer_past, + ) + + if use_cache: + hidden_states, layer_present = layer_output + present_key_values.append(layer_present) + else: + hidden_states = layer_output + + # Final norm + hidden_states = self.norm(hidden_states) + hidden_states_f32 = hidden_states.astype(mx.float32) + weight_f32 = self.lm_head.weight.astype(mx.float32) + logits_f32 = mx.matmul(hidden_states_f32, weight_f32.T) + logits = logits_f32.astype(hidden_states.dtype) + + if use_cache: + return logits, present_key_values + return logits + + def _get_image_features(self, pixel_values: mx.array, image_grid_thw: mx.array) -> list[mx.array]: + if self.visual is None: + raise RuntimeError("Vision transformer not initialized. Call load_visual_weights() first.") + + # Vision model can handle the original dtype (usually float16) + image_embeds, _ = self.visual(pixel_values, image_grid_thw) # Ignore deepstack for now + original_split_sizes = image_grid_thw.prod(axis=-1).astype(mx.int32) + spatial_merge_size = self.visual.spatial_merge_size + split_sizes = (original_split_sizes // (spatial_merge_size**2)).astype(mx.int32) + split_sizes = [int(s) for s in split_sizes.tolist()] + split_sizes = [s for s in split_sizes if s > 0] + + image_embeds_split = [] + start_idx = 0 + for split_size in split_sizes: + end_idx = start_idx + split_size + image_embeds_split.append(image_embeds[start_idx:end_idx]) + start_idx = end_idx + return image_embeds_split diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_decoder_layer.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_decoder_layer.py new file mode 100644 index 0000000..98419cd --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_decoder_layer.py @@ -0,0 +1,62 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo_vlm.model.qwen3_vl_attention import Qwen3VLAttention +from mflux.models.fibo_vlm.model.qwen3_vl_mlp import Qwen3VLMLP +from mflux.models.fibo_vlm.model.qwen3_vl_rms_norm import Qwen3VLRMSNorm + + +class Qwen3VLDecoderLayer(nn.Module): + def __init__( + self, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + head_dim: int, + max_position_embeddings: int, + rope_theta: float, + mrope_section: list[int] | None, + attention_bias: bool, + rms_norm_eps: float, + intermediate_size: int, + ): + super().__init__() + self.input_layernorm = Qwen3VLRMSNorm(hidden_size, eps=rms_norm_eps) + self.self_attn = Qwen3VLAttention( + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + num_key_value_heads=num_key_value_heads, + head_dim=head_dim, + max_position_embeddings=max_position_embeddings, + rope_theta=rope_theta, + mrope_section=mrope_section, + attention_bias=attention_bias, + rms_norm_eps=rms_norm_eps, + ) + self.post_attention_layernorm = Qwen3VLRMSNorm(hidden_size, eps=rms_norm_eps) + self.mlp = Qwen3VLMLP( + hidden_size=hidden_size, + intermediate_size=intermediate_size, + ) + + def __call__( + self, + hidden_states: mx.array, + attention_mask: mx.array | None = None, + position_embeddings: tuple[mx.array, mx.array] | None = None, + past_key_value: tuple[mx.array, mx.array] | None = None, + ) -> mx.array | tuple[mx.array, tuple[mx.array, mx.array]]: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + attn_output, past_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + past_key_value=past_key_value, + ) + hidden_states = residual + attn_output + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states, past_key_value diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_mlp.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_mlp.py new file mode 100644 index 0000000..4256ef7 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_mlp.py @@ -0,0 +1,17 @@ +import mlx.core as mx +from mlx import nn + + +class Qwen3VLMLP(nn.Module): + def __init__(self, hidden_size: int, intermediate_size: int): + super().__init__() + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + + def __call__(self, hidden_states: mx.array) -> mx.array: + gate_output = nn.silu(self.gate_proj(hidden_states)) + up_output = self.up_proj(hidden_states) + intermediate_output = gate_output * up_output + output = self.down_proj(intermediate_output) + return output diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_rms_norm.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_rms_norm.py new file mode 100644 index 0000000..04f8229 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_rms_norm.py @@ -0,0 +1,17 @@ +import mlx.core as mx +from mlx import nn + + +class Qwen3VLRMSNorm(nn.Module): + def __init__(self, hidden_size: int, eps: float = 1e-6): + super().__init__() + self.weight = mx.ones((hidden_size,)) + self.eps = eps + + def __call__(self, hidden_states: mx.array) -> mx.array: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.astype(mx.float32) + variance = mx.mean(mx.square(hidden_states), axis=-1, keepdims=True) + hidden_states = hidden_states * mx.rsqrt(variance + self.eps) + result = self.weight.astype(mx.float32) * hidden_states + return result.astype(input_dtype) diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_rope.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_rope.py new file mode 100644 index 0000000..8d9f6f1 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_rope.py @@ -0,0 +1,56 @@ +import mlx.core as mx +import numpy as np +from mlx import nn + + +class Qwen3VLRotaryEmbedding(nn.Module): + def __init__( + self, + dim: int, + max_position_embeddings: int = 262144, + base: float = 1000000.0, + scaling_factor: float = 1.0, + mrope_section: list[int] | None = None, + ): + super().__init__() + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + self.scaling_factor = scaling_factor + self.mrope_section = mrope_section or [24, 20, 20] + self.inv_freq = 1.0 / (base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)) + + def __call__(self, x: mx.array, position_ids: mx.array) -> tuple[mx.array, mx.array]: + if len(position_ids.shape) == 2: + batch_size, seq_len = position_ids.shape + position_ids = mx.broadcast_to(mx.expand_dims(position_ids, axis=0), (3, batch_size, seq_len)) + + inv_freq_expanded = mx.expand_dims(mx.expand_dims(self.inv_freq, axis=0), axis=0) + inv_freq_expanded = mx.expand_dims(inv_freq_expanded, axis=-1) + inv_freq_expanded = mx.broadcast_to(inv_freq_expanded, (3, position_ids.shape[1], self.inv_freq.shape[0], 1)) + inv_freq_expanded = inv_freq_expanded.astype(mx.float32) + + position_ids_expanded = mx.expand_dims(position_ids, axis=2) + position_ids_expanded = position_ids_expanded.astype(mx.float32) + freqs = mx.matmul(inv_freq_expanded, position_ids_expanded) + freqs = mx.transpose(freqs, (0, 1, 3, 2)) + freqs_interleaved = Qwen3VLRotaryEmbedding._apply_interleaved_mrope(freqs, self.mrope_section) + emb = mx.concatenate([freqs_interleaved, freqs_interleaved], axis=-1) + + cos = mx.cos(emb) * self.scaling_factor + sin = mx.sin(emb) * self.scaling_factor + return cos.astype(x.dtype), sin.astype(x.dtype) + + @staticmethod + def _apply_interleaved_mrope(freqs: mx.array, mrope_section: list[int]) -> mx.array: + freqs_t = freqs[0] + freqs_t_np = np.array(freqs_t) + + for dim, offset in enumerate((1, 2), start=1): + length = mrope_section[dim] * 3 + indices_np = np.arange(offset, length, 3) + freqs_dim_np = np.array(freqs[dim]) + freqs_t_np[..., indices_np] = freqs_dim_np[..., indices_np] + + freqs_t = mx.array(freqs_t_np) + return freqs_t diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_util.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_util.py new file mode 100644 index 0000000..9a177dd --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_util.py @@ -0,0 +1,155 @@ +import mlx.core as mx +import numpy as np + +from mflux.models.fibo_vlm.model.qwen3_vl_decoder import Qwen3VLDecoder + + +class Qwen3VLUtil: + @staticmethod + def sample_top_p( + logits: mx.array, + top_p: float, + temperature: float = 1.0, + ) -> mx.array: + if temperature != 1.0: + logits = logits / temperature + probs = mx.softmax(logits.astype(mx.float32), axis=-1) + probs_np = np.array(probs) + sorted_indices_np = np.argsort(probs_np)[::-1] + sorted_probs_np = probs_np[sorted_indices_np] + cumulative_probs_np = np.cumsum(sorted_probs_np) + sorted_indices_to_remove_np = cumulative_probs_np > top_p + sorted_indices_to_remove_np[0] = False + indices_to_remove_np = sorted_indices_np[sorted_indices_to_remove_np] + probs_np[indices_to_remove_np] = 0.0 + probs_np = probs_np / np.sum(probs_np) + token_id = np.random.choice(len(probs_np), p=probs_np) + return mx.array(token_id, dtype=mx.int32) + + @staticmethod + def generate_text( + decoder: Qwen3VLDecoder, + input_ids: mx.array, + attention_mask: mx.array | None = None, + pixel_values: mx.array | None = None, + image_grid_thw: mx.array | None = None, + max_new_tokens: int = 4096, + top_p: float = 0.9, + temperature: float = 0.2, + stop_token_sequences: list[list[int]] | None = None, + eos_token_id: int | None = None, + seed: int | None = None, + ) -> mx.array: + if stop_token_sequences is None: + stop_token_sequences = [] + if eos_token_id is not None: + stop_token_sequences.append([eos_token_id]) + + batch_size = input_ids.shape[0] + + # Avoid unnecessary conversion if already mx.array + if isinstance(input_ids, mx.array): + generated_ids = input_ids + else: + generated_ids = mx.array(input_ids) + + if attention_mask is None: + attention_mask = mx.ones_like(input_ids).astype(mx.int32) + + # Set random seed for deterministic generation if provided + if seed is not None: + np.random.seed(seed) + + # Pre-compute max stop sequence length (constant, don't recompute every iteration) + max_stop_len = 0 + if stop_token_sequences: + max_stop_len = max(len(seq) for seq in stop_token_sequences) + + # Pre-allocate reusable arrays for efficiency + ones_1x1 = mx.ones((batch_size, 1), dtype=mx.int32) + + # Initialize KV cache + past_key_values = None + iteration_count = 0 + + for iteration in range(max_new_tokens): + # On first iteration: process full sequence + # On subsequent iterations: only process new token (use cache) + if past_key_values is None: + # First iteration: process full input sequence + decoder_input_ids = generated_ids + decoder_attention_mask = attention_mask + else: + # Subsequent iterations: only process the new token + decoder_input_ids = generated_ids[:, -1:] # Only last token + decoder_attention_mask = ones_1x1 # Reuse pre-allocated array + + # Forward pass with KV cache + # Only pass pixel_values and image_grid_thw on first iteration (they're part of the input sequence) + decoder_kwargs = { + "input_ids": decoder_input_ids, + "attention_mask": decoder_attention_mask, + "use_cache": True, + "past_key_values": past_key_values, + } + if past_key_values is None: + # First iteration: include image data if present + if pixel_values is not None and image_grid_thw is not None: + decoder_kwargs["pixel_values"] = pixel_values + decoder_kwargs["image_grid_thw"] = image_grid_thw + + decoder_output = decoder(**decoder_kwargs) + + if isinstance(decoder_output, tuple): + logits, past_key_values = decoder_output + # Evaluate KV cache to ensure it's computed and not accumulating + if past_key_values is not None: + for kv_tuple in past_key_values: + mx.eval(kv_tuple[0], kv_tuple[1]) + else: + logits = decoder_output + past_key_values = None + + # Evaluate logits before sampling + mx.eval(logits) + + # Get logits for the last position + next_token_logits = logits[:, -1, :] + next_tokens = [] + for i in range(batch_size): + token_id = Qwen3VLUtil.sample_top_p(next_token_logits[i], top_p, temperature) + next_tokens.append(token_id) + + next_tokens = mx.stack(next_tokens, axis=0) + next_tokens = mx.expand_dims(next_tokens, axis=1) + + generated_ids = mx.concatenate([generated_ids, next_tokens], axis=1) + attention_mask = mx.concatenate([attention_mask, ones_1x1], axis=1) # Reuse pre-allocated array + iteration_count += 1 + + # Check for stop token sequences (only check last max_stop_len tokens for efficiency) + should_stop = False + matched_stop_sequence = None + if stop_token_sequences and max_stop_len > 0: + # Only convert the last max_stop_len tokens to numpy (much more efficient) + if generated_ids.shape[1] >= max_stop_len: + last_tokens_mx = generated_ids[0, -max_stop_len:] + last_tokens_np = np.array(last_tokens_mx) + for stop_sequence in stop_token_sequences: + seq_len = len(stop_sequence) + if len(last_tokens_np) >= seq_len: + # Check if the last seq_len tokens match this stop sequence + tokens_to_check = last_tokens_np[-seq_len:].tolist() + if tokens_to_check == stop_sequence: + should_stop = True + matched_stop_sequence = stop_sequence + break + + if should_stop: + # Exclude the stop sequence from the output (match PyTorch behavior) + if matched_stop_sequence is not None: + seq_len = len(matched_stop_sequence) + generated_ids = generated_ids[:, :-seq_len] + break + + return generated_ids diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_attention.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_attention.py new file mode 100644 index 0000000..ac628a4 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_attention.py @@ -0,0 +1,96 @@ +import mlx.core as mx +from mlx import nn +from mlx.core.fast import scaled_dot_product_attention + + +class Qwen3VLVisionAttention(nn.Module): + def __init__( + self, + hidden_size: int = 1024, + num_heads: int = 16, + ): + super().