Small additions before 0.8.0 (#209)

CHANGELOG.md

@@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-## [0.8.0] - 2025-01-20
+## [0.8.0] - 2025-06-14
 
 # MFLUX v.0.8.0 Release Notes
 
@@ -58,6 +58,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Better quality variations with adjustable parameters for more predictable results
 - Refined Redux algorithm for more natural image variations
 
+### 📥 Stdin Prompt Support
+- **LLM Integration Ready**: Added support for providing prompts via stdin using `--prompt -`
+- Enables seamless integration with LLMs and other text generation tools
+- Supports both single-line and multi-line prompts through stdin
+- Perfect for automation workflows and dynamic prompt generation
+- Example usage: `echo "A beautiful landscape" | mflux-generate --prompt -`
+
 ## Developer Experience
 
 ### 🔧 LORA_LIBRARY_PATH Improvements
@@ -78,6 +85,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Enhanced documentation structure for better organization and user navigation
 - Improved categorization of experimental vs stable features
 
+### 🔄 Code Architecture Changes
+- **Class rename**: `Flux1InContextLora` is now `Flux1InContextDev` to better reflect the dev model variant
+- **Module reorganization**: Moved from `mflux.community.in_context_lora.flux_in_context_lora` to `mflux.community.in_context.flux_in_context_dev`
+- **Breaking change for library users**: If you import the class directly, update your imports accordingly
+
+
 ### ⚡ Performance Optimizations
 - Updated MLX dependency to latest version for improved performance and stability
 - Removed PyTorch dependency for DepthPro model, significantly reducing installation requirements
@@ -90,8 +103,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## Contributors
 
 Special thanks to the following contributors for their exceptional work since v0.7.1:
-- **Anthony Wu (@anthonywu)**: Battery Saver implementation, Prompt File Support, LORA_LIBRARY_PATH improvements
-- **Alessandro (@alessandro)**: Redux Function Balancing enhancements
+- **Anthony Wu (@anthonywu)**: Battery Saver implementation, Prompt File Support, Stdin Prompt Support, LORA_LIBRARY_PATH improvements
+- **Alessandro (@azrahello)**: Redux Function Balancing enhancements
 - **Filip Strand (@filipstrand)**: Core development, experimental features integration, infrastructure improvements
 
 ## [0.7.1] - 2025-05-06

README.md

@@ -28,9 +28,12 @@ Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) models from [Black
   * [LoRA Library Path](#lora-library-path)
   * [Supported LoRA formats (updated)](#supported-lora-formats-updated)
 - [🎭 In-Context Generation](#-in-context-generation)
-  * [Available Styles](#available-styles)
-  * [How It Works](#how-it-works)
-  * [Tips for Best Results](#tips-for-best-results)
+  * [🎨 In-Context LoRA](#-in-context-lora)
+    + [Available Styles](#available-styles)
+    + [How It Works](#how-it-works)
+    + [Tips for Best Results](#tips-for-best-results)
+  * [👕 CatVTON (Virtual Try-On)](#-catvton-virtual-try-on)
+  * [✏️ IC-Edit (In-Context Editing)](#%EF%B8%8F-ic-edit-in-context-editing)
 - [🛠️ Flux Tools](#%EF%B8%8F-flux-tools)
   * [🖌️ Fill](#%EF%B8%8F-fill)
     + [Inpainting](#inpainting)
@@ -958,19 +961,42 @@ CatVTON enables virtual try-on capabilities using in-context learning. This appr
 
 The implementation is based on [@nftblackmagic/catvton-flux](https://github.com/nftblackmagic/catvton-flux) and uses the model weights from [xiaozaa/catvton-flux-beta](https://huggingface.co/xiaozaa/catvton-flux-beta) (approximately 24 GB download).
 
-##### How to Use CatVTON
+![catvton example](src/mflux/assets/catvton_example.jpg)
 
