diff --git a/README.md b/README.md
index c46036b..cd7cd6a 100644
--- a/README.md
+++ b/README.md
@@ -1591,16 +1591,16 @@ As of release [v.0.5.0](https://github.com/filipstrand/mflux/releases/tag/v.0.5.
 
 #### Training configuration
 
-To describe a training run, you need to provide a [training configuration](src/mflux/dreambooth/_example/train.json) file which specifies the details such as
+To describe a training run, you need to provide a [training configuration](src/mflux/models/flux/variants/dreambooth/_example/train.json) file which specifies the details such as
 what training data to use and various parameters. To try it out, one of the easiest ways is to start from the 
-provided [example configuration](src/mflux/dreambooth/_example/train.json) and simply use your own dataset and prompts by modifying the `examples` section of the json file.
+provided [example configuration](src/mflux/models/flux/variants/dreambooth/_example/train.json) and simply use your own dataset and prompts by modifying the `examples` section of the json file.
 
 #### Training example
 
-A complete example ([training configuration](src/mflux/dreambooth/_example/train.json) + [dataset](src/mflux/dreambooth/_example/images)) is provided in this repository. To start a training run, go to the project folder `cd mflux`, and simply run:
+A complete example ([training configuration](src/mflux/models/flux/variants/dreambooth/_example/train.json) + [dataset](src/mflux/models/flux/variants/dreambooth/_example/images)) is provided in this repository. To start a training run, go to the project folder `cd mflux`, and simply run:
 
 ```sh
-mflux-train --train-config src/mflux/dreambooth/_example/train.json
+mflux-train --train-config src/mflux/models/flux/variants/dreambooth/_example/train.json
 ```
 
 By default, this will train an adapter with images of size `512x512` with a batch size of 1 and can take up to several hours to fully complete depending on your machine.
@@ -1717,7 +1717,7 @@ To avoid this, consider some of the following strategies to reduce memory requir
 - Use a smaller batch size, for example `"batch_size": 1`
 - Make sure your Mac is not busy with other background tasks that holds memory.
 
-Applying some of these strategies, like how [train.json](src/mflux/dreambooth/_example/train.json) is set up by default,
+Applying some of these strategies, like how [train.json](src/mflux/models/flux/variants/dreambooth/_example/train.json) is set up by default,
 will allow a 32GB M1 Pro to perform a successful fine-tuning run. 
 Note, however, that reducing the trainable parameters might lead to worse performance.
  
@@ -1738,7 +1738,7 @@ The aim is to also gradually expand the scope of this feature with alternative t
 - Loss curve can be a bit misleading/hard to read, sometimes it conveys little improvement over time, but actual image samples show the real progress.
 - When plotting the loss during training, we label it as "validation loss" but it is actually only the first 10 elements of the training examples for now. Future updates should support user inputs of separate validation images.
 - Training also works with the original model as quantized!
-- [For the curious, a motivation for the loss function can be found here](src/mflux/dreambooth/optimization/_loss_derivation/dreambooth_loss.pdf).
+- [For the curious, a motivation for the loss function can be found here](src/mflux/models/flux/variants/dreambooth/optimization/_loss_derivation/dreambooth_loss.pdf).
 - Two great resources that heavily inspired this feature are: 
   - The fine-tuning script in [mlx-examples](https://github.com/ml-explore/mlx-examples/tree/main/flux#finetuning)
   - The original fine-tuning script in [Diffusers](https://huggingface.co/docs/diffusers/v0.11.0/en/training/dreambooth)
diff --git a/pyproject.toml b/pyproject.toml
index 519c963..e975519 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ source-exclude = [
     # documentation assets were 27MB on 2025-07-05
     "**/assets/**",
     # dreambooth examples ~=5MB on 2025-07-05
-    "**/dreambooth/_example/images/**",
+    "**/models/flux/variants/dreambooth/_example/images/**",
     # loss pdf/tex does not need to be distributed
     "**/optimization/_loss_derivation/**",
 ]
@@ -26,25 +26,31 @@ maintainers = [{ name = "Filip Strand", email = "strand.filip@gmail.com" }]
 license = { file = "LICENSE" }
 requires-python = ">=3.10"
 dependencies = [
+    "accelerate>=0.31.0",
+    "filelock>=3.18.0",
     "huggingface-hub>=0.24.5,<1.0",
     "matplotlib>=3.9.2,<4.0",
     "mlx>=0.27.0,<0.28.0",
     "numpy>=2.0.1,<3.0",
     "opencv-python>=4.10.0,<5.0",
     "piexif>=1.1.3,<2.0",
+    "pillow>=10.4.0",
     "pillow>=10.4.0,<11.0; python_version<'3.13'",
     "pillow>=11.0,<12.0; python_version>='3.13'",
     "platformdirs>=4.0,<5.0",
+    "regex>=2024.11.6",
+    "requests>=2.32.4",
     "safetensors>=0.4.4,<1.0",
     "sentencepiece>=0.2.0,<1.0; python_version<'3.13'",
     # sentencepiece 0.2.1 is first release with 3.13 and 3.14 wheels: https://pypi.org/project/sentencepiece/0.2.1/
     "sentencepiece>=0.2.1,<1.0; python_version>='3.13'",
     "tokenizers>=0.20.3; python_version>='3.13'", # transformers -> tokenizers
     "toml>=0.10.2,<1.0",
+    "torch>=2.7.1",
     "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>=4.44.0,<5.0",
+    "transformers>=4.55.0,<5.0",
     "twine>=6.1.0,<7.0",
 ]
 classifiers = [
@@ -84,7 +90,7 @@ mflux-save-depth = "mflux.save_depth:main"
 mflux-train = "mflux.train:main"
 mflux-upscale = "mflux.upscale:main"
 mflux-lora-library = "mflux.lora_library:main"
-mflux-completions = "mflux.completions.install:main"
+mflux-completions = "mflux.ui.cli.completions.install:main"
 
 
 [tool.ruff]
diff --git a/src/mflux/callbacks/callback_manager.py b/src/mflux/callbacks/callback_manager.py
index c00021f..c439260 100644
--- a/src/mflux/callbacks/callback_manager.py
+++ b/src/mflux/callbacks/callback_manager.py
@@ -12,7 +12,7 @@ class CallbackManager:
     @staticmethod
     def register_callbacks(
         args: Namespace,
-        flux,
+        model,
         enable_canny_saver: bool = False,
         enable_depth_saver: bool = False,
     ) -> MemorySaver | None:
@@ -20,7 +20,7 @@ class CallbackManager:
         CallbackManager._register_battery_saver(args)
 
         # VAE Tiling (if requested)
-        CallbackManager._register_vae_tiling(args, flux)
+        CallbackManager._register_vae_tiling(args, model)
 
         # Specialized savers (based on flags)
         if enable_canny_saver:
@@ -30,10 +30,10 @@ class CallbackManager:
             CallbackManager._register_depth_saver(args)
 
         # Stepwise handler (if requested)
-        CallbackManager._register_stepwise_handler(args, flux)
+        CallbackManager._register_stepwise_handler(args, model)
 
         # Memory saver (if requested)
-        return CallbackManager._register_memory_saver(args, flux)
+        return CallbackManager._register_memory_saver(args, model)
 
     @staticmethod
     def _register_battery_saver(args: Namespace) -> None:
@@ -41,10 +41,10 @@ class CallbackManager:
         CallbackRegistry.register_before_loop(battery_saver)
 
     @staticmethod
-    def _register_vae_tiling(args: Namespace, flux) -> None:
+    def _register_vae_tiling(args: Namespace, model) -> None:
         if args.vae_tiling:
-            flux.vae.decoder.enable_tiling = True
-            flux.vae.decoder.split_direction = args.vae_tiling_split
+            model.vae.decoder.enable_tiling = True
+            model.vae.decoder.split_direction = args.vae_tiling_split
 
     @staticmethod
     def _register_canny_saver(args: Namespace) -> None:
@@ -59,18 +59,18 @@ class CallbackManager:
             CallbackRegistry.register_before_loop(depth_image_saver)
 
     @staticmethod
-    def _register_stepwise_handler(args: Namespace, flux) -> None:
+    def _register_stepwise_handler(args: Namespace, model) -> None:
         if args.stepwise_image_output_dir:
-            handler = StepwiseHandler(flux=flux, output_dir=args.stepwise_image_output_dir)
+            handler = StepwiseHandler(model=model, output_dir=args.stepwise_image_output_dir)
             CallbackRegistry.register_before_loop(handler)
             CallbackRegistry.register_in_loop(handler)
             CallbackRegistry.register_interrupt(handler)
 
     @staticmethod
-    def _register_memory_saver(args: Namespace, flux) -> MemorySaver | None:
+    def _register_memory_saver(args: Namespace, model) -> MemorySaver | None:
         memory_saver = None
         if args.low_ram:
-            memory_saver = MemorySaver(flux=flux, keep_transformer=len(args.seed) > 1)
+            memory_saver = MemorySaver(model=model, keep_transformer=len(args.seed) > 1)
             CallbackRegistry.register_before_loop(memory_saver)
             CallbackRegistry.register_in_loop(memory_saver)
             CallbackRegistry.register_after_loop(memory_saver)
diff --git a/src/mflux/callbacks/instances/memory_saver.py b/src/mflux/callbacks/instances/memory_saver.py
index 537245d..82fbc88 100644
--- a/src/mflux/callbacks/instances/memory_saver.py
+++ b/src/mflux/callbacks/instances/memory_saver.py
@@ -14,8 +14,8 @@ class MemorySaver(BeforeLoopCallback, InLoopCallback, AfterLoopCallback):
     components at strategic points in the execution cycle.
     """
 
-    def __init__(self, flux, keep_transformer: bool = True, cache_limit_bytes: int = 1000**3):
-        self.flux = flux
+    def __init__(self, model, keep_transformer: bool = True, cache_limit_bytes: int = 1000**3):
+        self.model = model
         self.keep_transformer = keep_transformer
         self.peak_memory: int = 0
         mx.set_cache_limit(cache_limit_bytes)
@@ -32,7 +32,7 @@ class MemorySaver(BeforeLoopCallback, InLoopCallback, AfterLoopCallback):
         depth_image: PIL.Image.Image | None = None,
     ) -> None:
         self.peak_memory = mx.get_peak_memory()
-        self._delete_encoders()
+        self._delete_text_encoders()
 
     def call_in_loop(
         self,
@@ -56,17 +56,21 @@ class MemorySaver(BeforeLoopCallback, InLoopCallback, AfterLoopCallback):
         if not self.keep_transformer:
             self._delete_transformer()
 
-    def _delete_encoders(self) -> None:
+    def _delete_text_encoders(self) -> None:
         # repeated image generation only works with the same prompt (cache)
-        self.flux.clip_text_encoder = None
-        self.flux.t5_text_encoder = None
+        if hasattr(self.model, "clip_image_encoder"):
+            self.model.clip_image_encoder = None
+        if hasattr(self.model, "t5_image_encoder"):
+            self.model.t5_image_encoder = None
+        if hasattr(self.model, "text_encoder"):
+            self.model.text_encoder = None
         gc.collect()
         mx.clear_cache()
 
     def _delete_transformer(self) -> None:
-        self.flux.transformer = None
-        if hasattr(self.flux, "transformer_controlnet"):
-            self.flux.transformer_controlnet = None
+        self.model.transformer = None
+        if hasattr(self.model, "transformer_controlnet"):
+            self.model.transformer_controlnet = None
         gc.collect()
         mx.clear_cache()
 
diff --git a/src/mflux/callbacks/instances/stepwise_handler.py b/src/mflux/callbacks/instances/stepwise_handler.py
index a590baf..8a6902e 100644
--- a/src/mflux/callbacks/instances/stepwise_handler.py
+++ b/src/mflux/callbacks/instances/stepwise_handler.py
@@ -13,10 +13,10 @@ from mflux.post_processing.image_util import ImageUtil
 class StepwiseHandler(BeforeLoopCallback, InLoopCallback, InterruptCallback):
     def __init__(
         self,
-        flux,
+        model,
         output_dir: str,
     ):
-        self.flux = flux
+        self.model = model
         self.output_dir = Path(output_dir)
         self.step_wise_images = []
 
@@ -80,16 +80,16 @@ class StepwiseHandler(BeforeLoopCallback, InLoopCallback, InterruptCallback):
         time_steps: tqdm,
     ) -> None:
         unpack_latents = ArrayUtil.unpack_latents(latents=latents, height=config.height, width=config.width)
-        stepwise_decoded = self.flux.vae.decode(unpack_latents)
+        stepwise_decoded = self.model.vae.decode(unpack_latents)
         generation_time = time_steps.format_dict["elapsed"] if time_steps is not None else 0
         stepwise_img = ImageUtil.to_image(
             decoded_latents=stepwise_decoded,
             config=config,
             seed=seed,
             prompt=prompt,
-            quantization=self.flux.bits,
-            lora_paths=self.flux.lora_paths,
-            lora_scales=self.flux.lora_scales,
+            quantization=self.model.bits,
+            lora_paths=self.model.lora_paths,
+            lora_scales=self.model.lora_scales,
             generation_time=generation_time,
         )
         stepwise_img.save(
diff --git a/src/mflux/community/concept_attention/__init__.py b/src/mflux/community/concept_attention/__init__.py
deleted file mode 100644
index 397e2bb..0000000
--- a/src/mflux/community/concept_attention/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Concept Attention transformer models
diff --git a/src/mflux/concept.py b/src/mflux/concept.py
index a62223d..360acee 100644
--- a/src/mflux/concept.py
+++ b/src/mflux/concept.py
@@ -1,8 +1,8 @@
 from mflux.callbacks.callback_manager import CallbackManager
-from mflux.community.concept_attention.flux_concept import Flux1Concept
 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
@@ -34,7 +34,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/concept_from_image.py b/src/mflux/concept_from_image.py
index f7eef50..fa2699b 100644
--- a/src/mflux/concept_from_image.py
+++ b/src/mflux/concept_from_image.py
@@ -1,8 +1,8 @@
 from mflux.callbacks.callback_manager import CallbackManager
-from mflux.community.concept_attention.flux_concept_from_image import Flux1ConceptFromImage
 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
@@ -34,7 +34,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/config/model_config.py b/src/mflux/config/model_config.py
index 8136da3..092122e 100644
--- a/src/mflux/config/model_config.py
+++ b/src/mflux/config/model_config.py
@@ -84,6 +84,11 @@ class ModelConfig:
     def krea_dev() -> "ModelConfig":
         return AVAILABLE_MODELS["krea-dev"]
 
+    @staticmethod
+    @lru_cache
+    def qwen_image() -> "ModelConfig":
+        return AVAILABLE_MODELS["qwen-image"]
+
     def x_embedder_input_dim(self) -> int:
         if "Fill" in self.model_name:
             return 384
@@ -281,4 +286,16 @@ AVAILABLE_MODELS = {
         requires_sigma_shift=True,
         priority=10,
     ),
+    "qwen-image": ModelConfig(
+        aliases=["qwen-image", "qwen"],
+        model_name="Qwen/Qwen-Image",
+        base_model=None,
+        controlnet_model=None,
+        custom_transformer_model=None,
+        num_train_steps=None,
+        max_sequence_length=None,
+        supports_guidance=None,
+        requires_sigma_shift=None,
+        priority=11,
+    ),
 }
diff --git a/src/mflux/generate.py b/src/mflux/generate.py
index f2df4f7..37458db 100644
--- a/src/mflux/generate.py
+++ b/src/mflux/generate.py
@@ -2,10 +2,11 @@ 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.flux.flux import Flux1
+from mflux.models.flux.variants.txt2img.flux import Flux1
+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_prompt
+from mflux.ui.prompt_utils import get_effective_negative_prompt, get_effective_prompt
 
 
 def main():
@@ -24,7 +25,8 @@ def main():
         args.guidance = ui_defaults.GUIDANCE_SCALE
 
     # 1. Load the model
-    flux = Flux1(
+    model_class = QwenImage if "qwen" in args.model.lower() else Flux1
+    model = model_class(
         model_config=ModelConfig.from_name(model_name=args.model, base_model=args.base_model),
         quantize=args.quantize,
         local_path=args.path,
@@ -33,14 +35,15 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=model)
 
     try:
         for seed in args.seed:
             # 3. Generate an image for each seed value
-            image = flux.generate_image(
+            image = model.generate_image(
                 seed=seed,
                 prompt=get_effective_prompt(args),
+                negative_prompt=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 d03f0a1..d0872ab 100644
--- a/src/mflux/generate_controlnet.py
+++ b/src/mflux/generate_controlnet.py
@@ -1,8 +1,8 @@
 from mflux.callbacks.callback_manager import CallbackManager
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.controlnet.flux_controlnet import Flux1Controlnet
 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
@@ -33,7 +33,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux, enable_canny_saver=True)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux, enable_canny_saver=True)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_depth.py b/src/mflux/generate_depth.py
index 203ddcf..7890ab5 100644
--- a/src/mflux/generate_depth.py
+++ b/src/mflux/generate_depth.py
@@ -1,7 +1,7 @@
 from mflux.callbacks.callback_manager import CallbackManager
 from mflux.config.config import Config
 from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException
-from mflux.flux_tools.depth.flux_depth import Flux1Depth
+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
@@ -31,7 +31,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux, enable_depth_saver=True)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux, enable_depth_saver=True)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_fill.py b/src/mflux/generate_fill.py
index ba61d6b..34e9a40 100644
--- a/src/mflux/generate_fill.py
+++ b/src/mflux/generate_fill.py
@@ -1,7 +1,7 @@
 from mflux.callbacks.callback_manager import CallbackManager
 from mflux.config.config import Config
 from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException
-from mflux.flux_tools.fill.flux_fill import Flux1Fill
+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
@@ -31,7 +31,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_in_context_catvton.py b/src/mflux/generate_in_context_catvton.py
index cf38c23..7e27741 100644
--- a/src/mflux/generate_in_context_catvton.py
+++ b/src/mflux/generate_in_context_catvton.py
@@ -1,10 +1,10 @@
 from pathlib import Path
 
 from mflux.callbacks.callback_manager import CallbackManager
-from mflux.community.in_context.flux_in_context_fill import Flux1InContextFill
 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
@@ -44,7 +44,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_in_context_dev.py b/src/mflux/generate_in_context_dev.py
index 40dfab7..6e1f57f 100644
--- a/src/mflux/generate_in_context_dev.py
+++ b/src/mflux/generate_in_context_dev.py
@@ -1,11 +1,11 @@
 from pathlib import Path
 
 from mflux.callbacks.callback_manager import CallbackManager
-from mflux.community.in_context.flux_in_context_dev import Flux1InContextDev
-from mflux.community.in_context.utils.in_context_loras import LORA_REPO_ID, get_lora_filename
 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
@@ -42,7 +42,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_in_context_edit.py b/src/mflux/generate_in_context_edit.py
index 7ce958e..e1b0db7 100644
--- a/src/mflux/generate_in_context_edit.py
+++ b/src/mflux/generate_in_context_edit.py
@@ -3,11 +3,11 @@ from pathlib import Path
 from PIL import Image
 
 from mflux.callbacks.callback_manager import CallbackManager
-from mflux.community.in_context.flux_in_context_fill import Flux1InContextFill
-from mflux.community.in_context.utils.in_context_loras import prepare_ic_edit_loras
 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
@@ -46,7 +46,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_kontext.py b/src/mflux/generate_kontext.py
index ca27ce4..f979df0 100644
--- a/src/mflux/generate_kontext.py
+++ b/src/mflux/generate_kontext.py
@@ -3,7 +3,7 @@ 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.kontext.flux_kontext import Flux1Kontext
+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
@@ -33,7 +33,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/generate_redux.py b/src/mflux/generate_redux.py
index 09c5512..452aaef 100644
--- a/src/mflux/generate_redux.py
+++ b/src/mflux/generate_redux.py
@@ -4,7 +4,7 @@ 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.flux_tools.redux.flux_redux import Flux1Redux
+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
@@ -41,7 +41,7 @@ def main():
     )
 
     # 2. Register callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         for seed in args.seed:
diff --git a/src/mflux/latent_creator/latent_creator.py b/src/mflux/latent_creator/latent_creator.py
index b2d277b..9c8672a 100644
--- a/src/mflux/latent_creator/latent_creator.py
+++ b/src/mflux/latent_creator/latent_creator.py
@@ -1,8 +1,8 @@
 from pathlib import Path
 
 import mlx.core as mx
+from mlx import nn
 
-from mflux.models.vae.vae import VAE
 from mflux.post_processing.array_util import ArrayUtil
 from mflux.post_processing.image_util import ImageUtil
 
@@ -10,7 +10,7 @@ from mflux.post_processing.image_util import ImageUtil
 class Img2Img:
     def __init__(
         self,
-        vae: VAE,
+        vae: nn.Module,
         sigmas: mx.array,
         init_time_step: int,
         image_path: str | Path | None,
@@ -71,7 +71,7 @@ class LatentCreator:
             )
 
     @staticmethod
-    def encode_image(vae: VAE, image_path: str | Path, height: int, width: int):
+    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,
diff --git a/src/mflux/lora_library.py b/src/mflux/lora_library.py
index 11158c3..81e8ee6 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.weights.lora_library import _discover_lora_files
+from mflux.utils.lora_library import _discover_lora_files
 
 
 def list_loras(paths: list[str] | None = None) -> int:
diff --git a/src/mflux/community/__init__.py b/src/mflux/models/common/__init__.py
similarity index 100%
rename from src/mflux/community/__init__.py
rename to src/mflux/models/common/__init__.py
diff --git a/src/mflux/community/in_context/__init__.py b/src/mflux/models/common/lora/__init__.py
similarity index 100%
rename from src/mflux/community/in_context/__init__.py
rename to src/mflux/models/common/lora/__init__.py
diff --git a/src/mflux/community/in_context/utils/__init__.py b/src/mflux/models/common/lora/download/__init__.py
similarity index 100%
rename from src/mflux/community/in_context/utils/__init__.py
rename to src/mflux/models/common/lora/download/__init__.py
diff --git a/src/mflux/weights/weight_handler_lora_huggingface.py b/src/mflux/models/common/lora/download/lora_huggingface_downloader.py
similarity index 53%
rename from src/mflux/weights/weight_handler_lora_huggingface.py
rename to src/mflux/models/common/lora/download/lora_huggingface_downloader.py
index baa10d7..502ef69 100644
--- a/src/mflux/weights/weight_handler_lora_huggingface.py
+++ b/src/mflux/models/common/lora/download/lora_huggingface_downloader.py
@@ -2,28 +2,30 @@ import shutil
 from pathlib import Path
 
 from mflux.ui.defaults import MFLUX_LORA_CACHE_DIR
-from mflux.weights.download import snapshot_download
+from mflux.utils.download import snapshot_download
 
 
-class WeightHandlerLoRAHuggingFace:
+class LoRAHuggingFaceDownloader:
+
     @staticmethod
     def download_loras(
         lora_names: list[str] | None = None,
         repo_id: str | None = None,
         cache_dir: Path | str | None = None,
+        model_name: str = "LoRA",
     ) -> list[str]:
-        if repo_id is None:
+        if not lora_names or not repo_id:
             return []
 
         lora_paths = []
-        if lora_names:
-            for lora_name in lora_names:
-                lora_path = WeightHandlerLoRAHuggingFace.download_lora(
-                    repo_id=repo_id,
-                    lora_name=lora_name,
-                    cache_dir=cache_dir,
-                )
-                lora_paths.append(lora_path)
+        for lora_name in lora_names:
+            lora_path = LoRAHuggingFaceDownloader.download_lora(
+                repo_id=repo_id,
+                lora_name=lora_name,
+                cache_dir=cache_dir,
+                model_name=model_name,
+            )
+            lora_paths.append(lora_path)
 
         return lora_paths
 
@@ -32,6 +34,7 @@ class WeightHandlerLoRAHuggingFace:
         repo_id: str,
         lora_name: str,
         cache_dir: Path | str | None = None,
+        model_name: str = "LoRA",  # For logging purposes
     ) -> str:
         # Ensure cache_dir is a Path object
         if cache_dir is None:
@@ -41,25 +44,25 @@ class WeightHandlerLoRAHuggingFace:
 
         cache_path.mkdir(parents=True, exist_ok=True)
 
-        # Check if the file already exists in the cache
+        # Check if already cached
         cached_file_path = cache_path / lora_name
         if cached_file_path.exists() and cached_file_path.is_file():
             try:
                 # Verify the file is actually readable (catches broken symlinks)
                 with open(cached_file_path, "rb") as f:
                     f.read(1)  # Try to read just 1 byte to verify it works
-                print(f"Using cached LoRA: {cached_file_path}")
+                print(f"Using cached {model_name} LoRA: {cached_file_path}")
                 return str(cached_file_path)
             except (OSError, IOError):
                 # File exists but is not readable (broken symlink, permissions, etc.)
-                print(f"Cached LoRA file is corrupted or inaccessible, re-downloading: {cached_file_path}")
+                print(f"Cached {model_name} LoRA file is corrupted or inaccessible, re-downloading: {cached_file_path}")
                 try:
                     cached_file_path.unlink()  # Remove the broken file/symlink
                 except OSError:
                     pass  # Ignore if we can't remove it
 
         # Download the LoRA from Hugging Face
-        print(f"Downloading LoRA '{lora_name}' from {repo_id}...")
+        print(f"Downloading {model_name} LoRA '{lora_name}' from {repo_id}...")
         download_path = Path(
             snapshot_download(
                 repo_id=repo_id,
@@ -69,18 +72,30 @@ class WeightHandlerLoRAHuggingFace:
         )
 
         # Find the downloaded file
-        for file in download_path.glob(f"**/*{lora_name}*"):
+        print(f"🔍 Searching for downloaded files in: {download_path}")
+        found_files = list(download_path.glob(f"**/*{lora_name}*"))
+        print(f"📁 Found files matching pattern: {found_files}")
+
+        for file in found_files:
+            print(f"📄 Checking file: {file} (suffix: {file.suffix}, size: {file.stat().st_size} bytes)")
             if file.is_file() and file.suffix in [".safetensors", ".bin"]:
-                # Copy or link the file to the cache directory with the expected name
-                target_path = cache_path / lora_name
+                # Ensure the target path has the correct extension
+                if not lora_name.endswith(file.suffix):
+                    target_name = f"{lora_name}{file.suffix}"
+                else:
+                    target_name = lora_name
+
+                target_path = cache_path / target_name
                 if not target_path.exists():
                     # Create a symlink or copy the file
                     try:
                         target_path.symlink_to(file)
+                        print(f"🔗 Created symlink: {target_path} -> {file}")
                     except (OSError, AttributeError):
                         shutil.copy2(file, target_path)
+                        print(f"📋 Copied file: {file} -> {target_path}")
 
-                print(f"LoRA downloaded to: {target_path}")
+                print(f"{model_name} LoRA downloaded to: {target_path}")
                 return str(target_path)
 
-        raise FileNotFoundError(f"Could not find LoRA file '{lora_name}' in the downloaded repository.")
+        raise FileNotFoundError(f"Could not find {model_name} LoRA file '{lora_name}' in the downloaded repository.")
diff --git a/src/mflux/controlnet/__init__.py b/src/mflux/models/common/lora/layer/__init__.py
similarity index 100%
rename from src/mflux/controlnet/__init__.py
rename to src/mflux/models/common/lora/layer/__init__.py
diff --git a/src/mflux/dreambooth/lora_layers/fused_linear_lora_layer.py b/src/mflux/models/common/lora/layer/fused_linear_lora_layer.py
similarity index 88%
rename from src/mflux/dreambooth/lora_layers/fused_linear_lora_layer.py
rename to src/mflux/models/common/lora/layer/fused_linear_lora_layer.py
index 9a70e90..b830f11 100644
--- a/src/mflux/dreambooth/lora_layers/fused_linear_lora_layer.py
+++ b/src/mflux/models/common/lora/layer/fused_linear_lora_layer.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.dreambooth.lora_layers.linear_lora_layer import LoRALinear
+from mflux.models.common.lora.layer.linear_lora_layer import LoRALinear
 