__init__() + self.dim = hidden_size + self.num_heads = num_heads + self.head_dim = self.dim // self.num_heads + self.scaling = self.head_dim**-0.5 + + self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True) + self.proj = nn.Linear(self.dim, self.dim) + + def __call__( + self, + hidden_states: mx.array, + cu_seqlens: mx.array, + rotary_pos_emb: mx.array | None = None, + position_embeddings: tuple[mx.array, mx.array] | None = None, + ) -> mx.array: + seq_length = hidden_states.shape[0] + qkv = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, self.head_dim) + qkv = qkv.transpose(1, 0, 2, 3) + query_states, key_states, value_states = mx.split(qkv, 3, axis=0) + query_states = query_states.squeeze(0) + key_states = key_states.squeeze(0) + value_states = value_states.squeeze(0) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = Qwen3VLVisionAttention._apply_rotary_pos_emb_vision( + q=query_states, + k=key_states, + cos=cos, + sin=sin, + ) + + attn_outputs_chunks: list[mx.array] = [] + if cu_seqlens is not None and len(cu_seqlens) > 1: + lengths = [int((cu_seqlens[i + 1] - cu_seqlens[i]).item()) for i in range(len(cu_seqlens) - 1)] + offset = 0 + for length in lengths: + q_chunk = query_states[offset : offset + length] + k_chunk = key_states[offset : offset + length] + v_chunk = value_states[offset : offset + length] + offset += length + + q = q_chunk.transpose(1, 0, 2) + k = k_chunk.transpose(1, 0, 2) + v = v_chunk.transpose(1, 0, 2) + q = mx.expand_dims(q, axis=0) + k = mx.expand_dims(k, axis=0) + v = mx.expand_dims(v, axis=0) + + attn_output = scaled_dot_product_attention(q, k, v, scale=self.scaling, mask=None) + attn_output = attn_output.squeeze(0).transpose(1, 0, 2) + attn_outputs_chunks.append(attn_output) + + attn_output = mx.concatenate(attn_outputs_chunks, axis=0) + else: + q = query_states.transpose(1, 0, 2) + k = key_states.transpose(1, 0, 2) + v = value_states.transpose(1, 0, 2) + q = mx.expand_dims(q, axis=0) + k = mx.expand_dims(k, axis=0) + v = mx.expand_dims(v, axis=0) + + attn_output = scaled_dot_product_attention(q, k, v, scale=self.scaling, mask=None) + attn_output = attn_output.squeeze(0).transpose(1, 0, 2) + + attn_output = attn_output.reshape(seq_length, self.dim) + attn_output = self.proj(attn_output) + return attn_output + + @staticmethod + def _rotate_half(x: mx.array) -> mx.array: + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return mx.concatenate([-x2, x1], axis=-1) + + @staticmethod + def _apply_rotary_pos_emb_vision( + q: mx.array, k: mx.array, cos: mx.array, sin: mx.array + ) -> tuple[mx.array, mx.array]: + cos = cos[..., None, :] + sin = sin[..., None, :] + q_embed = (q * cos) + (Qwen3VLVisionAttention._rotate_half(q) * sin) + k_embed = (k * cos) + (Qwen3VLVisionAttention._rotate_half(k) * sin) + return q_embed, k_embed diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_block.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_block.py new file mode 100644 index 0000000..cb22729 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_block.py @@ -0,0 +1,40 @@ +import mlx.core as mx +from mlx import nn + +from .qwen3_vl_vision_attention import Qwen3VLVisionAttention +from .qwen3_vl_vision_mlp import Qwen3VLVisionMLP + + +class Qwen3VLVisionBlock(nn.Module): + def __init__( + self, + hidden_size: int = 1024, + num_heads: int = 16, + intermediate_size: int = 4096, + hidden_act: str = "gelu_pytorch_tanh", + ): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, eps=1e-6) + self.norm2 = nn.LayerNorm(hidden_size, eps=1e-6) + self.attn = Qwen3VLVisionAttention(hidden_size=hidden_size, num_heads=num_heads) + self.mlp = Qwen3VLVisionMLP( + hidden_size=hidden_size, + intermediate_size=intermediate_size, + hidden_act=hidden_act, + ) + + def __call__( + self, + hidden_states: mx.array, + cu_seqlens: mx.array, + rotary_pos_emb: mx.array | None = None, + position_embeddings: tuple[mx.array, mx.array] | None = None, + ) -> mx.array: + hidden_states = hidden_states + self.attn( + self.norm1(hidden_states), + cu_seqlens=cu_seqlens, + rotary_pos_emb=rotary_pos_emb, + position_embeddings=position_embeddings, + ) + hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) + return hidden_states diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_mlp.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_mlp.py new file mode 100644 index 0000000..41210e6 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_mlp.py @@ -0,0 +1,27 @@ +import mlx.core as mx +import numpy as np +from mlx import nn + + +class Qwen3VLVisionMLP(nn.Module): + def __init__( + self, + hidden_size: int = 1024, + intermediate_size: int = 4096, + hidden_act: str = "gelu_pytorch_tanh", + ): + super().__init__() + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.linear_fc1 = nn.Linear(hidden_size, intermediate_size, bias=True) + self.linear_fc2 = nn.Linear(intermediate_size, hidden_size, bias=True) + + if hidden_act == "gelu_pytorch_tanh": + self.act_fn = lambda x: 0.5 * x * (1 + mx.tanh(mx.sqrt(2 / np.pi) * (x + 0.044715 * x**3))) + elif hidden_act == "gelu": + self.act_fn = lambda x: x * 0.5 * (1.0 + mx.erf(x / mx.sqrt(2.0))) + else: + raise ValueError(f"Unsupported activation: {hidden_act}") + + def __call__(self, hidden_state: mx.array) -> mx.array: + return self.linear_fc2(self.act_fn(self.linear_fc1(hidden_state))) diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_model.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_model.py new file mode 100644 index 0000000..b6f3b0a --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_model.py @@ -0,0 +1,247 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.fibo_vlm.model.qwen3_vl_vision_block import Qwen3VLVisionBlock +from mflux.models.fibo_vlm.model.qwen3_vl_vision_patch_embed import Qwen3VLVisionPatchEmbed +from mflux.models.fibo_vlm.model.qwen3_vl_vision_patch_merger import Qwen3VLVisionPatchMerger +from mflux.models.fibo_vlm.model.qwen3_vl_vision_rotary_embedding import Qwen3VLVisionRotaryEmbedding + + +class Qwen3VLVisionModel(nn.Module): + def __init__( + self, + patch_size: int = 16, + temporal_patch_size: int = 2, + in_channels: int = 3, + hidden_size: int = 1024, + num_heads: int = 16, + intermediate_size: int = 4096, + depth: int = 24, + spatial_merge_size: int = 2, + num_position_embeddings: int = 2304, + out_hidden_size: int = 2560, + deepstack_visual_indexes: list[int] | None = None, + hidden_act: str = "gelu_pytorch_tanh", + ): + super().__init__() + self.spatial_merge_size = spatial_merge_size + self.patch_size = patch_size + self.spatial_merge_unit = spatial_merge_size * spatial_merge_size + + self.patch_embed = Qwen3VLVisionPatchEmbed( + patch_size=patch_size, + temporal_patch_size=temporal_patch_size, + in_channels=in_channels, + embed_dim=hidden_size, + ) + + self.pos_embed = nn.Embedding(num_position_embeddings, hidden_size) + self.num_grid_per_side = int(num_position_embeddings**0.5) + + head_dim = hidden_size // num_heads + self.rotary_pos_emb = Qwen3VLVisionRotaryEmbedding(head_dim // 2) + + self.blocks = [ + Qwen3VLVisionBlock( + hidden_size=hidden_size, + num_heads=num_heads, + intermediate_size=intermediate_size, + hidden_act=hidden_act, + ) + for _ in range(depth) + ] + self.merger = Qwen3VLVisionPatchMerger( + hidden_size=hidden_size, + spatial_merge_size=spatial_merge_size, + out_hidden_size=out_hidden_size, + use_postshuffle_norm=False, + ) + + self.deepstack_visual_indexes = deepstack_visual_indexes or [5, 11, 17] + self.deepstack_merger_list = [ + Qwen3VLVisionPatchMerger( + hidden_size=hidden_size, + spatial_merge_size=spatial_merge_size, + out_hidden_size=out_hidden_size, + use_postshuffle_norm=True, + ) + for _ in range(len(self.deepstack_visual_indexes)) + ] + + def __call__(self, hidden_states: mx.array, grid_thw: mx.array) -> tuple[mx.array, list[mx.array]]: + hidden_states = self.patch_embed(hidden_states) + pos_embeds = Qwen3VLVisionModel._fast_pos_embed_interpolate(self.spatial_merge_size, self.pos_embed, self.num_grid_per_side, grid_thw) # fmt: off + hidden_states = hidden_states + pos_embeds + rotary_pos_emb = Qwen3VLVisionModel._rot_pos_emb(self.rotary_pos_emb, self.spatial_merge_size, grid_thw) + + # Prepare position embeddings for attention + seq_len = hidden_states.shape[0] + hidden_states = hidden_states.reshape(seq_len, -1) + rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) + emb = mx.concatenate([rotary_pos_emb, rotary_pos_emb], axis=-1) + cos = mx.cos(emb) + sin = mx.sin(emb) + position_embeddings = (cos, sin) + + # Compute cu_seqlens for variable-length sequences + cu_seqlens_list = [] + for i in range(grid_thw.shape[0]): + t, h, w = int(grid_thw[i, 0].item()), int(grid_thw[i, 1].item()), int(grid_thw[i, 2].item()) + seq_len_img = h * w * t + cu_seqlens_list.extend([seq_len_img] * t) + cu_seqlens = mx.array( + [0] + [sum(cu_seqlens_list[: i + 1]) for i in range(len(cu_seqlens_list))], dtype=mx.int32 + ) + + # Process through blocks + deepstack_image_embeds = [] + for layer_idx, block in enumerate(self.blocks): + hidden_states = block( + hidden_states, + cu_seqlens=cu_seqlens, + position_embeddings=position_embeddings, + ) + + # Collect deepstack features + if layer_idx in self.deepstack_visual_indexes: + deepstack_idx = self.deepstack_visual_indexes.index(layer_idx) + deepstack_embeds = self.deepstack_merger_list[deepstack_idx](hidden_states) + deepstack_image_embeds.append(deepstack_embeds) + + image_embeds = self.merger(hidden_states) + return image_embeds, deepstack_image_embeds + + @staticmethod + def _fast_pos_embed_interpolate(spatial_merge_size, pos_embed, num_grid_per_side, grid_thw: mx.array) -> mx.array: + grid_ts = grid_thw[:, 0] + grid_hs = grid_thw[:, 1] + grid_ws = grid_thw[:, 2] + + idx_list = [[] for _ in range(4)] + weight_list = [[] for _ in range(4)] + + for t, h, w in zip(grid_ts, grid_hs, grid_ws): + t, h, w = int(t.item()), int(h.item()), int(w.item()) + h_idxs = mx.linspace(0, num_grid_per_side - 1, h) + w_idxs = mx.linspace(0, num_grid_per_side - 1, w) + + h_idxs_floor = h_idxs.astype(mx.int32) + w_idxs_floor = w_idxs.astype(mx.int32) + h_idxs_ceil = mx.clip(h_idxs_floor + 1, 0, num_grid_per_side - 1) + w_idxs_ceil = mx.clip(w_idxs_floor + 1, 0, num_grid_per_side - 1) + + dh = h_idxs - h_idxs_floor.astype(mx.float32) + dw = w_idxs - w_idxs_floor.astype(mx.float32) + + base_h = h_idxs_floor * num_grid_per_side + base_h_ceil = h_idxs_ceil * num_grid_per_side + + indices = [ + (base_h[:, None] + w_idxs_floor[None, :]).flatten(), + (base_h[:, None] + w_idxs_ceil[None, :]).flatten(), + (base_h_ceil[:, None] + w_idxs_floor[None, :]).flatten(), + (base_h_ceil[:, None] + w_idxs_ceil[None, :]).flatten(), + ] + + weights = [ + ((1 - dh)[:, None] * (1 - dw)[None, :]).flatten(), + ((1 - dh)[:, None] * dw[None, :]).flatten(), + (dh[:, None] * (1 - dw)[None, :]).flatten(), + (dh[:, None] * dw[None, :]).flatten(), + ] + + for i in range(4): + idx_list[i].extend(indices[i].tolist()) + weight_list[i].extend(weights[i].tolist()) + + # Convert to arrays + max_len = max(len(idx_list[0]), len(idx_list[1]), len(idx_list[2]), len(idx_list[3])) + idx_array = mx.zeros((4, max_len), dtype=mx.int32) + weight_array = mx.zeros((4, max_len), dtype=mx.float32) + + for i in range(4): + idx_array[i] = ( + mx.concatenate( + [ + mx.array(idx_list[i][:max_len], dtype=mx.int32), + mx.zeros(max_len - len(idx_list[i]), dtype=mx.int32), + ] + ) + if len(idx_list[i]) < max_len + else mx.array(idx_list[i][:max_len], dtype=mx.int32) + ) + weight_array[i] = ( + mx.concatenate( + [ + mx.array(weight_list[i][:max_len], dtype=mx.float32), + mx.zeros(max_len - len(weight_list[i]), dtype=mx.float32), + ] + ) + if len(weight_list[i]) < max_len + else mx.array(weight_list[i][:max_len], dtype=mx.float32) + ) + + # Get position embeddings + pos_embeds = pos_embed(idx_array) * weight_array[:, :, None] + patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3] + + # Split by image + patch_pos_embeds_list = [] + start = 0 + for h, w in zip(grid_hs, grid_ws): + h, w = int(h.item()), int(w.item()) + end = start + h * w + patch_pos_embeds_list.append(patch_pos_embeds[start:end]) + start = end + + # Permute and reshape for spatial merging + patch_pos_embeds_permute = [] + merge_size = spatial_merge_size + for pos_embed, t, h, w in zip(patch_pos_embeds_list, grid_ts, grid_hs, grid_ws): + t, h, w = int(t.item()), int(h.item()), int(w.item()) + pos_embed = mx.tile(pos_embed, (t, 1)) + pos_embed = pos_embed.reshape(t, h // merge_size, merge_size, w // merge_size, merge_size, -1) + pos_embed = pos_embed.transpose(0, 1, 3, 2, 4, 5) + pos_embed = pos_embed.reshape(-1, pos_embed.shape[-1]) + patch_pos_embeds_permute.append(pos_embed) + + patch_pos_embeds = mx.concatenate(patch_pos_embeds_permute) + return patch_pos_embeds + + @staticmethod + def _rot_pos_emb(rotary_pos_emb, spatial_merge_size, grid_thw: mx.array) -> mx.array: + pos_ids = [] + for i in range(grid_thw.shape[0]): + t, h, w = int(grid_thw[i, 0].item()), int(grid_thw[i, 1].item()), int(grid_thw[i, 2].item()) + + hpos_ids = mx.repeat(mx.arange(h, dtype=mx.int32)[..., None], w, axis=1) + wpos_ids = mx.repeat(mx.arange(w, dtype=mx.int32)[None, ...], h, axis=0) + + # Reshape for spatial merging + merge_h = h // spatial_merge_size + merge_w = w // spatial_merge_size + hpos_ids = hpos_ids.reshape(merge_h, spatial_merge_size, merge_w, spatial_merge_size) + wpos_ids = wpos_ids.reshape(merge_h, spatial_merge_size, merge_w, spatial_merge_size) + + hpos_ids = mx.transpose(hpos_ids, (0, 2, 1, 3)) + wpos_ids = mx.transpose(wpos_ids, (0, 2, 1, 3)) + hpos_ids = hpos_ids.reshape(-1) + wpos_ids = wpos_ids.reshape(-1) + + pos_id_pair = mx.stack([hpos_ids, wpos_ids], axis=-1) + if t > 1: + pos_id_pair = mx.tile(pos_id_pair, (t, 1)) + + pos_ids.append(pos_id_pair) + + pos_ids = mx.concatenate(pos_ids, axis=0) + max_grid_size = int(mx.max(grid_thw[:, 1:]).item()) + rotary_pos_emb_full = rotary_pos_emb(max_grid_size) + + h_indices = pos_ids[:, 0].astype(mx.int32) + w_indices = pos_ids[:, 1].astype(mx.int32) + h_emb = rotary_pos_emb_full[h_indices] + w_emb = rotary_pos_emb_full[w_indices] + rotary_pos_emb = mx.stack([h_emb, w_emb], axis=1) + rotary_pos_emb = rotary_pos_emb.reshape(rotary_pos_emb.shape[0], -1) + return rotary_pos_emb diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_patch_embed.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_patch_embed.py new file mode 100644 index 0000000..152ce1d --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_patch_embed.py @@ -0,0 +1,41 @@ +import mlx.core as mx +from mlx import nn + + +class Qwen3VLVisionPatchEmbed(nn.Module): + def __init__( + self, + patch_size: int = 16, + temporal_patch_size: int = 2, + in_channels: int = 3, + embed_dim: int = 1024, + ): + super().__init__() + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.in_channels = in_channels + self.embed_dim = embed_dim + + kernel_size = [temporal_patch_size, patch_size, patch_size] + stride = [temporal_patch_size, patch_size, patch_size] + self.proj = nn.Conv3d( + in_channels=in_channels, + out_channels=embed_dim, + kernel_size=kernel_size, + stride=stride, + bias=True, + ) + + def __call__(self, hidden_states: mx.array) -> mx.array: + seq_len = hidden_states.shape[0] + hidden_states = hidden_states.reshape( + seq_len, + self.in_channels, + self.temporal_patch_size, + self.patch_size, + self.patch_size, + ) + hidden_states = hidden_states.transpose(0, 2, 3, 4, 1) + output = self.proj(hidden_states) + output = output.reshape(seq_len, self.embed_dim) + return output diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_patch_merger.