-```sh
+##### How it works
+
+CatVTON uses a sophisticated in-context learning approach that creates a side-by-side composition for virtual try-on:
+
+1. **Image Layout**: The garment image is placed on the left side, while the person image is positioned on the right side of the composition.
+
+2. **Selective Processing**: The model treats each side differently:
+   - **Left side (garment)**: The model can only *read* information from this area - no inpainting or modifications occur here. This serves as a reference for the garment's appearance, texture, and style.
+   - **Right side (person)**: Only the areas highlighted in the person mask are inpainted. The mask determines exactly which parts of the person image should be modified to "wear" the garment.
+
+3. **Contextual Understanding**: By reading the garment characteristics from the left and selectively applying them to the masked regions on the right, the model creates a realistic virtual try-on that maintains proper lighting, shadows, and fabric behavior.
+
+This approach allows the model to understand the garment's properties while precisely controlling which parts of the person image should be transformed, resulting in natural-looking virtual clothing fitting.
+
+![catvton how it works](src/mflux/assets/catvton_how_it_works.jpg)
+
+##### Example Command
+
+```bash
 mflux-generate-in-context-catvton \
-  --person-image "person.jpg" \
-  --person-mask "person_mask.png" \
-  --garment-image "garment.jpg" \
+  --person-image "person.png" \
+  --person-mask "mask.png" \
+  --garment-image "garment.png" \
+  --prompt "The pair of images highlights a clothing and its styling on a model, high resolution, 4K, 8K; [IMAGE1] Detailed product shot of a light blue shirt with designer details such as white and pink patterns; [IMAGE2] The *exact* same cloth (the light blue shirt with designer details such as white and pink patterns) is worn by a model in a lifestyle setting." \
   --steps 20 \
-  --seed 42 \
+  --seed 6269363 \
+  --guidance 30 \
   --height 1024 \
-  --width 1024
+  --width 891 \
+  -q 8
 ```
 
+
+
 ##### Required Inputs
 
 - **Person Image**: A photo of the person who will "wear" the garment
@@ -990,6 +1016,8 @@ mflux-generate-in-context-catvton \
 2. **Accurate Masks**: Ensure the person mask precisely covers the areas where the garment should appear
 3. **Consistent Lighting**: Match lighting conditions between person and garment images when possible
 4. **Garment Type**: Works best with clearly defined clothing items (shirts, dresses, jackets, etc.)
+5. **Pattern Limitations**: Very intricate patterns, fine details, and complex textures may not transfer well - simpler, solid colors and basic patterns typically work better
+6. **Try Multiple Seeds**: Generate several variations with different seeds to find the best result, as outcomes can vary significantly
 
 #### ✏️ IC-Edit (In-Context Editing)
 
@@ -999,16 +1027,41 @@ IC-Edit provides intuitive image editing capabilities using natural language ins
 
 The implementation is based on [@River-Zhang/ICEdit](https://github.com/River-Zhang/ICEdit).
 
-##### How to Use IC-Edit
+![ic-edit example](src/mflux/assets/ic_edit_example.jpg)
+*Original images credit: [Jimmy Chang (flower)](https://unsplash.com/photos/closeup-photography-of-pink-lotus-flower-LP5WXkrnxX0), [Kar Ming Moo (tiger)](https://unsplash.com/photos/brown-and-black-tiger-lying-on-ground-Q_3WmguWgYg), [Martin Baron (car)](https://unsplash.com/photos/a-shiny-black-car-parks-on-a-street-8JoIWt2KzYI) on Unsplash*
 
-```sh
+##### How it works
+
+IC-Edit employs a streamlined diptych approach for natural language image editing:
+
+1. **Image Layout**: The reference image is placed on the left side, while the right side is reserved for the edited result.
+
+2. **Automatic Masking**: Unlike CatVTON, no user-provided mask is required. The system automatically creates a simple but effective mask:
+   - **Left side (reference)**: Completely black mask - no denoising occurs here, preserving the original image as reference
+   - **Right side (edit area)**: Completely white mask - the entire area is available for denoising and modification
+
+3. **Instruction Processing**: Your natural language instruction is automatically wrapped in a diptych template that tells the model to create the desired changes on the right side while using the left side as reference.
+
+4. **Guided Generation**: The model reads the visual information from the left side and applies the requested modifications to generate the edited result on the right side, maintaining visual coherence and context.
+
+This simplified approach removes the complexity of mask creation while still providing precise control through natural language instructions, making image editing accessible and intuitive.
+
+![ic-edit how it works](src/mflux/assets/ic_edit_how_it_works.jpg)
+
+##### Example Command
+
+```bash
 mflux-generate-in-context-edit \
-  --reference-image "original.jpg" \
-  --instruction "make the hair black" \
+  --reference-image "flower.jpg" \
+  --prompt "two images of the exact same flower in two different styles: On the left the photo has is in bright colors showing green leaves and a pink flower. On the right, the *exact* same photo (same flower, same leaves, same background, identical etc). but with where everything is black and white except for the flower which is still in color. The content is *exactly* the same between the left and right image, except only for the coloring (black and white for everything except for the colorful flower)" \
   --steps 20 \
-  --seed 42
+  --seed 8570325 \
+  --guidance 30 \
+  -q 8
 ```
 
+
+
 ##### Key Features
 
 - **Natural Language Instructions**: Use simple, descriptive instructions like "make the hair black", "add sunglasses", or "change the background to a beach"
@@ -1038,6 +1091,9 @@ mflux-generate-in-context-edit \
 2. **Single Changes**: Focus on one edit at a time for more predictable results
 3. **Reference Quality**: Higher quality reference images generally produce better edits
 4. **Iterative Editing**: Use the output of one edit as input for the next to build complex changes
+5. **Try Multiple Seeds**: Generate several variations with different seeds to find the best result, as outcomes can vary significantly
+
+⚠️ *Note: Using the IC-Edit tool requires an additional ~230MB download for the specialized LoRA weights. The download happens automatically on first use. This remarkably small LoRA (less than 0.7% of the base model's ~34GB) enables sophisticated image editing capabilities, demonstrating that the base FLUX model is already highly competent at style transfers and transformations - the LoRA simply provides the user control and interface to access these latent capabilities.*
 
 ---
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "mflux"
-version = "0.7.1"
+version = "0.8.0"
 description = "A MLX port of FLUX based on the Huggingface Diffusers implementation."
 readme = "README.md"
 keywords = ["diffusers", "flux", "mlx"]