 
 class FusedLoRALinear(nn.Module):
diff --git a/src/mflux/dreambooth/lora_layers/linear_lora_layer.py b/src/mflux/models/common/lora/layer/linear_lora_layer.py
similarity index 100%
rename from src/mflux/dreambooth/lora_layers/linear_lora_layer.py
rename to src/mflux/models/common/lora/layer/linear_lora_layer.py
diff --git a/src/mflux/dreambooth/__init__.py b/src/mflux/models/common/lora/mapping/__init__.py
similarity index 100%
rename from src/mflux/dreambooth/__init__.py
rename to src/mflux/models/common/lora/mapping/__init__.py
diff --git a/src/mflux/models/common/lora/mapping/lora_loader.py b/src/mflux/models/common/lora/mapping/lora_loader.py
new file mode 100644
index 0000000..8b7f7d4
--- /dev/null
+++ b/src/mflux/models/common/lora/mapping/lora_loader.py
@@ -0,0 +1,248 @@
+from pathlib import Path
+from typing import Dict, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from mflux.models.common.lora.layer.fused_linear_lora_layer import FusedLoRALinear
+from mflux.models.common.lora.layer.linear_lora_layer import LoRALinear
+from mflux.models.common.lora.mapping.lora_mapping import LoRATarget
+
+
+class LoRALoader:
+
+    @staticmethod
+    def load_and_apply_lora(
+        lora_mapping: list[LoRATarget],
+        transformer: nn.Module,
+        lora_files: list[str],
+        lora_scales: list[float] | None = None,
+    ) -> None:
+        if not lora_files:
+            return
+
+        # Validate scales - handle both None and empty list cases
+        if lora_scales is None or len(lora_scales) == 0:
+            lora_scales = [1.0] * len(lora_files)
+        elif len(lora_scales) != len(lora_files):
+            raise ValueError(
+                f"Number of LoRA scales ({len(lora_scales)}) must match number of LoRA files ({len(lora_files)})"
+            )
+
+        print(f"📦 Loading {len(lora_files)} LoRA file(s)...")
+
+        for lora_file, scale in zip(lora_files, lora_scales):
+            LoRALoader._apply_single_lora(transformer, lora_file, scale, lora_mapping)
+
+        print("✅ All LoRA weights applied successfully")
+
+    @staticmethod
+    def _apply_single_lora(
+        transformer: nn.Module,
+        lora_file: str,
+        scale: float,
+        lora_mapping: list[LoRATarget]
+    ) -> None:
+        # Load the LoRA weights
+        if not Path(lora_file).exists():
+            print(f"❌ LoRA file not found: {lora_file}")
+            return
+
+        print(f"🔧 Applying LoRA: {Path(lora_file).name} (scale={scale})")
+
+        try:
+            weights = dict(mx.load(lora_file, return_metadata=True)[0].items())
+            mx.eval(weights)
+        except (FileNotFoundError, ValueError, RuntimeError) as e:
+            print(f"❌ Failed to load LoRA file: {e}")
+            return
+
+        print(f"🔍 DEBUG: Found {len(weights)} LoRA weights")
+
+        # Apply LoRA using the provided mapping
+        flat_mapping = LoRALoader._get_flat_mapping(lora_mapping)
+        applied_count = LoRALoader._apply_lora_with_mapping(transformer, weights, scale, flat_mapping)
+
+        print(f"   ✅ Applied to {applied_count} layers")
+
+    @staticmethod
+    def _apply_lora_with_mapping(
+        transformer: nn.Module,
+        weights: dict,
+        scale: float,
+        lora_mappings: Dict[str, Tuple[str, str, bool]]
+    ) -> int:
+        applied_count = 0
+        lora_data_by_target = {}
+
+        # Group LoRA weights by their target layers
+        for weight_key, weight_value in weights.items():
+            found_mapping = None
+            block_idx = None
+
+            # Pattern matching logic
+            for pattern, mapping_info in lora_mappings.items():
+                if "{block}" in pattern:
+                    # Extract block number from the weight key
+                    parts = weight_key.split(".")
+                    for i, part in enumerate(parts):
+                        if part.isdigit():
+                            try:
+                                test_block_idx = int(part)
+                                concrete_pattern = pattern.format(block=test_block_idx)
+                                if weight_key == concrete_pattern:
+                                    found_mapping = mapping_info
+                                    block_idx = test_block_idx
+                                    break
+                            except (ValueError, KeyError):
+                                continue
+                    if found_mapping:
+                        break
+                else:
+                    if weight_key == pattern:
+                        found_mapping = mapping_info
+                        break
+
+            if found_mapping is None:
+                continue
+
+            target_path, matrix_name, transpose = found_mapping
+
+            # Handle block substitution in target path
+            if block_idx is not None and "{block}" in target_path:
+                target_path = target_path.format(block=block_idx)
+
+            if target_path not in lora_data_by_target:
+                lora_data_by_target[target_path] = {}
+
+            lora_data_by_target[target_path][matrix_name] = (weight_value, transpose)
+
+        print(f"🔍 DEBUG: Found {len(lora_data_by_target)} LoRA target layers")
+
+        # Apply LoRA to each target
+        for target_path, lora_data in lora_data_by_target.items():
+            if LoRALoader._apply_lora_matrices_to_target(transformer, target_path, lora_data, scale):
+                applied_count += 1
+
+        return applied_count
+
+    @staticmethod
+    def _apply_lora_matrices_to_target(
+        transformer: nn.Module,
+        target_path: str,
+        lora_data: dict,
+        scale: float
+    ) -> bool:
+        # Navigate to the target layer
+        current_module = transformer
+        path_parts = target_path.split(".")
+
+        try:
+            for part in path_parts:
+                if part.isdigit():
+                    current_module = current_module[int(part)]
+                else:
+                    current_module = getattr(current_module, part)
+        except (AttributeError, IndexError, KeyError):
+            print(f"❌ Could not find target path: {target_path}")
+            return False
+
+        # Check if we have the required matrices
+        if "lora_A" not in lora_data or "lora_B" not in lora_data:
+            print(f"❌ Missing required LoRA matrices for {target_path}")
+            return False
+
+        lora_A, transpose_A = lora_data["lora_A"]
+        lora_B, transpose_B = lora_data["lora_B"]
+
+        # Handle transposition
+        if transpose_A:
+            lora_A = lora_A.T
+        if transpose_B:
+            lora_B = lora_B.T
+
+        # Handle alpha scaling
+        alpha_scale = 1.0
+        if "alpha" in lora_data:
+            alpha_value, _ = lora_data["alpha"]
+            rank = lora_A.shape[1]
+            alpha_scale = float(alpha_value) / rank
+
+        # Calculate final scale - only use user scale, matching Diffusers approach
+        effective_scale = scale
+
+        # Create new LoRA layer
+        if hasattr(current_module, 'weight'):
+            # Create LoRA layer
+            lora_layer = LoRALinear.from_linear(
+                current_module,
+                r=lora_A.shape[1],
+                scale=effective_scale
+            )
+
+            # Set the LoRA matrices - use the correct dimensions from the LoRA file
+            lora_layer.lora_A = lora_A
+            lora_layer.lora_B = lora_B
+
+            # Apply alpha scaling to the matrices if present
+            if "alpha" in lora_data:
+                lora_layer.lora_B = lora_layer.lora_B * alpha_scale
+
+            # Handle fusion: if the current module is already a LoRA layer, fuse them
+            if isinstance(current_module, LoRALinear):
+                print(f"   🔀 Fusing with existing LoRA at {target_path}")
+                # Create fused layer with the existing LoRA and the new one
+                fused_layer = FusedLoRALinear(
+                    base_linear=current_module.linear,
+                    loras=[current_module, lora_layer]
+                )
+                replacement_layer = fused_layer
+            elif isinstance(current_module, FusedLoRALinear):
+                print(f"   🔀 Adding to existing fusion at {target_path}")
+                # Add to existing fusion
+                fused_layer = FusedLoRALinear(
+                    base_linear=current_module.base_linear,
+                    loras=current_module.loras + [lora_layer]
+                )
+                replacement_layer = fused_layer
+            else:
+                # First LoRA on this layer
+                replacement_layer = lora_layer
+
+            # Replace the layer in the parent module
+            parent_module = transformer
+            for part in path_parts[:-1]:
+                if part.isdigit():
+                    parent_module = parent_module[int(part)]
+                else:
+                    parent_module = getattr(parent_module, part)
+
+            final_attr = path_parts[-1]
+            if final_attr.isdigit():
+                parent_module[int(final_attr)] = replacement_layer
+            else:
+                setattr(parent_module, final_attr, replacement_layer)
+
+            return True
+        else:
+            print(f"❌ Target layer {target_path} is not a linear layer")
+            return False
+
+    @staticmethod
+    def _get_flat_mapping(targets: list[LoRATarget]) -> Dict[str, Tuple[str, str, bool]]:
+        flat_mapping = {}
+
+        for target in targets:
+            # Add up weight patterns (lora_B, transposed)
+            for pattern in target.possible_up_patterns:
+                flat_mapping[pattern] = (target.model_path, "lora_B", True)
+
+            # Add down weight patterns (lora_A, transposed)
+            for pattern in target.possible_down_patterns:
+                flat_mapping[pattern] = (target.model_path, "lora_A", True)
+
+            # Add alpha patterns (no transpose)
+            for pattern in target.possible_alpha_patterns:
+                flat_mapping[pattern] = (target.model_path, "alpha", False)
+
+        return flat_mapping
diff --git a/src/mflux/models/common/lora/mapping/lora_mapping.py b/src/mflux/models/common/lora/mapping/lora_mapping.py
new file mode 100644
index 0000000..8dde37d
--- /dev/null
+++ b/src/mflux/models/common/lora/mapping/lora_mapping.py
@@ -0,0 +1,17 @@
+from dataclasses import dataclass
+from typing import List, Protocol
+
+
+@dataclass
+class LoRATarget:
+    model_path: str
+    possible_up_patterns: List[str]
+    possible_down_patterns: List[str]
+    possible_alpha_patterns: List[str]
+
+
+class LoRAMapping(Protocol):
+
+    @staticmethod
+    def get_mapping() -> List[LoRATarget]:
+        return
diff --git a/src/mflux/models/depth_pro/depth_pro.py b/src/mflux/models/depth_pro/depth_pro.py
index f8e6030..bb3f2bf 100644
--- a/src/mflux/models/depth_pro/depth_pro.py
+++ b/src/mflux/models/depth_pro/depth_pro.py
@@ -7,8 +7,8 @@ import numpy as np
 from PIL import Image
 
 from mflux.models.depth_pro.depth_pro_initializer import DepthProInitializer
-from mflux.models.depth_pro.depth_pro_model import DepthProModel
-from mflux.models.depth_pro.depth_pro_util import DepthProUtil
+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
 
 
diff --git a/src/mflux/models/depth_pro/depth_pro_initializer.py b/src/mflux/models/depth_pro/depth_pro_initializer.py
index 6888fa4..d3edb92 100644
--- a/src/mflux/models/depth_pro/depth_pro_initializer.py
+++ b/src/mflux/models/depth_pro/depth_pro_initializer.py
@@ -1,7 +1,7 @@
 import mlx.nn as nn
 
-from mflux.models.depth_pro.depth_pro_model import DepthProModel
-from mflux.models.depth_pro.weight_handler_depth_pro import WeightHandlerDepthPro
+from mflux.models.depth_pro.model.depth_pro_model import DepthProModel
+from mflux.models.depth_pro.weights.weight_handler_depth_pro import WeightHandlerDepthPro
 
 
 class DepthProInitializer:
diff --git a/src/mflux/dreambooth/dataset/__init__.py b/src/mflux/models/depth_pro/model/__init__.py
similarity index 100%
rename from src/mflux/dreambooth/dataset/__init__.py
rename to src/mflux/models/depth_pro/model/__init__.py
diff --git a/src/mflux/models/depth_pro/conv_utils.py b/src/mflux/models/depth_pro/model/conv_utils.py
similarity index 100%
rename from src/mflux/models/depth_pro/conv_utils.py
rename to src/mflux/models/depth_pro/model/conv_utils.py
diff --git a/src/mflux/models/depth_pro/depth_pro_encoder.py b/src/mflux/models/depth_pro/model/depth_pro_encoder.py
similarity index 95%
rename from src/mflux/models/depth_pro/depth_pro_encoder.py
rename to src/mflux/models/depth_pro/model/depth_pro_encoder.py
index 5186d1b..fb0439d 100644
--- a/src/mflux/models/depth_pro/depth_pro_encoder.py
+++ b/src/mflux/models/depth_pro/model/depth_pro_encoder.py
@@ -3,9 +3,9 @@ import math
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.conv_utils import ConvUtils
-from mflux.models.depth_pro.dino_v2.dino_vision_transformer import DinoVisionTransformer
-from mflux.models.depth_pro.upsample_block import UpSampleBlock
+from mflux.models.depth_pro.model.conv_utils import ConvUtils
+from mflux.models.depth_pro.model.dino_v2.dino_vision_transformer import DinoVisionTransformer
+from mflux.models.depth_pro.model.upsample_block import UpSampleBlock
 
 
 class DepthProEncoder(nn.Module):
diff --git a/src/mflux/models/depth_pro/depth_pro_model.py b/src/mflux/models/depth_pro/model/depth_pro_model.py
similarity index 70%
rename from src/mflux/models/depth_pro/depth_pro_model.py
rename to src/mflux/models/depth_pro/model/depth_pro_model.py
index a751c13..d3bb45c 100644
--- a/src/mflux/models/depth_pro/depth_pro_model.py
+++ b/src/mflux/models/depth_pro/model/depth_pro_model.py
@@ -1,9 +1,9 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.depth_pro_encoder import DepthProEncoder
-from mflux.models.depth_pro.fov_head import FOVHead
-from mflux.models.depth_pro.multires_conv_decoder import MultiresConvDecoder
+from mflux.models.depth_pro.model.depth_pro_encoder import DepthProEncoder
+from mflux.models.depth_pro.model.fov_head import FOVHead
+from mflux.models.depth_pro.model.multires_conv_decoder import MultiresConvDecoder
 
 
 class DepthProModel(nn.Module):
diff --git a/src/mflux/models/depth_pro/depth_pro_util.py b/src/mflux/models/depth_pro/model/depth_pro_util.py
similarity index 100%
rename from src/mflux/models/depth_pro/depth_pro_util.py
rename to src/mflux/models/depth_pro/model/depth_pro_util.py
diff --git a/src/mflux/dreambooth/lora_layers/__init__.py b/src/mflux/models/depth_pro/model/dino_v2/__init__.py
similarity index 100%
rename from src/mflux/dreambooth/lora_layers/__init__.py
rename to src/mflux/models/depth_pro/model/dino_v2/__init__.py
diff --git a/src/mflux/models/depth_pro/dino_v2/attention.py b/src/mflux/models/depth_pro/model/dino_v2/attention.py
similarity index 100%
rename from src/mflux/models/depth_pro/dino_v2/attention.py
rename to src/mflux/models/depth_pro/model/dino_v2/attention.py
diff --git a/src/mflux/models/depth_pro/dino_v2/dino_vision_transformer.py b/src/mflux/models/depth_pro/model/dino_v2/dino_vision_transformer.py
similarity index 89%
rename from src/mflux/models/depth_pro/dino_v2/dino_vision_transformer.py
rename to src/mflux/models/depth_pro/model/dino_v2/dino_vision_transformer.py
index e179698..737295f 100644
--- a/src/mflux/models/depth_pro/dino_v2/dino_vision_transformer.py
+++ b/src/mflux/models/depth_pro/model/dino_v2/dino_vision_transformer.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.dino_v2.patch_embed import PatchEmbed
-from mflux.models.depth_pro.dino_v2.transformer_block import TransformerBlock
+from mflux.models.depth_pro.model.dino_v2.patch_embed import PatchEmbed
+from mflux.models.depth_pro.model.dino_v2.transformer_block import TransformerBlock
 
 
 class DinoVisionTransformer(nn.Module):
diff --git a/src/mflux/models/depth_pro/dino_v2/layer_scale.py b/src/mflux/models/depth_pro/model/dino_v2/layer_scale.py
similarity index 100%
rename from src/mflux/models/depth_pro/dino_v2/layer_scale.py
rename to src/mflux/models/depth_pro/model/dino_v2/layer_scale.py
diff --git a/src/mflux/models/depth_pro/dino_v2/mlp.py b/src/mflux/models/depth_pro/model/dino_v2/mlp.py
similarity index 100%
rename from src/mflux/models/depth_pro/dino_v2/mlp.py
rename to src/mflux/models/depth_pro/model/dino_v2/mlp.py
diff --git a/src/mflux/models/depth_pro/dino_v2/patch_embed.py b/src/mflux/models/depth_pro/model/dino_v2/patch_embed.py
similarity index 100%
rename from src/mflux/models/depth_pro/dino_v2/patch_embed.py
rename to src/mflux/models/depth_pro/model/dino_v2/patch_embed.py
diff --git a/src/mflux/models/depth_pro/dino_v2/transformer_block.py b/src/mflux/models/depth_pro/model/dino_v2/transformer_block.py
similarity index 75%
rename from src/mflux/models/depth_pro/dino_v2/transformer_block.py
rename to src/mflux/models/depth_pro/model/dino_v2/transformer_block.py
index a60039a..40db168 100644
--- a/src/mflux/models/depth_pro/dino_v2/transformer_block.py
+++ b/src/mflux/models/depth_pro/model/dino_v2/transformer_block.py
@@ -1,9 +1,9 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.dino_v2.attention import Attention
-from mflux.models.depth_pro.dino_v2.layer_scale import LayerScale
-from mflux.models.depth_pro.dino_v2.mlp import MLP
+from mflux.models.depth_pro.model.dino_v2.attention import Attention
+from mflux.models.depth_pro.model.dino_v2.layer_scale import LayerScale
+from mflux.models.depth_pro.model.dino_v2.mlp import MLP
 
 
 class TransformerBlock(nn.Module):
diff --git a/src/mflux/models/depth_pro/feature_fusion_block_2d.py b/src/mflux/models/depth_pro/model/feature_fusion_block_2d.py
similarity index 88%
rename from src/mflux/models/depth_pro/feature_fusion_block_2d.py
rename to src/mflux/models/depth_pro/model/feature_fusion_block_2d.py
index db500d3..5dba7d9 100644
--- a/src/mflux/models/depth_pro/feature_fusion_block_2d.py
+++ b/src/mflux/models/depth_pro/model/feature_fusion_block_2d.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.conv_utils import ConvUtils
-from mflux.models.depth_pro.residual_block import ResidualBlock
+from mflux.models.depth_pro.model.conv_utils import ConvUtils
+from mflux.models.depth_pro.model.residual_block import ResidualBlock
 
 
 class FeatureFusionBlock2d(nn.Module):
diff --git a/src/mflux/models/depth_pro/fov_head.py b/src/mflux/models/depth_pro/model/fov_head.py
similarity index 93%
rename from src/mflux/models/depth_pro/fov_head.py
rename to src/mflux/models/depth_pro/model/fov_head.py
index cb8b6a8..1d94074 100644
--- a/src/mflux/models/depth_pro/fov_head.py
+++ b/src/mflux/models/depth_pro/model/fov_head.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.conv_utils import ConvUtils
+from mflux.models.depth_pro.model.conv_utils import ConvUtils
 
 
 class FOVHead(nn.Module):
diff --git a/src/mflux/models/depth_pro/multires_conv_decoder.py b/src/mflux/models/depth_pro/model/multires_conv_decoder.py
similarity index 93%
rename from src/mflux/models/depth_pro/multires_conv_decoder.py
rename to src/mflux/models/depth_pro/model/multires_conv_decoder.py
index 7551ba3..655650b 100644
--- a/src/mflux/models/depth_pro/multires_conv_decoder.py
+++ b/src/mflux/models/depth_pro/model/multires_conv_decoder.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.conv_utils import ConvUtils
-from mflux.models.depth_pro.feature_fusion_block_2d import FeatureFusionBlock2d
+from mflux.models.depth_pro.model.conv_utils import ConvUtils
+from mflux.models.depth_pro.model.feature_fusion_block_2d import FeatureFusionBlock2d
 
 
 class MultiresConvDecoder(nn.Module):
diff --git a/src/mflux/models/depth_pro/residual_block.py b/src/mflux/models/depth_pro/model/residual_block.py
similarity index 94%
rename from src/mflux/models/depth_pro/residual_block.py
rename to src/mflux/models/depth_pro/model/residual_block.py
index ef1dc7e..9f4ca42 100644
--- a/src/mflux/models/depth_pro/residual_block.py
+++ b/src/mflux/models/depth_pro/model/residual_block.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.conv_utils import ConvUtils
+from mflux.models.depth_pro.model.conv_utils import ConvUtils
 
 
 class ResidualBlock(nn.Module):
diff --git a/src/mflux/models/depth_pro/upsample_block.py b/src/mflux/models/depth_pro/model/upsample_block.py
similarity index 95%
rename from src/mflux/models/depth_pro/upsample_block.py
rename to src/mflux/models/depth_pro/model/upsample_block.py
index ae647cd..3a31621 100644
--- a/src/mflux/models/depth_pro/upsample_block.py
+++ b/src/mflux/models/depth_pro/model/upsample_block.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 import mlx.nn as nn
 
-from mflux.models.depth_pro.conv_utils import ConvUtils
+from mflux.models.depth_pro.model.conv_utils import ConvUtils
 
 
 class UpSampleBlock(nn.Module):
diff --git a/src/mflux/dreambooth/optimization/__init__.py b/src/mflux/models/depth_pro/weights/__init__.py
similarity index 100%
rename from src/mflux/dreambooth/optimization/__init__.py
rename to src/mflux/models/depth_pro/weights/__init__.py
diff --git a/src/mflux/models/depth_pro/weight_handler_depth_pro.py b/src/mflux/models/depth_pro/weights/weight_handler_depth_pro.py
similarity index 97%
rename from src/mflux/models/depth_pro/weight_handler_depth_pro.py
rename to src/mflux/models/depth_pro/weights/weight_handler_depth_pro.py
index ddabbf4..244c853 100644
--- a/src/mflux/models/depth_pro/weight_handler_depth_pro.py
+++ b/src/mflux/models/depth_pro/weights/weight_handler_depth_pro.py
@@ -6,9 +6,9 @@ import mlx.core as mx
 import torch
 from mlx.utils import tree_unflatten
 
+from mflux.models.flux.weights.weight_handler import MetaData
+from mflux.models.flux.weights.weight_util import WeightUtil
 from mflux.ui.defaults import MFLUX_CACHE_DIR
-from mflux.weights.weight_handler import MetaData
-from mflux.weights.weight_util import WeightUtil
 
 logger = logging.getLogger(__name__)
 
diff --git a/src/mflux/dreambooth/state/__init__.py b/src/mflux/models/flux/__init__.py
similarity index 100%
rename from src/mflux/dreambooth/state/__init__.py
rename to src/mflux/models/flux/__init__.py
diff --git a/src/mflux/flux/flux_initializer.py b/src/mflux/models/flux/flux_initializer.py
similarity index 82%
rename from src/mflux/flux/flux_initializer.py
rename to src/mflux/models/flux/flux_initializer.py
index 7746b34..4db5cd3 100644
--- a/src/mflux/flux/flux_initializer.py
+++ b/src/mflux/models/flux/flux_initializer.py
@@ -1,21 +1,21 @@
 from mflux.config.model_config import ModelConfig
-from mflux.controlnet.transformer_controlnet import TransformerControlnet
-from mflux.controlnet.weight_handler_controlnet import WeightHandlerControlnet
-from mflux.flux_tools.redux.weight_handler_redux import WeightHandlerRedux
 from mflux.models.depth_pro.depth_pro import DepthPro
-from mflux.models.redux_encoder.redux_encoder import ReduxEncoder
-from mflux.models.siglip_vision_transformer.siglip_vision_transformer import SiglipVisionTransformer
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
-from mflux.tokenizer.clip_tokenizer import TokenizerCLIP
-from mflux.tokenizer.t5_tokenizer import TokenizerT5
-from mflux.tokenizer.tokenizer_handler import TokenizerHandler
-from mflux.weights.weight_handler import WeightHandler
-from mflux.weights.weight_handler_lora import WeightHandlerLoRA
-from mflux.weights.weight_handler_lora_huggingface import WeightHandlerLoRAHuggingFace
-from mflux.weights.weight_util import WeightUtil
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder
+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.model.redux_encoder.redux_encoder import ReduxEncoder
+from mflux.models.flux.model.siglip_vision_transformer.siglip_vision_transformer import SiglipVisionTransformer
+from mflux.models.flux.tokenizer.clip_tokenizer import TokenizerCLIP
+from mflux.models.flux.tokenizer.t5_tokenizer import TokenizerT5
+from mflux.models.flux.tokenizer.tokenizer_handler import TokenizerHandler
+from mflux.models.flux.variants.controlnet.transformer_controlnet import TransformerControlnet
+from mflux.models.flux.variants.controlnet.weight_handler_controlnet import WeightHandlerControlnet
+from mflux.models.flux.variants.redux.weight_handler_redux import WeightHandlerRedux
+from mflux.models.common.lora.download.lora_huggingface_downloader import LoRAHuggingFaceDownloader
+from mflux.models.flux.weights.weight_handler import WeightHandler
+from mflux.models.flux.weights.weight_handler_lora import WeightHandlerLoRA
+from mflux.models.flux.weights.weight_util import WeightUtil
 
 
 class FluxInitializer:
@@ -81,9 +81,10 @@ class FluxInitializer:
         )
 
         # 5. Set LoRA weights
-        hf_lora_paths = WeightHandlerLoRAHuggingFace.download_loras(
+        hf_lora_paths = LoRAHuggingFaceDownloader.download_loras(
             lora_names=lora_names,
             repo_id=lora_repo_id,
+            model_name="Flux",
         )
         flux_model.lora_paths = lora_paths + hf_lora_paths
         flux_model.lora_scales = (lora_scales or []) + [1.0] * len(hf_lora_paths)
@@ -206,9 +207,9 @@ class FluxInitializer:
         lora_repo_id: str | None = None,
     ):
         # Import here to avoid circular dependency
-        from mflux.community.concept_attention.transformer_concept import TransformerConcept
+        from mflux.models.flux.variants.concept_attention.transformer_concept import TransformerConcept
 
-        # 1. Load weights first to get transformer dimensions
+        # 1. Load weights first to get flux_transformer dimensions
         weights = WeightHandler.load_regular_weights(
             repo_id=model_config.model_name,
             local_path=local_path,
@@ -221,7 +222,7 @@ class FluxInitializer:
             num_single_transformer_blocks=weights.num_single_transformer_blocks(),
         )
 
-        # 3. Use the improved FluxInitializer with custom transformer
+        # 3. Use the improved FluxInitializer with custom flux_transformer
         FluxInitializer.init(
             flux_model=flux_model,
             model_config=model_config,
diff --git a/src/mflux/dreambooth/statistics/__init__.py b/src/mflux/models/flux/model/__init__.py
similarity index 100%
rename from src/mflux/dreambooth/statistics/__init__.py
rename to src/mflux/models/flux/model/__init__.py
diff --git a/src/mflux/flux/__init__.py b/src/mflux/models/flux/model/flux_text_encoder/__init__.py
similarity index 100%
rename from src/mflux/flux/__init__.py
rename to src/mflux/models/flux/model/flux_text_encoder/__init__.py
diff --git a/src/mflux/flux_tools/__init__.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/__init__.py
similarity index 100%
rename from src/mflux/flux_tools/__init__.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/__init__.py
diff --git a/src/mflux/models/text_encoder/clip_encoder/clip_embeddings.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_embeddings.py
similarity index 90%
rename from src/mflux/models/text_encoder/clip_encoder/clip_embeddings.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_embeddings.py
index 0410e13..cc6c553 100644
--- a/src/mflux/models/text_encoder/clip_encoder/clip_embeddings.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_embeddings.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.tokenizer.clip_tokenizer import TokenizerCLIP
+from mflux.models.flux.tokenizer.clip_tokenizer import TokenizerCLIP
 
 
 class CLIPEmbeddings(nn.Module):
diff --git a/src/mflux/models/text_encoder/clip_encoder/clip_encoder.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_encoder.py
similarity index 77%
rename from src/mflux/models/text_encoder/clip_encoder/clip_encoder.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_encoder.py
index 9b641e3..24eb6c6 100644
--- a/src/mflux/models/text_encoder/clip_encoder/clip_encoder.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_encoder.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.clip_encoder.clip_text_model import CLIPTextModel
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_text_model import CLIPTextModel
 
 
 class CLIPEncoder(nn.Module):
diff --git a/src/mflux/models/text_encoder/clip_encoder/clip_encoder_layer.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_encoder_layer.py
similarity index 81%
rename from src/mflux/models/text_encoder/clip_encoder/clip_encoder_layer.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_encoder_layer.py
index 55e2a3f..fbb0521 100644
--- a/src/mflux/models/text_encoder/clip_encoder/clip_encoder_layer.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_encoder_layer.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.clip_encoder.clip_mlp import CLIPMLP
-from mflux.models.text_encoder.clip_encoder.clip_sdpa_attention import CLIPSdpaAttention
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_mlp import CLIPMLP
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_sdpa_attention import CLIPSdpaAttention
 
 
 class CLIPEncoderLayer(nn.Module):
diff --git a/src/mflux/models/text_encoder/clip_encoder/clip_mlp.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_mlp.py
similarity index 100%
rename from src/mflux/models/text_encoder/clip_encoder/clip_mlp.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_mlp.py
diff --git a/src/mflux/models/text_encoder/clip_encoder/clip_sdpa_attention.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_sdpa_attention.py
similarity index 100%
rename from src/mflux/models/text_encoder/clip_encoder/clip_sdpa_attention.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_sdpa_attention.py
diff --git a/src/mflux/models/text_encoder/clip_encoder/clip_text_model.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_text_model.py
similarity index 86%
rename from src/mflux/models/text_encoder/clip_encoder/clip_text_model.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_text_model.py
index 2851073..c782776 100644
--- a/src/mflux/models/text_encoder/clip_encoder/clip_text_model.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/clip_text_model.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.clip_encoder.clip_embeddings import CLIPEmbeddings
-from mflux.models.text_encoder.clip_encoder.encoder_clip import EncoderCLIP
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_embeddings import CLIPEmbeddings
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.encoder_clip import EncoderCLIP
 
 
 class CLIPTextModel(nn.Module):
diff --git a/src/mflux/models/text_encoder/clip_encoder/encoder_clip.py b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/encoder_clip.py
similarity index 82%
rename from src/mflux/models/text_encoder/clip_encoder/encoder_clip.py
rename to src/mflux/models/flux/model/flux_text_encoder/clip_encoder/encoder_clip.py
index 2199732..ca33e47 100644
--- a/src/mflux/models/text_encoder/clip_encoder/encoder_clip.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/clip_encoder/encoder_clip.py
@@ -1,9 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.clip_encoder.clip_encoder_layer import (
-    CLIPEncoderLayer,
-)
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder_layer import CLIPEncoderLayer
 
 
 class EncoderCLIP(nn.Module):
diff --git a/src/mflux/models/text_encoder/prompt_encoder.py b/src/mflux/models/flux/model/flux_text_encoder/prompt_encoder.py
similarity index 74%
rename from src/mflux/models/text_encoder/prompt_encoder.py
rename to src/mflux/models/flux/model/flux_text_encoder/prompt_encoder.py
index 812f67f..5b702d6 100644
--- a/src/mflux/models/text_encoder/prompt_encoder.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/prompt_encoder.py
@@ -1,9 +1,9 @@
 import mlx.core as mx
 
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.tokenizer.clip_tokenizer import TokenizerCLIP
-from mflux.tokenizer.t5_tokenizer import TokenizerT5
+from mflux.models.flux.model.flux_text_encoder.clip_encoder.clip_encoder import CLIPEncoder
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_encoder import T5Encoder
+from mflux.models.flux.tokenizer.clip_tokenizer import TokenizerCLIP
+from mflux.models.flux.tokenizer.t5_tokenizer import TokenizerT5
 
 
 class PromptEncoder:
diff --git a/src/mflux/flux_tools/depth/__init__.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/__init__.py
similarity index 100%
rename from src/mflux/flux_tools/depth/__init__.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/__init__.py
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_attention.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_attention.py
similarity index 72%
rename from src/mflux/models/text_encoder/t5_encoder/t5_attention.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_attention.py
index 9280da5..23b2702 100644
--- a/src/mflux/models/text_encoder/t5_encoder/t5_attention.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_attention.py
@@ -1,10 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.t5_encoder.t5_layer_norm import T5LayerNorm
-from mflux.models.text_encoder.t5_encoder.t5_self_attention import (
-    T5SelfAttention,
-)
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_layer_norm import T5LayerNorm
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_self_attention import T5SelfAttention
 
 
 class T5Attention(nn.Module):
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_block.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_block.py
similarity index 69%
rename from src/mflux/models/text_encoder/t5_encoder/t5_block.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_block.py
index 64c789a..9483fbd 100644
--- a/src/mflux/models/text_encoder/t5_encoder/t5_block.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_block.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.t5_encoder.t5_attention import T5Attention
-from mflux.models.text_encoder.t5_encoder.t5_feed_forward import T5FeedForward
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_attention import T5Attention
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_feed_forward import T5FeedForward
 
 
 class T5Block(nn.Module):
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_dense_relu_dense.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_dense_relu_dense.py
similarity index 100%
rename from src/mflux/models/text_encoder/t5_encoder/t5_dense_relu_dense.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_dense_relu_dense.py
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_encoder.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_encoder.py
similarity index 76%
rename from src/mflux/models/text_encoder/t5_encoder/t5_encoder.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_encoder.py
index b053fc1..5e9fa02 100644
--- a/src/mflux/models/text_encoder/t5_encoder/t5_encoder.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_encoder.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.t5_encoder.t5_block import T5Block
-from mflux.models.text_encoder.t5_encoder.t5_layer_norm import T5LayerNorm
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_block import T5Block
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_layer_norm import T5LayerNorm
 
 
 class T5Encoder(nn.Module):
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_feed_forward.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_feed_forward.py
similarity index 69%
rename from src/mflux/models/text_encoder/t5_encoder/t5_feed_forward.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_feed_forward.py
index 03d6f5b..9760199 100644
--- a/src/mflux/models/text_encoder/t5_encoder/t5_feed_forward.py
+++ b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_feed_forward.py
@@ -1,10 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.text_encoder.t5_encoder.t5_dense_relu_dense import (
-    T5DenseReluDense,
-)
-from mflux.models.text_encoder.t5_encoder.t5_layer_norm import T5LayerNorm
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_dense_relu_dense import T5DenseReluDense
+from mflux.models.flux.model.flux_text_encoder.t5_encoder.t5_layer_norm import T5LayerNorm
 