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_patch_merger.py new file mode 100644 index 0000000..f9b5bdb --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_patch_merger.py @@ -0,0 +1,29 @@ +import mlx.core as mx +import numpy as np +from mlx import nn + + +class Qwen3VLVisionPatchMerger(nn.Module): + def __init__( + self, + hidden_size: int = 1024, + spatial_merge_size: int = 2, + out_hidden_size: int = 2560, + use_postshuffle_norm: bool = False, + ): + super().__init__() + self.hidden_size = hidden_size * (spatial_merge_size**2) + self.use_postshuffle_norm = use_postshuffle_norm + self.norm = nn.LayerNorm(self.hidden_size if use_postshuffle_norm else hidden_size, eps=1e-6) + self.linear_fc1 = nn.Linear(self.hidden_size, self.hidden_size) + self.act_fn = lambda x: 0.5 * x * (1 + mx.tanh(mx.sqrt(2 / np.pi) * (x + 0.044715 * x**3))) # GELU + self.linear_fc2 = nn.Linear(self.hidden_size, out_hidden_size) + + def __call__(self, x: mx.array) -> mx.array: + if self.use_postshuffle_norm: + x = self.norm(x.reshape(-1, self.hidden_size)).reshape(-1, self.hidden_size) + else: + x = self.norm(x) + x = x.reshape(-1, self.hidden_size) + x = self.linear_fc2(self.act_fn(self.linear_fc1(x))) + return x diff --git a/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_rotary_embedding.py b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_rotary_embedding.py new file mode 100644 index 0000000..0f7a971 --- /dev/null +++ b/src/mflux/models/fibo_vlm/model/qwen3_vl_vision_rotary_embedding.py @@ -0,0 +1,15 @@ +import mlx.core as mx +import numpy as np +from mlx import nn + + +class Qwen3VLVisionRotaryEmbedding(nn.Module): + def __init__(self, dim: int, theta: float = 10000.0): + super().__init__() + inv_freq = 1.0 / (theta ** (np.arange(0, dim, 2, dtype=np.float32) / dim)) + self.inv_freq = mx.array(inv_freq) + + def __call__(self, seqlen: int) -> mx.array: + seq = mx.arange(seqlen, dtype=mx.float32) + freqs = mx.outer(seq, self.inv_freq) + return freqs diff --git a/src/mflux/models/fibo_vlm/weights/__init__.py b/src/mflux/models/fibo_vlm/weights/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mflux/models/fibo_vlm/weights/fibo_vlm_weight_handler.py b/src/mflux/models/fibo_vlm/weights/fibo_vlm_weight_handler.py new file mode 100644 index 0000000..66531f8 --- /dev/null +++ b/src/mflux/models/fibo_vlm/weights/fibo_vlm_weight_handler.py @@ -0,0 +1,74 @@ +import mlx.core as mx +import torch +from transformers import Qwen3VLForConditionalGeneration + +from mflux.models.common.weights.mapping.weight_mapper import WeightMapper +from mflux.models.fibo.weights import FIBOWeightHandler +from mflux.models.fibo_vlm.weights.fibo_vlm_weight_mapping import FIBOVLMWeightMapping +from mflux.models.flux.weights.weight_handler import MetaData + + +class FIBOVLMWeightHandler: + @staticmethod + def load_vlm_regular_weights( + repo_id: str = "briaai/FIBO-vlm", + local_path: str | None = None, + ) -> "FIBOWeightHandler": + # Load model - try offline first (use cache), fall back to online if needed + pretrained_path = local_path or repo_id + if local_path: + # If explicit local path, use local_files_only + model = Qwen3VLForConditionalGeneration.from_pretrained( + pretrained_model_name_or_path=pretrained_path, + dtype=torch.bfloat16, + local_files_only=True, + ) + else: + # Try offline first (use cache), fall back to online if not cached + try: + model = Qwen3VLForConditionalGeneration.from_pretrained( + pretrained_model_name_or_path=pretrained_path, + dtype=torch.bfloat16, + local_files_only=True, + ) + except (FileNotFoundError, OSError): + # Model not in cache, allow download if online + model = Qwen3VLForConditionalGeneration.from_pretrained( + pretrained_model_name_or_path=pretrained_path, + dtype=torch.bfloat16, + local_files_only=False, + ) + num_layers = model.config.text_config.num_hidden_layers + depth = model.config.vision_config.depth + state_dict = model.state_dict() + + raw_decoder_weights = { + k: FIBOVLMWeightHandler._to_mlx(v) + for k, v in state_dict.items() + if k.startswith(("model.language_model", "lm_head")) + } + + raw_visual_weights = { + k: FIBOVLMWeightHandler._to_mlx(v) for k, v in state_dict.items() if k.startswith("model.visual") + } + + decoder_weights = WeightMapper.apply_mapping( + hf_weights=raw_decoder_weights, + mapping=FIBOVLMWeightMapping.get_vlm_decoder_mapping(num_layers=num_layers), + num_blocks=num_layers, + ) + visual_weights = WeightMapper.apply_mapping( + hf_weights=raw_visual_weights, + mapping=FIBOVLMWeightMapping.get_vlm_visual_mapping(depth=depth), + num_blocks=depth, + ) + + return FIBOWeightHandler( + decoder=decoder_weights, + visual=visual_weights, + meta_data=MetaData(quantization_level=None, scale=None, is_lora=False, mflux_version=None), + ) + + @staticmethod + def _to_mlx(tensor: torch.Tensor) -> mx.array: + return mx.array((tensor.to(torch.float16) if tensor.dtype == torch.bfloat16 else tensor).detach().cpu().numpy()) diff --git a/src/mflux/models/fibo_vlm/weights/fibo_vlm_weight_mapping.py b/src/mflux/models/fibo_vlm/weights/fibo_vlm_weight_mapping.py new file mode 100644 index 0000000..debf71d --- /dev/null +++ b/src/mflux/models/fibo_vlm/weights/fibo_vlm_weight_mapping.py @@ -0,0 +1,222 @@ +from typing import List + +from mflux.models.common.weights.mapping.weight_mapping import WeightMapping, WeightTarget +from mflux.models.fibo.weights.fibo_weight_mapping import transpose_conv3d_weight + + +class FIBOVLMWeightMapping(WeightMapping): + @staticmethod + def get_vlm_decoder_mapping(num_layers: int = 36) -> List[WeightTarget]: + return [ + WeightTarget( + mlx_path="embed_tokens.weight", + hf_patterns=["model.language_model.embed_tokens.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.q_proj.weight", + hf_patterns=["model.language_model.layers.{block}.self_attn.q_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.q_proj.bias", + hf_patterns=["model.language_model.layers.{block}.self_attn.q_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.k_proj.weight", + hf_patterns=["model.language_model.layers.{block}.self_attn.k_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.k_proj.bias", + hf_patterns=["model.language_model.layers.{block}.self_attn.k_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.v_proj.weight", + hf_patterns=["model.language_model.layers.{block}.self_attn.v_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.v_proj.bias", + hf_patterns=["model.language_model.layers.{block}.self_attn.v_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.o_proj.weight", + hf_patterns=["model.language_model.layers.{block}.self_attn.o_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.o_proj.bias", + hf_patterns=["model.language_model.layers.{block}.self_attn.o_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.q_norm.weight", + hf_patterns=["model.language_model.layers.{block}.self_attn.q_norm.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.self_attn.k_norm.weight", + hf_patterns=["model.language_model.layers.{block}.self_attn.k_norm.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.gate_proj.weight", + hf_patterns=["model.language_model.layers.{block}.mlp.gate_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.gate_proj.bias", + hf_patterns=["model.language_model.layers.{block}.mlp.gate_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.up_proj.weight", + hf_patterns=["model.language_model.layers.{block}.mlp.up_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.up_proj.bias", + hf_patterns=["model.language_model.layers.{block}.mlp.up_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.down_proj.weight", + hf_patterns=["model.language_model.layers.{block}.mlp.down_proj.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.mlp.down_proj.bias", + hf_patterns=["model.language_model.layers.{block}.mlp.down_proj.bias"], + ), + WeightTarget( + mlx_path="layers.{block}.input_layernorm.weight", + hf_patterns=["model.language_model.layers.{block}.input_layernorm.weight"], + ), + WeightTarget( + mlx_path="layers.{block}.post_attention_layernorm.weight", + hf_patterns=["model.language_model.layers.{block}.post_attention_layernorm.weight"], + ), + WeightTarget( + mlx_path="norm.weight", + hf_patterns=["model.language_model.norm.weight"], + ), + WeightTarget( + mlx_path="lm_head.weight", + hf_patterns=["lm_head.weight"], + ), + ] + + @staticmethod + def get_vlm_visual_mapping(depth: int = 24) -> List[WeightTarget]: + return [ + # Patch embedding + WeightTarget( + mlx_path="patch_embed.proj.weight", + hf_patterns=["model.visual.patch_embed.proj.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="patch_embed.proj.bias", + hf_patterns=["model.visual.patch_embed.proj.bias"], + ), + # Position embeddings + WeightTarget( + mlx_path="pos_embed.weight", + hf_patterns=["model.visual.pos_embed.weight"], + ), + # Vision transformer blocks + WeightTarget( + mlx_path="blocks.{block}.norm1.weight", + hf_patterns=["model.visual.blocks.{block}.norm1.weight"], + ), + WeightTarget( + mlx_path="blocks.{block}.norm1.bias", + hf_patterns=["model.visual.blocks.{block}.norm1.bias"], + ), + WeightTarget( + mlx_path="blocks.{block}.norm2.weight", + hf_patterns=["model.visual.blocks.{block}.norm2.weight"], + ), + WeightTarget( + mlx_path="blocks.{block}.norm2.bias", + hf_patterns=["model.visual.blocks.{block}.norm2.bias"], + ), + # Attention + WeightTarget( + mlx_path="blocks.{block}.attn.qkv.weight", + hf_patterns=["model.visual.blocks.{block}.attn.qkv.weight"], + ), + WeightTarget( + mlx_path="blocks.{block}.attn.qkv.bias", + hf_patterns=["model.visual.blocks.{block}.attn.qkv.bias"], + ), + WeightTarget( + mlx_path="blocks.{block}.attn.proj.weight", + hf_patterns=["model.visual.blocks.{block}.attn.proj.weight"], + ), + WeightTarget( + mlx_path="blocks.{block}.attn.proj.bias", + hf_patterns=["model.visual.blocks.{block}.attn.proj.bias"], + ), + # MLP + WeightTarget( + mlx_path="blocks.{block}.mlp.linear_fc1.weight", + hf_patterns=["model.visual.blocks.{block}.mlp.linear_fc1.weight"], + ), + WeightTarget( + mlx_path="blocks.{block}.mlp.linear_fc1.bias", + hf_patterns=["model.visual.blocks.{block}.mlp.linear_fc1.bias"], + ), + WeightTarget( + mlx_path="blocks.{block}.mlp.linear_fc2.weight", + hf_patterns=["model.visual.blocks.{block}.mlp.linear_fc2.weight"], + ), + WeightTarget( + mlx_path="blocks.{block}.mlp.linear_fc2.bias", + hf_patterns=["model.visual.blocks.{block}.mlp.linear_fc2.bias"], + ), + # Final merger + WeightTarget( + mlx_path="merger.norm.weight", + hf_patterns=["model.visual.merger.norm.weight"], + ), + WeightTarget( + mlx_path="merger.norm.bias", + hf_patterns=["model.visual.merger.norm.bias"], + ), + WeightTarget( + mlx_path="merger.linear_fc1.weight", + hf_patterns=["model.visual.merger.linear_fc1.weight"], + ), + WeightTarget( + mlx_path="merger.linear_fc1.bias", + hf_patterns=["model.visual.merger.linear_fc1.bias"], + ), + WeightTarget( + mlx_path="merger.linear_fc2.weight", + hf_patterns=["model.visual.merger.linear_fc2.weight"], + ), + WeightTarget( + mlx_path="merger.linear_fc2.bias", + hf_patterns=["model.visual.merger.linear_fc2.bias"], + ), + # DeepStack mergers (fixed 3 items, not depth-based) + WeightTarget( + mlx_path="deepstack_merger_list.{block}.norm.weight", + hf_patterns=["model.visual.deepstack_merger_list.{block}.norm.weight"], + max_blocks=3, # Fixed 3 items (0, 1, 2) + ), + WeightTarget( + mlx_path="deepstack_merger_list.{block}.norm.bias", + hf_patterns=["model.visual.deepstack_merger_list.{block}.norm.bias"], + max_blocks=3, + ), + WeightTarget( + mlx_path="deepstack_merger_list.{block}.linear_fc1.weight", + hf_patterns=["model.visual.deepstack_merger_list.{block}.linear_fc1.weight"], + max_blocks=3, + ), + WeightTarget( + mlx_path="deepstack_merger_list.{block}.linear_fc1.bias", + hf_patterns=["model.visual.deepstack_merger_list.{block}.linear_fc1.bias"], + max_blocks=3, + ), + WeightTarget( + mlx_path="deepstack_merger_list.{block}.linear_fc2.weight", + hf_patterns=["model.visual.deepstack_merger_list.{block}.linear_fc2.weight"], + max_blocks=3, + ), + WeightTarget( + mlx_path="deepstack_merger_list.{block}.linear_fc2.bias", + hf_patterns=["model.visual.deepstack_merger_list.{block}.linear_fc2.bias"], + max_blocks=3, + ), + ] diff --git a/src/mflux/models/flux/latent_creator/__init__.py b/src/mflux/models/flux/latent_creator/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mflux/models/flux/latent_creator/flux_latent_creator.py b/src/mflux/models/flux/latent_creator/flux_latent_creator.py new file mode 100644 index 0000000..89d393e --- /dev/null +++ b/src/mflux/models/flux/latent_creator/flux_latent_creator.py @@ -0,0 +1,16 @@ +import mlx.core as mx + + +class FluxLatentCreator: + @staticmethod + def create_noise(seed: int, height: int, width: int) -> mx.array: + return mx.random.normal( + shape=[1, (height // 16) * (width // 16), 64], + key=mx.random.key(seed), + ) + + @staticmethod + def pack_latents(latents: mx.array, height: int, width: int) -> mx.array: + latents = mx.reshape(latents, (1, 16, height // 16, 2, width // 16, 2)) + latents = mx.transpose(latents, (0, 2, 4, 1, 3, 5)) + return mx.reshape(latents, (1, (width // 16) * (height // 16), 64)) diff --git a/src/mflux/models/flux/variants/concept_attention/attention_data.py b/src/mflux/models/flux/variants/concept_attention/attention_data.py index 0c46e35..6f498c9 100644 --- a/src/mflux/models/flux/variants/concept_attention/attention_data.py +++ b/src/mflux/models/flux/variants/concept_attention/attention_data.py @@ -46,7 +46,7 @@ class ConceptHeatmap: width: int def save(self, path: str | Path, export_json_metadata: bool = False, overwrite: bool = False) -> None: - from mflux.post_processing.image_util import ImageUtil + from mflux.utils.image_util import ImageUtil ImageUtil.save_image( image=self.image, diff --git a/src/mflux/models/flux/variants/concept_attention/flux_concept.py b/src/mflux/models/flux/variants/concept_attention/flux_concept.py index d498003..98d107a 100644 --- a/src/mflux/models/flux/variants/concept_attention/flux_concept.py +++ b/src/mflux/models/flux/variants/concept_attention/flux_concept.py @@ -6,9 +6,9 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import Img2Img, LatentCreator +from mflux.models.common.latent_creator.latent_creator import Img2Img, LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder @@ -16,9 +16,10 @@ from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.concept_attention.attention_data