 
 class T5FeedForward(nn.Module):
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_layer_norm.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_layer_norm.py
similarity index 100%
rename from src/mflux/models/text_encoder/t5_encoder/t5_layer_norm.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_layer_norm.py
diff --git a/src/mflux/models/text_encoder/t5_encoder/t5_self_attention.py b/src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_self_attention.py
similarity index 100%
rename from src/mflux/models/text_encoder/t5_encoder/t5_self_attention.py
rename to src/mflux/models/flux/model/flux_text_encoder/t5_encoder/t5_self_attention.py
diff --git a/src/mflux/flux_tools/fill/__init__.py b/src/mflux/models/flux/model/flux_transformer/__init__.py
similarity index 100%
rename from src/mflux/flux_tools/fill/__init__.py
rename to src/mflux/models/flux/model/flux_transformer/__init__.py
diff --git a/src/mflux/models/transformer/ada_layer_norm_continuous.py b/src/mflux/models/flux/model/flux_transformer/ada_layer_norm_continuous.py
similarity index 100%
rename from src/mflux/models/transformer/ada_layer_norm_continuous.py
rename to src/mflux/models/flux/model/flux_transformer/ada_layer_norm_continuous.py
diff --git a/src/mflux/models/transformer/ada_layer_norm_zero.py b/src/mflux/models/flux/model/flux_transformer/ada_layer_norm_zero.py
similarity index 100%
rename from src/mflux/models/transformer/ada_layer_norm_zero.py
rename to src/mflux/models/flux/model/flux_transformer/ada_layer_norm_zero.py
diff --git a/src/mflux/models/transformer/ada_layer_norm_zero_single.py b/src/mflux/models/flux/model/flux_transformer/ada_layer_norm_zero_single.py
similarity index 100%
rename from src/mflux/models/transformer/ada_layer_norm_zero_single.py
rename to src/mflux/models/flux/model/flux_transformer/ada_layer_norm_zero_single.py
diff --git a/src/mflux/flux_tools/redux/__init__.py b/src/mflux/models/flux/model/flux_transformer/common/__init__.py
similarity index 100%
rename from src/mflux/flux_tools/redux/__init__.py
rename to src/mflux/models/flux/model/flux_transformer/common/__init__.py
diff --git a/src/mflux/models/flux/model/flux_transformer/common/attention_utils.py b/src/mflux/models/flux/model/flux_transformer/common/attention_utils.py
new file mode 100644
index 0000000..40241c8
--- /dev/null
+++ b/src/mflux/models/flux/model/flux_transformer/common/attention_utils.py
@@ -0,0 +1,98 @@
+import mlx.core as mx
+from mlx import nn
+from mlx.core.fast import scaled_dot_product_attention
+
+
+class AttentionUtils:
+    @staticmethod
+    def process_qkv(
+        hidden_states: mx.array,
+        to_q: nn.Linear,
+        to_k: nn.Linear,
+        to_v: nn.Linear,
+        norm_q: nn.RMSNorm,
+        norm_k: nn.RMSNorm,
+        num_heads: int,
+        head_dim: int,
+    ):
+        batch_size = hidden_states.shape[0]
+        seq_len = hidden_states.shape[1]
+
+        query = to_q(hidden_states)
+        key = to_k(hidden_states)
+        value = to_v(hidden_states)
+
+        # Reshape [B, S, H*D] -> [B, H, S, D]
+        query = mx.transpose(mx.reshape(query, (batch_size, seq_len, num_heads, head_dim)), (0, 2, 1, 3))
+        key = mx.transpose(mx.reshape(key, (batch_size, seq_len, num_heads, head_dim)), (0, 2, 1, 3))
+        value = mx.transpose(mx.reshape(value, (batch_size, seq_len, num_heads, head_dim)), (0, 2, 1, 3))
+
+        # Apply normalization in float32 for stability, then cast back (like working version)
+        q_dtype = query.dtype
+        k_dtype = key.dtype
+        query = norm_q(query.astype(mx.float32)).astype(q_dtype)
+        key = norm_k(key.astype(mx.float32)).astype(k_dtype)
+
+        return query, key, value
+
+    @staticmethod
+    def compute_attention(
+        query: mx.array,
+        key: mx.array,
+        value: mx.array,
+        batch_size: int,
+        num_heads: int,
+        head_dim: int,
+        mask: mx.array | None = None,
+    ) -> mx.array:
+        scale = 1 / mx.sqrt(query.shape[-1])
+        hidden_states = scaled_dot_product_attention(query, key, value, scale=scale, mask=mask)
+        hidden_states = mx.transpose(hidden_states, (0, 2, 1, 3))
+        hidden_states = mx.reshape(hidden_states, (batch_size, -1, num_heads * head_dim))
+        return hidden_states
+
+    @staticmethod
+    def convert_key_padding_mask_to_additive_mask(
+        mask: mx.array | None,
+        joint_seq_len: int,
+        txt_seq_len: int,
+    ) -> mx.array | None:
+        if mask is None:
+            return None
+
+        bsz = mask.shape[0]
+        img_seq_len = joint_seq_len - txt_seq_len
+
+        # Create joint mask: [text_mask, image_ones]
+        ones_img = mx.ones((bsz, img_seq_len), dtype=mx.float32)
+        joint_mask = mx.concatenate([mask.astype(mx.float32), ones_img], axis=1)
+
+        # Convert to additive mask for scaled_dot_product_attention
+        additive = (1.0 - joint_mask) * (-1e9)
+        return additive.reshape((additive.shape[0], 1, 1, additive.shape[1]))
+
+    @staticmethod
+    def apply_rope(xq: mx.array, xk: mx.array, freqs_cis: mx.array):
+        xq_ = xq.astype(mx.float32).reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.astype(mx.float32).reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).astype(mx.float32), xk_out.reshape(*xk.shape).astype(mx.float32)
+
+    @staticmethod
+    def apply_rope_bshd(xq: mx.array, xk: mx.array, cos: mx.array, sin: mx.array):
+        xq_f = xq.astype(mx.float32)
+        xk_f = xk.astype(mx.float32)
+        cos_b = cos.reshape(1, cos.shape[0], 1, cos.shape[1])
+        sin_b = sin.reshape(1, sin.shape[0], 1, sin.shape[1])
+
+        def mix(x: mx.array) -> mx.array:
+            x2 = x.reshape(*x.shape[:-1], -1, 2)
+            real = x2[..., 0]
+            imag = x2[..., 1]
+            out0 = real * cos_b + (-imag) * sin_b
+            out1 = imag * cos_b + (real) * sin_b
+            out2 = mx.stack([out0, out1], axis=-1)
+            return out2.reshape(*x.shape)
+
+        return mix(xq_f).astype(mx.float32), mix(xk_f).astype(mx.float32)
diff --git a/src/mflux/models/transformer/embed_nd.py b/src/mflux/models/flux/model/flux_transformer/embed_nd.py
similarity index 100%
rename from src/mflux/models/transformer/embed_nd.py
rename to src/mflux/models/flux/model/flux_transformer/embed_nd.py
diff --git a/src/mflux/models/transformer/feed_forward.py b/src/mflux/models/flux/model/flux_transformer/feed_forward.py
similarity index 100%
rename from src/mflux/models/transformer/feed_forward.py
rename to src/mflux/models/flux/model/flux_transformer/feed_forward.py
diff --git a/src/mflux/models/transformer/guidance_embedder.py b/src/mflux/models/flux/model/flux_transformer/guidance_embedder.py
similarity index 100%
rename from src/mflux/models/transformer/guidance_embedder.py
rename to src/mflux/models/flux/model/flux_transformer/guidance_embedder.py
diff --git a/src/mflux/models/transformer/joint_attention.py b/src/mflux/models/flux/model/flux_transformer/joint_attention.py
similarity index 96%
rename from src/mflux/models/transformer/joint_attention.py
rename to src/mflux/models/flux/model/flux_transformer/joint_attention.py
index a179d04..3472fe7 100644
--- a/src/mflux/models/transformer/joint_attention.py
+++ b/src/mflux/models/flux/model/flux_transformer/joint_attention.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.transformer.common.attention_utils import AttentionUtils
+from mflux.models.flux.model.flux_transformer.common.attention_utils import AttentionUtils
 
 
 class JointAttention(nn.Module):
diff --git a/src/mflux/models/transformer/joint_transformer_block.py b/src/mflux/models/flux/model/flux_transformer/joint_transformer_block.py
similarity index 92%
rename from src/mflux/models/transformer/joint_transformer_block.py
rename to src/mflux/models/flux/model/flux_transformer/joint_transformer_block.py
index f7b9836..789f1ae 100644
--- a/src/mflux/models/transformer/joint_transformer_block.py
+++ b/src/mflux/models/flux/model/flux_transformer/joint_transformer_block.py
@@ -1,9 +1,9 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.transformer.ada_layer_norm_zero import AdaLayerNormZero
-from mflux.models.transformer.feed_forward import FeedForward
-from mflux.models.transformer.joint_attention import JointAttention
+from mflux.models.flux.model.flux_transformer.ada_layer_norm_zero import AdaLayerNormZero
+from mflux.models.flux.model.flux_transformer.feed_forward import FeedForward
+from mflux.models.flux.model.flux_transformer.joint_attention import JointAttention
 
 
 class JointTransformerBlock(nn.Module):
diff --git a/src/mflux/models/transformer/single_block_attention.py b/src/mflux/models/flux/model/flux_transformer/single_block_attention.py
similarity index 93%
rename from src/mflux/models/transformer/single_block_attention.py
rename to src/mflux/models/flux/model/flux_transformer/single_block_attention.py
index a666850..a35beb3 100644
--- a/src/mflux/models/transformer/single_block_attention.py
+++ b/src/mflux/models/flux/model/flux_transformer/single_block_attention.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.transformer.common.attention_utils import AttentionUtils
+from mflux.models.flux.model.flux_transformer.common.attention_utils import AttentionUtils
 
 
 class SingleBlockAttention(nn.Module):
diff --git a/src/mflux/models/transformer/single_transformer_block.py b/src/mflux/models/flux/model/flux_transformer/single_transformer_block.py
similarity index 89%
rename from src/mflux/models/transformer/single_transformer_block.py
rename to src/mflux/models/flux/model/flux_transformer/single_transformer_block.py
index 2193a0e..c5f00e8 100644
--- a/src/mflux/models/transformer/single_transformer_block.py
+++ b/src/mflux/models/flux/model/flux_transformer/single_transformer_block.py
@@ -1,10 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.transformer.ada_layer_norm_zero_single import (
-    AdaLayerNormZeroSingle,
-)
-from mflux.models.transformer.single_block_attention import SingleBlockAttention
+from mflux.models.flux.model.flux_transformer.ada_layer_norm_zero_single import AdaLayerNormZeroSingle
+from mflux.models.flux.model.flux_transformer.single_block_attention import SingleBlockAttention
 
 
 class SingleTransformerBlock(nn.Module):
diff --git a/src/mflux/models/transformer/text_embedder.py b/src/mflux/models/flux/model/flux_transformer/text_embedder.py
similarity index 100%
rename from src/mflux/models/transformer/text_embedder.py
rename to src/mflux/models/flux/model/flux_transformer/text_embedder.py
diff --git a/src/mflux/models/transformer/time_text_embed.py b/src/mflux/models/flux/model/flux_transformer/time_text_embed.py
similarity index 85%
rename from src/mflux/models/transformer/time_text_embed.py
rename to src/mflux/models/flux/model/flux_transformer/time_text_embed.py
index eb6f24f..a979fd6 100644
--- a/src/mflux/models/transformer/time_text_embed.py
+++ b/src/mflux/models/flux/model/flux_transformer/time_text_embed.py
@@ -5,9 +5,9 @@ from mlx import nn
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.models.transformer.guidance_embedder import GuidanceEmbedder
-from mflux.models.transformer.text_embedder import TextEmbedder
-from mflux.models.transformer.timestep_embedder import TimestepEmbedder
+from mflux.models.flux.model.flux_transformer.guidance_embedder import GuidanceEmbedder
+from mflux.models.flux.model.flux_transformer.text_embedder import TextEmbedder
+from mflux.models.flux.model.flux_transformer.timestep_embedder import TimestepEmbedder
 
 
 class TimeTextEmbed(nn.Module):
diff --git a/src/mflux/models/transformer/timestep_embedder.py b/src/mflux/models/flux/model/flux_transformer/timestep_embedder.py
similarity index 100%
rename from src/mflux/models/transformer/timestep_embedder.py
rename to src/mflux/models/flux/model/flux_transformer/timestep_embedder.py
diff --git a/src/mflux/models/transformer/transformer.py b/src/mflux/models/flux/model/flux_transformer/transformer.py
similarity index 91%
rename from src/mflux/models/transformer/transformer.py
rename to src/mflux/models/flux/model/flux_transformer/transformer.py
index 0581647..bbb14d4 100644
--- a/src/mflux/models/transformer/transformer.py
+++ b/src/mflux/models/flux/model/flux_transformer/transformer.py
@@ -5,17 +5,11 @@ from mlx import nn
 
 from mflux.config.model_config import ModelConfig
 from mflux.config.runtime_config import RuntimeConfig
-from mflux.models.transformer.ada_layer_norm_continuous import (
-    AdaLayerNormContinuous,
-)
-from mflux.models.transformer.embed_nd import EmbedND
-from mflux.models.transformer.joint_transformer_block import (
-    JointTransformerBlock,
-)
-from mflux.models.transformer.single_transformer_block import (
-    SingleTransformerBlock,
-)
-from mflux.models.transformer.time_text_embed import TimeTextEmbed
+from mflux.models.flux.model.flux_transformer.ada_layer_norm_continuous import AdaLayerNormContinuous
+from mflux.models.flux.model.flux_transformer.embed_nd import EmbedND
+from mflux.models.flux.model.flux_transformer.joint_transformer_block import JointTransformerBlock
+from mflux.models.flux.model.flux_transformer.single_transformer_block import SingleTransformerBlock
+from mflux.models.flux.model.flux_transformer.time_text_embed import TimeTextEmbed
 
 
 class Transformer(nn.Module):
@@ -52,7 +46,7 @@ class Transformer(nn.Module):
         text_embeddings = Transformer.compute_text_embeddings(t, pooled_prompt_embeds, self.time_text_embed, config)
         image_rotary_embeddings = Transformer.compute_rotary_embeddings(prompt_embeds, self.pos_embed, config, kontext_image_ids)  # fmt: off
 
-        # 2. Run the joint transformer blocks
+        # 2. Run the joint flux_transformer blocks
         for idx, block in enumerate(self.transformer_blocks):
             encoder_hidden_states, hidden_states = self._apply_joint_transformer_block(
                 idx=idx,
@@ -67,7 +61,7 @@ class Transformer(nn.Module):
         # 3. Concat the hidden states
         hidden_states = mx.concatenate([encoder_hidden_states, hidden_states], axis=1)
 
-        # 4. Run the single transformer blocks
+        # 4. Run the single flux_transformer blocks
         for idx, block in enumerate(self.single_transformer_blocks):
             hidden_states = self._apply_single_transformer_block(
                 idx=idx,
@@ -95,7 +89,7 @@ class Transformer(nn.Module):
         image_rotary_embeddings: mx.array,
         controlnet_single_block_samples: list[mx.array],
     ) -> mx.array:
-        # 1. Apply single transformer block
+        # 1. Apply single flux_transformer block
         hidden_states = block(
             hidden_states=hidden_states,
             text_embeddings=text_embeddings,
@@ -118,7 +112,7 @@ class Transformer(nn.Module):
         image_rotary_embeddings: mx.array,
         controlnet_block_samples: list[mx.array],
     ) -> mx.array:
-        # 1. Apply joint transformer block
+        # 1. Apply joint flux_transformer block
         encoder_hidden_states, hidden_states = block(
             hidden_states=hidden_states,
             encoder_hidden_states=encoder_hidden_states,
diff --git a/src/mflux/kontext/__init__.py b/src/mflux/models/flux/model/flux_vae/__init__.py
similarity index 100%
rename from src/mflux/kontext/__init__.py
rename to src/mflux/models/flux/model/flux_vae/__init__.py
diff --git a/src/mflux/kontext/utils/__init__.py b/src/mflux/models/flux/model/flux_vae/common/__init__.py
similarity index 100%
rename from src/mflux/kontext/utils/__init__.py
rename to src/mflux/models/flux/model/flux_vae/common/__init__.py
diff --git a/src/mflux/models/vae/common/attention.py b/src/mflux/models/flux/model/flux_vae/common/attention.py
similarity index 100%
rename from src/mflux/models/vae/common/attention.py
rename to src/mflux/models/flux/model/flux_vae/common/attention.py
diff --git a/src/mflux/models/vae/common/resnet_block_2d.py b/src/mflux/models/flux/model/flux_vae/common/resnet_block_2d.py
similarity index 100%
rename from src/mflux/models/vae/common/resnet_block_2d.py
rename to src/mflux/models/flux/model/flux_vae/common/resnet_block_2d.py
diff --git a/src/mflux/models/vae/common/unet_mid_block.py b/src/mflux/models/flux/model/flux_vae/common/unet_mid_block.py
similarity index 81%
rename from src/mflux/models/vae/common/unet_mid_block.py
rename to src/mflux/models/flux/model/flux_vae/common/unet_mid_block.py
index f6af721..f0ffb9a 100644
--- a/src/mflux/models/vae/common/unet_mid_block.py
+++ b/src/mflux/models/flux/model/flux_vae/common/unet_mid_block.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.attention import Attention
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.common.attention import Attention
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
 
 
 class UnetMidBlock(nn.Module):
diff --git a/src/mflux/models/depth_pro/dino_v2/__init__.py b/src/mflux/models/flux/model/flux_vae/decoder/__init__.py
similarity index 100%
rename from src/mflux/models/depth_pro/dino_v2/__init__.py
rename to src/mflux/models/flux/model/flux_vae/decoder/__init__.py
diff --git a/src/mflux/models/vae/decoder/conv_in.py b/src/mflux/models/flux/model/flux_vae/decoder/conv_in.py
similarity index 100%
rename from src/mflux/models/vae/decoder/conv_in.py
rename to src/mflux/models/flux/model/flux_vae/decoder/conv_in.py
diff --git a/src/mflux/models/vae/decoder/conv_norm_out.py b/src/mflux/models/flux/model/flux_vae/decoder/conv_norm_out.py
similarity index 100%
rename from src/mflux/models/vae/decoder/conv_norm_out.py
rename to src/mflux/models/flux/model/flux_vae/decoder/conv_norm_out.py
diff --git a/src/mflux/models/vae/decoder/conv_out.py b/src/mflux/models/flux/model/flux_vae/decoder/conv_out.py
similarity index 100%
rename from src/mflux/models/vae/decoder/conv_out.py
rename to src/mflux/models/flux/model/flux_vae/decoder/conv_out.py
diff --git a/src/mflux/models/vae/decoder/decoder.py b/src/mflux/models/flux/model/flux_vae/decoder/decoder.py
similarity index 86%
rename from src/mflux/models/vae/decoder/decoder.py
rename to src/mflux/models/flux/model/flux_vae/decoder/decoder.py
index 87e8bef..e06a83f 100644
--- a/src/mflux/models/vae/decoder/decoder.py
+++ b/src/mflux/models/flux/model/flux_vae/decoder/decoder.py
@@ -1,13 +1,13 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.unet_mid_block import UnetMidBlock
-from mflux.models.vae.decoder.conv_in import ConvIn
-from mflux.models.vae.decoder.conv_norm_out import ConvNormOut
-from mflux.models.vae.decoder.conv_out import ConvOut
-from mflux.models.vae.decoder.up_block_1_or_2 import UpBlock1Or2
-from mflux.models.vae.decoder.up_block_3 import UpBlock3
-from mflux.models.vae.decoder.up_block_4 import UpBlock4
+from mflux.models.flux.model.flux_vae.common.unet_mid_block import UnetMidBlock
+from mflux.models.flux.model.flux_vae.decoder.conv_in import ConvIn
+from mflux.models.flux.model.flux_vae.decoder.conv_norm_out import ConvNormOut
+from mflux.models.flux.model.flux_vae.decoder.conv_out import ConvOut
+from mflux.models.flux.model.flux_vae.decoder.up_block_1_or_2 import UpBlock1Or2
+from mflux.models.flux.model.flux_vae.decoder.up_block_3 import UpBlock3
+from mflux.models.flux.model.flux_vae.decoder.up_block_4 import UpBlock4
 
 
 class Decoder(nn.Module):
diff --git a/src/mflux/models/vae/decoder/up_block_1_or_2.py b/src/mflux/models/flux/model/flux_vae/decoder/up_block_1_or_2.py
similarity index 85%
rename from src/mflux/models/vae/decoder/up_block_1_or_2.py
rename to src/mflux/models/flux/model/flux_vae/decoder/up_block_1_or_2.py
index 9de27af..e2f4aa3 100644
--- a/src/mflux/models/vae/decoder/up_block_1_or_2.py
+++ b/src/mflux/models/flux/model/flux_vae/decoder/up_block_1_or_2.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
-from mflux.models.vae.decoder.up_sampler import UpSampler
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.decoder.up_sampler import UpSampler
 
 
 class UpBlock1Or2(nn.Module):
diff --git a/src/mflux/models/vae/decoder/up_block_3.py b/src/mflux/models/flux/model/flux_vae/decoder/up_block_3.py
similarity index 86%
rename from src/mflux/models/vae/decoder/up_block_3.py
rename to src/mflux/models/flux/model/flux_vae/decoder/up_block_3.py
index b7c45ff..364c5b9 100644
--- a/src/mflux/models/vae/decoder/up_block_3.py
+++ b/src/mflux/models/flux/model/flux_vae/decoder/up_block_3.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
-from mflux.models.vae.decoder.up_sampler import UpSampler
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.decoder.up_sampler import UpSampler
 
 
 class UpBlock3(nn.Module):
diff --git a/src/mflux/models/vae/decoder/up_block_4.py b/src/mflux/models/flux/model/flux_vae/decoder/up_block_4.py
similarity index 90%
rename from src/mflux/models/vae/decoder/up_block_4.py
rename to src/mflux/models/flux/model/flux_vae/decoder/up_block_4.py
index 4d9b6a9..4c52736 100644
--- a/src/mflux/models/vae/decoder/up_block_4.py
+++ b/src/mflux/models/flux/model/flux_vae/decoder/up_block_4.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
 
 
 class UpBlock4(nn.Module):
diff --git a/src/mflux/models/vae/decoder/up_sampler.py b/src/mflux/models/flux/model/flux_vae/decoder/up_sampler.py
similarity index 100%
rename from src/mflux/models/vae/decoder/up_sampler.py
rename to src/mflux/models/flux/model/flux_vae/decoder/up_sampler.py
diff --git a/src/mflux/models/redux_encoder/__init__.py b/src/mflux/models/flux/model/flux_vae/encoder/__init__.py
similarity index 100%
rename from src/mflux/models/redux_encoder/__init__.py
rename to src/mflux/models/flux/model/flux_vae/encoder/__init__.py
diff --git a/src/mflux/models/vae/encoder/conv_in.py b/src/mflux/models/flux/model/flux_vae/encoder/conv_in.py
similarity index 100%
rename from src/mflux/models/vae/encoder/conv_in.py
rename to src/mflux/models/flux/model/flux_vae/encoder/conv_in.py
diff --git a/src/mflux/models/vae/encoder/conv_norm_out.py b/src/mflux/models/flux/model/flux_vae/encoder/conv_norm_out.py
similarity index 100%
rename from src/mflux/models/vae/encoder/conv_norm_out.py
rename to src/mflux/models/flux/model/flux_vae/encoder/conv_norm_out.py
diff --git a/src/mflux/models/vae/encoder/conv_out.py b/src/mflux/models/flux/model/flux_vae/encoder/conv_out.py
similarity index 100%
rename from src/mflux/models/vae/encoder/conv_out.py
rename to src/mflux/models/flux/model/flux_vae/encoder/conv_out.py
diff --git a/src/mflux/models/vae/encoder/down_block_1.py b/src/mflux/models/flux/model/flux_vae/encoder/down_block_1.py
similarity index 82%
rename from src/mflux/models/vae/encoder/down_block_1.py
rename to src/mflux/models/flux/model/flux_vae/encoder/down_block_1.py
index 2abd5a8..0b9faa0 100644
--- a/src/mflux/models/vae/encoder/down_block_1.py
+++ b/src/mflux/models/flux/model/flux_vae/encoder/down_block_1.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
-from mflux.models.vae.encoder.down_sampler import DownSampler
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.encoder.down_sampler import DownSampler
 
 
 class DownBlock1(nn.Module):
diff --git a/src/mflux/models/vae/encoder/down_block_2.py b/src/mflux/models/flux/model/flux_vae/encoder/down_block_2.py
similarity index 83%
rename from src/mflux/models/vae/encoder/down_block_2.py
rename to src/mflux/models/flux/model/flux_vae/encoder/down_block_2.py
index 88beddc..8d2d93c 100644
--- a/src/mflux/models/vae/encoder/down_block_2.py
+++ b/src/mflux/models/flux/model/flux_vae/encoder/down_block_2.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
-from mflux.models.vae.encoder.down_sampler import DownSampler
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.encoder.down_sampler import DownSampler
 
 
 class DownBlock2(nn.Module):
diff --git a/src/mflux/models/vae/encoder/down_block_3.py b/src/mflux/models/flux/model/flux_vae/encoder/down_block_3.py
similarity index 83%
rename from src/mflux/models/vae/encoder/down_block_3.py
rename to src/mflux/models/flux/model/flux_vae/encoder/down_block_3.py
index 80cca20..452fbc5 100644
--- a/src/mflux/models/vae/encoder/down_block_3.py
+++ b/src/mflux/models/flux/model/flux_vae/encoder/down_block_3.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
-from mflux.models.vae.encoder.down_sampler import DownSampler
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.encoder.down_sampler import DownSampler
 
 
 class DownBlock3(nn.Module):
diff --git a/src/mflux/models/vae/encoder/down_block_4.py b/src/mflux/models/flux/model/flux_vae/encoder/down_block_4.py
similarity index 87%
rename from src/mflux/models/vae/encoder/down_block_4.py
rename to src/mflux/models/flux/model/flux_vae/encoder/down_block_4.py
index 95c9bf5..6eee6f5 100644
--- a/src/mflux/models/vae/encoder/down_block_4.py
+++ b/src/mflux/models/flux/model/flux_vae/encoder/down_block_4.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.resnet_block_2d import ResnetBlock2D
+from mflux.models.flux.model.flux_vae.common.resnet_block_2d import ResnetBlock2D
 
 
 class DownBlock4(nn.Module):
diff --git a/src/mflux/models/vae/encoder/down_sampler.py b/src/mflux/models/flux/model/flux_vae/encoder/down_sampler.py
similarity index 100%
rename from src/mflux/models/vae/encoder/down_sampler.py
rename to src/mflux/models/flux/model/flux_vae/encoder/down_sampler.py
diff --git a/src/mflux/models/vae/encoder/encoder.py b/src/mflux/models/flux/model/flux_vae/encoder/encoder.py
similarity index 56%
rename from src/mflux/models/vae/encoder/encoder.py
rename to src/mflux/models/flux/model/flux_vae/encoder/encoder.py
index c3b3859..d55c638 100644
--- a/src/mflux/models/vae/encoder/encoder.py
+++ b/src/mflux/models/flux/model/flux_vae/encoder/encoder.py
@@ -1,14 +1,14 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.common.unet_mid_block import UnetMidBlock
-from mflux.models.vae.encoder.conv_in import ConvIn
-from mflux.models.vae.encoder.conv_norm_out import ConvNormOut
-from mflux.models.vae.encoder.conv_out import ConvOut
-from mflux.models.vae.encoder.down_block_1 import DownBlock1
-from mflux.models.vae.encoder.down_block_2 import DownBlock2
-from mflux.models.vae.encoder.down_block_3 import DownBlock3
-from mflux.models.vae.encoder.down_block_4 import DownBlock4
+from mflux.models.flux.model.flux_vae.common.unet_mid_block import UnetMidBlock
+from mflux.models.flux.model.flux_vae.encoder.conv_in import ConvIn
+from mflux.models.flux.model.flux_vae.encoder.conv_norm_out import ConvNormOut
+from mflux.models.flux.model.flux_vae.encoder.conv_out import ConvOut
+from mflux.models.flux.model.flux_vae.encoder.down_block_1 import DownBlock1
+from mflux.models.flux.model.flux_vae.encoder.down_block_2 import DownBlock2
+from mflux.models.flux.model.flux_vae.encoder.down_block_3 import DownBlock3
+from mflux.models.flux.model.flux_vae.encoder.down_block_4 import DownBlock4
 
 
 class Encoder(nn.Module):
diff --git a/src/mflux/models/vae/vae.py b/src/mflux/models/flux/model/flux_vae/vae.py
similarity index 81%
rename from src/mflux/models/vae/vae.py
rename to src/mflux/models/flux/model/flux_vae/vae.py
index 9ac7af5..7d8d833 100644
--- a/src/mflux/models/vae/vae.py
+++ b/src/mflux/models/flux/model/flux_vae/vae.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.vae.decoder.decoder import Decoder
-from mflux.models.vae.encoder.encoder import Encoder
+from mflux.models.flux.model.flux_vae.decoder.decoder import Decoder
+from mflux.models.flux.model.flux_vae.encoder.encoder import Encoder
 
 
 class VAE(nn.Module):
diff --git a/src/mflux/models/siglip_vision_transformer/__init__.py b/src/mflux/models/flux/model/redux_encoder/__init__.py
similarity index 100%
rename from src/mflux/models/siglip_vision_transformer/__init__.py
rename to src/mflux/models/flux/model/redux_encoder/__init__.py
diff --git a/src/mflux/models/redux_encoder/redux_encoder.py b/src/mflux/models/flux/model/redux_encoder/redux_encoder.py
similarity index 100%
rename from src/mflux/models/redux_encoder/redux_encoder.py
rename to src/mflux/models/flux/model/redux_encoder/redux_encoder.py
diff --git a/src/mflux/models/text_encoder/__init__.py b/src/mflux/models/flux/model/siglip_vision_transformer/__init__.py
similarity index 100%
rename from src/mflux/models/text_encoder/__init__.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/__init__.py
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_encoder.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_encoder.py
similarity index 79%
rename from src/mflux/models/siglip_vision_transformer/siglip_encoder.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_encoder.py
index 34059ae..fb4e3c5 100644
--- a/src/mflux/models/siglip_vision_transformer/siglip_encoder.py
+++ b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_encoder.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.siglip_vision_transformer.siglip_encoder_layer import SiglipEncoderLayer
+from mflux.models.flux.model.siglip_vision_transformer.siglip_encoder_layer import SiglipEncoderLayer
 
 
 class SiglipEncoder(nn.Module):
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_encoder_layer.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_encoder_layer.py
similarity index 81%
rename from src/mflux/models/siglip_vision_transformer/siglip_encoder_layer.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_encoder_layer.py
index c47a78c..de95f5f 100644
--- a/src/mflux/models/siglip_vision_transformer/siglip_encoder_layer.py
+++ b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_encoder_layer.py
@@ -1,8 +1,8 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.siglip_vision_transformer.siglip_mlp import SiglipMLP
-from mflux.models.siglip_vision_transformer.siglip_sdpa_attention import SiglipSdpaAttention
+from mflux.models.flux.model.siglip_vision_transformer.siglip_mlp import SiglipMLP
+from mflux.models.flux.model.siglip_vision_transformer.siglip_sdpa_attention import SiglipSdpaAttention
 
 
 class SiglipEncoderLayer(nn.Module):
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_mlp.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_mlp.py
similarity index 100%
rename from src/mflux/models/siglip_vision_transformer/siglip_mlp.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_mlp.py
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_multi_head_attention_pooling_head.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_multi_head_attention_pooling_head.py
similarity index 90%
rename from src/mflux/models/siglip_vision_transformer/siglip_multi_head_attention_pooling_head.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_multi_head_attention_pooling_head.py
index dfb1da2..cc1929a 100644
--- a/src/mflux/models/siglip_vision_transformer/siglip_multi_head_attention_pooling_head.py
+++ b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_multi_head_attention_pooling_head.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.siglip_vision_transformer.siglip_mlp import SiglipMLP
+from mflux.models.flux.model.siglip_vision_transformer.siglip_mlp import SiglipMLP
 
 
 class SiglipMultiHeadAttentionPoolingHead(nn.Module):
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_sdpa_attention.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_sdpa_attention.py
similarity index 100%
rename from src/mflux/models/siglip_vision_transformer/siglip_sdpa_attention.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_sdpa_attention.py
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_vision_embeddings.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_vision_embeddings.py
similarity index 100%
rename from src/mflux/models/siglip_vision_transformer/siglip_vision_embeddings.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_vision_embeddings.py
diff --git a/src/mflux/models/siglip_vision_transformer/siglip_vision_transformer.py b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_vision_transformer.py
similarity index 69%
rename from src/mflux/models/siglip_vision_transformer/siglip_vision_transformer.py
rename to src/mflux/models/flux/model/siglip_vision_transformer/siglip_vision_transformer.py
index a7d9e88..abbcbea 100644
--- a/src/mflux/models/siglip_vision_transformer/siglip_vision_transformer.py
+++ b/src/mflux/models/flux/model/siglip_vision_transformer/siglip_vision_transformer.py
@@ -1,11 +1,11 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.siglip_vision_transformer.siglip_encoder import SiglipEncoder
-from mflux.models.siglip_vision_transformer.siglip_multi_head_attention_pooling_head import (
+from mflux.models.flux.model.siglip_vision_transformer.siglip_encoder import SiglipEncoder
+from mflux.models.flux.model.siglip_vision_transformer.siglip_multi_head_attention_pooling_head import (
     SiglipMultiHeadAttentionPoolingHead,
 )
-from mflux.models.siglip_vision_transformer.siglip_vision_embeddings import SiglipVisionEmbeddings
+from mflux.models.flux.model.siglip_vision_transformer.siglip_vision_embeddings import SiglipVisionEmbeddings
 
 
 class SiglipVisionTransformer(nn.Module):
diff --git a/src/mflux/models/text_encoder/clip_encoder/__init__.py b/src/mflux/models/flux/tokenizer/__init__.py
similarity index 100%
rename from src/mflux/models/text_encoder/clip_encoder/__init__.py
rename to src/mflux/models/flux/tokenizer/__init__.py
diff --git a/src/mflux/tokenizer/clip_tokenizer.py b/src/mflux/models/flux/tokenizer/clip_tokenizer.py
similarity index 100%
rename from src/mflux/tokenizer/clip_tokenizer.py
rename to src/mflux/models/flux/tokenizer/clip_tokenizer.py
diff --git a/src/mflux/tokenizer/t5_tokenizer.py b/src/mflux/models/flux/tokenizer/t5_tokenizer.py
similarity index 100%
rename from src/mflux/tokenizer/t5_tokenizer.py
rename to src/mflux/models/flux/tokenizer/t5_tokenizer.py
diff --git a/src/mflux/tokenizer/tokenizer_handler.py b/src/mflux/models/flux/tokenizer/tokenizer_handler.py
similarity index 89%
rename from src/mflux/tokenizer/tokenizer_handler.py
rename to src/mflux/models/flux/tokenizer/tokenizer_handler.py
index d90accd..e144025 100644
--- a/src/mflux/tokenizer/tokenizer_handler.py
+++ b/src/mflux/models/flux/tokenizer/tokenizer_handler.py
@@ -2,8 +2,8 @@ from pathlib import Path
 
 import transformers
 
-from mflux.tokenizer.clip_tokenizer import TokenizerCLIP
-from mflux.weights.download import snapshot_download
+from mflux.models.flux.tokenizer.clip_tokenizer import TokenizerCLIP
+from mflux.utils.download import snapshot_download
 
 
 class TokenizerHandler:
diff --git a/src/mflux/models/text_encoder/t5_encoder/__init__.py b/src/mflux/models/flux/variants/__init__.py
similarity index 100%
rename from src/mflux/models/text_encoder/t5_encoder/__init__.py
rename to src/mflux/models/flux/variants/__init__.py
diff --git a/src/mflux/models/flux/variants/concept_attention/__init__.py b/src/mflux/models/flux/variants/concept_attention/__init__.py
new file mode 100644
index 0000000..f2779a2
--- /dev/null
+++ b/src/mflux/models/flux/variants/concept_attention/__init__.py
@@ -0,0 +1 @@
+# Concept Attention flux_transformer models
diff --git a/src/mflux/community/concept_attention/attention_data.py b/src/mflux/models/flux/variants/concept_attention/attention_data.py
similarity index 94%
rename from src/mflux/community/concept_attention/attention_data.py
rename to src/mflux/models/flux/variants/concept_attention/attention_data.py
index 13697fa..0c46e35 100644
--- a/src/mflux/community/concept_attention/attention_data.py
+++ b/src/mflux/models/flux/variants/concept_attention/attention_data.py
@@ -5,9 +5,7 @@ from typing import List
 import mlx.core as mx
 import PIL.Image
 
-from mflux.community.concept_attention.joint_transformer_block_concept import (
-    LayerAttentionData,
-)
+from mflux.models.flux.variants.concept_attention.joint_transformer_block_concept import LayerAttentionData
 
 
 @dataclass
diff --git a/src/mflux/community/concept_attention/concept_util.py b/src/mflux/models/flux/variants/concept_attention/concept_util.py
similarity index 96%
rename from src/mflux/community/concept_attention/concept_util.py
rename to src/mflux/models/flux/variants/concept_attention/concept_util.py
index d5c1e6b..3896b48 100644
--- a/src/mflux/community/concept_attention/concept_util.py
+++ b/src/mflux/models/flux/variants/concept_attention/concept_util.py
@@ -3,10 +3,7 @@ import mlx.core as mx
 import numpy as np
 import PIL.Image
 
-from mflux.community.concept_attention.attention_data import (
-    ConceptHeatmap,
-    GenerationAttentionData,
-)
+from mflux.models.flux.variants.concept_attention.attention_data import ConceptHeatmap, GenerationAttentionData
 
 
 class ConceptUtil:
diff --git a/src/mflux/community/concept_attention/flux_concept.py b/src/mflux/models/flux/variants/concept_attention/flux_concept.py
similarity index 90%
rename from src/mflux/community/concept_attention/flux_concept.py
rename to src/mflux/models/flux/variants/concept_attention/flux_concept.py
index 6fd1171..7f985b8 100644
--- a/src/mflux/community/concept_attention/flux_concept.py
+++ b/src/mflux/models/flux/variants/concept_attention/flux_concept.py
@@ -3,21 +3,19 @@ from mlx import nn
 from tqdm import tqdm
 
 from mflux.callbacks.callbacks import Callbacks
-from mflux.community.concept_attention.attention_data import (
-    GenerationAttentionData,
-)
-from mflux.community.concept_attention.concept_util import ConceptUtil
-from mflux.community.concept_attention.transformer_concept import TransformerConcept
 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.flux.flux_initializer import FluxInitializer
 from mflux.latent_creator.latent_creator import Img2Img, LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.vae.vae import VAE
+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_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
diff --git a/src/mflux/community/concept_attention/flux_concept_from_image.py b/src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py
similarity index 91%
rename from src/mflux/community/concept_attention/flux_concept_from_image.py
rename to src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py
index 23cb35c..410be6e 100644
--- a/src/mflux/community/concept_attention/flux_concept_from_image.py
+++ b/src/mflux/models/flux/variants/concept_attention/flux_concept_from_image.py
@@ -3,19 +3,19 @@ from mlx import nn
 from tqdm import tqdm
 
 from mflux.callbacks.callbacks import Callbacks
-from mflux.community.concept_attention.attention_data import GenerationAttentionData
-from mflux.community.concept_attention.concept_util import ConceptUtil
-from mflux.community.concept_attention.transformer_concept import TransformerConcept
 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.flux.flux_initializer import FluxInitializer
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.vae.vae import VAE
+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_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
diff --git a/src/mflux/community/concept_attention/joint_attention_concept.py b/src/mflux/models/flux/variants/concept_attention/joint_attention_concept.py
similarity index 98%
rename from src/mflux/community/concept_attention/joint_attention_concept.py
rename to src/mflux/models/flux/variants/concept_attention/joint_attention_concept.py
index 4cc1696..47ce17c 100644
--- a/src/mflux/community/concept_attention/joint_attention_concept.py
+++ b/src/mflux/models/flux/variants/concept_attention/joint_attention_concept.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.models.transformer.common.attention_utils import AttentionUtils
+from mflux.models.flux.model.flux_transformer.common.attention_utils import AttentionUtils
 
 
 class JointAttentionConcept(nn.Module):
diff --git a/src/mflux/community/concept_attention/joint_transformer_block_concept.py b/src/mflux/models/flux/variants/concept_attention/joint_transformer_block_concept.py
similarity index 91%
rename from src/mflux/community/concept_attention/joint_transformer_block_concept.py
rename to src/mflux/models/flux/variants/concept_attention/joint_transformer_block_concept.py
index a51f61f..71e31d0 100644
--- a/src/mflux/community/concept_attention/joint_transformer_block_concept.py
+++ b/src/mflux/models/flux/variants/concept_attention/joint_transformer_block_concept.py
@@ -3,10 +3,10 @@ from dataclasses import dataclass
 import mlx.core as mx
 from mlx import nn
 
-from mflux.community.concept_attention.joint_attention_concept import JointAttentionConcept
-from mflux.models.transformer.ada_layer_norm_zero import AdaLayerNormZero
-from mflux.models.transformer.feed_forward import FeedForward
-from mflux.models.transformer.joint_transformer_block import JointTransformerBlock
+from mflux.models.flux.model.flux_transformer.ada_layer_norm_zero import AdaLayerNormZero
+from mflux.models.flux.model.flux_transformer.feed_forward import FeedForward
+from mflux.models.flux.model.flux_transformer.joint_transformer_block import JointTransformerBlock
+from mflux.models.flux.variants.concept_attention.joint_attention_concept import JointAttentionConcept
 
 
 @dataclass
diff --git a/src/mflux/community/concept_attention/transformer_concept.py b/src/mflux/models/flux/variants/concept_attention/transformer_concept.py
similarity index 82%
rename from src/mflux/community/concept_attention/transformer_concept.py
rename to src/mflux/models/flux/variants/concept_attention/transformer_concept.py
index 6b0bfa5..6e452c1 100644
--- a/src/mflux/community/concept_attention/transformer_concept.py
+++ b/src/mflux/models/flux/variants/concept_attention/transformer_concept.py
@@ -1,23 +1,15 @@
 import mlx.core as mx
 from mlx import nn
 
-from mflux.community.concept_attention.attention_data import (
-    TimestepAttentionData,
-)
-from mflux.community.concept_attention.joint_transformer_block_concept import (
-    JointTransformerBlockConcept,
-)
 from mflux.config.model_config import ModelConfig
 from mflux.config.runtime_config import RuntimeConfig
-from mflux.models.transformer.ada_layer_norm_continuous import (
-    AdaLayerNormContinuous,
-)
-from mflux.models.transformer.embed_nd import EmbedND
-from mflux.models.transformer.single_transformer_block import (
-    SingleTransformerBlock,
-)
-from mflux.models.transformer.time_text_embed import TimeTextEmbed
-from mflux.models.transformer.transformer import Transformer
+from mflux.models.flux.model.flux_transformer.ada_layer_norm_continuous import AdaLayerNormContinuous
+from mflux.models.flux.model.flux_transformer.embed_nd import EmbedND
+from mflux.models.flux.model.flux_transformer.single_transformer_block import SingleTransformerBlock
+from mflux.models.flux.model.flux_transformer.time_text_embed import TimeTextEmbed
+from mflux.models.flux.model.flux_transformer.transformer import Transformer
+from mflux.models.flux.variants.concept_attention.attention_data import TimestepAttentionData
+from mflux.models.flux.variants.concept_attention.joint_transformer_block_concept import JointTransformerBlockConcept
 
 
 class TransformerConcept(nn.Module):
@@ -56,7 +48,7 @@ class TransformerConcept(nn.Module):
         image_rotary_embeddings = Transformer.compute_rotary_embeddings(prompt_embeds, self.pos_embed, config, None)  # fmt: off
         image_rotary_embeddings_concept = Transformer.compute_rotary_embeddings(prompt_embeds_concept, self.pos_embed, config, None)  # fmt: off
 
-        # 2. Run the joint transformer blocks
+        # 2. Run the joint flux_transformer blocks
         attention_information = []
         for idx, block in enumerate(self.transformer_blocks):
             encoder_hidden_states, hidden_states, encoder_hidden_states_concept, attn = block(
@@ -74,7 +66,7 @@ class TransformerConcept(nn.Module):
         # 3. Concat the hidden states
         hidden_states = mx.concatenate([encoder_hidden_states, hidden_states], axis=1)
 
-        # 4. Run the single transformer blocks
+        # 4. Run the single flux_transformer blocks
         for idx, block in enumerate(self.single_transformer_blocks):
             hidden_states = block(
                 hidden_states=hidden_states,
diff --git a/src/mflux/models/transformer/__init__.py b/src/mflux/models/flux/variants/controlnet/__init__.py
similarity index 100%
rename from src/mflux/models/transformer/__init__.py
rename to src/mflux/models/flux/variants/controlnet/__init__.py
diff --git a/src/mflux/controlnet/controlnet_util.py b/src/mflux/models/flux/variants/controlnet/controlnet_util.py
similarity index 97%
rename from src/mflux/controlnet/controlnet_util.py
rename to src/mflux/models/flux/variants/controlnet/controlnet_util.py
index da38d70..964dcf3 100644
--- a/src/mflux/controlnet/controlnet_util.py
+++ b/src/mflux/models/flux/variants/controlnet/controlnet_util.py
@@ -5,7 +5,7 @@ import mlx.core as mx
 import numpy as np
 import PIL.Image
 
-from mflux.models.vae.vae import VAE
+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
 
diff --git a/src/mflux/controlnet/flux_controlnet.py b/src/mflux/models/flux/variants/controlnet/flux_controlnet.py
similarity index 89%
rename from src/mflux/controlnet/flux_controlnet.py
rename to src/mflux/models/flux/variants/controlnet/flux_controlnet.py
index 3a29d02..678a7cd 100644
--- a/src/mflux/controlnet/flux_controlnet.py
+++ b/src/mflux/models/flux/variants/controlnet/flux_controlnet.py
@@ -6,20 +6,20 @@ 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.controlnet.controlnet_util import ControlnetUtil
-from mflux.controlnet.transformer_controlnet import TransformerControlnet
 from mflux.error.exceptions import StopImageGenerationException
-from mflux.flux.flux_initializer import FluxInitializer
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.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.weights.model_saver import ModelSaver
 
 
 class Flux1Controlnet(nn.Module):
diff --git a/src/mflux/controlnet/transformer_controlnet.py b/src/mflux/models/flux/variants/controlnet/transformer_controlnet.py
similarity index 88%
rename from src/mflux/controlnet/transformer_controlnet.py
rename to src/mflux/models/flux/variants/controlnet/transformer_controlnet.py
index 9b0e3ec..39da5a0 100644
--- a/src/mflux/controlnet/transformer_controlnet.py
+++ b/src/mflux/models/flux/variants/controlnet/transformer_controlnet.py
@@ -3,11 +3,11 @@ from mlx import nn
 
 from mflux.config.model_config import ModelConfig
 from mflux.config.runtime_config import RuntimeConfig
-from mflux.models.transformer.embed_nd import EmbedND
-from mflux.models.transformer.joint_transformer_block import JointTransformerBlock
-from mflux.models.transformer.single_transformer_block import SingleTransformerBlock
-from mflux.models.transformer.time_text_embed import TimeTextEmbed
-from mflux.models.transformer.transformer import Transformer
+from mflux.models.flux.model.flux_transformer.embed_nd import EmbedND
+from mflux.models.flux.model.flux_transformer.joint_transformer_block import JointTransformerBlock
+from mflux.models.flux.model.flux_transformer.single_transformer_block import SingleTransformerBlock
+from mflux.models.flux.model.flux_transformer.time_text_embed import TimeTextEmbed
+from mflux.models.flux.model.flux_transformer.transformer import Transformer
 
 
 class TransformerControlnet(nn.Module):
@@ -44,7 +44,7 @@ class TransformerControlnet(nn.Module):
         text_embeddings = Transformer.compute_text_embeddings(t, pooled_prompt_embeds, self.time_text_embed, config)
         image_rotary_embeddings = Transformer.compute_rotary_embeddings(prompt_embeds, self.pos_embed, config, None)
 
-        # 2. Run the joint transformer blocks
+        # 2. Run the joint flux_transformer blocks
         controlnet_block_samples = []
         for idx, block in enumerate(self.transformer_blocks):
             encoder_hidden_states, hidden_states = self._apply_joint_transformer_block(
@@ -61,7 +61,7 @@ class TransformerControlnet(nn.Module):
         # 3. Concat the hidden states
         hidden_states = mx.concatenate([encoder_hidden_states, hidden_states], axis=1)
 
-        # 4. Run the single transformer blocks
+        # 4. Run the single flux_transformer blocks
         controlnet_single_block_samples = []
         for idx, block in enumerate(self.single_transformer_blocks):
             hidden_states = self._apply_single_transformer_block(
@@ -88,7 +88,7 @@ class TransformerControlnet(nn.Module):
         image_rotary_embeddings: mx.array,
         controlnet_single_block_samples: list[mx.array],
     ) -> mx.array:
-        # 1. Apply single transformer block
+        # 1. Apply single flux_transformer block
         hidden_states = block(
             hidden_states=hidden_states,
             text_embeddings=text_embeddings,
@@ -114,7 +114,7 @@ class TransformerControlnet(nn.Module):
         image_rotary_embeddings: mx.array,
         controlnet_block_samples: list[mx.array],
     ) -> tuple[mx.array, mx.array]:
-        # 1. Apply joint transformer block
+        # 1. Apply joint flux_transformer block
         encoder_hidden_states, hidden_states = block(
             hidden_states=hidden_states,
             encoder_hidden_states=encoder_hidden_states,
diff --git a/src/mflux/controlnet/weight_handler_controlnet.py b/src/mflux/models/flux/variants/controlnet/weight_handler_controlnet.py
similarity index 93%
rename from src/mflux/controlnet/weight_handler_controlnet.py
rename to src/mflux/models/flux/variants/controlnet/weight_handler_controlnet.py
index e4ab354..59a3bc3 100644
--- a/src/mflux/controlnet/weight_handler_controlnet.py
+++ b/src/mflux/models/flux/variants/controlnet/weight_handler_controlnet.py
@@ -4,9 +4,9 @@ from pathlib import Path
 import mlx.core as mx
 from mlx.utils import tree_unflatten
 
-from mflux.weights.download import snapshot_download
-from mflux.weights.weight_handler import MetaData
-from mflux.weights.weight_util import WeightUtil
+from mflux.models.flux.weights.weight_handler import MetaData
+from mflux.models.flux.weights.weight_util import WeightUtil
+from mflux.utils.download import snapshot_download
 
 
 class WeightHandlerControlnet:
diff --git a/src/mflux/models/transformer/common/__init__.py b/src/mflux/models/flux/variants/depth/__init__.py
similarity index 100%
rename from src/mflux/models/transformer/common/__init__.py
rename to src/mflux/models/flux/variants/depth/__init__.py
diff --git a/src/mflux/flux_tools/depth/depth_util.py b/src/mflux/models/flux/variants/depth/depth_util.py
similarity index 97%
rename from src/mflux/flux_tools/depth/depth_util.py
rename to src/mflux/models/flux/variants/depth/depth_util.py
index 89fa96c..8149af5 100644
--- a/src/mflux/flux_tools/depth/depth_util.py
+++ b/src/mflux/models/flux/variants/depth/depth_util.py
@@ -7,7 +7,7 @@ import PIL.Image
 
 from mflux.config.runtime_config import RuntimeConfig
 from mflux.models.depth_pro.depth_pro import DepthPro
-from mflux.models.vae.vae import VAE
+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
 
diff --git a/src/mflux/flux_tools/depth/flux_depth.py b/src/mflux/models/flux/variants/depth/flux_depth.py
similarity index 90%
rename from src/mflux/flux_tools/depth/flux_depth.py
rename to src/mflux/models/flux/variants/depth/flux_depth.py
index 1af8415..4279560 100644
--- a/src/mflux/flux_tools/depth/flux_depth.py
+++ b/src/mflux/models/flux/variants/depth/flux_depth.py
@@ -7,15 +7,15 @@ 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.flux.flux_initializer import FluxInitializer
-from mflux.flux_tools.depth.depth_util import DepthUtil
 from mflux.latent_creator.latent_creator import LatentCreator
 from mflux.models.depth_pro.depth_pro import DepthPro
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.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
diff --git a/src/mflux/models/vae/__init__.py b/src/mflux/models/flux/variants/dreambooth/__init__.py
similarity index 100%
rename from src/mflux/models/vae/__init__.py
rename to src/mflux/models/flux/variants/dreambooth/__init__.py
diff --git a/src/mflux/dreambooth/_example/images/01.jpeg b/src/mflux/models/flux/variants/dreambooth/_example/images/01.jpeg
similarity index 100%
rename from src/mflux/dreambooth/_example/images/01.jpeg
rename to src/mflux/models/flux/variants/dreambooth/_example/images/01.jpeg
diff --git a/src/mflux/dreambooth/_example/images/02.jpeg b/src/mflux/models/flux/variants/dreambooth/_example/images/02.jpeg
similarity index 100%
rename from src/mflux/dreambooth/_example/images/02.jpeg
rename to src/mflux/models/flux/variants/dreambooth/_example/images/02.jpeg
diff --git a/src/mflux/dreambooth/_example/images/03.jpeg b/src/mflux/models/flux/variants/dreambooth/_example/images/03.jpeg
similarity index 100%
rename from src/mflux/dreambooth/_example/images/03.jpeg
rename to src/mflux/models/flux/variants/dreambooth/_example/images/03.jpeg
diff --git a/src/mflux/dreambooth/_example/images/04.jpeg b/src/mflux/models/flux/variants/dreambooth/_example/images/04.jpeg
similarity index 100%
rename from src/mflux/dreambooth/_example/images/04.jpeg
rename to src/mflux/models/flux/variants/dreambooth/_example/images/04.jpeg
diff --git a/src/mflux/dreambooth/_example/images/05.jpeg b/src/mflux/models/flux/variants/dreambooth/_example/images/05.jpeg
similarity index 100%
rename from src/mflux/dreambooth/_example/images/05.jpeg
rename to src/mflux/models/flux/variants/dreambooth/_example/images/05.jpeg
diff --git a/src/mflux/dreambooth/_example/train.json b/src/mflux/models/flux/variants/dreambooth/_example/train.json
similarity index 100%
rename from src/mflux/dreambooth/_example/train.json
rename to src/mflux/models/flux/variants/dreambooth/_example/train.json
diff --git a/src/mflux/models/vae/common/__init__.py b/src/mflux/models/flux/variants/dreambooth/dataset/__init__.py
similarity index 100%
rename from src/mflux/models/vae/common/__init__.py
rename to src/mflux/models/flux/variants/dreambooth/dataset/__init__.py
diff --git a/src/mflux/dreambooth/dataset/batch.py b/src/mflux/models/flux/variants/dreambooth/dataset/batch.py
similarity index 100%
rename from src/mflux/dreambooth/dataset/batch.py
rename to src/mflux/models/flux/variants/dreambooth/dataset/batch.py
diff --git a/src/mflux/dreambooth/dataset/dataset.py b/src/mflux/models/flux/variants/dreambooth/dataset/dataset.py
similarity index 89%
rename from src/mflux/dreambooth/dataset/dataset.py
rename to src/mflux/models/flux/variants/dreambooth/dataset/dataset.py
index 82d3bf4..14c7630 100644
--- a/src/mflux/dreambooth/dataset/dataset.py
+++ b/src/mflux/models/flux/variants/dreambooth/dataset/dataset.py
@@ -5,10 +5,10 @@ import PIL.Image
 from mlx import nn
 from tqdm import tqdm
 
-from mflux.dreambooth.dataset.batch import Example
-from mflux.dreambooth.dataset.dreambooth_preprocessing import DreamBoothPreProcessing
-from mflux.dreambooth.state.training_spec import ExampleSpec
-from mflux.flux.flux import Flux1
+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
 
diff --git a/src/mflux/dreambooth/dataset/dreambooth_preprocessing.py b/src/mflux/models/flux/variants/dreambooth/dataset/dreambooth_preprocessing.py
similarity index 69%
rename from src/mflux/dreambooth/dataset/dreambooth_preprocessing.py
rename to src/mflux/models/flux/variants/dreambooth/dataset/dreambooth_preprocessing.py
index 30f62bd..1ceb6c6 100644
--- a/src/mflux/dreambooth/dataset/dreambooth_preprocessing.py
+++ b/src/mflux/models/flux/variants/dreambooth/dataset/dreambooth_preprocessing.py
@@ -1,4 +1,4 @@
-from mflux.dreambooth.dataset.batch import Example
+from mflux.models.flux.variants.dreambooth.dataset.batch import Example
 
 
 class DreamBoothPreProcessing:
diff --git a/src/mflux/dreambooth/dataset/iterator.py b/src/mflux/models/flux/variants/dreambooth/dataset/iterator.py
similarity index 94%
rename from src/mflux/dreambooth/dataset/iterator.py
rename to src/mflux/models/flux/variants/dreambooth/dataset/iterator.py
index c058d5b..c605ba3 100644
--- a/src/mflux/dreambooth/dataset/iterator.py
+++ b/src/mflux/models/flux/variants/dreambooth/dataset/iterator.py
@@ -4,10 +4,10 @@ import random
 from pathlib import Path
 from typing import Any
 
-from mflux.dreambooth.dataset.batch import Batch
-from mflux.dreambooth.dataset.dataset import Dataset
-from mflux.dreambooth.state.training_spec import TrainingSpec
-from mflux.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.dreambooth.dataset.batch import Batch
+from mflux.models.flux.variants.dreambooth.dataset.dataset import Dataset
+from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
 
 
 class Iterator:
diff --git a/src/mflux/dreambooth/dreambooth.py b/src/mflux/models/flux/variants/dreambooth/dreambooth.py
similarity index 84%
rename from src/mflux/dreambooth/dreambooth.py
rename to src/mflux/models/flux/variants/dreambooth/dreambooth.py
index 16bf853..7ba51a3 100644
--- a/src/mflux/dreambooth/dreambooth.py
+++ b/src/mflux/models/flux/variants/dreambooth/dreambooth.py
@@ -2,12 +2,12 @@ from mlx import nn
 from tqdm import tqdm
 
 from mflux.config.runtime_config import RuntimeConfig
-from mflux.dreambooth.optimization.dreambooth_loss import DreamBoothLoss
-from mflux.dreambooth.state.training_spec import TrainingSpec
-from mflux.dreambooth.state.training_state import TrainingState
-from mflux.dreambooth.statistics.plotter import Plotter
-from mflux.flux.flux import Flux1
-from mflux.weights.weight_handler_lora import WeightHandlerLoRA
+from mflux.models.flux.variants.dreambooth.optimization.dreambooth_loss import DreamBoothLoss
+from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
+from mflux.models.flux.variants.dreambooth.state.training_state import TrainingState
+from mflux.models.flux.variants.dreambooth.statistics.plotter import Plotter
+from mflux.models.flux.variants.txt2img.flux import Flux1
+from mflux.models.flux.weights.weight_handler_lora import WeightHandlerLoRA
 
 
 class DreamBooth:
diff --git a/src/mflux/dreambooth/dreambooth_initializer.py b/src/mflux/models/flux/variants/dreambooth/dreambooth_initializer.py
similarity index 79%
rename from src/mflux/dreambooth/dreambooth_initializer.py
rename to src/mflux/models/flux/variants/dreambooth/dreambooth_initializer.py
index 629a969..d8c1e5f 100644
--- a/src/mflux/dreambooth/dreambooth_initializer.py
+++ b/src/mflux/models/flux/variants/dreambooth/dreambooth_initializer.py
@@ -3,14 +3,14 @@ import mlx.core.random as random
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
 from mflux.config.runtime_config import RuntimeConfig
-from mflux.dreambooth.dataset.dataset import Dataset
-from mflux.dreambooth.dataset.iterator import Iterator
-from mflux.dreambooth.lora_layers.lora_layers import LoRALayers
-from mflux.dreambooth.optimization.optimizer import Optimizer
-from mflux.dreambooth.state.training_spec import TrainingSpec
-from mflux.dreambooth.state.training_state import TrainingState
-from mflux.dreambooth.statistics.statistics import Statistics
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.dreambooth.dataset.dataset import Dataset
+from mflux.models.flux.variants.dreambooth.dataset.iterator import Iterator
+from mflux.models.flux.variants.dreambooth.lora_layers.lora_layers import LoRALayers
+from mflux.models.flux.variants.dreambooth.optimization.optimizer import Optimizer
+from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
+from mflux.models.flux.variants.dreambooth.state.training_state import TrainingState
+from mflux.models.flux.variants.dreambooth.statistics.statistics import Statistics
+from mflux.models.flux.variants.txt2img.flux import Flux1
 
 
 class DreamBoothInitializer:
diff --git a/src/mflux/models/vae/decoder/__init__.py b/src/mflux/models/flux/variants/dreambooth/lora_layers/__init__.py
similarity index 100%
rename from src/mflux/models/vae/decoder/__init__.py
rename to src/mflux/models/flux/variants/dreambooth/lora_layers/__init__.py
diff --git a/src/mflux/dreambooth/lora_layers/lora_layers.py b/src/mflux/models/flux/variants/dreambooth/lora_layers/lora_layers.py
similarity index 94%
rename from src/mflux/dreambooth/lora_layers/lora_layers.py
rename to src/mflux/models/flux/variants/dreambooth/lora_layers/lora_layers.py
index 493110f..6628f93 100644
--- a/src/mflux/dreambooth/lora_layers/lora_layers.py
+++ b/src/mflux/models/flux/variants/dreambooth/lora_layers/lora_layers.py
@@ -6,16 +6,20 @@ import mlx.core as mx
 from mlx import nn
 from mlx.utils import tree_flatten
 
-from mflux.dreambooth.lora_layers.linear_lora_layer import LoRALinear
-from mflux.dreambooth.state.training_spec import SingleTransformerBlocks, TrainingSpec, TransformerBlocks
-from mflux.dreambooth.state.zip_util import ZipUtil
-from mflux.models.transformer.joint_transformer_block import JointTransformerBlock
-from mflux.models.transformer.single_transformer_block import SingleTransformerBlock
+from mflux.models.common.lora.layer.linear_lora_layer import LoRALinear
+from mflux.models.flux.model.flux_transformer.joint_transformer_block import JointTransformerBlock
+from mflux.models.flux.model.flux_transformer.single_transformer_block import SingleTransformerBlock
+from mflux.models.flux.variants.dreambooth.state.training_spec import (
+    SingleTransformerBlocks,
+    TrainingSpec,
+    TransformerBlocks,
+)
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.weights.weight_handler import MetaData, WeightHandler
 from mflux.utils.version_util import VersionUtil
-from mflux.weights.weight_handler import MetaData, WeightHandler
 
 if TYPE_CHECKING:
-    from mflux.flux.flux import Flux1
+    from mflux.models.flux.variants.txt2img.flux import Flux1
 
 
 class LoRALayers:
@@ -26,7 +30,7 @@ class LoRALayers:
     def from_spec(flux: "Flux1", training_spec: TrainingSpec) -> "LoRALayers":
         if training_spec.lora_layers.state_path is not None:
             # Load from state if present in the spec
-            from mflux.weights.weight_handler_lora import WeightHandlerLoRA
+            from mflux.models.flux.weights.weight_handler_lora import WeightHandlerLoRA
 
             weights = ZipUtil.unzip(
                 zip_path=training_spec.checkpoint_path,
diff --git a/src/mflux/models/vae/encoder/__init__.py b/src/mflux/models/flux/variants/dreambooth/optimization/__init__.py
similarity index 100%
rename from src/mflux/models/vae/encoder/__init__.py
rename to src/mflux/models/flux/variants/dreambooth/optimization/__init__.py
diff --git a/src/mflux/dreambooth/optimization/_loss_derivation/dreambooth_loss.pdf b/src/mflux/models/flux/variants/dreambooth/optimization/_loss_derivation/dreambooth_loss.pdf
similarity index 100%
rename from src/mflux/dreambooth/optimization/_loss_derivation/dreambooth_loss.pdf
rename to src/mflux/models/flux/variants/dreambooth/optimization/_loss_derivation/dreambooth_loss.pdf
diff --git a/src/mflux/dreambooth/optimization/_loss_derivation/dreambooth_loss.tex b/src/mflux/models/flux/variants/dreambooth/optimization/_loss_derivation/dreambooth_loss.tex
similarity index 100%
rename from src/mflux/dreambooth/optimization/_loss_derivation/dreambooth_loss.tex
rename to src/mflux/models/flux/variants/dreambooth/optimization/_loss_derivation/dreambooth_loss.tex
diff --git a/src/mflux/dreambooth/optimization/dreambooth_loss.py b/src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py
similarity index 94%
rename from src/mflux/dreambooth/optimization/dreambooth_loss.py
rename to src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py
index 6014ae3..7a3fac3 100644
--- a/src/mflux/dreambooth/optimization/dreambooth_loss.py
+++ b/src/mflux/models/flux/variants/dreambooth/optimization/dreambooth_loss.py
@@ -4,10 +4,9 @@ import mlx.core as mx
 
 from mflux.config.config import Config
 from mflux.config.runtime_config import RuntimeConfig
-from mflux.dreambooth.dataset.batch import Batch
-from mflux.dreambooth.dataset.dataset import Example
-from mflux.flux.flux import Flux1
 from mflux.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
 
 
 class DreamBoothLoss:
diff --git a/src/mflux/dreambooth/optimization/optimizer.py b/src/mflux/models/flux/variants/dreambooth/optimization/optimizer.py
similarity index 91%
rename from src/mflux/dreambooth/optimization/optimizer.py
rename to src/mflux/models/flux/variants/dreambooth/optimization/optimizer.py
index 5cddeea..e45a541 100644
--- a/src/mflux/dreambooth/optimization/optimizer.py
+++ b/src/mflux/models/flux/variants/dreambooth/optimization/optimizer.py
@@ -6,8 +6,8 @@ import mlx.optimizers
 import mlx.optimizers as optim
 from mlx.utils import tree_flatten, tree_unflatten
 