import GenerationAttentionData from mflux.models.flux.variants.concept_attention.concept_util import ConceptUtil from mflux.models.flux.variants.concept_attention.transformer_concept import TransformerConcept -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1Concept(nn.Module): @@ -65,6 +66,7 @@ class Flux1Concept(nn.Module): width=config.width, img2img=Img2Img( vae=self.vae, + latent_creator=FluxLatentCreator, image_path=config.image_path, sigmas=config.scheduler.sigmas, init_time_step=config.init_time_step, diff --git a/src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py b/src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py index 491c301..36a4035 100644 --- a/src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py +++ b/src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py @@ -6,9 +6,9 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.common.latent_creator.latent_creator import LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder @@ -16,9 +16,10 @@ from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.concept_attention.attention_data import GenerationAttentionData from mflux.models.flux.variants.concept_attention.concept_util import ConceptUtil from mflux.models.flux.variants.concept_attention.transformer_concept import TransformerConcept -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1ConceptFromImage(nn.Module): @@ -68,7 +69,7 @@ class Flux1ConceptFromImage(nn.Module): ) # Create static noise for blending at each timestep - static_noise = LatentCreator.create( + static_noise = FluxLatentCreator.create_noise( seed=seed, height=config.height, width=config.width, diff --git a/src/mflux/models/flux/variants/controlnet/controlnet_util.py b/src/mflux/models/flux/variants/controlnet/controlnet_util.py index 964dcf3..911d436 100644 --- a/src/mflux/models/flux/variants/controlnet/controlnet_util.py +++ b/src/mflux/models/flux/variants/controlnet/controlnet_util.py @@ -6,8 +6,8 @@ import numpy as np import PIL.Image from mflux.models.flux.model.flux_vae.vae import VAE -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import StrOrBytesPath +from mflux.utils.array_util import ArrayUtil +from mflux.utils.image_util import StrOrBytesPath log = logging.getLogger(__name__) @@ -21,7 +21,7 @@ class ControlnetUtil: controlnet_image_path: StrOrBytesPath, is_canny: bool, ) -> tuple[mx.array, PIL.Image.Image]: - from mflux.post_processing.image_util import ImageUtil + from mflux.utils.image_util import ImageUtil control_image = ImageUtil.load_image(controlnet_image_path) control_image = ControlnetUtil._scale_image(height=height, width=width, img=control_image) diff --git a/src/mflux/models/flux/variants/controlnet/flux_controlnet.py b/src/mflux/models/flux/variants/controlnet/flux_controlnet.py index 8521b30..2061a18 100644 --- a/src/mflux/models/flux/variants/controlnet/flux_controlnet.py +++ b/src/mflux/models/flux/variants/controlnet/flux_controlnet.py @@ -6,9 +6,9 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.common.weights.model_saver import ModelSaver from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder @@ -16,10 +16,10 @@ from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.controlnet.controlnet_util import ControlnetUtil from mflux.models.flux.variants.controlnet.transformer_controlnet import TransformerControlnet -from mflux.models.flux.weights.model_saver import ModelSaver -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil, StrOrBytesPath +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil, StrOrBytesPath class Flux1Controlnet(nn.Module): @@ -69,7 +69,7 @@ class Flux1Controlnet(nn.Module): ) # 2. Create the initial latents - latents = LatentCreator.create( + latents = FluxLatentCreator.create_noise( seed=seed, height=config.height, width=config.width, @@ -175,5 +175,19 @@ class Flux1Controlnet(nn.Module): ) def save_model(self, base_path: str) -> None: - ModelSaver.save_model(self, self.bits, base_path) - ModelSaver.save_weights(base_path, self.bits, self.transformer_controlnet, "transformer_controlnet") + ModelSaver.save_model( + model=self, + bits=self.bits, + base_path=base_path, + tokenizers=[ + ("clip_tokenizer.tokenizer", "tokenizer"), + ("t5_tokenizer.tokenizer", "tokenizer_2"), + ], + components=[ + ("vae", "vae"), + ("transformer", "transformer"), + ("clip_text_encoder", "text_encoder"), + ("t5_text_encoder", "text_encoder_2"), + ("transformer_controlnet", "transformer_controlnet"), + ], + ) diff --git a/src/mflux/models/flux/variants/depth/depth_util.py b/src/mflux/models/flux/variants/depth/depth_util.py index 8149af5..dae93c1 100644 --- a/src/mflux/models/flux/variants/depth/depth_util.py +++ b/src/mflux/models/flux/variants/depth/depth_util.py @@ -8,8 +8,8 @@ import PIL.Image from mflux.config.runtime_config import RuntimeConfig from mflux.models.depth_pro.depth_pro import DepthPro from mflux.models.flux.model.flux_vae.vae import VAE -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.image_util import ImageUtil logger = logging.getLogger(__name__) diff --git a/src/mflux/models/flux/variants/depth/flux_depth.py b/src/mflux/models/flux/variants/depth/flux_depth.py index 5e68fc0..a415058 100644 --- a/src/mflux/models/flux/variants/depth/flux_depth.py +++ b/src/mflux/models/flux/variants/depth/flux_depth.py @@ -6,19 +6,19 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator from mflux.models.depth_pro.depth_pro import DepthPro from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.depth.depth_util import DepthUtil -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1Depth(nn.Module): @@ -56,7 +56,7 @@ class Flux1Depth(nn.Module): time_steps = tqdm(range(config.init_time_step, config.num_inference_steps)) # 1. Create the initial latents - latents = LatentCreator.create( + latents = FluxLatentCreator.create_noise( seed=seed, height=config.height, width=config.width, diff --git a/src/mflux/models/flux/variants/dreambooth/dataset/dataset.py b/src/mflux/models/flux/variants/dreambooth/dataset/dataset.py index 14c7630..3d6a66f 100644 --- a/src/mflux/models/flux/variants/dreambooth/dataset/dataset.py +++ b/src/mflux/models/flux/variants/dreambooth/dataset/dataset.py @@ -9,8 +9,8 @@ from mflux.models.flux.variants.dreambooth.dataset.batch import Example from mflux.models.flux.variants.dreambooth.dataset.dreambooth_preprocessing import DreamBoothPreProcessing from mflux.models.flux.variants.dreambooth.state.training_spec import ExampleSpec from mflux.models.flux.variants.txt2img.flux import Flux1 -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.image_util import ImageUtil class Dataset: diff --git a/src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py b/src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py index 10c804f..b2b49a4 100644 --- a/src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py +++ b/src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py @@ -4,7 +4,7 @@ import mlx.core as mx from mflux.config.config import Config from mflux.config.runtime_config import RuntimeConfig -from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.common.latent_creator.latent_creator import LatentCreator from mflux.models.flux.variants.dreambooth.dataset.batch import Batch, Example from mflux.models.flux.variants.txt2img.flux import Flux1 diff --git a/src/mflux/models/flux/variants/fill/flux_fill.py b/src/mflux/models/flux/variants/fill/flux_fill.py index 9d9703e..261c8b4 100644 --- a/src/mflux/models/flux/variants/fill/flux_fill.py +++ b/src/mflux/models/flux/variants/fill/flux_fill.py @@ -6,18 +6,18 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.fill.mask_util import MaskUtil -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1Fill(nn.Module): @@ -54,7 +54,7 @@ class Flux1Fill(nn.Module): time_steps = tqdm(range(config.init_time_step, config.num_inference_steps)) # 1. Create the initial latents - latents = LatentCreator.create( + latents = FluxLatentCreator.create_noise( seed=seed, height=config.height, width=config.width, diff --git a/src/mflux/models/flux/variants/fill/mask_util.py b/src/mflux/models/flux/variants/fill/mask_util.py index 13818b1..90da69e 100644 --- a/src/mflux/models/flux/variants/fill/mask_util.py +++ b/src/mflux/models/flux/variants/fill/mask_util.py @@ -3,8 +3,8 @@ from pathlib import Path import mlx.core as mx from mflux.models.flux.model.flux_vae.vae import VAE -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.image_util import ImageUtil class MaskUtil: diff --git a/src/mflux/models/flux/variants/in_context/flux_in_context_dev.py b/src/mflux/models/flux/variants/in_context/flux_in_context_dev.py index ec75526..03a7f27 100644 --- a/src/mflux/models/flux/variants/in_context/flux_in_context_dev.py +++ b/src/mflux/models/flux/variants/in_context/flux_in_context_dev.py @@ -6,17 +6,17 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.common.latent_creator.latent_creator import LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1InContextDev(nn.Module): diff --git a/src/mflux/models/flux/variants/in_context/flux_in_context_fill.py b/src/mflux/models/flux/variants/in_context/flux_in_context_fill.py index 8073acd..1655fb0 100644 --- a/src/mflux/models/flux/variants/in_context/flux_in_context_fill.py +++ b/src/mflux/models/flux/variants/in_context/flux_in_context_fill.py @@ -6,18 +6,18 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.in_context.utils.in_context_mask_util import InContextMaskUtil -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1InContextFill(nn.Module): @@ -62,7 +62,7 @@ class Flux1InContextFill(nn.Module): time_steps = tqdm(range(config.init_time_step, config.num_inference_steps)) # 1. Create the initial latents - latents = LatentCreator.create( + latents = FluxLatentCreator.create_noise( seed=seed, height=config.height, width=config.width, diff --git a/src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py b/src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py index 97b56b5..2484d84 100644 --- a/src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py +++ b/src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py @@ -2,8 +2,8 @@ import mlx.core as mx from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.fill.mask_util import MaskUtil -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.image_util import ImageUtil class InContextMaskUtil: diff --git a/src/mflux/models/flux/variants/kontext/flux_kontext.py b/src/mflux/models/flux/variants/kontext/flux_kontext.py index 694c3aa..9827e12 100644 --- a/src/mflux/models/flux/variants/kontext/flux_kontext.py +++ b/src/mflux/models/flux/variants/kontext/flux_kontext.py @@ -6,18 +6,18 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE from mflux.models.flux.variants.kontext.utils.kontext_util import KontextUtil -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1Kontext(nn.Module): @@ -54,7 +54,7 @@ class Flux1Kontext(nn.Module): time_steps = tqdm(range(config.init_time_step, config.num_inference_steps)) # 1. Create the initial latents - latents = LatentCreator.create( + latents = FluxLatentCreator.create_noise( seed=seed, height=config.height, width=config.width, diff --git a/src/mflux/models/flux/variants/kontext/utils/kontext_util.py b/src/mflux/models/flux/variants/kontext/utils/kontext_util.py index 15d0f75..fd67393 100644 --- a/src/mflux/models/flux/variants/kontext/utils/kontext_util.py +++ b/src/mflux/models/flux/variants/kontext/utils/kontext_util.py @@ -1,7 +1,7 @@ import mlx.core as mx -from mflux.latent_creator.latent_creator import LatentCreator -from mflux.post_processing.array_util import ArrayUtil +from mflux.models.common.latent_creator.latent_creator import LatentCreator +from mflux.utils.array_util import ArrayUtil class KontextUtil: diff --git a/src/mflux/models/flux/variants/redux/flux_redux.py b/src/mflux/models/flux/variants/redux/flux_redux.py index b9deeb2..d590a4f 100644 --- a/src/mflux/models/flux/variants/redux/flux_redux.py +++ b/src/mflux/models/flux/variants/redux/flux_redux.py @@ -8,9 +8,8 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder @@ -21,9 +20,10 @@ from mflux.models.flux.model.siglip_vision_transformer.siglip_vision_transformer from mflux.models.flux.tokenizer.clip_tokenizer import TokenizerCLIP from mflux.models.flux.tokenizer.t5_tokenizer import TokenizerT5 from mflux.models.flux.variants.redux.redux_util import ReduxUtil -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1Redux(nn.Module): @@ -62,7 +62,7 @@ class Flux1Redux(nn.Module): time_steps = tqdm(range(runtime_config.init_time_step, runtime_config.num_inference_steps)) # 1. Create the initial latents - latents = LatentCreator.create( + latents = FluxLatentCreator.create_noise( seed=seed, height=runtime_config.height, width=runtime_config.width, diff --git a/src/mflux/models/flux/variants/redux/redux_util.py b/src/mflux/models/flux/variants/redux/redux_util.py index 1c38696..cd12353 100644 --- a/src/mflux/models/flux/variants/redux/redux_util.py +++ b/src/mflux/models/flux/variants/redux/redux_util.py @@ -4,7 +4,7 @@ import mlx.core as mx from mflux.models.flux.model.redux_encoder.redux_encoder import ReduxEncoder from mflux.models.flux.model.siglip_vision_transformer.siglip_vision_transformer import SiglipVisionTransformer -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.image_util import ImageUtil class ReduxUtil: diff --git a/src/mflux/models/flux/variants/txt2img/flux.py b/src/mflux/models/flux/variants/txt2img/flux.py index 313f4d2..adb1986 100644 --- a/src/mflux/models/flux/variants/txt2img/flux.py +++ b/src/mflux/models/flux/variants/txt2img/flux.py @@ -6,18 +6,19 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import Img2Img, LatentCreator +from mflux.models.common.latent_creator.latent_creator import Img2Img, LatentCreator +from mflux.models.common.weights.model_saver import ModelSaver from mflux.models.flux.flux_initializer import FluxInitializer +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder from mflux.models.flux.model.flux_text_encoder.prompt_encoder import PromptEncoder from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder from mflux.models.flux.model.flux_transformer.transformer import Transformer from mflux.models.flux.model.flux_vae.vae import VAE -from mflux.models.flux.weights.model_saver import ModelSaver -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class Flux1(nn.Module): @@ -62,6 +63,7 @@ class