-from mflux.dreambooth.state.training_spec import TrainingSpec
-from mflux.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
 
 
 class Optimizers(Enum):
diff --git a/src/mflux/tokenizer/__init__.py b/src/mflux/models/flux/variants/dreambooth/state/__init__.py
similarity index 100%
rename from src/mflux/tokenizer/__init__.py
rename to src/mflux/models/flux/variants/dreambooth/state/__init__.py
diff --git a/src/mflux/dreambooth/state/training_spec.py b/src/mflux/models/flux/variants/dreambooth/state/training_spec.py
similarity index 99%
rename from src/mflux/dreambooth/state/training_spec.py
rename to src/mflux/models/flux/variants/dreambooth/state/training_spec.py
index a98e996..e241b14 100644
--- a/src/mflux/dreambooth/state/training_spec.py
+++ b/src/mflux/models/flux/variants/dreambooth/state/training_spec.py
@@ -5,7 +5,7 @@ from dataclasses import asdict, dataclass
 from pathlib import Path
 from typing import List
 
-from mflux.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
 
 
 @dataclass
diff --git a/src/mflux/dreambooth/state/training_state.py b/src/mflux/models/flux/variants/dreambooth/state/training_state.py
similarity index 93%
rename from src/mflux/dreambooth/state/training_state.py
rename to src/mflux/models/flux/variants/dreambooth/state/training_state.py
index 2d3a0f5..824a298 100644
--- a/src/mflux/dreambooth/state/training_state.py
+++ b/src/mflux/models/flux/variants/dreambooth/state/training_state.py
@@ -4,12 +4,12 @@ import tempfile
 import zipfile
 from pathlib import Path
 
-from mflux.dreambooth.dataset.iterator import Iterator
-from mflux.dreambooth.lora_layers.lora_layers import LoRALayers
-from mflux.dreambooth.optimization.optimizer import Optimizer
-from mflux.dreambooth.state.training_spec import TrainingSpec
-from mflux.dreambooth.state.zip_util import ZipUtil
-from mflux.dreambooth.statistics.statistics import Statistics
+from mflux.models.flux.variants.dreambooth.dataset.iterator import Iterator
+from mflux.models.flux.variants.dreambooth.lora_layers.lora_layers import LoRALayers
+from mflux.models.flux.variants.dreambooth.optimization.optimizer import Optimizer
+from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.dreambooth.statistics.statistics import Statistics
 
 DREAMBOOTH_PATH_CHECKPOINTS = "_checkpoints"
 DREAMBOOTH_FILE_NAME_CHECKPOINT = "checkpoint"
diff --git a/src/mflux/dreambooth/state/zip_util.py b/src/mflux/models/flux/variants/dreambooth/state/zip_util.py
similarity index 100%
rename from src/mflux/dreambooth/state/zip_util.py
rename to src/mflux/models/flux/variants/dreambooth/state/zip_util.py
diff --git a/src/mflux/weights/__init__.py b/src/mflux/models/flux/variants/dreambooth/statistics/__init__.py
similarity index 100%
rename from src/mflux/weights/__init__.py
rename to src/mflux/models/flux/variants/dreambooth/statistics/__init__.py
diff --git a/src/mflux/dreambooth/statistics/plotter.py b/src/mflux/models/flux/variants/dreambooth/statistics/plotter.py
similarity index 96%
rename from src/mflux/dreambooth/statistics/plotter.py
rename to src/mflux/models/flux/variants/dreambooth/statistics/plotter.py
index 1bb4929..3b799bc 100644
--- a/src/mflux/dreambooth/statistics/plotter.py
+++ b/src/mflux/models/flux/variants/dreambooth/statistics/plotter.py
@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 
-from mflux.dreambooth.state.training_spec import TrainingSpec
-from mflux.dreambooth.state.training_state import TrainingState
+from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
+from mflux.models.flux.variants.dreambooth.state.training_state import TrainingState
 
 
 class Plotter:
diff --git a/src/mflux/dreambooth/statistics/statistics.py b/src/mflux/models/flux/variants/dreambooth/statistics/statistics.py
similarity index 90%
rename from src/mflux/dreambooth/statistics/statistics.py
rename to src/mflux/models/flux/variants/dreambooth/statistics/statistics.py
index 9cfbce2..d17d6c6 100644
--- a/src/mflux/dreambooth/statistics/statistics.py
+++ b/src/mflux/models/flux/variants/dreambooth/statistics/statistics.py
@@ -3,10 +3,10 @@ import json
 from pathlib import Path
 from typing import TYPE_CHECKING
 
-from mflux.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
 
 if TYPE_CHECKING:
-    from mflux.dreambooth.state.training_spec import TrainingSpec
+    from mflux.models.flux.variants.dreambooth.state.training_spec import TrainingSpec
 
 
 class Statistics:
diff --git a/src/mflux/models/flux/variants/fill/__init__.py b/src/mflux/models/flux/variants/fill/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/flux_tools/fill/flux_fill.py b/src/mflux/models/flux/variants/fill/flux_fill.py
similarity index 90%
rename from src/mflux/flux_tools/fill/flux_fill.py
rename to src/mflux/models/flux/variants/fill/flux_fill.py
index 92336c3..c9b8f78 100644
--- a/src/mflux/flux_tools/fill/flux_fill.py
+++ b/src/mflux/models/flux/variants/fill/flux_fill.py
@@ -7,14 +7,14 @@ 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.flux.flux_initializer import FluxInitializer
-from mflux.flux_tools.fill.mask_util import MaskUtil
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.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
diff --git a/src/mflux/flux_tools/fill/mask_util.py b/src/mflux/models/flux/variants/fill/mask_util.py
similarity index 97%
rename from src/mflux/flux_tools/fill/mask_util.py
rename to src/mflux/models/flux/variants/fill/mask_util.py
index 37e747d..13818b1 100644
--- a/src/mflux/flux_tools/fill/mask_util.py
+++ b/src/mflux/models/flux/variants/fill/mask_util.py
@@ -2,7 +2,7 @@ from pathlib import Path
 
 import mlx.core as mx
 
-from mflux.models.vae.vae import VAE
+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
 
diff --git a/src/mflux/models/flux/variants/in_context/__init__.py b/src/mflux/models/flux/variants/in_context/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/community/in_context/flux_in_context_dev.py b/src/mflux/models/flux/variants/in_context/flux_in_context_dev.py
similarity index 94%
rename from src/mflux/community/in_context/flux_in_context_dev.py
rename to src/mflux/models/flux/variants/in_context/flux_in_context_dev.py
index 07f2564..c15f977 100644
--- a/src/mflux/community/in_context/flux_in_context_dev.py
+++ b/src/mflux/models/flux/variants/in_context/flux_in_context_dev.py
@@ -7,13 +7,13 @@ 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.flux.flux_initializer import FluxInitializer
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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
diff --git a/src/mflux/community/in_context/flux_in_context_fill.py b/src/mflux/models/flux/variants/in_context/flux_in_context_fill.py
similarity index 90%
rename from src/mflux/community/in_context/flux_in_context_fill.py
rename to src/mflux/models/flux/variants/in_context/flux_in_context_fill.py
index 627364a..d9da0f8 100644
--- a/src/mflux/community/in_context/flux_in_context_fill.py
+++ b/src/mflux/models/flux/variants/in_context/flux_in_context_fill.py
@@ -3,18 +3,18 @@ from mlx import nn
 from tqdm import tqdm
 
 from mflux.callbacks.callbacks import Callbacks
-from mflux.community.in_context.utils.in_context_mask_util import InContextMaskUtil
 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.flux.flux_initializer import FluxInitializer
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.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
diff --git a/src/mflux/models/flux/variants/in_context/utils/__init__.py b/src/mflux/models/flux/variants/in_context/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/community/in_context/utils/in_context_loras.py b/src/mflux/models/flux/variants/in_context/utils/in_context_loras.py
similarity index 91%
rename from src/mflux/community/in_context/utils/in_context_loras.py
rename to src/mflux/models/flux/variants/in_context/utils/in_context_loras.py
index ec620c7..f82351b 100644
--- a/src/mflux/community/in_context/utils/in_context_loras.py
+++ b/src/mflux/models/flux/variants/in_context/utils/in_context_loras.py
@@ -1,6 +1,5 @@
 """Configuration for In-Context LoRAs from Hugging Face."""
-
-from mflux.weights.weight_handler_lora_huggingface import WeightHandlerLoRAHuggingFace
+from mflux.models.common.lora.download.lora_huggingface_downloader import LoRAHuggingFaceDownloader
 
 # Default Hugging Face repository for In-Context LoRAs
 LORA_REPO_ID = "ali-vilab/In-Context-LoRA"
@@ -36,7 +35,7 @@ def prepare_ic_edit_loras(additional_lora_paths: list[str] | None = None) -> lis
     print(f"🔍 Downloading IC-Edit LoRA from {IC_EDIT_LORA_REPO_ID}")
 
     # Download the required IC-Edit LoRA
-    ic_edit_lora_path = WeightHandlerLoRAHuggingFace.download_lora(
+    ic_edit_lora_path = LoRAHuggingFaceDownloader.download_lora(
         repo_id=IC_EDIT_LORA_REPO_ID,
         lora_name=IC_EDIT_LORA_FILENAME,
     )
diff --git a/src/mflux/community/in_context/utils/in_context_mask_util.py b/src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py
similarity index 96%
rename from src/mflux/community/in_context/utils/in_context_mask_util.py
rename to src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py
index 33aff52..97b56b5 100644
--- a/src/mflux/community/in_context/utils/in_context_mask_util.py
+++ b/src/mflux/models/flux/variants/in_context/utils/in_context_mask_util.py
@@ -1,7 +1,7 @@
 import mlx.core as mx
 
-from mflux.flux_tools.fill.mask_util import MaskUtil
-from mflux.models.vae.vae import VAE
+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
 
diff --git a/src/mflux/models/flux/variants/kontext/__init__.py b/src/mflux/models/flux/variants/kontext/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/kontext/flux_kontext.py b/src/mflux/models/flux/variants/kontext/flux_kontext.py
similarity index 90%
rename from src/mflux/kontext/flux_kontext.py
rename to src/mflux/models/flux/variants/kontext/flux_kontext.py
index 73f9b56..ae0ec09 100644
--- a/src/mflux/kontext/flux_kontext.py
+++ b/src/mflux/models/flux/variants/kontext/flux_kontext.py
@@ -7,14 +7,14 @@ 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.flux.flux_initializer import FluxInitializer
-from mflux.kontext.utils.kontext_util import KontextUtil
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.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
diff --git a/src/mflux/models/flux/variants/kontext/utils/__init__.py b/src/mflux/models/flux/variants/kontext/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/kontext/utils/kontext_util.py b/src/mflux/models/flux/variants/kontext/utils/kontext_util.py
similarity index 100%
rename from src/mflux/kontext/utils/kontext_util.py
rename to src/mflux/models/flux/variants/kontext/utils/kontext_util.py
diff --git a/src/mflux/models/flux/variants/redux/__init__.py b/src/mflux/models/flux/variants/redux/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/flux_tools/redux/flux_redux.py b/src/mflux/models/flux/variants/redux/flux_redux.py
similarity index 88%
rename from src/mflux/flux_tools/redux/flux_redux.py
rename to src/mflux/models/flux/variants/redux/flux_redux.py
index ded162b..08f233c 100644
--- a/src/mflux/flux_tools/redux/flux_redux.py
+++ b/src/mflux/models/flux/variants/redux/flux_redux.py
@@ -9,21 +9,21 @@ 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.flux.flux_initializer import FluxInitializer
-from mflux.flux_tools.redux.redux_util import ReduxUtil
 from mflux.latent_creator.latent_creator import LatentCreator
-from mflux.models.redux_encoder.redux_encoder import ReduxEncoder
-from mflux.models.siglip_vision_transformer.siglip_vision_transformer import SiglipVisionTransformer
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.models.flux.model.redux_encoder.redux_encoder import ReduxEncoder
+from mflux.models.flux.model.siglip_vision_transformer.siglip_vision_transformer import SiglipVisionTransformer
+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.tokenizer.clip_tokenizer import TokenizerCLIP
-from mflux.tokenizer.t5_tokenizer import TokenizerT5
 
 
 class Flux1Redux(nn.Module):
diff --git a/src/mflux/flux_tools/redux/redux_util.py b/src/mflux/models/flux/variants/redux/redux_util.py
similarity index 89%
rename from src/mflux/flux_tools/redux/redux_util.py
rename to src/mflux/models/flux/variants/redux/redux_util.py
index 0d3e27b..1c38696 100644
--- a/src/mflux/flux_tools/redux/redux_util.py
+++ b/src/mflux/models/flux/variants/redux/redux_util.py
@@ -2,8 +2,8 @@ from pathlib import Path
 
 import mlx.core as mx
 
-from mflux.models.redux_encoder.redux_encoder import ReduxEncoder
-from mflux.models.siglip_vision_transformer.siglip_vision_transformer import SiglipVisionTransformer
+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
 
 
diff --git a/src/mflux/flux_tools/redux/weight_handler_redux.py b/src/mflux/models/flux/variants/redux/weight_handler_redux.py
similarity index 90%
rename from src/mflux/flux_tools/redux/weight_handler_redux.py
rename to src/mflux/models/flux/variants/redux/weight_handler_redux.py
index 05e3ca5..aee798b 100644
--- a/src/mflux/flux_tools/redux/weight_handler_redux.py
+++ b/src/mflux/models/flux/variants/redux/weight_handler_redux.py
@@ -1,8 +1,8 @@
 from pathlib import Path
 
 from mflux.config.model_config import ModelConfig
-from mflux.weights.download import snapshot_download
-from mflux.weights.weight_handler import MetaData, WeightHandler
+from mflux.models.flux.weights.weight_handler import MetaData, WeightHandler
+from mflux.utils.download import snapshot_download
 
 
 class WeightHandlerRedux:
diff --git a/src/mflux/models/flux/variants/txt2img/__init__.py b/src/mflux/models/flux/variants/txt2img/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/flux/flux.py b/src/mflux/models/flux/variants/txt2img/flux.py
similarity index 90%
rename from src/mflux/flux/flux.py
rename to src/mflux/models/flux/variants/txt2img/flux.py
index 7e8838b..8df2686 100644
--- a/src/mflux/flux/flux.py
+++ b/src/mflux/models/flux/variants/txt2img/flux.py
@@ -7,17 +7,17 @@ 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.flux.flux_initializer import FluxInitializer
 from mflux.latent_creator.latent_creator import Img2Img, LatentCreator
-from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
-from mflux.models.text_encoder.prompt_encoder import PromptEncoder
-from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
-from mflux.models.transformer.transformer import Transformer
-from mflux.models.vae.vae import VAE
+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.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.weights.model_saver import ModelSaver
 
 
 class Flux1(nn.Module):
@@ -49,6 +49,7 @@ class Flux1(nn.Module):
         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
         config = RuntimeConfig(config, self.model_config)
diff --git a/src/mflux/models/flux/weights/__init__.py b/src/mflux/models/flux/weights/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/weights/lora_converter.py b/src/mflux/models/flux/weights/lora_converter.py
similarity index 100%
rename from src/mflux/weights/lora_converter.py
rename to src/mflux/models/flux/weights/lora_converter.py
diff --git a/src/mflux/weights/model_saver.py b/src/mflux/models/flux/weights/model_saver.py
similarity index 100%
rename from src/mflux/weights/model_saver.py
rename to src/mflux/models/flux/weights/model_saver.py
diff --git a/src/mflux/weights/weight_handler.py b/src/mflux/models/flux/weights/weight_handler.py
similarity index 97%
rename from src/mflux/weights/weight_handler.py
rename to src/mflux/models/flux/weights/weight_handler.py
index bc4de4d..d796b28 100644
--- a/src/mflux/weights/weight_handler.py
+++ b/src/mflux/models/flux/weights/weight_handler.py
@@ -4,9 +4,9 @@ from pathlib import Path
 import mlx.core as mx
 from mlx.utils import tree_unflatten
 
-from mflux.weights.download import snapshot_download
-from mflux.weights.lora_converter import LoRAConverter
-from mflux.weights.weight_util import WeightUtil
+from mflux.models.flux.weights.lora_converter import LoRAConverter
+from mflux.models.flux.weights.weight_util import WeightUtil
+from mflux.utils.download import snapshot_download
 
 
 @dataclass
@@ -39,7 +39,7 @@ class WeightHandler:
         transformer_repo_id: str | None = None,
     ) -> "WeightHandler":
         # Load the weights from disk, huggingface cache, or download from huggingface
-        root_path = Path(local_path) if local_path else WeightHandler._download_or_get_cached_weights(repo_id)
+        root_path = Path(local_path) if local_path else WeightHandler.download_or_get_cached_weights(repo_id)
 
         # Some custom models might have a different specific transformer setup
         if transformer_repo_id:
@@ -211,7 +211,7 @@ class WeightHandler:
         return unflatten, quantization_level, mflux_version
 
     @staticmethod
-    def _download_or_get_cached_weights(repo_id: str) -> Path:
+    def download_or_get_cached_weights(repo_id: str) -> Path:
         return Path(
             snapshot_download(
                 repo_id=repo_id,
diff --git a/src/mflux/weights/weight_handler_lora.py b/src/mflux/models/flux/weights/weight_handler_lora.py
similarity index 95%
rename from src/mflux/weights/weight_handler_lora.py
rename to src/mflux/models/flux/weights/weight_handler_lora.py
index 0c8f830..eb13432 100644
--- a/src/mflux/weights/weight_handler_lora.py
+++ b/src/mflux/models/flux/weights/weight_handler_lora.py
@@ -2,10 +2,10 @@ import mlx.core as mx
 import mlx.nn as nn
 from mlx.utils import tree_flatten, tree_unflatten
 
-from mflux.dreambooth.lora_layers.fused_linear_lora_layer import FusedLoRALinear
-from mflux.dreambooth.lora_layers.linear_lora_layer import LoRALinear
-from mflux.dreambooth.lora_layers.lora_layers import LoRALayers
-from mflux.weights.weight_handler import MetaData, WeightHandler
+from mflux.models.common.lora.layer.fused_linear_lora_layer import FusedLoRALinear
+from mflux.models.common.lora.layer.linear_lora_layer import LoRALinear
+from mflux.models.flux.variants.dreambooth.lora_layers.lora_layers import LoRALayers
+from mflux.models.flux.weights.weight_handler import MetaData, WeightHandler
 
 
 class WeightHandlerLoRA:
diff --git a/src/mflux/weights/weight_util.py b/src/mflux/models/flux/weights/weight_util.py
similarity index 93%
rename from src/mflux/weights/weight_util.py
rename to src/mflux/models/flux/weights/weight_util.py
index 5745c47..7c9c902 100644
--- a/src/mflux/weights/weight_util.py
+++ b/src/mflux/models/flux/weights/weight_util.py
@@ -3,12 +3,12 @@ from typing import TYPE_CHECKING
 import mlx.nn as nn
 
 from mflux.config.config import Config
-from mflux.weights.quantization_util import QuantizationUtil
+from mflux.utils.quantization_util import QuantizationUtil
 
 if TYPE_CHECKING:
-    from mflux.controlnet.weight_handler_controlnet import WeightHandlerControlnet
-    from mflux.flux_tools.redux.weight_handler_redux import WeightHandlerRedux
-    from mflux.weights.weight_handler import WeightHandler
+    from mflux.models.flux.variants.controlnet.weight_handler_controlnet import WeightHandlerControlnet
+    from mflux.models.flux.variants.redux.weight_handler_redux import WeightHandlerRedux
+    from mflux.models.flux.weights.weight_handler import WeightHandler
 