Flux1(nn.Module): width=config.width, img2img=Img2Img( vae=self.vae, + latent_creator=FluxLatentCreator, image_path=config.image_path, sigmas=config.scheduler.sigmas, init_time_step=config.init_time_step, @@ -163,7 +165,21 @@ class Flux1(nn.Module): ) def save_model(self, base_path: str) -> None: - ModelSaver.save_model(self, self.bits, base_path) + ModelSaver.save_model( + model=self, + bits=self.bits, + base_path=base_path, + tokenizers=[ + ("clip_tokenizer.tokenizer", "tokenizer"), + ("t5_tokenizer.tokenizer", "tokenizer_2"), + ], + components=[ + ("vae", "vae"), + ("transformer", "transformer"), + ("clip_text_encoder", "text_encoder"), + ("t5_text_encoder", "text_encoder_2"), + ], + ) def freeze(self, **kwargs): self.vae.freeze() diff --git a/src/mflux/models/flux/weights/model_saver.py b/src/mflux/models/flux/weights/model_saver.py deleted file mode 100644 index deb8e6d..0000000 --- a/src/mflux/models/flux/weights/model_saver.py +++ /dev/null @@ -1,62 +0,0 @@ -from pathlib import Path - -import mlx.core as mx -from mlx import nn -from mlx.utils import tree_flatten -from transformers import CLIPTokenizer, T5Tokenizer - -from mflux.utils.version_util import VersionUtil - - -class ModelSaver: - @staticmethod - def save_model(model, bits: int, base_path: str): - # Save the tokenizers - ModelSaver._save_tokenizer(base_path, model.clip_tokenizer.tokenizer, "tokenizer") - ModelSaver._save_tokenizer(base_path, model.t5_tokenizer.tokenizer, "tokenizer_2") - - # Save the models - ModelSaver.save_weights(base_path, bits, model.vae, "vae") - ModelSaver.save_weights(base_path, bits, model.transformer, "transformer") - ModelSaver.save_weights(base_path, bits, model.clip_text_encoder, "text_encoder") - ModelSaver.save_weights(base_path, bits, model.t5_text_encoder, "text_encoder_2") - - @staticmethod - def _save_tokenizer(base_path: str, tokenizer: CLIPTokenizer | T5Tokenizer, subdir: str): - path = Path(base_path) / subdir - path.mkdir(parents=True, exist_ok=True) - tokenizer.save_pretrained(path) - - @staticmethod - def save_weights(base_path: str, bits: int, model: nn.Module, subdir: str): - path = Path(base_path) / subdir - path.mkdir(parents=True, exist_ok=True) - weights = ModelSaver._split_weights(base_path, dict(tree_flatten(model.parameters()))) - for i, weight in enumerate(weights): - mx.save_safetensors( - # usage: save_safetensors(file: str, arrays: dict[str, array], metadata: Optional[dict[str, str]] = None) - str(path / f"{i}.safetensors"), - # arrays (dict(str, array)): The dictionary of names to arrays to be saved. - weight, - # [save_safetensors] Metadata must be a dictionary with string keys and values - # i.e. 'None' and other special values are string-ified and need to be parsed by readers - { - "quantization_level": str(bits), - "mflux_version": VersionUtil.get_mflux_version(), - }, - ) - - @staticmethod - def _split_weights(base_path: str, weights: dict, max_file_size_gb: int = 2) -> list: - # Copied from mlx-examples repo - max_file_size_bytes = max_file_size_gb << 30 - shards = [] - shard, shard_size = {}, 0 - for k, v in weights.items(): - if shard_size + v.nbytes > max_file_size_bytes: - shards.append(shard) - shard, shard_size = {}, 0 - shard[k] = v - shard_size += v.nbytes - shards.append(shard) - return shards diff --git a/src/mflux/models/flux/weights/weight_util.py b/src/mflux/models/flux/weights/weight_util.py index 7c9c902..17722c3 100644 --- a/src/mflux/models/flux/weights/weight_util.py +++ b/src/mflux/models/flux/weights/weight_util.py @@ -3,7 +3,7 @@ from typing import TYPE_CHECKING import mlx.nn as nn from mflux.config.config import Config -from mflux.utils.quantization_util import QuantizationUtil +from mflux.models.common.quantization.quantization_util import QuantizationUtil if TYPE_CHECKING: from mflux.models.flux.variants.controlnet.weight_handler_controlnet import WeightHandlerControlnet diff --git a/src/mflux/models/qwen/latent_creator/__init__.py b/src/mflux/models/qwen/latent_creator/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mflux/models/qwen/latent_creator/qwen_latent_creator.py b/src/mflux/models/qwen/latent_creator/qwen_latent_creator.py new file mode 100644 index 0000000..52e4a3f --- /dev/null +++ b/src/mflux/models/qwen/latent_creator/qwen_latent_creator.py @@ -0,0 +1,13 @@ +import mlx.core as mx + +from mflux.models.flux.latent_creator.flux_latent_creator import FluxLatentCreator + + +class QwenLatentCreator: + @staticmethod + def create_noise(seed: int, height: int, width: int) -> mx.array: + return FluxLatentCreator.create_noise(seed, height, width) + + @staticmethod + def pack_latents(latents: mx.array, height: int, width: int) -> mx.array: + return FluxLatentCreator.pack_latents(latents, height, width) diff --git a/src/mflux/models/qwen/variants/edit/qwen_image_edit.py b/src/mflux/models/qwen/variants/edit/qwen_image_edit.py index 148af91..0d0689b 100644 --- a/src/mflux/models/qwen/variants/edit/qwen_image_edit.py +++ b/src/mflux/models/qwen/variants/edit/qwen_image_edit.py @@ -8,17 +8,17 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.qwen.latent_creator.qwen_latent_creator import QwenLatentCreator from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenTextEncoder from mflux.models.qwen.model.qwen_transformer.qwen_transformer import QwenTransformer from mflux.models.qwen.model.qwen_vae.qwen_vae import QwenVAE from mflux.models.qwen.qwen_edit_initializer import QwenImageEditInitializer from mflux.models.qwen.variants.edit.utils.qwen_edit_util import QwenEditUtil from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class QwenImageEdit(nn.Module): @@ -56,14 +56,14 @@ class QwenImageEdit(nn.Module): image_paths: list[str] | None = None, ) -> GeneratedImage: if image_paths is None: - image_paths = [config.image_path] + image_paths = [str(config.image_path)] runtime_config, vl_width, vl_height, vae_width, vae_height = self._compute_dimensions(config, image_paths) timesteps = runtime_config.scheduler.timesteps time_steps = tqdm(range(len(timesteps))) # 1. Create initial latents - latents = LatentCreator.create( + latents = QwenLatentCreator.create_noise( seed=seed, height=runtime_config.height, width=runtime_config.width, diff --git a/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py b/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py index 1763c90..e362e1d 100644 --- a/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py +++ b/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py @@ -2,8 +2,8 @@ import os import mlx.core as mx -from mflux.latent_creator.latent_creator import LatentCreator -from mflux.post_processing.array_util import ArrayUtil +from mflux.models.common.latent_creator.latent_creator import LatentCreator +from mflux.utils.array_util import ArrayUtil class QwenEditUtil: @@ -22,7 +22,7 @@ class QwenEditUtil: if vl_width is not None and vl_height is not None: calc_w, calc_h = vl_width, vl_height else: - from mflux.post_processing.image_util import ImageUtil + from mflux.utils.image_util import ImageUtil pil_image = ImageUtil.load_image(image_paths[-1]).convert("RGB") img_w, img_h = pil_image.size diff --git a/src/mflux/models/qwen/variants/txt2img/qwen_image.py b/src/mflux/models/qwen/variants/txt2img/qwen_image.py index 3365bc2..5a9c0c8 100644 --- a/src/mflux/models/qwen/variants/txt2img/qwen_image.py +++ b/src/mflux/models/qwen/variants/txt2img/qwen_image.py @@ -6,17 +6,18 @@ from mflux.callbacks.callbacks import Callbacks from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.error.exceptions import StopImageGenerationException -from mflux.latent_creator.latent_creator import Img2Img, LatentCreator +from mflux.models.common.latent_creator.latent_creator import Img2Img, LatentCreator +from mflux.models.common.weights.model_saver import ModelSaver +from mflux.models.qwen.latent_creator.qwen_latent_creator import QwenLatentCreator from mflux.models.qwen.model.qwen_text_encoder.qwen_prompt_encoder import QwenPromptEncoder from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenTextEncoder from mflux.models.qwen.model.qwen_transformer.qwen_transformer import QwenTransformer from mflux.models.qwen.model.qwen_vae.qwen_vae import QwenVAE from mflux.models.qwen.qwen_initializer import QwenImageInitializer -from mflux.models.qwen.weights.qwen_model_saver import QwenModelSaver -from mflux.post_processing.array_util import ArrayUtil -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.array_util import ArrayUtil +from mflux.utils.exceptions import StopImageGenerationException +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.image_util import ImageUtil class QwenImage(nn.Module): @@ -64,6 +65,7 @@ class QwenImage(nn.Module): width=runtime_config.width, img2img=Img2Img( vae=self.vae, + latent_creator=QwenLatentCreator, sigmas=runtime_config.scheduler.sigmas, init_time_step=runtime_config.init_time_step, image_path=runtime_config.image_path, @@ -166,7 +168,19 @@ class QwenImage(nn.Module): ) def save_model(self, base_path: str) -> None: - QwenModelSaver.save_model(self, self.bits, base_path) + ModelSaver.save_model( + model=self, + bits=self.bits, + base_path=base_path, + tokenizers=[ + ("qwen_tokenizer.tokenizer", "tokenizer"), + ], + components=[ + ("vae", "vae"), + ("transformer", "transformer"), + ("text_encoder", "text_encoder"), + ], + ) @staticmethod def compute_guided_noise( diff --git a/src/mflux/models/qwen/weights/qwen_model_saver.py b/src/mflux/models/qwen/weights/qwen_model_saver.py deleted file mode 100644 index be98a63..0000000 --- a/src/mflux/models/qwen/weights/qwen_model_saver.py +++ /dev/null @@ -1,56 +0,0 @@ -from pathlib import Path - -import mlx.core as mx -from mlx import nn -from mlx.utils import tree_flatten -from transformers import Qwen2Tokenizer - -from mflux.utils.version_util import VersionUtil - - -class QwenModelSaver: - @staticmethod - def save_model(model, bits: int, base_path: str): - # Save the tokenizer - QwenModelSaver._save_tokenizer(base_path, model.qwen_tokenizer.tokenizer, "tokenizer") - - # Save the models - QwenModelSaver.save_weights(base_path, bits, model.vae, "vae") - QwenModelSaver.save_weights(base_path, bits, model.transformer, "transformer") - QwenModelSaver.save_weights(base_path, bits, model.text_encoder, "text_encoder") - - @staticmethod - def _save_tokenizer(base_path: str, tokenizer: Qwen2Tokenizer, subdir: str): - path = Path(base_path) / subdir - tokenizer.save_pretrained(path) - - @staticmethod - def save_weights(base_path: str, bits: int, model: nn.Module, subdir: str): - path = Path(base_path) / subdir - path.mkdir(parents=True, exist_ok=True) - weights = QwenModelSaver._split_weights(base_path, dict(tree_flatten(model.parameters()))) - for i, weight in enumerate(weights): - mx.save_safetensors( - str(path / f"{i}.safetensors"), - weight, - { - "quantization_level": str(bits), - "mflux_version": VersionUtil.get_mflux_version(), - }, - ) - - @staticmethod - def _split_weights(base_path: str, weights: dict, max_file_size_gb: int = 2) -> list: - # Copied from mlx-examples repo - max_file_size_bytes = max_file_size_gb << 30 - shards = [] - shard, shard_size = {}, 0 - for k, v in weights.items(): - if shard_size + v.nbytes > max_file_size_bytes: - shards.append(shard) - shard, shard_size = {}, 0 - shard[k] = v - shard_size += v.nbytes - shards.append(shard) - return shards - diff --git a/src/mflux/models/qwen/weights/qwen_weight_util.py b/src/mflux/models/qwen/weights/qwen_weight_util.py index 2ea99f3..dde5011 100644 --- a/src/mflux/models/qwen/weights/qwen_weight_util.py +++ b/src/mflux/models/qwen/weights/qwen_weight_util.py @@ -5,8 +5,8 @@ import mlx.nn as nn from mlx.utils import tree_flatten # noqa: F401 from mflux.config.config import Config +from mflux.models.common.quantization.quantization_util import QuantizationUtil from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_transformer import VisionTransformer -from mflux.utils.quantization_util import QuantizationUtil if TYPE_CHECKING: from mflux.models.qwen.weights.qwen_weight_handler import QwenWeightHandler @@ -82,7 +82,7 @@ class QwenWeightUtil: transformer.update(weights.transformer, strict=False) # Check if visual weights are present and create visual transformer if needed - if text_encoder is not None: + if text_encoder is not None and weights.qwen_text_encoder is not None: has_visual_weights = ( "encoder" in weights.qwen_text_encoder and "visual" in weights.qwen_text_encoder["encoder"] ) diff --git a/src/mflux/refine_fibo.py b/src/mflux/refine_fibo.py new file mode 100644 index 0000000..eb4b689 --- /dev/null +++ b/src/mflux/refine_fibo.py @@ -0,0 +1,68 @@ +import json +from pathlib import Path + +from mflux.models.fibo_vlm.model.fibo_vlm import FiboVLM +from mflux.ui.cli.parsers import CommandLineParser +from mflux.utils.exceptions import PromptFileReadError + + +def main(): + # 0. Parse command line arguments + parser = CommandLineParser(description="Refine FIBO JSON prompts using VLM.") + # fmt: off + parser.add_argument("--prompt-file", type=Path, required=True, help="Path to JSON prompt file to refine") + parser.add_argument("--instructions", type=str, required=True, help="Text instructions for how to refine the prompt (e.g., 'make the dragon blue')") + parser.add_argument("--output", type=Path, default=Path("refined.json"), help="Output path for refined JSON prompt (default: refined.json)") + parser.add_argument("--path", type=str, default=None, help="Local path for loading the VLM model from disk") + parser.add_argument("--top-p", type=float, default=0.9, help="Top-p sampling for VLM (default: 0.9)") + parser.add_argument("--temperature", type=float, default=0.2, help="Temperature for VLM (default: 0.2)") + parser.add_argument("--max-tokens", type=int, default=4096, help="Max tokens for VLM generation (default: 4096)") + parser.add_argument("--seed", type=int, default=None, help="Seed for VLM generation") + # fmt: on + args = parser.parse_args() + + try: + # 1. Refine the JSON prompt + vlm = FiboVLM(local_path=args.path) + + refined_json = vlm.refine( + structured_prompt=_get_structured_prompt(args), + editing_instructions=args.instructions, + top_p=args.top_p, + temperature=args.temperature, + max_tokens=args.max_tokens, + seed=args.seed, + ) + + # 2. Parse and save refined JSON prompt + _save_prompt(args, refined_json) + except (PromptFileReadError, ValueError) as exc: + print(exc) + + +def _save_prompt(args, refined_json): + try: + refined_json_parsed = json.loads(refined_json) + except json.JSONDecodeError as