 
 class WeightUtil:
diff --git a/src/mflux/models/qwen/__init__.py b/src/mflux/models/qwen/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/model/__init__.py b/src/mflux/models/qwen/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/__init__.py b/src/mflux/models/qwen/model/qwen_text_encoder/__init__.py
new file mode 100644
index 0000000..cc7bab9
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/__init__.py
@@ -0,0 +1 @@
+# Qwen Text Encoder components
diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py
new file mode 100644
index 0000000..8ce5ea2
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py
@@ -0,0 +1,143 @@
+import math
+
+import mlx.core as mx
+from mlx import nn
+
+from .qwen_rope import QwenRotaryEmbedding
+
+
+class QwenAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        num_key_value_heads: int = None,
+        max_position_embeddings: int = 128000,
+        rope_theta: float = 1000000.0,
+        rope_scaling: dict = None,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads or num_attention_heads
+        self.head_dim = hidden_size // num_attention_heads
+        self.num_key_value_groups = num_attention_heads // self.num_key_value_heads
+        self.scaling = 1.0 / math.sqrt(self.head_dim)
+        self.q_proj = nn.Linear(hidden_size, num_attention_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(num_attention_heads * self.head_dim, hidden_size, bias=False)
+        self.rotary_emb = QwenRotaryEmbedding(
+            dim=self.head_dim,
+            max_position_embeddings=max_position_embeddings,
+            base=rope_theta,
+            rope_type="default",
+        )
+        self.rope_scaling = rope_scaling or {"mrope_section": [16, 24, 24]}
+
+    def __call__(
+        self,
+        hidden_states: mx.array,
+        attention_mask: mx.array | None = None,
+        position_embeddings: tuple[mx.array, mx.array] | None = None,
+    ) -> mx.array:
+        bsz, q_len, _ = hidden_states.shape
+
+        # Q, K, V projections
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Reshape and transpose
+        query_states = query_states.reshape(bsz, q_len, self.num_attention_heads, self.head_dim).transpose(0, 2, 1, 3)
+        key_states = key_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(0, 2, 1, 3)
+        value_states = value_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(0, 2, 1, 3)
+
+        # Apply RoPE
+        query_states, key_states = QwenAttention._apply_multimodal_rotary_pos_emb(
+            q=query_states,
+            k=key_states,
+            position_embeddings=position_embeddings,
+            mrope_section=self.rope_scaling["mrope_section"],
+        )
+
+        # GQA expansion using repeat_interleave semantics
+        if self.num_key_value_heads != self.num_attention_heads:
+            key_states = QwenAttention._repeat_kv(key_states, self.num_key_value_groups)
+            value_states = QwenAttention._repeat_kv(value_states, self.num_key_value_groups)
+
+        # Attention computation
+        attn_weights = mx.matmul(query_states, key_states.transpose(0, 1, 3, 2)) * self.scaling
+
+        # Apply attention mask
+        if attention_mask is not None:
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # Softmax and output
+        attn_weights = mx.softmax(attn_weights.astype(mx.float32), axis=-1).astype(query_states.dtype)
+        attn_output = mx.matmul(attn_weights, value_states)
+        attn_output = attn_output.transpose(0, 2, 1, 3).reshape(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+        return attn_output
+
+    @staticmethod
+    def _repeat_kv(hidden_states: mx.array, n_rep: int) -> mx.array:
+        batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+        if n_rep == 1:
+            return hidden_states
+        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_multimodal_rotary_pos_emb(
+        q: mx.array,
+        k: mx.array,
+        position_embeddings: tuple[mx.array, mx.array],
+        mrope_section: list[int],
+        unsqueeze_dim: int = 1,
+    ) -> tuple[mx.array, mx.array]:
+        mrope_section_doubled = [s * 2 for s in mrope_section]
+
+        cos_chunks = []
+        sin_chunks = []
+        start_idx = 0
+
+        cos, sin = position_embeddings
+        for section_size in mrope_section_doubled:
+            end_idx = start_idx + section_size
+            cos_chunk = cos[..., start_idx:end_idx]
+            sin_chunk = sin[..., start_idx:end_idx]
+            cos_chunks.append(cos_chunk)
+            sin_chunks.append(sin_chunk)
+            start_idx = end_idx
+
+        # For each chunk position, select the corresponding modality
+        # chunk 0 -> modality 0, chunk 1 -> modality 1, chunk 2 -> modality 2, etc.
+        cos_selected = [chunk[i % 3] for i, chunk in enumerate(cos_chunks)]
+        sin_selected = [chunk[i % 3] for i, chunk in enumerate(sin_chunks)]
+
+        # Concatenate back together
+        cos_combined = mx.concatenate(cos_selected, axis=-1)
+        sin_combined = mx.concatenate(sin_selected, axis=-1)
+
+        # Add head dimension if needed
+        if unsqueeze_dim == 1:
+            cos_combined = mx.expand_dims(cos_combined, axis=1)
+            sin_combined = mx.expand_dims(sin_combined, axis=1)
+        elif unsqueeze_dim == 2:
+            cos_combined = mx.expand_dims(cos_combined, axis=2)
+            sin_combined = mx.expand_dims(sin_combined, axis=2)
+
+        # Apply rotary embedding with properly processed cos/sin
+        q_embed = (q * cos_combined) + (QwenAttention._rotate_half(q) * sin_combined)
+        k_embed = (k * cos_combined) + (QwenAttention._rotate_half(k) * sin_combined)
+
+        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/qwen/model/qwen_text_encoder/qwen_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder.py
new file mode 100644
index 0000000..b93afa1
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder.py
@@ -0,0 +1,74 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_text_encoder.qwen_encoder_layer import QwenEncoderLayer
+from mflux.models.qwen.model.qwen_text_encoder.qwen_rms_norm import QwenRMSNorm
+from mflux.models.qwen.model.qwen_text_encoder.qwen_rope import QwenRotaryEmbedding
+
+
+class QwenEncoder(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int = 152064,
+        hidden_size: int = 3584,
+        num_hidden_layers: int = 28,
+        max_position_embeddings: int = 128000,
+        rope_theta: float = 1000000.0,
+    ):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.embed_tokens = nn.Embedding(vocab_size, hidden_size)
+        self.layers = [QwenEncoderLayer() for i in range(num_hidden_layers)]
+        self.norm = QwenRMSNorm(hidden_size, eps=1e-6)
+        self.rotary_emb = QwenRotaryEmbedding(
+            dim=hidden_size // 28,
+            max_position_embeddings=max_position_embeddings,
+            base=rope_theta,
+            rope_type="default",
+        )
+
+    def __call__(
+        self,
+        input_ids: mx.array,
+        attention_mask: mx.array | None = None,
+    ) -> mx.array:
+        batch_size, seq_len = input_ids.shape
+        inputs_embeds = self.embed_tokens(input_ids)
+        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))
+        if attention_mask is not None:
+            padding_mask = mx.where(
+                attention_mask == 1,
+                mx.zeros_like(attention_mask).astype(mx.float32),
+                mx.ones_like(attention_mask).astype(mx.float32) * (-float("inf")),
+            )
+            padding_mask = mx.expand_dims(mx.expand_dims(padding_mask, axis=1), axis=1)
+        else:
+            padding_mask = None
+
+        # Create causal triangular mask
+        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)).astype(mx.float32)
+        neginf_2d = mx.ones((seq_len, seq_len)).astype(mx.float32) * (-float("inf"))
+        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)  # [1,1,S,S]
+        causal_tri_mask = mx.broadcast_to(causal_tri_mask, (batch_size, 1, seq_len, seq_len))
+
+        # Final attention mask: causal triangle + padding mask
+        if padding_mask is not None:
+            attention_mask_4d = causal_tri_mask + padding_mask
+        else:
+            attention_mask_4d = causal_tri_mask
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, attention_mask_4d, position_embeddings)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder_layer.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder_layer.py
new file mode 100644
index 0000000..686ec02
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder_layer.py
@@ -0,0 +1,54 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_text_encoder.qwen_attention import QwenAttention
+from mflux.models.qwen.model.qwen_text_encoder.qwen_mlp import QwenMLP
+from mflux.models.qwen.model.qwen_text_encoder.qwen_rms_norm import QwenRMSNorm
+
+
+class QwenEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int = 3584,
+        num_attention_heads: int = 28,
+        num_key_value_heads: int = 4,
+        intermediate_size: int = 18944,
+        rms_norm_eps: float = 1e-6,
+        max_position_embeddings: int = 128000,
+        rope_theta: float = 1000000.0,
+    ):
+        super().__init__()
+        self.input_layernorm = QwenRMSNorm(hidden_size, eps=rms_norm_eps)
+        self.self_attn = QwenAttention(
+            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,
+            rope_scaling={"mrope_section": [16, 24, 24]},
+        )
+        self.post_attention_layernorm = QwenRMSNorm(hidden_size, eps=rms_norm_eps)
+        self.mlp = QwenMLP(
+            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,
+    ) -> mx.array:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_embeddings=position_embeddings,
+        )
+        hidden_states = residual + hidden_states
+        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/qwen/model/qwen_text_encoder/qwen_mlp.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_mlp.py
new file mode 100644
index 0000000..e12a931
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_mlp.py
@@ -0,0 +1,17 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenMLP(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/qwen/model/qwen_text_encoder/qwen_prompt_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_prompt_encoder.py
new file mode 100644
index 0000000..5eb92cd
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_prompt_encoder.py
@@ -0,0 +1,34 @@
+import mlx.core as mx
+
+from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenTextEncoder
+from mflux.models.qwen.tokenizer.qwen_tokenizer import TokenizerQwen
+
+
+class QwenPromptEncoder:
+    @staticmethod
+    def encode_prompt(
+        prompt: str,
+        negative_prompt: str,
+        prompt_cache: dict[str, tuple[mx.array, mx.array, mx.array, mx.array]],
+        qwen_tokenizer: TokenizerQwen,
+        qwen_text_encoder: QwenTextEncoder,
+    ) -> tuple[mx.array, mx.array, mx.array, mx.array]:
+        # 0. Create a cache key that combines both prompts
+        cache_key = f"{prompt}|NEG|{negative_prompt}"
+
+        # 1. Return prompt encodings if already cached
+        if cache_key in prompt_cache:
+            return prompt_cache[cache_key]
+
+        # 2. Encode the positive prompt
+        input_ids, attention_mask = qwen_tokenizer.tokenize(prompt)
+        neg_input_ids, neg_attention_mask = qwen_tokenizer.tokenize(negative_prompt)
+        prompt_embeds, prompt_mask = qwen_text_encoder(input_ids=input_ids, attention_mask=attention_mask)
+        neg_prompt_embeds, neg_prompt_mask = qwen_text_encoder(
+            input_ids=neg_input_ids, attention_mask=neg_attention_mask
+        )
+
+        # 3. Cache the result (all 4 values)
+        result = (prompt_embeds, prompt_mask, neg_prompt_embeds, neg_prompt_mask)
+        prompt_cache[cache_key] = result
+        return result
diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_rms_norm.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_rms_norm.py
new file mode 100644
index 0000000..238767a
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_rms_norm.py
@@ -0,0 +1,17 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenRMSNorm(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/qwen/model/qwen_text_encoder/qwen_rope.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_rope.py
new file mode 100644
index 0000000..c4d1400
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_rope.py
@@ -0,0 +1,36 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenRotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_position_embeddings: int = 128000,
+        base: float = 1000000.0,
+        device: str = None,
+        scaling_factor: float = 1.0,
+        rope_type: str = "default",
+        config=None,
+    ):
+        super().__init__()
+        self.inv_freq = 1.0 / (base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim))
+        self.attention_scaling = scaling_factor
+
+    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))
+        emb = mx.concatenate([freqs, freqs], axis=-1)
+        cos = mx.cos(emb) * self.attention_scaling
+        sin = mx.sin(emb) * self.attention_scaling
+        return cos.astype(x.dtype), sin.astype(x.dtype)
diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py
new file mode 100644
index 0000000..fc468fb
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py
@@ -0,0 +1,70 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_text_encoder.qwen_encoder import QwenEncoder
+
+
+class QwenTextEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.encoder = QwenEncoder()
+
+    def __call__(
+        self,
+        input_ids: mx.array,
+        attention_mask: mx.array | None = None,
+    ) -> tuple[mx.array, mx.array]:
+        hidden_states = self.encoder(input_ids, attention_mask)
+        prompt_embeds, encoder_attention_mask = QwenTextEncoder._process_text_embeddings_mlx(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            drop_idx=34,
+            dtype=mx.bfloat16,
+        )
+        return prompt_embeds, encoder_attention_mask
+
+    @staticmethod
+    def _process_text_embeddings_mlx(hidden_states, attention_mask, drop_idx=1, dtype=mx.float32):
+        split_hidden_states = QwenTextEncoder._extract_masked_hidden(hidden_states, attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        attn_mask_list = [mx.ones(e.shape[0], dtype=mx.int32) for e in split_hidden_states]
+        max_seq_len = max([e.shape[0] for e in split_hidden_states])
+
+        padded_embeds = []
+        for u in split_hidden_states:
+            current_len = u.shape[0]
+            hidden_dim = u.shape[1]
+            if current_len < max_seq_len:
+                padding = mx.zeros((max_seq_len - current_len, hidden_dim), dtype=u.dtype)
+                padded = mx.concatenate([u, padding], axis=0)
+            else:
+                padded = u
+            padded_embeds.append(padded)
+
+        prompt_embeds = mx.stack(padded_embeds, axis=0)
+
+        padded_masks = []
+        for mask in attn_mask_list:
+            current_len = mask.shape[0]
+            if current_len < max_seq_len:
+                padding = mx.zeros(max_seq_len - current_len, dtype=mask.dtype)
+                padded = mx.concatenate([mask, padding], axis=0)
+            else:
+                padded = mask
+            padded_masks.append(padded)
+
+        encoder_attention_mask = mx.stack(padded_masks, axis=0)
+        prompt_embeds = prompt_embeds.astype(dtype)
+        return prompt_embeds, encoder_attention_mask
+
+    @staticmethod
+    def _extract_masked_hidden(hidden_states, attention_mask):
+        batch_size = hidden_states.shape[0]
+        split_hidden_states = []
+        for i in range(batch_size):
+            mask = attention_mask[i]
+            valid_length = mx.sum(mask).item()
+            valid_length = int(valid_length)
+            valid_hidden = hidden_states[i, :valid_length, :]
+            split_hidden_states.append(valid_hidden)
+        return split_hidden_states
diff --git a/src/mflux/models/qwen/model/qwen_transformer/__init__.py b/src/mflux/models/qwen/model/qwen_transformer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py
new file mode 100644
index 0000000..11a638c
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py
@@ -0,0 +1,150 @@
+from __future__ import annotations
+
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.flux.model.flux_transformer.common.attention_utils import AttentionUtils
+
+
+class QwenAttention(nn.Module):
+    def __init__(self, dim: int = 3072, num_heads: int = 24, head_dim: int = 128):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        # Attention projections for image stream
+        self.to_q = nn.Linear(dim, dim)
+        self.to_k = nn.Linear(dim, dim)
+        self.to_v = nn.Linear(dim, dim)
+
+        # Attention projections for text stream
+        self.add_q_proj = nn.Linear(dim, dim)
+        self.add_k_proj = nn.Linear(dim, dim)
+        self.add_v_proj = nn.Linear(dim, dim)
+
+        # Query/Key normalization
+        self.norm_q = nn.RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_k = nn.RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_added_q = nn.RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_added_k = nn.RMSNorm(self.head_dim, eps=1e-6)
+
+        # Output projections
+        self.attn_to_out = [nn.Linear(dim, dim)]
+        self.to_add_out = nn.Linear(dim, dim)
+
+    def __call__(
+        self,
+        img_modulated: mx.array,
+        txt_modulated: mx.array,
+        encoder_hidden_states_mask: mx.array | None,
+        image_rotary_emb: tuple[mx.array, mx.array],
+    ) -> tuple[mx.array, mx.array]:
+        # 1a. Compute Q,K,V for image stream (hidden_states)
+        img_q, img_k, img_v = AttentionUtils.process_qkv(
+            hidden_states=img_modulated,
+            to_q=self.to_q,
+            to_k=self.to_k,
+            to_v=self.to_v,
+            norm_q=self.norm_q,
+            norm_k=self.norm_k,
+            num_heads=self.num_heads,
+            head_dim=self.head_dim,
+        )
+
+        # 1b. Compute Q,K,V for text stream (encoder_hidden_states)
+        txt_q, txt_k, txt_v = AttentionUtils.process_qkv(
+            hidden_states=txt_modulated,
+            to_q=self.add_q_proj,
+            to_k=self.add_k_proj,
+            to_v=self.add_v_proj,
+            norm_q=self.norm_added_q,
+            norm_k=self.norm_added_k,
+            num_heads=self.num_heads,
+            head_dim=self.head_dim,
+        )
+
+        # 1c. Concatenate results [text, image] like Flux
+        joint_q = mx.concatenate([txt_q, img_q], axis=2)
+        joint_k = mx.concatenate([txt_k, img_k], axis=2)
+        joint_v = mx.concatenate([txt_v, img_v], axis=2)
+
+        # 1d. Apply RoPE to concatenated Q,K
+        joint_q, joint_k = QwenAttention._apply_rotary_embeddings_joint(
+            joint_q=joint_q,
+            joint_k=joint_k,
+            txt_seq_len=txt_q.shape[2],
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        # 2. Compute attention with optional masking
+        mask = AttentionUtils.convert_key_padding_mask_to_additive_mask(
+            mask=encoder_hidden_states_mask,
+            joint_seq_len=joint_q.shape[2],
+            txt_seq_len=txt_q.shape[2],
+        )
+
+        # 3. Compute attention
+        hidden_states = AttentionUtils.compute_attention(
+            query=joint_q,
+            key=joint_k,
+            value=joint_v,
+            batch_size=1,
+            num_heads=self.num_heads,
+            head_dim=self.head_dim,
+            mask=mask,
+        )
+
+        # 4. Separate the results
+        txt_seq_len = txt_modulated.shape[1]
+        txt_attn_output = hidden_states[:, :txt_seq_len]
+        img_attn_output = hidden_states[:, txt_seq_len:]
+
+        # 5. Project the output (Flux-style)
+        img_attn_output = self.attn_to_out[0](img_attn_output.astype(mx.float32)).astype(img_modulated.dtype)
+        txt_attn_output = self.to_add_out(txt_attn_output.astype(mx.float32)).astype(txt_modulated.dtype)
+
+        return img_attn_output, txt_attn_output
+
+    @staticmethod
+    def _apply_rotary_embeddings_joint(
+        joint_q: mx.array,
+        joint_k: mx.array,
+        txt_seq_len: int,
+        image_rotary_emb: tuple[mx.array, mx.array],
+    ) -> tuple[mx.array, mx.array]:
+        img_rot, txt_rot = image_rotary_emb
+
+        # Extract separate parts
+        txt_q = joint_q[:, :, :txt_seq_len, :]
+        img_q = joint_q[:, :, txt_seq_len:, :]
+        txt_k = joint_k[:, :, :txt_seq_len, :]
+        img_k = joint_k[:, :, txt_seq_len:, :]
+
+        # Prepare cos/sin for text and image
+        img_cos = img_rot[..., 0, 0].reshape(img_rot.shape[2], img_rot.shape[3])
+        img_sin = img_rot[..., 1, 0].reshape(img_rot.shape[2], img_rot.shape[3])
+        txt_cos = txt_rot[..., 0, 0].reshape(txt_rot.shape[2], txt_rot.shape[3])
+        txt_sin = txt_rot[..., 1, 0].reshape(txt_rot.shape[2], txt_rot.shape[3])
+
+        # Transpose [B,H,S,D] -> [B,S,H,D] for RoPE application
+        img_q_bshd = mx.transpose(img_q, (0, 2, 1, 3))
+        img_k_bshd = mx.transpose(img_k, (0, 2, 1, 3))
+        txt_q_bshd = mx.transpose(txt_q, (0, 2, 1, 3))
+        txt_k_bshd = mx.transpose(txt_k, (0, 2, 1, 3))
+
+        # Apply RoPE
+        img_q_bshd, img_k_bshd = AttentionUtils.apply_rope_bshd(img_q_bshd, img_k_bshd, img_cos, img_sin)
+        txt_q_bshd, txt_k_bshd = AttentionUtils.apply_rope_bshd(txt_q_bshd, txt_k_bshd, txt_cos, txt_sin)
+
+        # Back to [B,H,S,D]
+        img_q = mx.transpose(img_q_bshd, (0, 2, 1, 3))
+        img_k = mx.transpose(img_k_bshd, (0, 2, 1, 3))
+        txt_q = mx.transpose(txt_q_bshd, (0, 2, 1, 3))
+        txt_k = mx.transpose(txt_k_bshd, (0, 2, 1, 3))
+
+        # Concatenate back [text, image]
+        joint_q = mx.concatenate([txt_q, img_q], axis=2)
+        joint_k = mx.concatenate([txt_k, img_k], axis=2)
+
+        return joint_q, joint_k
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py
new file mode 100644
index 0000000..02a906b
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py
@@ -0,0 +1,15 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenFeedForward(nn.Module):
+    def __init__(self, dim: int = 3072):
+        super().__init__()
+        self.mlp_in = nn.Linear(dim, 4 * dim)
+        self.mlp_out = nn.Linear(4 * dim, dim)
+
+    def __call__(self, hidden_states: mx.array) -> mx.array:
+        hidden_states = self.mlp_in(hidden_states.astype(mx.float32))
+        hidden_states = nn.gelu_approx(hidden_states)
+        hidden_states = self.mlp_out(hidden_states)
+        return hidden_states
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py
new file mode 100644
index 0000000..d3d81ab
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py
@@ -0,0 +1,21 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenLayerNorm(nn.Module):
+    def __init__(self, dim: int = 3072):
+        super().__init__()
+        self.mod_linear = nn.Linear(dim, 6 * dim)
+        self.norm1 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False)
+
+    def __call__(self, hidden_states: mx.array, text_embeddings: mx.array) -> tuple[mx.array, mx.array, mx.array]:
+        # Compute modulation parameters from text embeddings
+        mod_params = self.mod_linear(nn.silu(text_embeddings))
+        mod1, mod2 = mx.split(mod_params, 2, axis=-1)
+
+        # Stage 1: Apply normalization and modulation for attention
+        normed1 = self.norm1(hidden_states)
+        shift1, scale1, gate1 = mx.split(mod1, 3, axis=-1)
+        normed_stage1 = normed1 * (1 + scale1[:, None, :]) + shift1[:, None, :]
+
+        return normed_stage1, gate1, mod2
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py
new file mode 100644
index 0000000..9a78cfa
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py
@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+import mlx.core as mx
+import numpy as np
+from mlx import nn
+
+
+class QwenEmbedRopeMLX(nn.Module):
+    """
+    Faithful MLX port of Qwen's RoPE embedding helper used in the flux_transformer.
+
+    Exposes a forward(video_fhw, txt_seq_lens) API that returns rotation matrices
+    for image and text streams with shape [1, 1, S, D/2, 2, 2].
+    """
+
+    def __init__(self, theta: int, axes_dim: list[int], scale_rope: bool = True):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        self.scale_rope = scale_rope
+
+        # Precompute positive and negative index caches up to 1024 as in reference
+        pos_index = np.arange(1024, dtype=np.int32)
+        neg_index = (np.arange(1024, dtype=np.int32)[::-1] * -1) - 1
+
+        self._pos_cos = []
+        self._pos_sin = []
+        self._neg_cos = []
+        self._neg_sin = []
+        for dim in axes_dim:
+            cos_p, sin_p = self._rope_params(pos_index, dim)
+            cos_n, sin_n = self._rope_params(neg_index, dim)
+            self._pos_cos.append(cos_p)
+            self._pos_sin.append(sin_p)
+            self._neg_cos.append(cos_n)
+            self._neg_sin.append(sin_n)
+
+    @staticmethod
+    def _rope_params(index: np.ndarray, dim: int, theta: int = 10000) -> tuple[np.ndarray, np.ndarray]:
+        assert dim % 2 == 0
+        scales = np.arange(0, dim, 2, dtype=np.float32) / dim
+        omega = 1.0 / (theta**scales)
+        out = np.outer(index.astype(np.float32), omega).astype(np.float32)
+        return np.cos(out), np.sin(out)
+
+    def _build_video_freqs(self, frame: int, height: int, width: int) -> tuple[np.ndarray, np.ndarray]:
+        dim_f, dim_h, dim_w = self.axes_dim
+
+        cos_f = self._pos_cos[0][:frame].reshape(frame, 1, 1, -1)
+        sin_f = self._pos_sin[0][:frame].reshape(frame, 1, 1, -1)
+
+        if self.scale_rope:
+            cos_h = np.concatenate(
+                [self._neg_cos[1][-(height - height // 2) :], self._pos_cos[1][: height // 2]], axis=0
+            )
+            sin_h = np.concatenate(
+                [self._neg_sin[1][-(height - height // 2) :], self._pos_sin[1][: height // 2]], axis=0
+            )
+        else:
+            cos_h = self._pos_cos[1][:height]
+            sin_h = self._pos_sin[1][:height]
+        cos_h = cos_h.reshape(1, height, 1, -1)
+        sin_h = sin_h.reshape(1, height, 1, -1)
+
+        if self.scale_rope:
+            cos_w = np.concatenate([self._neg_cos[2][-(width - width // 2) :], self._pos_cos[2][: width // 2]], axis=0)
+            sin_w = np.concatenate([self._neg_sin[2][-(width - width // 2) :], self._pos_sin[2][: width // 2]], axis=0)
+        else:
+            cos_w = self._pos_cos[2][:width]
+            sin_w = self._pos_sin[2][:width]
+        cos_w = cos_w.reshape(1, 1, width, -1)
+        sin_w = sin_w.reshape(1, 1, width, -1)
+
+        cos = np.concatenate(
+            [
+                np.broadcast_to(cos_f, (frame, height, width, cos_f.shape[-1])),
+                np.broadcast_to(cos_h, (frame, height, width, cos_h.shape[-1])),
+                np.broadcast_to(cos_w, (frame, height, width, cos_w.shape[-1])),
+            ],
+            axis=-1,
+        )
+        sin = np.concatenate(
+            [
+                np.broadcast_to(sin_f, (frame, height, width, sin_f.shape[-1])),
+                np.broadcast_to(sin_h, (frame, height, width, sin_h.shape[-1])),
+                np.broadcast_to(sin_w, (frame, height, width, sin_w.shape[-1])),
+            ],
+            axis=-1,
+        )
+
+        # Flatten to [S, D/2]
+        cos = cos.reshape(-1, cos.shape[-1])
+        sin = sin.reshape(-1, sin.shape[-1])
+        return cos, sin
+
+    def __call__(self, video_fhw: tuple[int, int, int] | list[tuple[int, int, int]], txt_seq_lens: list[int]):
+        if isinstance(video_fhw, list):
+            video_fhw = video_fhw[0]
+        frame, height, width = video_fhw
+
+        cos_v, sin_v = self._build_video_freqs(frame, height, width)
+
+        # Build text freqs using max_vid_index rule
+        max_vid_index = max(height // 2, width // 2) if self.scale_rope else max(height, width)
+        txt_len = max(txt_seq_lens)
+        # Combine axes cos/sin for positions starting at max_vid_index
+        cos_full = np.concatenate(self._pos_cos, axis=1)  # [1024, 128]
+        sin_full = np.concatenate(self._pos_sin, axis=1)
+        cos_t = cos_full[max_vid_index : max_vid_index + txt_len]
+        sin_t = sin_full[max_vid_index : max_vid_index + txt_len]
+
+        def to_rot(cos: np.ndarray, sin: np.ndarray) -> mx.array:
+            row0 = np.stack([cos, -sin], axis=-1)
+            row1 = np.stack([sin, cos], axis=-1)
+            rot = np.stack([row0, row1], axis=-2)
+            rot = rot[None, None, ...].astype(np.float32)
+            return mx.array(rot)
+
+        return to_rot(cos_v, sin_v), to_rot(cos_t, sin_t)
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py
new file mode 100644
index 0000000..8423fe0
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py
@@ -0,0 +1,17 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_transformer.qwen_timestep_embedding import QwenTimestepEmbedding
+from mflux.models.qwen.model.qwen_transformer.qwen_timesteps import QwenTimesteps
+
+
+class QwenTimeTextEmbed(nn.Module):
+    def __init__(self, timestep_proj_dim: int = 256, inner_dim: int = 3072):
+        super().__init__()
+        self.time_proj = QwenTimesteps(proj_dim=timestep_proj_dim)
+        self.timestep_embedder = QwenTimestepEmbedding(proj_dim=timestep_proj_dim, inner_dim=inner_dim)
+
+    def __call__(self, timestep: mx.array, hidden_states: mx.array) -> mx.array:
+        time_proj = self.time_proj(timestep)
+        time_emb = self.timestep_embedder(time_proj.astype(hidden_states.dtype))
+        return time_emb
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py
new file mode 100644
index 0000000..875e4fe
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py
@@ -0,0 +1,14 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenTimestepEmbedding(nn.Module):
+    def __init__(self, proj_dim: int, inner_dim: int):
+        super().__init__()
+        self.linear_1 = nn.Linear(proj_dim, inner_dim)
+        self.linear_2 = nn.Linear(inner_dim, inner_dim)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        x = nn.silu(self.linear_1(x))
+        x = self.linear_2(x)
+        return x
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py
new file mode 100644
index 0000000..05184be
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py
@@ -0,0 +1,21 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenTimesteps(nn.Module):
+    def __init__(self, proj_dim: int = 256, scale: float = 1000.0):
+        super().__init__()
+        self.proj_dim = proj_dim
+        self.scale = scale
+
+    def __call__(self, timesteps: mx.array) -> mx.array:
+        half_dim = self.proj_dim // 2
+        max_period = 10000.0
+        exponent = -mx.log(mx.array(max_period)) * mx.arange(0, half_dim, dtype=mx.float32)
+        exponent = exponent / (half_dim - 0.0)
+        freqs = mx.exp(exponent)
+        emb = timesteps.astype(mx.float32)[:, None] * freqs[None, :]
+        emb = self.scale * emb
+        emb = mx.concatenate([mx.sin(emb), mx.cos(emb)], axis=-1)
+        emb = mx.concatenate([emb[:, half_dim:], emb[:, :half_dim]], axis=-1)
+        return emb
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py
new file mode 100644
index 0000000..9472fb5
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py
@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+import mlx.core as mx
+import numpy as np
+from mlx import nn
+
+from mflux.config.runtime_config import RuntimeConfig
+from mflux.models.flux.model.flux_transformer.ada_layer_norm_continuous import AdaLayerNormContinuous
+from mflux.models.qwen.model.qwen_transformer.qwen_rope import QwenEmbedRopeMLX
+from mflux.models.qwen.model.qwen_transformer.qwen_time_text_embed import QwenTimeTextEmbed
+from mflux.models.qwen.model.qwen_transformer.qwen_transformer_block import QwenTransformerBlock
+
+
+class QwenTransformer(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 64,
+        out_channels: int = 16,
+        num_layers: int = 60,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 3584,
+        patch_size: int = 2,
+    ) -> None:
+        super().__init__()
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.img_in = nn.Linear(in_channels, self.inner_dim)
+        self.txt_norm = nn.RMSNorm(joint_attention_dim, eps=1e-6)
+        self.txt_in = nn.Linear(joint_attention_dim, self.inner_dim)
+        self.time_text_embed = QwenTimeTextEmbed(timestep_proj_dim=256, inner_dim=self.inner_dim)
+        self.pos_embed = QwenEmbedRopeMLX(theta=10000, axes_dim=[16, 56, 56], scale_rope=True)
+        self.transformer_blocks = [QwenTransformerBlock(dim=self.inner_dim, num_heads=num_attention_heads, head_dim=attention_head_dim) for i in range(num_layers)]  # fmt: off
+        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * out_channels)
+
+    def __call__(
+        self,
+        t: int,
+        config: RuntimeConfig,
+        hidden_states: mx.array,
+        encoder_hidden_states: mx.array,
+        encoder_hidden_states_mask: mx.array,
+    ) -> mx.array:
+        # 1. Create embeddings
+        hidden_states = self.img_in(hidden_states)
+        encoder_hidden_states = self.txt_in(self.txt_norm(encoder_hidden_states))
+        text_embeddings = QwenTransformer._compute_text_embeddings(t, hidden_states, self.time_text_embed, config)
+        image_rotary_embeddings = QwenTransformer._compute_rotary_embeddings(encoder_hidden_states_mask, self.pos_embed, config)  # fmt: off
+
+        # 2. Run the transformer blocks
+        for idx, block in enumerate(self.transformer_blocks):
+            encoder_hidden_states, hidden_states = QwenTransformer._apply_transformer_block(
+                idx=idx,
+                block=block,
+                hidden_states=hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_hidden_states_mask=encoder_hidden_states_mask,
+                text_embeddings=text_embeddings,
+                image_rotary_embeddings=image_rotary_embeddings,
+            )
+
+        # 3. Apply output normalization and projection
+        hidden_states = self.norm_out(hidden_states, text_embeddings)
+        hidden_states = self.proj_out(hidden_states)
+        return hidden_states
+
+    @staticmethod
+    def _apply_transformer_block(
+        idx: int,
+        block: QwenTransformerBlock,
+        hidden_states: mx.array,
+        encoder_hidden_states: mx.array,
+        encoder_hidden_states_mask: mx.array,
+        text_embeddings: mx.array,
+        image_rotary_embeddings: tuple[mx.array, mx.array],
+    ) -> tuple[mx.array, mx.array]:
+        return block(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_hidden_states_mask=encoder_hidden_states_mask,
+            text_embeddings=text_embeddings,
+            image_rotary_emb=image_rotary_embeddings,
+        )
+
+    @staticmethod
+    def _compute_text_embeddings(
+        t: int,
+        hidden_states: mx.array,
+        time_text_embed: QwenTimeTextEmbed,
+        config: RuntimeConfig,
+    ) -> mx.array:
+        time_step = config.sigmas[t]
+        batch = hidden_states.shape[0]
+        timestep = mx.array(np.full((batch,), time_step, dtype=np.float32))
+        text_embeddings = time_text_embed(timestep, hidden_states)
+        return text_embeddings
+
+    @staticmethod
+    def _compute_rotary_embeddings(
+        encoder_hidden_states_mask: mx.array,
+        pos_embed: QwenEmbedRopeMLX,
+        config: RuntimeConfig,
+    ) -> tuple[mx.array, mx.array]:
+        latent_height = config.height // 16
+        latent_width = config.width // 16
+        img_shapes = [(1, latent_height, latent_width)]
+        txt_seq_lens = [int(mx.sum(encoder_hidden_states_mask[i]).item()) for i in range(encoder_hidden_states_mask.shape[0])]  # fmt: off
+        img_rotary_emb, txt_rotary_emb = pos_embed(video_fhw=img_shapes, txt_seq_lens=txt_seq_lens)
+        return img_rotary_emb, txt_rotary_emb
diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py
new file mode 100644
index 0000000..ddaf46f
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py
@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_transformer.qwen_attention import QwenAttention
+from mflux.models.qwen.model.qwen_transformer.qwen_feed_forward import QwenFeedForward
+from mflux.models.qwen.model.qwen_transformer.qwen_layer_norm import QwenLayerNorm
+
+
+class QwenTransformerBlock(nn.Module):
+    def __init__(self, dim: int = 3072, num_heads: int = 24, head_dim: int = 128):
+        super().__init__()
+        self.img_norm1 = QwenLayerNorm(dim=dim)
+        self.txt_norm1 = QwenLayerNorm(dim=dim)
+        self.attn = QwenAttention(dim=dim, num_heads=num_heads, head_dim=head_dim)
+        self.img_norm2 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False)
+        self.txt_norm2 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False)
+        self.img_ff = QwenFeedForward(dim=dim)
+        self.txt_ff = QwenFeedForward(dim=dim)
+
+    def __call__(
+        self,
+        hidden_states: mx.array,
+        encoder_hidden_states: mx.array,
+        encoder_hidden_states_mask: mx.array | None,
+        text_embeddings: mx.array,
+        image_rotary_emb: tuple[mx.array, mx.array],
+    ) -> tuple[mx.array, mx.array]:
+        # 1. Compute stage 1 normalization and modulation
+        img_modulated, img_gate1, img_mod2 = self.img_norm1(hidden_states, text_embeddings)
+        txt_modulated, txt_gate1, txt_mod2 = self.txt_norm1(encoder_hidden_states, text_embeddings)
+
+        # 2. Compute attention
+        img_attn_output, txt_attn_output = self.attn(
+            img_modulated=img_modulated,
+            txt_modulated=txt_modulated,
+            encoder_hidden_states_mask=encoder_hidden_states_mask,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        # 3. Apply attention residual and feed forward
+        hidden_states = QwenTransformerBlock._apply_residual_and_feed_forward(
+            hidden_states=hidden_states,
+            output=img_attn_output,
+            gate_attn=img_gate1,
+            mod_params=img_mod2,
+            norm2=self.img_norm2,
+            ff_module=self.img_ff,
+        )
+        encoder_hidden_states = QwenTransformerBlock._apply_residual_and_feed_forward(
+            hidden_states=encoder_hidden_states,
+            output=txt_attn_output,
+            gate_attn=txt_gate1,
+            mod_params=txt_mod2,
+            norm2=self.txt_norm2,
+            ff_module=self.txt_ff,
+        )
+
+        return encoder_hidden_states, hidden_states
+
+    @staticmethod
+    def _apply_residual_and_feed_forward(
+        hidden_states: mx.array,
+        output: mx.array,
+        gate_attn: mx.array,
+        mod_params: mx.array,
+        norm2: nn.LayerNorm,
+        ff_module: QwenFeedForward,
+    ) -> mx.array:
+        hidden_states = hidden_states + gate_attn[:, None, :] * output
+        shift2, scale2, gate_ff = mx.split(mod_params, 3, axis=-1)
+        normed = norm2(hidden_states)
+        modulated = normed * (1 + scale2[:, None, :]) + shift2[:, None, :]
+        ff_output = ff_module(modulated)
+        return hidden_states + gate_ff[:, None, :] * ff_output.astype(hidden_states.dtype)
diff --git a/src/mflux/models/qwen/model/qwen_vae/__init__.py b/src/mflux/models/qwen/model/qwen_vae/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py
new file mode 100644
index 0000000..cab0d3c
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py
@@ -0,0 +1,41 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_rms_norm import QwenImageRMSNorm
+
+
+class QwenImageAttentionBlock3D(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dim = dim
+        self.norm = QwenImageRMSNorm(dim, images=True)  # Attention blocks use images=True
+        self.to_qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, stride=1, padding=0)
+        self.proj = nn.Conv2d(dim, dim, kernel_size=1, stride=1, padding=0)
+
+    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_5d = mx.expand_dims(x, axis=2)
+        x_5d = self.norm(x_5d)
+        x = mx.squeeze(x_5d, axis=2)
+        x = mx.transpose(x, (0, 2, 3, 1))
+        qkv = self.to_qkv(x)  # [bt, h, w, 3c]
+        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=-1)
+        scale = 1.0 / mx.sqrt(mx.array(float(channels)))
+        scores = mx.matmul(q, mx.transpose(k, (0, 1, 3, 2))) * scale
+        attn_weights = mx.softmax(scores, axis=-1)
+        attn_out = mx.matmul(attn_weights, v)
+        attn_out = mx.squeeze(attn_out, axis=1)
+        attn_out = mx.transpose(attn_out, (0, 2, 1))
+        attn_out = mx.reshape(attn_out, (batch_size * time, channels, height, width))
+        attn_out = mx.transpose(attn_out, (0, 2, 3, 1))
+        attn_out = self.proj(attn_out)
+        attn_out = mx.transpose(attn_out, (0, 3, 1, 2))
+        attn_out = mx.reshape(attn_out, (batch_size, time, channels, height, width))
+        attn_out = mx.transpose(attn_out, (0, 2, 1, 3, 4))
+        return attn_out + identity
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py
new file mode 100644
index 0000000..6a3d0f1
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py
@@ -0,0 +1,48 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenImageCausalConv3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 1,
+    ):
+        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
+
+    def __call__(self, x: mx.array) -> mx.array:
+        if isinstance(self.padding, int):
+            pad_t = pad_h = pad_w = self.padding
+        elif isinstance(self.padding, (tuple, list)) and len(self.padding) == 3:
+            pad_t, pad_h, pad_w = self.padding
+        else:
+            pad_t = pad_h = pad_w = self.padding
+
+        # Apply causal padding in channels-first format (B, C, T, H, W)
+        if pad_t > 0 or pad_h > 0 or pad_w > 0:
+            pad_spec = [
+                (0, 0),  # Batch dimension (B)
+                (0, 0),  # Channel dimension (C)
+                (2 * pad_t, 0),  # Time dimension (T) - causal: pad past, not future
+                (pad_h, pad_h),  # Height dimension (H)
+                (pad_w, pad_w),  # Width dimension (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/qwen/model/qwen_vae/qwen_image_decoder_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_decoder_3d.py
new file mode 100644
index 0000000..aea0240
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_decoder_3d.py
@@ -0,0 +1,32 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_causal_conv_3d import QwenImageCausalConv3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_mid_block_3d import QwenImageMidBlock3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_rms_norm import QwenImageRMSNorm
+from mflux.models.qwen.model.qwen_vae.qwen_image_up_block_3d import QwenImageUpBlock3D
+
+
+class QwenImageDecoder3D(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv_in = QwenImageCausalConv3D(16, 384, 3, 1, 1)
+        self.mid_block = QwenImageMidBlock3D(384, num_layers=1)
+        self.up_block0 = QwenImageUpBlock3D(384, 384, num_res_blocks=2, upsample_mode="upsample3d")
+        self.up_block1 = QwenImageUpBlock3D(192, 384, num_res_blocks=2, upsample_mode="upsample3d")
+        self.up_block2 = QwenImageUpBlock3D(192, 192, num_res_blocks=2, upsample_mode="upsample2d")
+        self.up_block3 = QwenImageUpBlock3D(96, 96, num_res_blocks=2, upsample_mode=None)
+        self.norm_out = QwenImageRMSNorm(96, images=False)  # Decoder norm_out uses images=False
+        self.conv_out = QwenImageCausalConv3D(96, 3, 3, 1, 1)  # RGB output
+
+    def __call__(self, x: mx.array) -> mx.array:
+        x = self.conv_in(x)
+        x = self.mid_block(x)
+        x = self.up_block0(x)
+        x = self.up_block1(x)
+        x = self.up_block2(x)
+        x = self.up_block3(x)
+        x = self.norm_out(x)
+        x = nn.silu(x)
+        x = self.conv_out(x)
+        return x
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py
new file mode 100644
index 0000000..a9b8955
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py
@@ -0,0 +1,26 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_res_block_3d import QwenImageResBlock3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_resample_3d import QwenImageResample3D
+
+
+class QwenImageDownBlock3D(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int = 2, downsample_mode: str = None):
+        super().__init__()
+        self.resnets = []
+        current_dim = in_channels
+        for _ in range(num_res_blocks):  # FIXED: Use num_res_blocks, not num_res_blocks + 1
+            self.resnets.append(QwenImageResBlock3D(current_dim, out_channels))
+            current_dim = out_channels
+
+        self.downsamplers = None
+        if downsample_mode is not None:
+            self.downsamplers = [QwenImageResample3D(out_channels, mode=downsample_mode)]
+
+    def __call__(self, x: mx.array) -> mx.array:
+        for resnet in self.resnets:
+            x = resnet(x)
+        if self.downsamplers is not None:
+            x = self.downsamplers[0](x)
+        return x
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py
new file mode 100644
index 0000000..a4ef063
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py
@@ -0,0 +1,71 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_causal_conv_3d import QwenImageCausalConv3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_down_block_3d import QwenImageDownBlock3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_mid_block_3d import QwenImageMidBlock3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_rms_norm import QwenImageRMSNorm
+
+
+class QwenImageEncoder3D(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.dim = 96
+        self.z_dim = 32
+        self.dim_mult = [1, 2, 4, 4]
+        self.num_res_blocks = [2, 2, 2, 2]
+        self.attn_scales = []
+        self.temporal_downsample = [False, False, True, True]
+        self.dropout = 0.0
+
+        dims = [self.dim * u for u in [1] + self.dim_mult]
+        self.conv_in = QwenImageCausalConv3D(3, dims[0], 3, 1, 1)
+
+        down_blocks = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            if i < len(self.temporal_downsample):
+                downsample_mode = "downsample3d" if self.temporal_downsample[i] else "downsample2d"
+            else:
+                downsample_mode = "downsample2d"
+            # Do not downsample on the final stage only
+            if i == len(dims) - 2:
+                downsample_mode = None
+            # Use per-stage num_res_blocks
+            stage_res_blocks = self.num_res_blocks[i] if isinstance(self.num_res_blocks, list) else self.num_res_blocks
+            down_block = QwenImageDownBlock3D(
+                in_dim, out_dim, num_res_blocks=stage_res_blocks, downsample_mode=downsample_mode
+            )
+            down_blocks.append(down_block)
+        self.down_blocks = down_blocks
+
+        self.mid_block = QwenImageMidBlock3D(dims[-1], num_layers=1)
+        self.norm_out = QwenImageRMSNorm(dims[-1], images=False)  # Encoder norm_out uses images=False
+        self.conv_out = QwenImageCausalConv3D(dims[-1], 32, 3, 1, 1)  # Changed z_dim to 32
+
+    def __call__(self, x: mx.array) -> mx.array:
+        x = self.conv_in(x)
+        for stage_idx, down_block in enumerate(self.down_blocks):
+            for res_idx, resnet in enumerate(down_block.resnets):
+                if stage_idx == 3:
+                    residual = x
+                    n1 = resnet.norm1(x)
+                    a1 = nn.silu(n1)
+                    c1 = resnet.conv1(a1)
+                    n2 = resnet.norm2(c1)
+                    a2 = nn.silu(n2)
+                    c2 = resnet.conv2(a2)
+                    if resnet.skip_conv is not None:
+                        residual = resnet.skip_conv(residual)
+                    y = c2 + residual
+                    x = y
+                else:
+                    x = resnet(x)
+            if down_block.downsamplers is not None:
+                x = down_block.downsamplers[0](x)
+
+        x = self.mid_block(x)
+        norm_in = x
+        x = self.norm_out(norm_in)
+        x = nn.silu(x)
+        encoded = self.conv_out(x)
+        return encoded
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_mid_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_mid_block_3d.py
new file mode 100644
index 0000000..ba0125e
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_mid_block_3d.py
@@ -0,0 +1,26 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_attention_block_3d import QwenImageAttentionBlock3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_res_block_3d import QwenImageResBlock3D
+
+
+class QwenImageMidBlock3D(nn.Module):
+    def __init__(self, dim: int, num_layers: int = 1):
+        super().__init__()
+        self.dim = dim
+        resnets = [QwenImageResBlock3D(dim, dim)]
+        attentions = []
+        for _ in range(num_layers):
+            attentions.append(QwenImageAttentionBlock3D(dim))
+            resnets.append(QwenImageResBlock3D(dim, dim))
+        self.attentions = attentions
+        self.resnets = resnets
+
+    def __call__(self, x: mx.array) -> mx.array:
+        x = self.resnets[0](x)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                x = attn(x)
+            x = resnet(x)
+        return x
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_res_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_res_block_3d.py
new file mode 100644
index 0000000..fa65c00
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_res_block_3d.py
@@ -0,0 +1,30 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_causal_conv_3d import QwenImageCausalConv3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_rms_norm import QwenImageRMSNorm
+
+
+class QwenImageResBlock3D(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.norm1 = QwenImageRMSNorm(in_channels, images=False)
+        self.conv1 = QwenImageCausalConv3D(in_channels, out_channels, 3, 1, 1)
+        self.norm2 = QwenImageRMSNorm(out_channels, images=False)
+        self.conv2 = QwenImageCausalConv3D(out_channels, out_channels, 3, 1, 1)
+        if in_channels != out_channels:
+            self.skip_conv = QwenImageCausalConv3D(in_channels, out_channels, 1, 1, 0)
+        else:
+            self.skip_conv = None
+
+    def __call__(self, x: mx.array) -> mx.array:
+        residual = 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)
+        if self.skip_conv is not None:
+            residual = self.skip_conv(residual)
+        return x + residual
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_resample_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_resample_3d.py
new file mode 100644
index 0000000..2f4a133
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_resample_3d.py
@@ -0,0 +1,50 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_causal_conv_3d import QwenImageCausalConv3D
+
+
+class QwenImageResample3D(nn.Module):
+    def __init__(self, dim: int, mode: str):
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+        if mode == "upsample3d":
+            self.time_conv = QwenImageCausalConv3D(dim, dim * 2, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0))
+            self.resample_conv = nn.Conv2d(dim, dim // 2, kernel_size=3, stride=1, padding=1)
+        elif mode == "upsample2d":
+            self.resample_conv = nn.Conv2d(dim, dim // 2, kernel_size=3, stride=1, padding=1)
+        elif mode == "downsample3d":
+            self.time_conv = QwenImageCausalConv3D(dim, dim, kernel_size=(3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
+            self.resample_conv = nn.Conv2d(dim, dim, kernel_size=3, stride=2, padding=0)
+        elif mode == "downsample2d":
+            self.resample_conv = nn.Conv2d(dim, dim, kernel_size=3, stride=2, padding=0)
+        else:
+            raise ValueError(f"Unsupported resample mode: {mode}")
+
+    def __call__(self, x: mx.array) -> mx.array:
+        b, c, t, h, w = x.shape
+        t = x.shape[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))
+        if self.mode in ["upsample3d", "upsample2d"]:
+            x = self._up_sample_nearest_2x(x)
+
+        if self.mode in ["downsample2d", "downsample3d"]:
+            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
+
+    @staticmethod
+    def _up_sample_nearest_2x(x: mx.array) -> mx.array:
+        B_T, H, W, C = x.shape
+        x = mx.repeat(x, 2, axis=1)
+        x = mx.repeat(x, 2, axis=2)
+        return x
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_rms_norm.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_rms_norm.py
new file mode 100644
index 0000000..a4daef8
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_rms_norm.py
@@ -0,0 +1,33 @@
+import mlx.core as mx
+from mlx import nn
+
+
+class QwenImageRMSNorm(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-12, images: bool = True):
+        super().__init__()
+        self.eps = eps
+        self.scale = float(num_channels) ** 0.5
+        self.images = images
+        if images:
+            self.weight = mx.ones((num_channels, 1, 1))
+        else:
+            self.weight = mx.ones((num_channels, 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/qwen/model/qwen_vae/qwen_image_up_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_up_block_3d.py
new file mode 100644
index 0000000..5d9faa7
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_up_block_3d.py
@@ -0,0 +1,26 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_res_block_3d import QwenImageResBlock3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_resample_3d import QwenImageResample3D
+
+
+class QwenImageUpBlock3D(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int = 2, upsample_mode: str = None):
+        super().__init__()
+        self.resnets = []
+        current_dim = in_channels
+        for _ in range(num_res_blocks + 1):
+            self.resnets.append(QwenImageResBlock3D(current_dim, out_channels))
+            current_dim = out_channels
+
+        self.upsamplers = None
+        if upsample_mode is not None:
+            self.upsamplers = [QwenImageResample3D(out_channels, mode=upsample_mode)]
+
+    def __call__(self, x: mx.array) -> mx.array:
+        for resnet in self.resnets:
+            x = resnet(x)
+        if self.upsamplers is not None:
+            x = self.upsamplers[0](x)
+        return x
diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_vae.py b/src/mflux/models/qwen/model/qwen_vae/qwen_vae.py
new file mode 100644
index 0000000..11c92f9
--- /dev/null
+++ b/src/mflux/models/qwen/model/qwen_vae/qwen_vae.py
@@ -0,0 +1,32 @@
+import mlx.core as mx
+from mlx import nn
+
+from mflux.models.qwen.model.qwen_vae.qwen_image_causal_conv_3d import QwenImageCausalConv3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_decoder_3d import QwenImageDecoder3D