e: + raise ValueError(f"VLM did not return valid JSON: {e}") + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(args.output, "w") as f: + json.dump(refined_json_parsed, f, indent=2, ensure_ascii=False) + + +def _get_structured_prompt(args): + if not args.prompt_file.exists(): + raise PromptFileReadError(f"Prompt file does not exist: {args.prompt_file}") + with open(args.prompt_file, "rt") as f: + structured_prompt = f.read().strip() + if not structured_prompt: + raise PromptFileReadError(f"Prompt file is empty: {args.prompt_file}") + try: + json.loads(structured_prompt) + except json.JSONDecodeError as e: + raise PromptFileReadError(f"Prompt file does not contain valid JSON: {e}") + return structured_prompt + + +if __name__ == "__main__": + main() diff --git a/src/mflux/release/git_operations.py b/src/mflux/release/git_operations.py index eb81663..1b90ecf 100644 --- a/src/mflux/release/git_operations.py +++ b/src/mflux/release/git_operations.py @@ -1,6 +1,6 @@ import subprocess -from mflux.error.exceptions import CommandExecutionError +from mflux.utils.exceptions import CommandExecutionError class GitOperations: diff --git a/src/mflux/save.py b/src/mflux/save.py index 2c6ab7d..a157767 100644 --- a/src/mflux/save.py +++ b/src/mflux/save.py @@ -1,4 +1,5 @@ from mflux.config.model_config import ModelConfig +from mflux.models.fibo.variants.txt2img.fibo import FIBO from mflux.models.flux.variants.txt2img.flux import Flux1 from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage from mflux.ui.cli.parsers import CommandLineParser @@ -12,15 +13,24 @@ def main(): args = parser.parse_args() # 1. Determine model class based on model name - model_class = QwenImage if "qwen" in args.model.lower() else Flux1 + model_name_lower = args.model.lower() + if "qwen" in model_name_lower: + model_class = QwenImage + elif "fibo" in model_name_lower: + model_class = FIBO + else: + model_class = Flux1 # 2. Load, quantize and save the model - model = model_class( - model_config=ModelConfig.from_name(args.model, base_model=args.base_model), - quantize=args.quantize, - lora_paths=args.lora_paths, - lora_scales=args.lora_scales, - ) + model_kwargs = { + "model_config": ModelConfig.from_name(args.model, base_model=args.base_model), + "quantize": args.quantize, + } + if args.lora_paths and model_class != FIBO: + model_kwargs["lora_paths"] = args.lora_paths + model_kwargs["lora_scales"] = args.lora_scales + + model = model_class(**model_kwargs) model.save_model(args.path) diff --git a/src/mflux/train.py b/src/mflux/train.py index 4255aba..0dce622 100644 --- a/src/mflux/train.py +++ b/src/mflux/train.py @@ -1,7 +1,7 @@ -from mflux.error.exceptions import StopTrainingException from mflux.models.flux.variants.dreambooth.dreambooth import DreamBooth from mflux.models.flux.variants.dreambooth.dreambooth_initializer import DreamBoothInitializer from mflux.ui.cli.parsers import CommandLineParser +from mflux.utils.exceptions import StopTrainingException def main(): diff --git a/src/mflux/ui/cli/parsers.py b/src/mflux/ui/cli/parsers.py index 4b39e4b..19ace37 100644 --- a/src/mflux/ui/cli/parsers.py +++ b/src/mflux/ui/cli/parsers.py @@ -5,13 +5,13 @@ import time import typing as t from pathlib import Path +from mflux.models.common.lora.download.lora_library import get_lora_path from mflux.models.flux.variants.in_context.utils.in_context_loras import LORA_NAME_MAP, LORA_REPO_ID from mflux.ui import ( box_values, defaults as ui_defaults, scale_factor, ) -from mflux.utils.lora_library import get_lora_path class ModelSpecAction(argparse.Action): @@ -274,8 +274,14 @@ class CommandLineParser(argparse.ArgumentParser): self.error("--model / -m must be provided, or 'model' must be specified in the config file.") if self.supports_image_generation and namespace.seed is None and namespace.auto_seeds > 0: - # choose N int seeds in the range of 0 < value < 1 billion - namespace.seed = [random.randint(0, int(1e7)) for _ in range(namespace.auto_seeds)] + # choose N unique int seeds in the range of 0 < value < 1 billion + # Use random.sample to guarantee uniqueness + max_seed_value = int(1e7) + if namespace.auto_seeds > max_seed_value + 1: + # If requesting more seeds than possible unique values, allow duplicates + namespace.seed = [random.randint(0, max_seed_value) for _ in range(namespace.auto_seeds)] + else: + namespace.seed = random.sample(range(max_seed_value + 1), namespace.auto_seeds) if self.supports_image_generation and namespace.seed is None: # final default: did not obtain seed from metadata, --seed, or --auto-seeds diff --git a/src/mflux/ui/prompt_utils.py b/src/mflux/ui/prompt_utils.py index fd542c2..54015de 100644 --- a/src/mflux/ui/prompt_utils.py +++ b/src/mflux/ui/prompt_utils.py @@ -3,51 +3,53 @@ import sys from argparse import Namespace from pathlib import Path -from mflux.error.exceptions import PromptFileReadError +from mflux.utils.exceptions import PromptFileReadError logger = logging.getLogger(__name__) -def read_prompt_file(prompt_file_path: Path) -> str: - # Check if file exists - if not prompt_file_path.exists(): - raise PromptFileReadError(f"Prompt file does not exist: {prompt_file_path}") +class PromptUtils: + @staticmethod + def get_effective_prompt(args: Namespace) -> str: + # Handle stdin input when prompt is "-" + if args.prompt == "-": + try: + content = sys.stdin.read().strip() + if not content: + raise PromptFileReadError("No prompt provided via stdin") + logger.info("Using prompt from stdin") + return content + except (IOError, OSError, KeyboardInterrupt) as e: + raise PromptFileReadError(f"Error reading from stdin: {e}") - try: - with open(prompt_file_path, "rt") as f: - content: str = f.read().strip() + # Handle prompt file + if args.prompt_file is not None: + prompt = PromptUtils._read_prompt_file(args.prompt_file) + return prompt - # Validate content - if not content: - raise PromptFileReadError(f"Prompt file is empty: {prompt_file_path}") + # Return regular prompt + return args.prompt - logger.info(f"Using prompt from file: {prompt_file_path}") - return content - except (IOError, OSError) as e: - raise PromptFileReadError(f"Error reading prompt file '{prompt_file_path}': {e}") + @staticmethod + def get_effective_negative_prompt(args: Namespace) -> str: + # Return negative prompt or empty string if not provided + return getattr(args, "negative_prompt", "") + @staticmethod + def _read_prompt_file(prompt_file_path: Path) -> str: + # Check if file exists + if not prompt_file_path.exists(): + raise PromptFileReadError(f"Prompt file does not exist: {prompt_file_path}") -def get_effective_prompt(args: Namespace) -> str: - # Handle stdin input when prompt is "-" - if args.prompt == "-": try: - content = sys.stdin.read().strip() + with open(prompt_file_path, "rt") as f: + content: str = f.read().strip() + + # Validate content if not content: - raise PromptFileReadError("No prompt provided via stdin") - logger.info("Using prompt from stdin") + raise PromptFileReadError(f"Prompt file is empty: {prompt_file_path}") + + logger.info(f"Using prompt from file: {prompt_file_path}") return content - except (IOError, OSError, KeyboardInterrupt) as e: - raise PromptFileReadError(f"Error reading from stdin: {e}") - - # Handle prompt file - if args.prompt_file is not None: - prompt = read_prompt_file(args.prompt_file) - return prompt - - # Return regular prompt - return args.prompt - - -def get_effective_negative_prompt(args: Namespace) -> str: - # Return negative prompt or empty string if not provided - return getattr(args, "negative_prompt", "") + except (IOError, OSError) as e: + raise PromptFileReadError(f"Error reading prompt file '{prompt_file_path}': {e}") diff --git a/src/mflux/upscale.py b/src/mflux/upscale.py index 52acfa7..c8086ad 100644 --- a/src/mflux/upscale.py +++ b/src/mflux/upscale.py @@ -5,12 +5,12 @@ import PIL.Image from mflux.callbacks.callback_manager import CallbackManager from mflux.config.config import Config from mflux.config.model_config import ModelConfig -from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException from mflux.models.flux.variants.controlnet.flux_controlnet import Flux1Controlnet from mflux.ui import defaults as ui_defaults from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils from mflux.ui.scale_factor import ScaleFactor +from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException def main(): @@ -44,7 +44,7 @@ def main(): # 3. Generate an upscaled image for each seed value image = flux.generate_image( seed=seed, - prompt=get_effective_prompt(args), + prompt=PromptUtils.get_effective_prompt(args), controlnet_image_path=args.controlnet_image_path, config=Config( num_inference_steps=args.steps, diff --git a/src/mflux/post_processing/array_util.py b/src/mflux/utils/array_util.py similarity index 100% rename from src/mflux/post_processing/array_util.py rename to src/mflux/utils/array_util.py diff --git a/src/mflux/error/exceptions.py b/src/mflux/utils/exceptions.py similarity index 88% rename from src/mflux/error/exceptions.py rename to src/mflux/utils/exceptions.py index 1a35a57..bdcc694 100644 --- a/src/mflux/error/exceptions.py +++ b/src/mflux/utils/exceptions.py @@ -41,3 +41,11 @@ class CommandExecutionError(MFluxException): class ReferenceVsOutputImageError(AssertionError): """Raised when reference and output images don't match within the allowed threshold.""" + + +class ModelConfigError(ValueError): + """User error in model config.""" + + +class InvalidBaseModel(ModelConfigError): + """Invalid base model, cannot infer model properties.""" diff --git a/src/mflux/post_processing/generated_image.py b/src/mflux/utils/generated_image.py similarity index 80% rename from src/mflux/post_processing/generated_image.py rename to src/mflux/utils/generated_image.py index 5f2cb86..e165fde 100644 --- a/src/mflux/post_processing/generated_image.py +++ b/src/mflux/utils/generated_image.py @@ -1,3 +1,5 @@ +import json +import logging from datetime import datetime from pathlib import Path @@ -8,6 +10,8 @@ from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.concept_attention.attention_data import ConceptHeatmap from mflux.utils.version_util import VersionUtil +log = logging.getLogger(__name__) + class GeneratedImage: def __init__( @@ -97,7 +101,11 @@ class GeneratedImage: export_json_metadata: bool = False, overwrite: bool = False, ) -> None: - from mflux.post_processing.image_util import ImageUtil + from mflux.utils.image_util import ImageUtil + + # Always save prompt file for FIBO models + if self._is_fibo_model(): + self._save_prompt_file(path, overwrite) ImageUtil.save_image(self.image, path, self._get_metadata(), export_json_metadata, overwrite) @@ -123,7 +131,7 @@ class GeneratedImage: overwrite: bool = False, ) -> None: if self.concept_heatmap: - from mflux.post_processing.image_util import ImageUtil + from mflux.utils.image_util import ImageUtil ImageUtil.save_image( image=self.concept_heatmap.image, @@ -140,6 +148,37 @@ class GeneratedImage: return None return [round(scale, 2) for scale in self.redux_image_strengths] + def _is_fibo_model(self) -> bool: + return self.model_config.model_name == "briaai/FIBO" or str(self.model_config.base_model) == "fibo" + + def _save_prompt_file(self, image_path: str | Path, overwrite: bool) -> None: + file_path = Path(image_path) + # For FIBO models, use .json instead of .prompt.json + prompt_path = file_path.with_suffix(".json") + + # Handle overwrite logic similar to image saving + if not overwrite: + counter = 1 + while prompt_path.exists(): + new_name = f"{file_path.stem}_{counter}.json" + prompt_path = file_path.parent / new_name + counter += 1 + + try: + # Parse and pretty-print the JSON prompt + try: + prompt_json = json.loads(self.prompt) + with open(prompt_path, "w") as f: + json.dump(prompt_json, f, indent=2, ensure_ascii=False) + except (json.JSONDecodeError, ValueError): + # If prompt is not valid JSON, save as-is (shouldn't happen for FIBO) + with open(prompt_path, "w") as f: + f.write(self.prompt) + + log.info(f"Prompt file saved successfully at: {prompt_path}") + except Exception as e: # noqa: BLE001 + log.error(f"Error saving prompt file: {e}") + def _get_metadata(self) -> dict: return { "mflux_version": VersionUtil.get_mflux_version(), diff --git a/src/mflux/post_processing/image_util.py b/src/mflux/utils/image_util.py similarity index 98% rename from src/mflux/post_processing/image_util.py rename to src/mflux/utils/image_util.py index d3c2be8..ff9e726 100644 --- a/src/mflux/post_processing/image_util.py +++ b/src/mflux/utils/image_util.py @@ -11,9 +11,9 @@ from PIL._typing import StrOrBytesPath from mflux.config.runtime_config import RuntimeConfig from mflux.models.flux.variants.concept_attention.attention_data import ConceptHeatmap -from mflux.post_processing.generated_image import GeneratedImage -from mflux.post_processing.metadata_builder import MetadataBuilder from mflux.ui.box_values import AbsoluteBoxValues, BoxValues +from mflux.utils.generated_image import GeneratedImage +from mflux.utils.metadata_builder import MetadataBuilder log = logging.getLogger(__name__) diff --git a/src/mflux/post_processing/metadata_builder.py b/src/mflux/utils/metadata_builder.py similarity index 99% rename from src/mflux/post_processing/metadata_builder.py rename to src/mflux/utils/metadata_builder.py index 9818f86..4d4e9c3 100644 --- a/src/mflux/post_processing/metadata_builder.py +++ b/src/mflux/utils/metadata_builder.py @@ -229,4 +229,3 @@ class MetadataBuilder: lora_list.append(f"{lora_name}:{scale}") return ", ".join(lora_list) - diff --git a/src/mflux/post_processing/metadata_reader.py b/src/mflux/utils/metadata_reader.py similarity index 92% rename from src/mflux/post_processing/metadata_reader.py rename to src/mflux/utils/metadata_reader.py index 0fb53d4..f3406c3 100644 --- a/src/mflux/post_processing/metadata_reader.py +++ b/src/mflux/utils/metadata_reader.py @@ -44,7 +44,7 @@ class MetadataReader: return None - except Exception as e: + except (OSError, KeyError, json.JSONDecodeError, UnicodeDecodeError) as e: log.debug(f"Error reading EXIF metadata: {e}") return None @@ -71,9 +71,9 @@ class MetadataReader: # Simple XML parsing for common fields fields = { - "description": "", + "description": '', "creator": "", - "rights": "", + "rights": '', "creator_tool": "", "category": "", "credit": "", @@ -104,7 +104,7 @@ class MetadataReader: return xmp_dict if xmp_dict else None - except Exception as e: + except (OSError, KeyError, ValueError) as e: log.debug(f"Error reading XMP metadata: {e}") return None @@ -123,4 +123,3 @@ class MetadataReader: "exif": MetadataReader.read_exif_metadata(image_path), "xmp": MetadataReader.read_xmp_metadata(image_path), } - diff --git a/tests/arg_parser/test_lora_library_integration.py b/tests/arg_parser/test_lora_library_integration.py index 0437474..976e9d3 100644 --- a/tests/arg_parser/test_lora_library_integration.py +++ b/tests/arg_parser/test_lora_library_integration.py @@ -6,8 +6,8 @@ from unittest import mock import pytest +from mflux.models.common.lora.download import lora_library from mflux.ui.cli.parsers import CommandLineParser -from mflux.utils import lora_library @pytest.fixture diff --git a/tests/arg_parser/test_stdin_prompt.py b/tests/arg_parser/test_stdin_prompt.py index 53cf589..8fe179f 100644 --- a/tests/arg_parser/test_stdin_prompt.py +++ b/tests/arg_parser/test_stdin_prompt.py @@ -5,7 +5,7 @@ from unittest.mock import patch import pytest from mflux.ui.cli.parsers import CommandLineParser -from mflux.ui.prompt_utils import get_effective_prompt +from mflux.ui.prompt_utils import PromptUtils @pytest.fixture @@ -46,7 +46,7 @@ def test_prompt_stdin_vs_regular(mflux_generate_parser): with patch("sys.argv", ["mflux-generate", "--prompt", regular_prompt, "--model", "dev"]): args = mflux_generate_parser.parse_args() assert args.prompt == regular_prompt - assert get_effective_prompt(args) == regular_prompt + assert PromptUtils.get_effective_prompt(args) == regular_prompt def test_prompt_stdin_with_whitespace(mflux_generate_parser): @@ -57,7 +57,7 @@ def test_prompt_stdin_with_whitespace(mflux_generate_parser): with patch("sys.stdin", StringIO(stdin_content)): with patch("sys.argv", ["mflux-generate", "--prompt", "-", "--model", "dev"]): args = mflux_generate_parser.parse_args() - effective_prompt = get_effective_prompt(args) + effective_prompt = PromptUtils.get_effective_prompt(args) assert effective_prompt == expected_prompt @@ -73,5 +73,5 @@ def test_prompt_file_takes_precedence_over_stdin(mflux_generate_parser, temp_out with patch("sys.stdin", StringIO(stdin_content)): with patch("sys.argv", ["mflux-generate", "--prompt-file", str(prompt_file), "--model", "dev"]): args = mflux_generate_parser.parse_args() - effective_prompt = get_effective_prompt(args) + effective_prompt = PromptUtils.get_effective_prompt(args) assert effective_prompt == file_prompt diff --git a/tests/callbacks/test_battery_saver.py b/tests/callbacks/test_battery_saver.py index 6531508..479345c 100644 --- a/tests/callbacks/test_battery_saver.py +++ b/tests/callbacks/test_battery_saver.py @@ -3,7 +3,7 @@ from unittest.mock import MagicMock, patch import pytest from mflux.callbacks.instances.battery_saver import BatterySaver, get_battery_percentage -from mflux.error.exceptions import StopImageGenerationException +from mflux.utils.exceptions import StopImageGenerationException def test_get_battery_percentage_while_charging(): diff --git a/tests/image_generation/helpers/image_generation_fibo_test_helper.py b/tests/image_generation/helpers/image_generation_fibo_test_helper.py new file mode 100644 index 0000000..7369b0d --- /dev/null +++ b/tests/image_generation/helpers/image_generation_fibo_test_helper.py @@ -0,0 +1,73 @@ +import os +from pathlib import Path +from typing import Optional + +from mflux.config.config import Config +from mflux.config.model_config import ModelConfig +from mflux.models.fibo.variants.txt2img.fibo import FIBO +from mflux.utils.image_compare import ImageCompare + + +class ImageGeneratorFiboTestHelper: + @staticmethod + def assert_matches_reference_image( + reference_image_path: str, + output_image_path: str, + prompt: str, + steps: int, + seed: int, + height: int, + width: int, + guidance: float = 4.0, + negative_prompt: Optional[str] = None, + mismatch_threshold: Optional[float] = None, + quantize: Optional[int] = None, + ): + # resolve paths + reference_image_path = ImageGeneratorFiboTestHelper.resolve_path(reference_image_path) + output_image_path = ImageGeneratorFiboTestHelper.resolve_path(output_image_path) + + try: + # Step 1: Create FIBO model + model = FIBO( + model_config=ModelConfig.fibo(), + quantize=quantize, + local_path=None, + ) + + # Step 2: Generate image from prompt + image = model.generate_image( + seed=seed, + prompt=prompt, + negative_prompt=negative_prompt, + config=Config( + num_inference_steps=steps, + height=height, + width=width, + guidance=guidance, + image_path=None, + image_strength=None, + scheduler="flow_match_euler_discrete", + ), + ) + + # Step 3: Save output image + image.save(path=output_image_path, overwrite=True) + + # Step 4: Compare with reference + ImageCompare.check_images_close_enough( + output_image_path, + reference_image_path, + "Generated image doesn't match reference image.", + mismatch_threshold=mismatch_threshold, + ) + finally: + # cleanup + if os.path.exists(output_image_path) and "MFLUX_PRESERVE_TEST_OUTPUT" not in os.environ: + os.remove(output_image_path) + + @staticmethod + def resolve_path(path) -> Path | None: + if path is None: + return None + return Path(__file__).parent.parent.parent / "resources" / path diff --git a/tests/image_generation/test_generate_image_fibo.py b/tests/image_generation/test_generate_image_fibo.py new file mode 100644 index 0000000..3fe1683 --- /dev/null +++ b/tests/image_generation/test_generate_image_fibo.py @@ -0,0 +1,111 @@ +from tests.image_generation.helpers.image_generation_fibo_test_helper import ImageGeneratorFiboTestHelper + +OWL_PROMPT = """ +{ + "short_description": "A hyper-detailed, ultra-fluffy owl sitting in the trees at night, looking directly at the camera with wide, adorable, expressive eyes. Its feathers are soft and voluminous, catching the cool moonlight with subtle silver highlights. The owl's gaze is curious and full of charm, giving it a whimsical, storybook-like personality.", + "objects": [ + { + "description": "An adorable, fluffy owl with large, expressive eyes and soft, voluminous feathers. Its plumage is a mix of warm browns, grays, and subtle silver highlights from the moonlight.", + "location": "center", + "relationship": "The owl is the sole subject, perched comfortably within its environment.", + "relative_size": "large within frame", + "shape_and_color": "Round head, large eyes, bulky body, predominantly brown and grey with silver accents.", + "texture": "Extremely soft, fluffy, and detailed feathers, giving a plush toy-like appearance.", + "appearance_details": "The eyes are wide, dark, and reflective, conveying a sense of wonder and curiosity. The beak is small and light-colored, almost hidden by the feathers. Subtle silver highlights catch the moonlight on its feathers.", + "orientation": "upright, facing forward" + } + ], + "background_setting": "A dark, nocturnal forest setting with blurred trees and foliage, illuminated by a soft, cool moonlight. The background is out of focus, emphasizing the owl.", + "lighting": { + "conditions": "moonlight", + "direction": "backlit and side-lit from the left", + "shadows": "soft, diffused shadows on the right side of the owl and within the background foliage, indicating a single light source." + }, + "aesthetics": { + "composition": "centered, portrait composition", + "color_scheme": "cool blues and silvers from the moonlight contrasting with warm browns and grays of the owl and forest.", + "mood_atmosphere": "mysterious, enchanting, whimsical, and serene.", + "aesthetic_score": "very high", + "preference_score": "very high" + }, + "photographic_characteristics": { + "depth_of_field": "shallow", + "focus": "sharp focus on the owl's face and eyes, with a soft blur in the background.", + "camera_angle": "eye-level", + "lens_focal_length": "portrait lens (e.g., 50mm-85mm)" + }, + "style_medium": "digital illustration", + "text_render": [], + "context": "A whimsical character illustration, possibly for a children's book, animated film, or fantasy art collection.", + "artistic_style": "fantasy, illustrative, detailed" +} +""" + +OWL_PROMPT_REFINED = """ +{ + "short_description": "A hyper-detailed, ultra-fluffy owl sitting in the trees at night, looking directly at the camera with wide, adorable, expressive eyes. Its feathers are soft and voluminous, catching the cool moonlight with subtle silver highlights. The owl's gaze is curious and full of charm, giving it a whimsical, storybook-like personality.", + "objects": [ + { + "description": "An adorable, fluffy owl with large, expressive eyes and soft, voluminous feathers. Its plumage is a mix of white and subtle silver highlights from the moonlight.", + "location": "center", + "relationship": "The owl is the sole subject, perched comfortably within its environment.", + "relative_size": "large within frame", + "shape_and_color": "Round head, large eyes, bulky body, predominantly white with silver accents.", + "texture": "Extremely soft, fluffy, and detailed feathers, giving a plush toy-like appearance.", + "appearance_details": "The eyes are wide, dark, and reflective, conveying a sense of wonder and curiosity. The beak is small and light-colored, almost hidden by the feathers. Subtle silver highlights catch the moonlight on its feathers.", + "orientation": "upright, facing forward" + } + ], + "background_setting": "A dark, nocturnal forest setting with blurred trees and foliage, illuminated by a soft, cool moonlight. The background is out of focus, emphasizing the owl.", + "lighting": { + "conditions": "moonlight", + "direction": "backlit and side-lit from the left", + "shadows": "soft, diffused shadows on the right side of the owl and within the background foliage, indicating a single light source." + }, + "aesthetics": { + "composition": "centered, portrait composition", + "color_scheme": "cool blues and silvers from the moonlight contrasting with white of the owl and forest.", + "mood_atmosphere": "mysterious, enchanting, whimsical, and serene.", + "aesthetic_score": "very high", + "preference_score": "very high" + }, + "photographic_characteristics": { + "depth_of_field": "shallow", + "focus": "sharp focus on the owl's face and eyes, with a soft blur in the background.", + "camera_angle": "eye-level", + "lens_focal_length": "portrait lens (e.g., 50mm-85mm)" + }, + "style_medium": "digital illustration", + "text_render": [], + "context": "A whimsical character illustration, possibly for a children's book, animated film, or fantasy art collection.", + "artistic_style": "fantasy, illustrative, detailed" +} +""" + + +class TestImageGeneratorFibo: + def test_image_generation_fibo(self): + ImageGeneratorFiboTestHelper.assert_matches_reference_image( + reference_image_path="reference_fibo.png", + output_image_path="output_fibo.png", + prompt=OWL_PROMPT, # Assume this has been generated by the VLM, actual VLM tests are separate + steps=20, + seed=42, + height=176, + width=320, + guidance=4.0, + quantize=8, + ) + + def test_image_generation_fibo_refined_white_owl(self): + ImageGeneratorFiboTestHelper.assert_matches_reference_image( + reference_image_path="reference_fibo_white_owl.png", + output_image_path="output_fibo_white_owl.png", + prompt=OWL_PROMPT_REFINED, # Assume this has been refined by the VLM, actual VLM tests are separate + steps=20, + seed=42, + height=176, + width=320, + guidance=4.0, + quantize=8, + ) diff --git a/tests/image_generation/test_image_util.py b/tests/image_generation/test_image_util.py index 9d8f0a0..1ba0714 100644 --- a/tests/image_generation/test_image_util.py +++ b/tests/image_generation/test_image_util.py @@ -3,7 +3,7 @@ import numpy as np import PIL.Image import pytest -from mflux.post_processing.image_util import ImageUtil +from mflux.utils.image_util import ImageUtil @pytest.fixture diff --git a/tests/image_generation/test_upscale_dimensions.py b/tests/image_generation/test_upscale_dimensions.py index 47424ee..7fae51e 100644 --- a/tests/image_generation/test_upscale_dimensions.py +++ b/tests/image_generation/test_upscale_dimensions.py @@ -57,7 +57,7 @@ def test_upscale_passes_correct_dimensions_to_generate_image( mock_parser.parse_args.return_value = mock_args mock_parser_class.return_value = mock_parser - with patch("mflux.upscale.get_effective_prompt", return_value="test prompt"): + with patch("mflux.upscale.PromptUtils.get_effective_prompt", return_value="test prompt"): # Call the main function main() diff --git a/tests/metadata/test_metadata.py b/tests/metadata/test_metadata.py index 59c2707..eec3d53 100644 --- a/tests/metadata/test_metadata.py +++ b/tests/metadata/test_metadata.py @@ -14,7 +14,7 @@ from pathlib import Path from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.txt2img.flux import Flux1 -from mflux.post_processing.metadata_reader import MetadataReader +from mflux.utils.metadata_reader import MetadataReader class TestMetadata: diff --git a/tests/model_config/test_model_config.py b/tests/model_config/test_model_config.py index 315b72a..3b0aebc 100644 --- a/tests/model_config/test_model_config.py +++ b/tests/model_config/test_model_config.py @@ -1,7 +1,7 @@ import pytest from mflux.config.model_config import ModelConfig -from mflux.error.error import InvalidBaseModel, ModelConfigError +from mflux.utils.exceptions import InvalidBaseModel, ModelConfigError def test_bfl_dev(): diff --git a/tests/resources/reference_fibo.png b/tests/resources/reference_fibo.png new file mode 100644 index 0000000..55ade30 Binary files /dev/null and b/tests/resources/reference_fibo.png differ diff --git a/tests/resources/reference_fibo_white_owl.png b/tests/resources/reference_fibo_white_owl.png new file mode 100644 index 0000000..994fb3d Binary files /dev/null and b/tests/resources/reference_fibo_white_owl.png differ diff --git a/tests/resources/skyscrapers.jpg b/tests/resources/skyscrapers.jpg new file mode 100644 index 0000000..51f189a Binary files /dev/null and b/tests/resources/skyscrapers.jpg differ diff --git a/tests/schedulers/__init__.py b/tests/schedulers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/schedulers/test_base_scheduler.py b/tests/schedulers/test_base_scheduler.py index f8773b4..c9f08a0 100644 --- a/tests/schedulers/test_base_scheduler.py +++ b/tests/schedulers/test_base_scheduler.py @@ -1,7 +1,6 @@ -import mlx.core as mx import pytest -from mflux.schedulers.base_scheduler import BaseScheduler +from mflux.models.common.schedulers.base_scheduler import BaseScheduler def