+from mflux.models.qwen.model.qwen_vae.qwen_image_encoder_3d import QwenImageEncoder3D
+
+
+class QwenVAE(nn.Module):
+    LATENTS_MEAN = mx.array([-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921]).reshape(1, 16, 1, 1, 1)  # fmt: off
+    LATENTS_STD = mx.array([2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.916]).reshape(1, 16, 1, 1, 1)  # fmt: off
+
+    def __init__(self):
+        super().__init__()
+        self.decoder = QwenImageDecoder3D()
+        self.encoder = QwenImageEncoder3D()
+        self.post_quant_conv = QwenImageCausalConv3D(16, 16, 1, 1, 0)
+        self.quant_conv = QwenImageCausalConv3D(32, 32, 1, 1, 0)  # Keep 32 channels like diffusers
+
+    def decode(self, latents: mx.array) -> mx.array:
+        latents = latents.reshape(latents.shape[0], latents.shape[1], 1, latents.shape[2], latents.shape[3])
+        latents = latents * QwenVAE.LATENTS_STD + QwenVAE.LATENTS_MEAN
+        latents = self.post_quant_conv(latents)
+        decoded = self.decoder(latents)
+        return decoded[:, :, 0, :, :]
+
+    def encode(self, latents: mx.array) -> mx.array:
+        latents = latents.reshape(latents.shape[0], latents.shape[1], 1, latents.shape[2], latents.shape[3])
+        latents = self.encoder(latents)
+        latents = self.quant_conv(latents)
+        latents = latents[:, :16, :, :, :][:, :, 0, :, :]
+        return (latents - QwenVAE.LATENTS_MEAN[:, :, 0, :, :]) / QwenVAE.LATENTS_STD[:, :, 0, :, :]
diff --git a/src/mflux/models/qwen/qwen_initializer.py b/src/mflux/models/qwen/qwen_initializer.py
new file mode 100644
index 0000000..855d78f
--- /dev/null
+++ b/src/mflux/models/qwen/qwen_initializer.py
@@ -0,0 +1,74 @@
+from mflux.config.model_config import ModelConfig
+from mflux.models.common.lora.download.lora_huggingface_downloader import LoRAHuggingFaceDownloader
+from mflux.models.common.lora.mapping.lora_loader import LoRALoader
+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.tokenizer.qwen_tokenizer_handler import QwenTokenizerHandler
+from mflux.models.qwen.weights.qwen_weight_handler import QwenWeightHandler
+from mflux.models.qwen.weights.qwen_lora_mapping import QwenLoRAMapping
+from mflux.models.qwen.weights.qwen_weight_util import QwenWeightUtil
+
+
+class QwenImageInitializer:
+    @staticmethod
+    def init(
+        qwen_model,
+        model_config: ModelConfig,
+        quantize: int | None,
+        local_path: str | None,
+        lora_paths: list[str] | None = None,
+        lora_scales: list[float] | None = None,
+        lora_names: list[str] | None = None,
+        lora_repo_id: str | None = None,
+    ) -> None:
+        # 0. Set paths, configs, and prompt_cache for later
+        qwen_model.prompt_cache = {}
+        qwen_model.model_config = model_config
+
+        # 1. Load the regular weights
+        weights = QwenWeightHandler.load_regular_weights(
+            repo_id=model_config.model_name,
+            local_path=local_path,
+        )
+
+        # 2. Initialize tokenizers
+        tokenizer_handler = QwenTokenizerHandler(
+            repo_id=model_config.model_name,
+            local_path=local_path,
+        )
+        qwen_model.qwen_tokenizer = tokenizer_handler.qwen
+
+        # 3. Initialize all models
+        qwen_model.vae = QwenVAE()
+        qwen_model.transformer = QwenTransformer()
+        qwen_model.text_encoder = QwenTextEncoder()
+
+        # 4. Apply weights and quantize the models
+        qwen_model.bits = QwenWeightUtil.set_weights_and_quantize(
+            quantize_arg=quantize,
+            weights=weights,
+            vae=qwen_model.vae,
+            transformer=qwen_model.transformer,
+            text_encoder=qwen_model.text_encoder,
+        )
+
+        # 5. Set LoRA weights
+        hf_lora_paths = LoRAHuggingFaceDownloader.download_loras(
+            lora_names=lora_names,
+            repo_id=lora_repo_id,
+            model_name="Qwen",
+        )
+        qwen_model.lora_paths = (lora_paths or []) + hf_lora_paths
+        qwen_model.lora_scales = (lora_scales or []) + [1.0] * len(hf_lora_paths)
+        if qwen_model.lora_paths:
+            LoRALoader.load_and_apply_lora(
+                lora_mapping=QwenLoRAMapping.get_mapping(),
+                transformer=qwen_model.transformer,
+                lora_files=qwen_model.lora_paths,
+                lora_scales=qwen_model.lora_scales,
+            )
+        else:
+            print("⚠️  No LoRA paths provided, skipping LoRA setup")
+
+        print("🔧 === END QWEN LORA WEIGHT SETUP DEBUG ===\n")
diff --git a/src/mflux/models/qwen/tokenizer/__init__.py b/src/mflux/models/qwen/tokenizer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py b/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py
new file mode 100644
index 0000000..29bbbb1
--- /dev/null
+++ b/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py
@@ -0,0 +1,23 @@
+import mlx.core as mx
+from transformers import Qwen2Tokenizer
+
+
+class TokenizerQwen:
+    def __init__(self, tokenizer: Qwen2Tokenizer, max_length: int = 1024):
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.prompt_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.template_start_idx = 34
+
+    def tokenize(self, prompt: str) -> tuple[mx.array, mx.array]:
+        formatted_text = self.prompt_template.format(prompt)
+        tokens = self.tokenizer(
+            formatted_text,
+            max_length=self.max_length + self.template_start_idx,
+            padding=True,
+            truncation=True,
+            return_tensors="np",
+        )
+        input_ids = mx.array(tokens["input_ids"])
+        attention_mask = mx.array(tokens["attention_mask"])
+        return input_ids, attention_mask
diff --git a/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py b/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py
new file mode 100644
index 0000000..c7711a7
--- /dev/null
+++ b/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+
+import transformers
+
+from mflux.models.qwen.tokenizer.qwen_tokenizer import TokenizerQwen
+from mflux.utils.download import snapshot_download
+
+
+class QwenTokenizerHandler:
+    """
+    Handler for Qwen tokenizer, following the same pattern as TokenizerHandler.
+
+    This loads the Qwen2Tokenizer from the model repository and wraps it
+    in our TokenizerQwen class for consistent interface.
+    """
+
+    def __init__(
+        self,
+        repo_id: str,
+        max_length: int = 1024,
+        local_path: str | None = None,
+    ):
+        root_path = Path(local_path) if local_path else QwenTokenizerHandler._download_or_get_cached_tokenizer(repo_id)
+
+        # Load the Qwen2 tokenizer
+        tokenizer_path = root_path / "tokenizer"
+        self.tokenizer_raw = transformers.Qwen2Tokenizer.from_pretrained(
+            pretrained_model_name_or_path=tokenizer_path,
+            local_files_only=True,
+        )
+
+        # Wrap in our TokenizerQwen class
+        self.qwen = TokenizerQwen(self.tokenizer_raw, max_length)
+
+    @staticmethod
+    def _download_or_get_cached_tokenizer(repo_id: str) -> Path:
+        """Download or get cached tokenizer files."""
+        return Path(
+            snapshot_download(
+                repo_id=repo_id,
+                allow_patterns=[
+                    "tokenizer/**",
+                    "added_tokens.json",
+                    "chat_template.jinja",
+                ],
+            )
+        )
diff --git a/src/mflux/models/qwen/variants/__init__.py b/src/mflux/models/qwen/variants/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/variants/txt2img/__init__.py b/src/mflux/models/qwen/variants/txt2img/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/variants/txt2img/qwen_image.py b/src/mflux/models/qwen/variants/txt2img/qwen_image.py
new file mode 100644
index 0000000..e79d8de
--- /dev/null
+++ b/src/mflux/models/qwen/variants/txt2img/qwen_image.py
@@ -0,0 +1,175 @@
+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.error.exceptions import StopImageGenerationException
+from mflux.latent_creator.latent_creator import Img2Img, LatentCreator
+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.post_processing.array_util import ArrayUtil
+from mflux.post_processing.generated_image import GeneratedImage
+from mflux.post_processing.image_util import ImageUtil
+
+
+class QwenImage(nn.Module):
+    vae: QwenVAE
+    transformer: QwenTransformer
+    text_encoder: QwenTextEncoder
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        quantize: int | None = None,
+        local_path: str | None = None,
+        lora_paths: list[str] | None = None,
+        lora_scales: list[float] | None = None,
+        lora_names: list[str] | None = None,
+        lora_repo_id: str | None = None,
+    ):
+        super().__init__()
+        QwenImageInitializer.init(
+            qwen_model=self,
+            model_config=model_config,
+            quantize=quantize,
+            local_path=local_path,
+            lora_paths=lora_paths,
+            lora_scales=lora_scales,
+            lora_names=lora_names,
+            lora_repo_id=lora_repo_id,
+        )
+
+    def generate_image(
+        self,
+        seed: int,
+        prompt: str,
+        config: Config,
+        negative_prompt: str | None = None,
+        prompt_embeds: mx.array | None = None,
+        prompt_mask: mx.array | None = None,
+        negative_prompt_embeds: mx.array | None = None,
+        negative_prompt_mask: mx.array | 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,
+                sigmas=runtime_config.sigmas,
+                init_time_step=runtime_config.init_time_step,
+                image_path=runtime_config.image_path,
+            ),
+        )
+
+        # 2. Encode the prompt
+        prompt_embeds, prompt_mask, negative_prompt_embeds, negative_prompt_mask = QwenPromptEncoder.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_cache=self.prompt_cache,
+            qwen_tokenizer=self.qwen_tokenizer,
+            qwen_text_encoder=self.text_encoder,
+        )
+
+        # (Optional) Call subscribers for beginning of loop
+        Callbacks.before_loop(
+            seed=seed,
+            prompt=prompt,
+            latents=latents,
+            config=runtime_config,
+        )
+
+        for t in time_steps:
+            try:
+                # 3. Predict the noise
+                noise = self.transformer(
+                    t=t,
+                    config=runtime_config,
+                    hidden_states=latents,
+                    encoder_hidden_states=prompt_embeds,
+                    encoder_hidden_states_mask=prompt_mask,
+                )
+                noise_negative = self.transformer(
+                    t=t,
+                    config=runtime_config,
+                    hidden_states=latents,
+                    encoder_hidden_states=negative_prompt_embeds,
+                    encoder_hidden_states_mask=negative_prompt_mask,
+                )
+                guided_noise = QwenImage._compute_guided_noise(noise, noise_negative, runtime_config.guidance)
+
+                # 4.t Take one denoise step
+                dt = runtime_config.sigmas[t + 1] - runtime_config.sigmas[t]
+                latents = latents + guided_noise * dt
+
+                # (Optional) Call subscribers in-loop
+                Callbacks.in_loop(
+                    t=t,
+                    seed=seed,
+                    prompt=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=prompt,
+                    latents=latents,
+                    config=runtime_config,
+                    time_steps=time_steps,
+                )
+                raise StopImageGenerationException(f"Stopping image generation at step {t + 1}/{len(time_steps)}")
+
+        # (Optional) Call subscribers after loop
+        Callbacks.after_loop(
+            seed=seed,
+            prompt=prompt,
+            latents=latents,
+            config=runtime_config,
+        )
+
+        # 7. Decode the latent array and return the image
+        latents = ArrayUtil.unpack_latents(latents=latents, height=runtime_config.height, width=runtime_config.width)
+        decoded = self.vae.decode(latents)
+        return ImageUtil.to_image(
+            decoded_latents=decoded,
+            config=runtime_config,
+            seed=seed,
+            prompt=prompt,
+            quantization=self.bits,
+            lora_paths=self.lora_paths,
+            lora_scales=self.lora_scales,
+            image_path=runtime_config.image_path,
+            image_strength=runtime_config.image_strength,
+            generation_time=time_steps.format_dict["elapsed"],
+            negative_prompt=negative_prompt,
+        )
+
+    @staticmethod
+    def _compute_guided_noise(
+        noise: mx.array,
+        noise_negative: mx.array,
+        guidance: float,
+    ) -> mx.array:
+        combined = noise_negative + guidance * (noise - noise_negative)
+        cond_norm = mx.sqrt(mx.sum(noise * noise, axis=-1, keepdims=True) + 1e-12)
+        noise_norm = mx.sqrt(mx.sum(combined * combined, axis=-1, keepdims=True) + 1e-12)
+        noise = combined * (cond_norm / noise_norm)
+        return noise
diff --git a/src/mflux/models/qwen/weights/__init__.py b/src/mflux/models/qwen/weights/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/mflux/models/qwen/weights/qwen_lora_mapping.py b/src/mflux/models/qwen/weights/qwen_lora_mapping.py
new file mode 100644
index 0000000..d1a8822
--- /dev/null
+++ b/src/mflux/models/qwen/weights/qwen_lora_mapping.py
@@ -0,0 +1,164 @@
+from typing import List
+
+from mflux.models.common.lora.mapping.lora_mapping import LoRAMapping, LoRATarget
+
+
+class QwenLoRAMapping(LoRAMapping):
+
+    @staticmethod
+    def get_mapping() -> List[LoRATarget]:
+        return [
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.to_q",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.to_q.lora_up.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_q.lora.up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.to_q.lora_down.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_q.lora.down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.to_q.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.to_k",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.to_k.lora_up.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_k.lora.up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.to_k.lora_down.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_k.lora.down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.to_k.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.to_v",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.to_v.lora_up.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_v.lora.up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.to_v.lora_down.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_v.lora.down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.to_v.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.attn_to_out.0",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.to_out.0.lora_up.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_out.0.lora.up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.to_out.0.lora_down.weight",
+                    "transformer.transformer_blocks.{block}.attn.to_out.0.lora.down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.to_out.0.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.add_q_proj",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.add_q_proj.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.add_q_proj.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.add_q_proj.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.add_k_proj",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.add_k_proj.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.add_k_proj.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.add_k_proj.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.add_v_proj",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.add_v_proj.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.add_v_proj.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.add_v_proj.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.attn.to_add_out",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.attn.to_add_out.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.attn.to_add_out.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.attn.to_add_out.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.img_ff.mlp_in",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.img_mlp.net.0.proj.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.img_mlp.net.0.proj.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.img_mlp.net.0.proj.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.img_ff.mlp_out",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.img_mlp.net.2.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.img_mlp.net.2.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.img_mlp.net.2.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.txt_ff.mlp_in",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.txt_mlp.net.0.proj.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.txt_mlp.net.0.proj.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.txt_mlp.net.0.proj.alpha",
+                ]
+            ),
+            LoRATarget(
+                model_path="transformer_blocks.{block}.txt_ff.mlp_out",
+                possible_up_patterns=[
+                    "transformer_blocks.{block}.txt_mlp.net.2.lora_up.weight",
+                ],
+                possible_down_patterns=[
+                    "transformer_blocks.{block}.txt_mlp.net.2.lora_down.weight",
+                ],
+                possible_alpha_patterns=[
+                    "transformer_blocks.{block}.txt_mlp.net.2.alpha",
+                ]
+        ),
+    ]
+
diff --git a/src/mflux/models/qwen/weights/qwen_weight_handler.py b/src/mflux/models/qwen/weights/qwen_weight_handler.py
new file mode 100644
index 0000000..b70e04d
--- /dev/null
+++ b/src/mflux/models/qwen/weights/qwen_weight_handler.py
@@ -0,0 +1,740 @@
+import json
+from pathlib import Path
+
+import mlx.core as mx
+from safetensors.mlx import load_file as mlx_load_file
+
+from mflux.models.flux.weights.weight_handler import MetaData, WeightHandler
+from mflux.models.qwen.weights.qwen_weight_util import QwenWeightUtil
+
+
+class QwenWeightHandler:
+    def __init__(
+        self,
+        meta_data: MetaData,
+        qwen_text_encoder: dict | None = None,
+        transformer: dict | None = None,
+        vae: dict | None = None,
+    ):
+        self.qwen_text_encoder = qwen_text_encoder
+        self.transformer = transformer
+        self.vae = vae
+        self.meta_data = meta_data
+
+    def num_transformer_blocks(self) -> int:
+        return (
+            len(self.transformer["transformer_blocks"])
+            if self.transformer and "transformer_blocks" in self.transformer
+            else 0
+        )
+
+    @staticmethod
+    def load_regular_weights(
+        repo_id: str | None = None,
+        local_path: str | None = None,
+    ) -> "QwenWeightHandler":
+        # Load the weights from disk, huggingface cache, or download from huggingface
+        root_path = Path(local_path) if local_path else WeightHandler.download_or_get_cached_weights(repo_id)
+
+        # Load the weights
+        transformer, quantization_level, mflux_version = QwenWeightHandler.load_transformer(root_path=root_path)
+        qwen_text_encoder, _, _ = QwenWeightHandler._load_qwen_text_encoder(root_path=root_path)
+        vae, _, _ = QwenWeightHandler._load_vae(root_path=root_path)
+
+        return QwenWeightHandler(
+            qwen_text_encoder=qwen_text_encoder,
+            transformer=transformer,
+            vae=vae,
+            meta_data=MetaData(
+                quantization_level=quantization_level,
+                scale=None,
+                is_lora=False,
+                mflux_version=mflux_version,
+            ),
+        )
+
+    @staticmethod
+    def load_transformer(root_path: Path) -> tuple[dict, int | None, str | None]:
+        flat = QwenWeightHandler._load_safetensors_shards(root_path / "transformer", loading_mode="multi_glob")
+        mapped_weights = QwenWeightHandler._manual_transformer_mapping(flat)
+        return mapped_weights, None, None
+
+    @staticmethod
+    def _load_qwen_text_encoder(root_path: Path) -> tuple[dict, int | None, str | None]:
+        all_weights = QwenWeightHandler._load_safetensors_shards(root_path / "text_encoder", loading_mode="multi_json")
+        mapped_weights = QwenWeightHandler._manual_text_encoder_mapping(all_weights)
+        return mapped_weights, None, None
+
+    @staticmethod
+    def _load_vae(root_path: Path) -> tuple[dict, int | None, str | None]:
+        weights = QwenWeightHandler._load_safetensors_shards(root_path / "vae", loading_mode="single")
+        reshaped_weights = [QwenWeightUtil.reshape_weights(k, v) for k, v in weights.items()]
+        reshaped_weights = QwenWeightUtil.flatten(reshaped_weights)
+        weights = dict(reshaped_weights)
+        mapped_weights = QwenWeightHandler._manual_flux_style_mapping(weights)
+        return mapped_weights, None, None
+
+    @staticmethod
+    def _manual_flux_style_mapping(diffusers_weights: dict) -> dict:
+        weights = {}
+
+        # 1. Simple direct mappings (like Flux does)
+        weights["decoder"] = {}
+
+        # conv_in: decoder.conv_in.weight -> decoder.conv_in.conv3d.weight
+        weights["decoder"]["conv_in"] = {
+            "conv3d": {
+                "weight": diffusers_weights["decoder.conv_in.weight"],
+                "bias": diffusers_weights["decoder.conv_in.bias"],
+            }
+        }
+
+        # conv_out: decoder.conv_out.weight -> decoder.conv_out.conv3d.weight
+        weights["decoder"]["conv_out"] = {
+            "conv3d": {
+                "weight": diffusers_weights["decoder.conv_out.weight"],
+                "bias": diffusers_weights["decoder.conv_out.bias"],
+            }
+        }
+
+        # norm_out: decoder.norm_out.gamma -> decoder.norm_out.weight (flatten to 1D like MLX expects)
+        dec_gamma = diffusers_weights["decoder.norm_out.gamma"]
+        if len(dec_gamma.shape) > 1:
+            dec_gamma = mx.reshape(dec_gamma, (dec_gamma.shape[0],))
+        weights["decoder"]["norm_out"] = {"weight": dec_gamma}
+
+        # post_quant_conv: post_quant_conv.weight -> post_quant_conv.conv3d.weight
+        weights["post_quant_conv"] = {
+            "conv3d": {
+                "weight": diffusers_weights["post_quant_conv.weight"],
+                "bias": diffusers_weights["post_quant_conv.bias"],
+            }
+        }
+
+        # 2. Mid block (manual structure building)
+        weights["decoder"]["mid_block"] = {}
+
+        # mid_block.resnets (list of 2 resnets)
+        weights["decoder"]["mid_block"]["resnets"] = [{}, {}]
+        for i in range(2):
+            resnet = weights["decoder"]["mid_block"]["resnets"][i]
+            # conv1.weight -> conv1.conv3d.weight
+            resnet["conv1"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv1.weight"],
+                    "bias": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv1.bias"],
+                }
+            }
+            # conv2.weight -> conv2.conv3d.weight
+            resnet["conv2"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv2.weight"],
+                    "bias": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv2.bias"],
+                }
+            }
+            # norm gammas -> 1D weights
+            g1 = diffusers_weights[f"decoder.mid_block.resnets.{i}.norm1.gamma"]
+            if len(g1.shape) > 1:
+                g1 = mx.reshape(g1, (g1.shape[0],))
+            g2 = diffusers_weights[f"decoder.mid_block.resnets.{i}.norm2.gamma"]
+            if len(g2.shape) > 1:
+                g2 = mx.reshape(g2, (g2.shape[0],))
+            resnet["norm1"] = {"weight": g1}
+            resnet["norm2"] = {"weight": g2}
+
+        # mid_block.attentions (list of 1 attention)
+        weights["decoder"]["mid_block"]["attentions"] = [{}]
+        attn = weights["decoder"]["mid_block"]["attentions"][0]
+        g = diffusers_weights["decoder.mid_block.attentions.0.norm.gamma"]
+        if len(g.shape) > 1:
+            g = mx.reshape(g, (g.shape[0],))
+        attn["norm"] = {"weight": g}
+        # Note: to_qkv and proj are Conv2d, not Conv3d - no conv3d wrapper
+        attn["to_qkv"] = {
+            "weight": diffusers_weights["decoder.mid_block.attentions.0.to_qkv.weight"],
+            "bias": diffusers_weights["decoder.mid_block.attentions.0.to_qkv.bias"],
+        }
+        attn["proj"] = {
+            "weight": diffusers_weights["decoder.mid_block.attentions.0.proj.weight"],
+            "bias": diffusers_weights["decoder.mid_block.attentions.0.proj.bias"],
+        }
+
+        # 3. Up blocks (manual structure building)
+        for block_idx in range(4):
+            up_block_key = f"up_block{block_idx}"
+            weights["decoder"][up_block_key] = {}
+
+            # resnets (list of 3 resnets)
+            weights["decoder"][up_block_key]["resnets"] = [{}, {}, {}]
+            for res_idx in range(3):
+                resnet = weights["decoder"][up_block_key]["resnets"][res_idx]
+
+                # conv1.weight -> conv1.conv3d.weight
+                resnet["conv1"] = {
+                    "conv3d": {
+                        "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv1.weight"],
+                        "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv1.bias"],
+                    }
+                }
+                # conv2.weight -> conv2.conv3d.weight
+                resnet["conv2"] = {
+                    "conv3d": {
+                        "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv2.weight"],
+                        "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv2.bias"],
+                    }
+                }
+                # norm gammas -> 1D weights
+                g1 = diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.norm1.gamma"]
+                if len(g1.shape) > 1:
+                    g1 = mx.reshape(g1, (g1.shape[0],))
+                g2 = diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.norm2.gamma"]
+                if len(g2.shape) > 1:
+                    g2 = mx.reshape(g2, (g2.shape[0],))
+                resnet["norm1"] = {"weight": g1}
+                resnet["norm2"] = {"weight": g2}
+
+                # Handle optional conv_shortcut -> skip_conv (only exists for some resnets)
+                shortcut_key = f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv_shortcut.weight"
+                if shortcut_key in diffusers_weights:
+                    resnet["skip_conv"] = {
+                        "conv3d": {
+                            "weight": diffusers_weights[shortcut_key],
+                            "bias": diffusers_weights[
+                                f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv_shortcut.bias"
+                            ],
+                        }
+                    }
+
+            # upsamplers (only for blocks 0, 1, 2)
+            if block_idx <= 2:
+                weights["decoder"][up_block_key]["upsamplers"] = [{}]
+                upsampler = weights["decoder"][up_block_key]["upsamplers"][0]
+
+                # resample.1.weight -> resample_conv.weight (Conv2d, no conv3d wrapper)
+                upsampler["resample_conv"] = {
+                    "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.resample.1.weight"],
+                    "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.resample.1.bias"],
+                }
+
+                # time_conv (only for blocks 0, 1)
+                if block_idx <= 1:
+                    upsampler["time_conv"] = {
+                        "conv3d": {
+                            "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.time_conv.weight"],
+                            "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.time_conv.bias"],
+                        }
+                    }
+
+        # 4. Encoder mappings (mirror structure of model parameter names)
+        weights["encoder"] = {}
+
+        # conv_in: encoder.conv_in.weight -> encoder.conv_in.conv3d.weight
+        weights["encoder"]["conv_in"] = {
+            "conv3d": {
+                "weight": diffusers_weights["encoder.conv_in.weight"],
+                "bias": diffusers_weights["encoder.conv_in.bias"],
+            }
+        }
+
+        # conv_out: encoder.conv_out.weight -> encoder.conv_out.conv3d.weight
+        weights["encoder"]["conv_out"] = {
+            "conv3d": {
+                "weight": diffusers_weights["encoder.conv_out.weight"],
+                "bias": diffusers_weights["encoder.conv_out.bias"],
+            }
+        }
+
+        # norm_out: encoder.norm_out.gamma -> encoder.norm_out.weight (flatten to 1D)
+        enc_gamma = diffusers_weights["encoder.norm_out.gamma"]
+        if len(enc_gamma.shape) > 1:
+            enc_gamma = mx.reshape(enc_gamma, (enc_gamma.shape[0],))
+        weights["encoder"]["norm_out"] = {"weight": enc_gamma}
+
+        # mid_block
+        weights["encoder"]["mid_block"] = {}
+        # mid_block.attentions (list of 1)
+        weights["encoder"]["mid_block"]["attentions"] = [{}]
+        enc_attn = weights["encoder"]["mid_block"]["attentions"][0]
+        g = diffusers_weights["encoder.mid_block.attentions.0.norm.gamma"]
+        if len(g.shape) > 1:
+            g = mx.reshape(g, (g.shape[0],))
+        enc_attn["norm"] = {"weight": g}
+        enc_attn["to_qkv"] = {
+            "weight": diffusers_weights["encoder.mid_block.attentions.0.to_qkv.weight"],
+            "bias": diffusers_weights["encoder.mid_block.attentions.0.to_qkv.bias"],
+        }
+        enc_attn["proj"] = {
+            "weight": diffusers_weights["encoder.mid_block.attentions.0.proj.weight"],
+            "bias": diffusers_weights["encoder.mid_block.attentions.0.proj.bias"],
+        }
+        # mid_block.resnets (list of 2)
+        weights["encoder"]["mid_block"]["resnets"] = [{}, {}]
+        for i in range(2):
+            res = weights["encoder"]["mid_block"]["resnets"][i]
+            g1 = diffusers_weights[f"encoder.mid_block.resnets.{i}.norm1.gamma"]
+            if len(g1.shape) > 1:
+                g1 = mx.reshape(g1, (g1.shape[0],))
+            g2 = diffusers_weights[f"encoder.mid_block.resnets.{i}.norm2.gamma"]
+            if len(g2.shape) > 1:
+                g2 = mx.reshape(g2, (g2.shape[0],))
+            res["norm1"] = {"weight": g1}
+            res["norm2"] = {"weight": g2}
+            res["conv1"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv1.weight"],
+                    "bias": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv1.bias"],
+                }
+            }
+            res["conv2"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv2.weight"],
+                    "bias": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv2.bias"],
+                }
+            }
+
+        # down_blocks - simplified mapping like decoder
+        # From safetensor keys, encoder has flattened indices 0-9 that need to be grouped into 4 stages
+        # Stage 0: indices 0,1 (2 resnets, no downsampler)
+        # Stage 1: indices 2,3,4 (downsampler at 2, resnets at 3,4)
+        # Stage 2: indices 5,6,7 (downsampler at 5, resnets at 6,7)
+        # Stage 3: indices 8,9 (downsampler at 8, resnet at 9)
+
+        weights["encoder"]["down_blocks"] = [{}, {}, {}, {}]
+
+        # Stage 0: 2 resnets + downsampler (2D)
+        weights["encoder"]["down_blocks"][0]["resnets"] = [{}, {}]
+        for res_idx in range(2):
+            flat_idx = res_idx  # 0, 1
+            resnet = weights["encoder"]["down_blocks"][0]["resnets"][res_idx]
+            g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"]
+            if len(g1.shape) > 1:
+                g1 = mx.reshape(g1, (g1.shape[0],))
+            g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"]
+            if len(g2.shape) > 1:
+                g2 = mx.reshape(g2, (g2.shape[0],))
+            resnet["norm1"] = {"weight": g1}
+            resnet["norm2"] = {"weight": g2}
+            resnet["conv1"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"],
+                }
+            }
+            resnet["conv2"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"],
+                }
+            }
+        # Downsampler appears after indices 0,1 at flattened index 2
+        weights["encoder"]["down_blocks"][0]["downsamplers"] = [{}]
+        d0 = weights["encoder"]["down_blocks"][0]["downsamplers"][0]
+        d0["resample_conv"] = {
+            "weight": diffusers_weights["encoder.down_blocks.2.resample.1.weight"],
+            "bias": diffusers_weights["encoder.down_blocks.2.resample.1.bias"],
+        }
+
+        # Stage 1: downsampler + 2 resnets
+        weights["encoder"]["down_blocks"][1]["resnets"] = [{}, {}]
+        weights["encoder"]["down_blocks"][1]["downsamplers"] = [{}]
+        # Downsampler at flattened index 5 (after stage0's 0,1,2 and stage1's 3,4)
+        d = weights["encoder"]["down_blocks"][1]["downsamplers"][0]
+        d["resample_conv"] = {
+            "weight": diffusers_weights["encoder.down_blocks.5.resample.1.weight"],
+            "bias": diffusers_weights["encoder.down_blocks.5.resample.1.bias"],
+        }
+        # Resnets at indices 3, 4
+        for res_idx in range(2):
+            flat_idx = 3 + res_idx  # 3, 4
+            resnet = weights["encoder"]["down_blocks"][1]["resnets"][res_idx]
+            g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"]
+            if len(g1.shape) > 1:
+                g1 = mx.reshape(g1, (g1.shape[0],))
+            g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"]
+            if len(g2.shape) > 1:
+                g2 = mx.reshape(g2, (g2.shape[0],))
+            resnet["norm1"] = {"weight": g1}
+            resnet["norm2"] = {"weight": g2}
+            resnet["conv1"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"],
+                }
+            }
+            resnet["conv2"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"],
+                }
+            }
+            # Skip conv only for first resnet (index 3)
+            if flat_idx == 3:
+                resnet["skip_conv"] = {
+                    "conv3d": {
+                        "weight": diffusers_weights["encoder.down_blocks.3.conv_shortcut.weight"],
+                        "bias": diffusers_weights["encoder.down_blocks.3.conv_shortcut.bias"],
+                    }
+                }
+
+        # Stage 2: downsampler + 2 resnets
+        weights["encoder"]["down_blocks"][2]["resnets"] = [{}, {}]
+        weights["encoder"]["down_blocks"][2]["downsamplers"] = [{}]
+        # Downsampler at flattened index 8
+        d = weights["encoder"]["down_blocks"][2]["downsamplers"][0]
+        d["resample_conv"] = {
+            "weight": diffusers_weights["encoder.down_blocks.8.resample.1.weight"],
+            "bias": diffusers_weights["encoder.down_blocks.8.resample.1.bias"],
+        }
+        d["time_conv"] = {
+            "conv3d": {
+                "weight": diffusers_weights["encoder.down_blocks.8.time_conv.weight"],
+                "bias": diffusers_weights["encoder.down_blocks.8.time_conv.bias"],
+            }
+        }
+        # Resnets at indices 6, 7
+        for res_idx in range(2):
+            flat_idx = 6 + res_idx  # 6, 7
+            resnet = weights["encoder"]["down_blocks"][2]["resnets"][res_idx]
+            g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"]
+            if len(g1.shape) > 1:
+                g1 = mx.reshape(g1, (g1.shape[0],))
+            g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"]
+            if len(g2.shape) > 1:
+                g2 = mx.reshape(g2, (g2.shape[0],))
+            resnet["norm1"] = {"weight": g1}
+            resnet["norm2"] = {"weight": g2}
+            resnet["conv1"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"],
+                }
+            }
+            resnet["conv2"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"],
+                }
+            }
+            # Skip conv only for first resnet (index 6)
+            if flat_idx == 6:
+                resnet["skip_conv"] = {
+                    "conv3d": {
+                        "weight": diffusers_weights["encoder.down_blocks.6.conv_shortcut.weight"],
+                        "bias": diffusers_weights["encoder.down_blocks.6.conv_shortcut.bias"],
+                    }
+                }
+
+        # Stage 3: no downsampler + 2 resnets (like Diffusers)
+        weights["encoder"]["down_blocks"][3]["resnets"] = [{}, {}]
+        # No downsampler in final stage (mirrors diffusers)
+        # Resnets at indices 9, 10
+        for res_idx in range(2):
+            flat_idx = 9 + res_idx  # 9, 10
+            resnet = weights["encoder"]["down_blocks"][3]["resnets"][res_idx]
+            g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"]
+            if len(g1.shape) > 1:
+                g1 = mx.reshape(g1, (g1.shape[0],))
+            g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"]
+            if len(g2.shape) > 1:
+                g2 = mx.reshape(g2, (g2.shape[0],))
+            resnet["norm1"] = {"weight": g1}
+            resnet["norm2"] = {"weight": g2}
+            resnet["conv1"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"],
+                }
+            }
+            resnet["conv2"] = {
+                "conv3d": {
+                    "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"],
+                    "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"],
+                }
+            }
+            # No skip_conv for stage 3 (channels don't change: 384->384)
+
+        # 5. Quant conv
+        weights["quant_conv"] = {
+            "conv3d": {
+                "weight": diffusers_weights["quant_conv.weight"],
+                "bias": diffusers_weights["quant_conv.bias"],
+            }
+        }
+
+        return weights
+
+    @staticmethod
+    def _manual_transformer_mapping(diffusers_weights: dict) -> dict:
+        weights = {}
+
+        # 1. Top-level mappings (exact MLX parameter names)
+        weights["img_in"] = {"weight": diffusers_weights["img_in.weight"], "bias": diffusers_weights["img_in.bias"]}
+        weights["txt_norm"] = {"weight": diffusers_weights["txt_norm.weight"]}
+        weights["txt_in"] = {"weight": diffusers_weights["txt_in.weight"], "bias": diffusers_weights["txt_in.bias"]}
+
+        # 2. Time text embedder (exact MLX structure)
+        weights["time_text_embed"] = {
+            "timestep_embedder": {
+                "linear_1": {
+                    "weight": diffusers_weights["time_text_embed.timestep_embedder.linear_1.weight"],
+                    "bias": diffusers_weights["time_text_embed.timestep_embedder.linear_1.bias"],
+                },
+                "linear_2": {
+                    "weight": diffusers_weights["time_text_embed.timestep_embedder.linear_2.weight"],
+                    "bias": diffusers_weights["time_text_embed.timestep_embedder.linear_2.bias"],
+                },
+            }
+        }
+
+        # 3. Output head (exact MLX structure)
+        weights["norm_out"] = {
+            "linear": {
+                "weight": diffusers_weights["norm_out.linear.weight"],
+                "bias": diffusers_weights["norm_out.linear.bias"],
+            }
+        }
+        weights["proj_out"] = {
+            "weight": diffusers_weights["proj_out.weight"],
+            "bias": diffusers_weights["proj_out.bias"],
+        }
+
+        # 4. Transformer blocks (exact MLX parameter names - no applier needed!)
+        transformer_blocks = []
+        for block_idx in range(60):  # 60 blocks based on debug output
+            block = {}
+
+            # Stage 1: Normalization + modulation modules (QwenLayerNorm)
+            block["img_norm1"] = {
+                "mod_linear": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.img_mod.1.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.img_mod.1.bias"],
+                },
+                "norm1": {},  # LayerNorm with affine=False has no weights
+                "norm2": {},  # LayerNorm with affine=False has no weights
+            }
+            block["txt_norm1"] = {
+                "mod_linear": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mod.1.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mod.1.bias"],
+                },
+                "norm1": {},  # LayerNorm with affine=False has no weights
+                "norm2": {},  # LayerNorm with affine=False has no weights
+            }
+
+            # Stage 2: Separate normalization modules (Flux-style)
+            block["img_norm2"] = {}  # LayerNorm with affine=False has no weights
+            block["txt_norm2"] = {}  # LayerNorm with affine=False has no weights
+
+            # Attention module (nested under "attn" like Flux)
+            block["attn"] = {
+                "to_q": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_q.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_q.bias"],
+                },
+                "to_k": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_k.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_k.bias"],
+                },
+                "to_v": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_v.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_v.bias"],
+                },
+                "add_q_proj": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_q_proj.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_q_proj.bias"],
+                },
+                "add_k_proj": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_k_proj.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_k_proj.bias"],
+                },
+                "add_v_proj": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_v_proj.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_v_proj.bias"],
+                },
+                "norm_q": {"weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_q.weight"]},
+                "norm_k": {"weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_k.weight"]},
+                "norm_added_q": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_added_q.weight"]
+                },
+                "norm_added_k": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_added_k.weight"]
+                },
+                "attn_to_out": [  # type: ignore[dict-item]
+                    {
+                        "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_out.0.weight"],
+                        "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_out.0.bias"],
+                    }
+                ],
+                "to_add_out": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_add_out.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_add_out.bias"],
+                },
+            }
+
+            # Feed Forward modules (nested under img_ff/txt_ff)
+            block["img_ff"] = {
+                "mlp_in": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.0.proj.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.0.proj.bias"],
+                },
+                "mlp_out": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.2.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.2.bias"],
+                },
+            }
+            block["txt_ff"] = {
+                "mlp_in": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.0.proj.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.0.proj.bias"],
+                },
+                "mlp_out": {
+                    "weight": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.2.weight"],
+                    "bias": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.2.bias"],
+                },
+            }
+
+            transformer_blocks.append(block)
+
+        weights["transformer_blocks"] = transformer_blocks
+        return weights
+
+    @staticmethod
+    def _load_safetensors_shards(path: Path, loading_mode: str = "multi_glob") -> dict[str, mx.array]:
+        all_weights = {}
+
+        if loading_mode == "single":
+            # VAE style: Single file loading
+            safetensors_files = list(path.glob("*.safetensors"))
+            if not safetensors_files:
+                raise FileNotFoundError(f"No safetensors files found in {path}")
+
+            weights_file = safetensors_files[0]
+            data = mx.load(str(weights_file), return_metadata=True)
+            all_weights = dict(data[0].items())
+
+        elif loading_mode == "multi_json":
+            # Text encoder style: Use JSON index to map params to files
+            index_path = path / "model.safetensors.index.json"
+            with open(index_path) as f:
+                index = json.load(f)
+
+            # Group weights by file
+            files_to_load = {}
+            for param_name, file_name in index["weight_map"].items():
+                if file_name not in files_to_load:
+                    files_to_load[file_name] = []
+                files_to_load[file_name].append(param_name)
+
+            # Load weights from each file
+            for file_name, param_names in files_to_load.items():
+                file_path = path / file_name
+
+                # Load the safetensor file with fallback to torch conversion
+                try:
+                    file_weights = mlx_load_file(str(file_path))
+                except Exception:  # noqa: BLE001
+                    # If MLX can't load directly, try with torch and convert
+                    import torch
+                    from safetensors.torch import load_file as torch_load_file
+
+                    torch_weights = torch_load_file(str(file_path))
+                    file_weights = {}
+                    for name, tensor in torch_weights.items():
+                        # Convert to float32 if bfloat16, then to MLX
+                        if tensor.dtype == torch.bfloat16:
+                            tensor = tensor.to(torch.float32)
+                        file_weights[name] = mx.array(tensor.numpy())
+
+                # Add requested parameters to combined weights
+                for param_name in param_names:
+                    if param_name in file_weights:
+                        all_weights[param_name] = file_weights[param_name]
+        else:  # "multi_glob"
+            # Transformer style: Directly glob all safetensors files
+            shard_files = sorted([f for f in path.glob("*.safetensors") if not f.name.startswith("._")])
+            if not shard_files:
+                raise FileNotFoundError(f"No safetensors found in {path}")
+
+            for shard in shard_files:
+                data, metadata = mx.load(str(shard), return_metadata=True)
+                all_weights.update(dict(data.items()))
+
+        return all_weights
+
+    @staticmethod
+    def _manual_text_encoder_mapping(hf_weights: dict[str, mx.array]) -> dict:
+        weights = {}
+        converted_count = 0
+        skipped_count = 0
+
+        # Skip LM head and vision encoder weights - we only need the text encoder
+        filtered_weights = {}
+        for hf_name, weight in hf_weights.items():
+            if hf_name.startswith("lm_head") or hf_name.startswith("visual."):
+                skipped_count += 1
+                continue
+            filtered_weights[hf_name] = weight
+
+        # 1. Top-level embeddings (exact MLX parameter names)
+        weights["encoder"] = {}
+        weights["encoder"]["embed_tokens"] = {"weight": filtered_weights["model.embed_tokens.weight"]}
+        converted_count += 1
+
+        # 2. Final norm (exact MLX parameter names)
+        weights["encoder"]["norm"] = {"weight": filtered_weights["model.norm.weight"]}
+        converted_count += 1
+
+        # 3. Encoder layers (exact MLX parameter names - 28 layers)
+        layers = []
+        for layer_idx in range(28):  # 28 text encoder layers
+            layer = {}
+
+            # Layer norms
+            layer["input_layernorm"] = {"weight": filtered_weights[f"model.layers.{layer_idx}.input_layernorm.weight"]}
+            layer["post_attention_layernorm"] = {
+                "weight": filtered_weights[f"model.layers.{layer_idx}.post_attention_layernorm.weight"]
+            }
+            converted_count += 2
+
+            # Self attention (exact MLX parameter names)
+            layer["self_attn"] = {
+                "q_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.q_proj.weight"],
+                    "bias": filtered_weights[f"model.layers.{layer_idx}.self_attn.q_proj.bias"],
+                },
+                "k_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.k_proj.weight"],
+                    "bias": filtered_weights[f"model.layers.{layer_idx}.self_attn.k_proj.bias"],
+                },
+                "v_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.v_proj.weight"],
+                    "bias": filtered_weights[f"model.layers.{layer_idx}.self_attn.v_proj.bias"],
+                },
+                "o_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.o_proj.weight"]
+                    # Note: o_proj has no bias in MLX structure
+                },
+            }
+            converted_count += 7
+
+            # MLP (exact MLX parameter names)
+            layer["mlp"] = {
+                "gate_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.mlp.gate_proj.weight"]
+                    # Note: gate_proj has no bias in MLX structure
+                },
+                "up_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.mlp.up_proj.weight"]
+                    # Note: up_proj has no bias in MLX structure
+                },
+                "down_proj": {
+                    "weight": filtered_weights[f"model.layers.{layer_idx}.mlp.down_proj.weight"]
+                    # Note: down_proj has no bias in MLX structure
+                },
+            }
+            converted_count += 3
+
+            layers.append(layer)
+
+        weights["encoder"]["layers"] = layers
+        return weights
diff --git a/src/mflux/models/qwen/weights/qwen_weight_util.py b/src/mflux/models/qwen/weights/qwen_weight_util.py
new file mode 100644
index 0000000..e7454fd
--- /dev/null
+++ b/src/mflux/models/qwen/weights/qwen_weight_util.py
@@ -0,0 +1,62 @@
+from typing import TYPE_CHECKING
+
+import mlx.nn as nn
+from mlx.utils import tree_flatten  # noqa: F401
+
+from mflux.config.config import Config
+from mflux.utils.quantization_util import QuantizationUtil
+
+if TYPE_CHECKING:
+    from mflux.models.qwen.weights.qwen_weight_handler import QwenWeightHandler
+
+
+class QwenWeightUtil:
+    @staticmethod
+    def flatten(params):
+        return [(k, v) for p in params for (k, v) in p]
+
+    @staticmethod
+    def reshape_weights(key, value):
+        if len(value.shape) == 4:
+            value = value.transpose(0, 2, 3, 1)
+        elif len(value.shape) == 5:
+            value = value.transpose(0, 2, 3, 4, 1)
+        value = value.reshape(-1).reshape(value.shape).astype(Config.precision)
+        return [(key, value)]
+
+    @staticmethod
+    def set_weights_and_quantize(
+        quantize_arg: int | None,
+        weights: "QwenWeightHandler",
+        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:
+            QwenWeightUtil._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
+            QwenWeightUtil._set_model_weights(weights, vae, transformer, text_encoder)
+            QuantizationUtil.quantize_qwen_models(text_encoder, vae, transformer, bits, weights)
+            return bits
+
+        if weights.meta_data.quantization_level is not None:
+            bits = weights.meta_data.quantization_level
+            QuantizationUtil.quantize_qwen_models(text_encoder, vae, transformer, bits, weights)
+            QwenWeightUtil._set_model_weights(weights, vae, transformer, text_encoder)
+            return bits
+
+        raise Exception("Error setting weights")
+
+    @staticmethod
+    def _set_model_weights(
+        weights: "QwenWeightHandler",
+        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.qwen_text_encoder, strict=False)
diff --git a/src/mflux/models/transformer/common/attention_utils.py b/src/mflux/models/transformer/common/attention_utils.py
deleted file mode 100644
index 7674424..0000000
--- a/src/mflux/models/transformer/common/attention_utils.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import mlx.core as mx
-from mlx import nn
-from mlx.core.fast import scaled_dot_product_attention
-
-
-class AttentionUtils:
-    @staticmethod
-    def process_qkv(
-        hidden_states: mx.array,
-        to_q: nn.Linear,
-        to_k: nn.Linear,
-        to_v: nn.Linear,
-        norm_q: nn.RMSNorm,
-        norm_k: nn.RMSNorm,
-        num_heads: int,
-        head_dim: int,
-    ):
-        query = to_q(hidden_states)
-        key = to_k(hidden_states)
-        value = to_v(hidden_states)
-
-        # Reshape and transpose
-        query = mx.transpose(mx.reshape(query, (1, -1, num_heads, head_dim)), (0, 2, 1, 3))
-        key = mx.transpose(mx.reshape(key, (1, -1, num_heads, head_dim)), (0, 2, 1, 3))
-        value = mx.transpose(mx.reshape(value, (1, -1, num_heads, head_dim)), (0, 2, 1, 3))
-
-        # Apply normalization
-        query = norm_q(query)
-        key = norm_k(key)
-
-        return query, key, value
-
-    @staticmethod
-    def compute_attention(
-        query: mx.array,
-        key: mx.array,
-        value: mx.array,
-        batch_size: int,
-        num_heads: int,
-        head_dim: int,
-    ) -> mx.array:
-        scale = 1 / mx.sqrt(query.shape[-1])
-        hidden_states = scaled_dot_product_attention(query, key, value, scale=scale)
-
-        hidden_states = mx.transpose(hidden_states, (0, 2, 1, 3))
-        hidden_states = mx.reshape(
-            hidden_states,
-            (batch_size, -1, num_heads * head_dim),
-        )
-
-        return hidden_states
-
-    @staticmethod
-    def apply_rope(xq: mx.array, xk: mx.array, freqs_cis: mx.array):
-        xq_ = xq.astype(mx.float32).reshape(*xq.shape[:-1], -1, 1, 2)
-        xk_ = xk.astype(mx.float32).reshape(*xk.shape[:-1], -1, 1, 2)
-        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-        return xq_out.reshape(*xq.shape).astype(mx.float32), xk_out.reshape(*xk.shape).astype(mx.float32)
diff --git a/src/mflux/post_processing/generated_image.py b/src/mflux/post_processing/generated_image.py
index 91fb352..e682009 100644
--- a/src/mflux/post_processing/generated_image.py
+++ b/src/mflux/post_processing/generated_image.py
@@ -3,8 +3,8 @@ from pathlib import Path
 import mlx.core as mx
 import PIL.Image
 