test_base_scheduler_is_abstract(): diff --git a/tests/schedulers/test_linear_scheduler.py b/tests/schedulers/test_linear_scheduler.py index 71eea90..f71467e 100644 --- a/tests/schedulers/test_linear_scheduler.py +++ b/tests/schedulers/test_linear_scheduler.py @@ -7,12 +7,15 @@ import pytest from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.config.runtime_config import RuntimeConfig -from mflux.schedulers import try_import_external_scheduler -from mflux.schedulers.linear_scheduler import LinearScheduler +from mflux.models.common.schedulers import try_import_external_scheduler +from mflux.models.common.schedulers.linear_scheduler import LinearScheduler def test_linear_scheduler_import_by_name(): - assert try_import_external_scheduler("mflux.schedulers.linear_scheduler.LinearScheduler") == LinearScheduler + assert ( + try_import_external_scheduler("mflux.models.common.schedulers.linear_scheduler.LinearScheduler") + == LinearScheduler + ) @pytest.fixture diff --git a/tests/schedulers/test_scheduler_lookup.py b/tests/schedulers/test_scheduler_lookup.py index 8c16a51..b369ac2 100644 --- a/tests/schedulers/test_scheduler_lookup.py +++ b/tests/schedulers/test_scheduler_lookup.py @@ -1,17 +1,17 @@ import pytest -import mflux.schedulers +import mflux.models.common.schedulers as schedulers def test_scheduler_by_path(): - mflux.schedulers.try_import_external_scheduler("mflux.schedulers.linear_scheduler.LinearScheduler") + schedulers.try_import_external_scheduler("mflux.models.common.schedulers.linear_scheduler.LinearScheduler") def test_scheduler_bad_module(): - with pytest.raises(mflux.schedulers.SchedulerModuleNotFound): - mflux.schedulers.try_import_external_scheduler("someone.other.project.BarScheduler") + with pytest.raises(schedulers.SchedulerModuleNotFound): + schedulers.try_import_external_scheduler("someone.other.project.BarScheduler") def test_scheduler_bad_classname(): - with pytest.raises(mflux.schedulers.SchedulerClassNotFound): - mflux.schedulers.try_import_external_scheduler("mflux.schedulers.linear_scheduler.FooBarScheduler") + with pytest.raises(schedulers.SchedulerClassNotFound): + schedulers.try_import_external_scheduler("mflux.models.common.schedulers.linear_scheduler.FooBarScheduler") diff --git a/tests/ui/__init__.py b/tests/ui/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/vlm/__init__.py b/tests/vlm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/vlm/test_fibo_vlm.py b/tests/vlm/test_fibo_vlm.py new file mode 100644 index 0000000..9f6e8c4 --- /dev/null +++ b/tests/vlm/test_fibo_vlm.py @@ -0,0 +1,209 @@ +import json +from pathlib import Path + +import pytest +from PIL import Image + +from mflux.models.fibo_vlm.model.fibo_vlm import FiboVLM +from tests.image_generation.test_generate_image_fibo import OWL_PROMPT, OWL_PROMPT_REFINED + +INSPIRE_PROMPT = """ +{ + "short_description": "A charming, stylized illustration of a young owl perched on a mossy tree stump in a dimly lit forest. The owl has large, expressive eyes and soft, feathered textures. The background is a blur of dark trees and foliage, creating a serene and mysterious atmosphere. The overall impression is one of innocence and nature's quiet beauty.", + "objects": [ + { + "description": "A young owl with large, round, golden eyes and a small yellow beak. Its plumage is a mix of soft browns, creams, and subtle blues, with distinct feather patterns. It has prominent ear tufts and a fluffy chest.", + "location": "center", + "relationship": "perched on a tree stump", + "relative_size": "medium within frame", + "shape_and_color": "Rounded body shape, predominantly brown and cream with blueish undertones. Large, round eyes with dark pupils.", + "texture": "soft, feathery, detailed", + "appearance_details": "Its eyes have a glossy sheen and appear to be looking directly at the viewer. The feathers have a layered, textured look.", + "number_of_objects": 1, + "pose": "Sitting upright, with its body facing forward and its head slightly tilted.", + "expression": "curious and gentle", + "action": "perching", + "gender": "unknown", + "orientation": "upright" + }, + { + "description": "A weathered tree stump covered in lush green moss. It provides a natural perch for the owl.", + "location": "bottom-center foreground", + "relationship": "supports the owl", + "relative_size": "medium", + "shape_and_color": "Irregular, rounded shape, dark brown wood with vibrant green moss.", + "texture": "rough wood, soft mossy texture", + "appearance_details": "The moss is thick and detailed, with small blades of grass growing around the base.", + "number_of_objects": 1, + "orientation": "lying on its side" + }, + { + "description": "Several slender tree trunks and branches, rendered in dark, muted tones.", + "location": "background and midground", + "relationship": "forms the forest environment", + "relative_size": "large, forming the environment", + "shape_and_color": "Vertical, elongated shapes in shades of dark brown and grey.", + "texture": "smooth, bark-like texture", + "appearance_details": "Some branches have sparse green leaves. The trees are out of focus, creating depth.", + "orientation": "vertical" + } + ], + "background_setting": "A dense, dark forest with tall trees and undergrowth. The atmosphere is slightly misty or foggy, softening the distant elements.", + "lighting": { + "conditions": "dim, atmospheric lighting", + "direction": "soft, diffused light from the front-left", + "shadows": "soft, subtle shadows that enhance the depth and mood" + }, + "aesthetics": { + "composition": "centered composition with the owl as the primary focal point", + "color_scheme": "muted earth tones with pops of green and soft blues/greys", + "mood_atmosphere": "serene, mysterious, innocent", + "aesthetic_score": "high", + "preference_score": "high" + }, + "photographic_characteristics": { + "depth_of_field": "shallow, with a blurred background", + "focus": "sharp focus on the owl", + "camera_angle": "eye-level", + "lens_focal_length": "standard lens (e.g., 50mm)" + }, + "style_medium": "digital illustration", + "text_render": [], + "context": "This image is suitable for children's book illustrations, nature-themed art, or decorative prints.", + "artistic_style": "stylized, painterly, cute" +} +""" + +SKYSCRAPERS_INSPIRE_PROMPT = """ +{ + "short_description": "A dramatic, low-angle shot of several modern skyscrapers in black and white, emphasizing their towering height and geometric forms against a bright, overcast sky. The buildings feature repetitive window patterns and varying architectural details, creating a sense of urban grandeur and scale.", + "objects": [ + { + "description": "A tall skyscraper with a facade of numerous rectangular windows, arranged in a grid pattern. The building has a slightly curved or angled design, giving it a dynamic appearance.", + "location": "center-right foreground", + "relationship": "This is the most prominent building, dominating the right side of the frame and serving as a primary focal point.", + "relative_size": "large within frame", + "shape_and_color": "Rectangular and angular forms, dark grey to black with bright white window reflections.", + "texture": "Smooth concrete or glass, with visible mullions creating a textured grid.", + "appearance_details": "The windows reflect the bright sky, appearing as glowing rectangles. The building's structure is robust and imposing.", + "orientation": "vertical" + }, + { + "description": "A large, flat-roofed skyscraper with a simpler, more rectilinear design compared to the central-right building. Its facade also features windows, but they are less prominent due to the building's angle and distance.", + "location": "left foreground", + "relationship": "It stands to the left of the central skyscraper, partially obscuring other buildings behind it and contributing to the layered effect.", + "relative_size": "large within frame", + "shape_and_color": "Large, flat, angular forms, dark grey to black.", + "texture": "Rough concrete texture on the upper sections, smoother on the windowed parts.", + "appearance_details": "The building has a distinct overhang or cantilevered section at the top-left, adding architectural interest.", + "orientation": "diagonal, leaning towards the right" + }, + { + "description": "A cluster of mid-rise buildings, less distinct than the foreground structures, with visible window patterns.", + "location": "midground, behind the foreground buildings", + "relationship": "These buildings provide depth and context, suggesting a dense urban environment behind the prominent foreground structures.", + "relative_size": "medium", + "shape_and_color": "Rectangular forms, dark grey with hints of white from windows.", + "texture": "Less defined, appearing smoother due to distance.", + "appearance_details": "Their details are softened by distance and atmospheric haze.", + "number_of_objects": 3, + "orientation": "vertical" + }, + { + "description": "A tall, slender skyscraper with a facade composed of many small, closely spaced rectangular windows, creating a dense, textured pattern.", + "location": "bottom-right midground", + "relationship": "It stands slightly behind and to the right of the main central skyscraper, adding to the overall height of the urban landscape.", + "relative_size": "medium", + "shape_and_color": "Tall, slender, rectangular, dark grey with bright white window reflections.", + "texture": "Densely packed, creating a fine-grained texture from the windows.", + "appearance_details": "Its vertical lines are emphasized by the window arrangement.", + "orientation": "vertical" + } + ], + "background_setting": "A bright, uniformly white sky, indicative of an overcast day or a foggy atmosphere, which provides a stark contrast to the dark buildings.", + "lighting": { + "conditions": "overcast daylight", + "direction": "diffused from above", + "shadows": "soft, subtle shadows on the buildings, mainly emphasizing their geometric forms rather than sharp lines" + }, + "aesthetics": { + "composition": "dynamic and asymmetrical, with buildings angled and overlapping, creating a sense of depth and scale. The low angle emphasizes the height of the structures.", + "color_scheme": "monochromatic (black and white) with a wide range of greys, emphasizing form and texture.", + "mood_atmosphere": "grand, imposing, architectural, and slightly dramatic.", + "aesthetic_score": "very high", + "preference_score": "very high" + }, + "photographic_characteristics": { + "depth_of_field": "deep", + "focus": "sharp focus on the foreground and midground buildings, with slight softening towards the background.", + "camera_angle": "very low angle, looking upwards from ground level.", + "lens_focal_length": "wide-angle" + }, + "style_medium": "photograph", + "text_render": [], + "context": "An architectural photograph, likely intended for art prints, urban exploration photography, or a design magazine, focusing on the abstract beauty of modern cityscapes.", + "artistic_style": "minimalist, high-contrast, geometric" +} +""" + + +@pytest.fixture +def vlm(): + return FiboVLM(model_id="briaai/FIBO-vlm") + + +def test_vlm_generate_json(vlm): + # given + input_prompt = "A hyper-detailed, ultra-fluffy owl sitting in the trees at night, looking directly at the camera with wide, adorable, expressive eyes. Its feathers are soft and voluminous, catching the cool moonlight with subtle silver highlights. The owl's gaze is curious and full of charm, giving it a whimsical, storybook-like personality." + + # when + json_output = vlm.generate(prompt=input_prompt, seed=42) + + # then + assert json_output == OWL_PROMPT, "Generated JSON output does not match expected output exactly." + assert isinstance(json.loads(json_output), dict), "Output should be a valid JSON object" + + +def test_vlm_refine_json_text_only(vlm): + # given + editing_instructions = "make the owl white color but keep everything else exactly the same" + + # when + json_output = vlm.refine( + structured_prompt=OWL_PROMPT, + editing_instructions=editing_instructions, + seed=42, + ) + + # then + assert json_output == OWL_PROMPT_REFINED, "Generated JSON output does not match expected output exactly." + assert isinstance(json.loads(json_output), dict), "Output should be a valid JSON object" + + +def test_vlm_inspire_json_from_image(vlm): + # given + resource_dir = Path(__file__).parent.parent / "resources" + image_path = resource_dir / "reference_fibo.png" + image = Image.open(image_path).convert("RGB") + + # when - test without prompt to verify image processing works correctly + json_output = vlm.inspire(image=image, prompt=None, seed=42) + + # then + assert json_output == INSPIRE_PROMPT, "Generated JSON output does not match expected output exactly." + assert isinstance(json.loads(json_output), dict), "Output should be a valid JSON object" + + +def test_vlm_inspire_json_from_image_with_prompt(vlm): + # given + resource_dir = Path(__file__).parent.parent / "resources" + image_path = resource_dir / "skyscrapers.jpg" + image = Image.open(image_path).convert("RGB") + prompt = "an image of skyscrapers" + + # when - test with prompt to verify prompt-guided image processing works correctly + json_output = vlm.inspire(image=image, prompt=prompt, seed=42) + + # then + assert json_output == SKYSCRAPERS_INSPIRE_PROMPT, "Generated JSON output does not match expected output exactly." + assert isinstance(json.loads(json_output), dict), "Output should be a valid JSON object" diff --git a/tests/weights/test_lora_library.py b/tests/weights/test_lora_library.py index e43d7a0..d78ded5 100644 --- a/tests/weights/test_lora_library.py +++ b/tests/weights/test_lora_library.py @@ -5,7 +5,7 @@ from unittest import mock import pytest -from mflux.utils import lora_library +from mflux.models.common.lora.download import lora_library @pytest.fixture(autouse=True) diff --git a/tools/create_outpaint_image_canvas_and_mask.py b/tools/create_outpaint_image_canvas_and_mask.py index 76d8722..b04ffa9 100755 --- a/tools/create_outpaint_image_canvas_and_mask.py +++ b/tools/create_outpaint_image_canvas_and_mask.py @@ -1,9 +1,9 @@ import sys from pathlib import Path -from mflux.post_processing.image_util import ImageUtil from mflux.ui.box_values import AbsoluteBoxValues, BoxValues from mflux.ui.cli.parsers import CommandLineParser +from mflux.utils.image_util import ImageUtil def main(): diff --git 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