-from mflux.community.concept_attention.attention_data import ConceptHeatmap
 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
 
 
@@ -31,6 +31,7 @@ class GeneratedImage:
         redux_image_paths: list[str] | list[Path] | None = None,
         redux_image_strengths: list[float] | None = None,
         concept_heatmap: ConceptHeatmap | None = None,
+        negative_prompt: str | None = None,
     ):
         self.image = image
         self.model_config = model_config
@@ -52,6 +53,7 @@ class GeneratedImage:
         self.redux_image_paths = redux_image_paths
         self.redux_image_strengths = redux_image_strengths
         self.concept_heatmap = concept_heatmap
+        self.negative_prompt = negative_prompt
 
     def get_right_half(self) -> "GeneratedImage":
         # Calculate the coordinates for the right half
@@ -151,4 +153,5 @@ class GeneratedImage:
             "redux_image_paths": str(self.redux_image_paths) if self.redux_image_paths else None,
             "redux_image_strengths": self._format_redux_strengths(),
             "prompt": self.prompt,
+            "negative_prompt": self.negative_prompt if self.negative_prompt else None,
         }
diff --git a/src/mflux/post_processing/image_util.py b/src/mflux/post_processing/image_util.py
index c465108..ce9f483 100644
--- a/src/mflux/post_processing/image_util.py
+++ b/src/mflux/post_processing/image_util.py
@@ -9,8 +9,8 @@ import PIL.Image
 import PIL.ImageDraw
 from PIL._typing import StrOrBytesPath
 
-from mflux.community.concept_attention.attention_data import ConceptHeatmap
 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.ui.box_values import AbsoluteBoxValues, BoxValues
 
@@ -36,6 +36,7 @@ class ImageUtil:
         masked_image_path: str | Path | None = None,
         depth_image_path: str | Path | None = None,
         concept_heatmap: ConceptHeatmap | None = None,
+        negative_prompt: str | None = None,
     ) -> GeneratedImage:
         normalized = ImageUtil._denormalize(decoded_latents)
         normalized_numpy = ImageUtil._to_numpy(normalized)
@@ -61,6 +62,7 @@ class ImageUtil:
             redux_image_paths=redux_image_paths,
             redux_image_strengths=redux_image_strengths,
             concept_heatmap=concept_heatmap,
+            negative_prompt=negative_prompt,
         )
 
     @staticmethod
diff --git a/src/mflux/save.py b/src/mflux/save.py
index 6fd0e81..13790b4 100644
--- a/src/mflux/save.py
+++ b/src/mflux/save.py
@@ -1,5 +1,5 @@
 from mflux.config.model_config import ModelConfig
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.txt2img.flux import Flux1
 from mflux.ui.cli.parsers import CommandLineParser
 
 
diff --git a/src/mflux/train.py b/src/mflux/train.py
index b09aee0..4255aba 100644
--- a/src/mflux/train.py
+++ b/src/mflux/train.py
@@ -1,6 +1,6 @@
-from mflux.dreambooth.dreambooth import DreamBooth
-from mflux.dreambooth.dreambooth_initializer import DreamBoothInitializer
 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
 
 
diff --git a/src/mflux/completions/README.md b/src/mflux/ui/cli/completions/README.md
similarity index 100%
rename from src/mflux/completions/README.md
rename to src/mflux/ui/cli/completions/README.md
diff --git a/src/mflux/completions/__init__.py b/src/mflux/ui/cli/completions/__init__.py
similarity index 100%
rename from src/mflux/completions/__init__.py
rename to src/mflux/ui/cli/completions/__init__.py
diff --git a/src/mflux/completions/generator.py b/src/mflux/ui/cli/completions/generator.py
similarity index 99%
rename from src/mflux/completions/generator.py
rename to src/mflux/ui/cli/completions/generator.py
index 34b0f36..263901b 100644
--- a/src/mflux/completions/generator.py
+++ b/src/mflux/ui/cli/completions/generator.py
@@ -3,7 +3,7 @@
 import argparse
 from pathlib import Path
 
-from mflux.community.in_context.utils.in_context_loras import LORA_NAME_MAP
+from mflux.models.flux.variants.in_context.utils.in_context_loras import LORA_NAME_MAP
 from mflux.ui import defaults as ui_defaults
 from mflux.ui.cli.parsers import CommandLineParser
 
diff --git a/src/mflux/completions/install.py b/src/mflux/ui/cli/completions/install.py
similarity index 98%
rename from src/mflux/completions/install.py
rename to src/mflux/ui/cli/completions/install.py
index 1b0393a..3d6c47d 100644
--- a/src/mflux/completions/install.py
+++ b/src/mflux/ui/cli/completions/install.py
@@ -7,7 +7,7 @@ import subprocess
 import sys
 from pathlib import Path
 
-from mflux.completions.generator import CompletionGenerator
+from mflux.ui.cli.completions.generator import CompletionGenerator
 
 
 def get_zsh_fpath():
diff --git a/src/mflux/ui/cli/parsers.py b/src/mflux/ui/cli/parsers.py
index f904b90..005f340 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.community.in_context.utils.in_context_loras import LORA_NAME_MAP, LORA_REPO_ID
+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.weights.lora_library import get_lora_path
+from mflux.utils.lora_library import get_lora_path
 
 
 class ModelSpecAction(argparse.Action):
@@ -105,6 +105,7 @@ class CommandLineParser(argparse.ArgumentParser):
         prompt_group = self.add_mutually_exclusive_group(required=(require_prompt and not supports_metadata_config))
         prompt_group.add_argument("--prompt", type=str, help="The textual description of the image to generate.")
         prompt_group.add_argument("--prompt-file", type=Path, help="Path to a file containing the prompt text. The file will be re-read before each generation, allowing you to edit the prompt between iterations when using multiple seeds without restarting the program.")
+        self.add_argument("--negative-prompt", type=str, default="", help="The negative prompt to guide what the model should not generate.")
         self.add_argument("--seed", type=int, default=None, nargs='+', help="Specify 1+ Entropy Seeds (Default is 1 time-based random-seed)")
         self.add_argument("--auto-seeds", type=int, default=-1, help="Auto generate N Entropy Seeds (random ints between 0 and 1 billion")
         self._add_image_generator_common_arguments(supports_dimension_scale_factor=supports_dimension_scale_factor)
diff --git a/src/mflux/ui/defaults.py b/src/mflux/ui/defaults.py
index 00dd4d6..80d4205 100644
--- a/src/mflux/ui/defaults.py
+++ b/src/mflux/ui/defaults.py
@@ -17,11 +17,12 @@ GUIDANCE_SCALE_KONTEXT = 2.5
 HEIGHT, WIDTH = 1024, 1024
 MAX_PIXELS_WARNING_THRESHOLD = 2048 * 2048
 IMAGE_STRENGTH = 0.4
-MODEL_CHOICES = ["dev", "schnell", "krea-dev", "dev-krea"]
+MODEL_CHOICES = ["dev", "schnell", "krea-dev", "dev-krea", "qwen"]
 MODEL_INFERENCE_STEPS = {
     "dev": 25,
     "krea-dev": 25,
     "schnell": 4,
+    "qwen": 25,
 }
 QUANTIZE_CHOICES = [3, 5, 4, 6, 8]
 
diff --git a/src/mflux/ui/prompt_utils.py b/src/mflux/ui/prompt_utils.py
index 97a9fb7..fd542c2 100644
--- a/src/mflux/ui/prompt_utils.py
+++ b/src/mflux/ui/prompt_utils.py
@@ -46,3 +46,8 @@ def get_effective_prompt(args: Namespace) -> str:
 
     # 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", "")
diff --git a/src/mflux/upscale.py b/src/mflux/upscale.py
index a28fa81..52acfa7 100644
--- a/src/mflux/upscale.py
+++ b/src/mflux/upscale.py
@@ -5,8 +5,8 @@ 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.controlnet.flux_controlnet import Flux1Controlnet
 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
@@ -34,7 +34,7 @@ def main():
     )
 
     # 2. Register the optional callbacks
-    memory_saver = CallbackManager.register_callbacks(args=args, flux=flux)
+    memory_saver = CallbackManager.register_callbacks(args=args, model=flux)
 
     try:
         # Calculate output dimensions and handle safety warnings
diff --git a/src/mflux/weights/download.py b/src/mflux/utils/download.py
similarity index 100%
rename from src/mflux/weights/download.py
rename to src/mflux/utils/download.py
diff --git a/src/mflux/weights/lora_library.py b/src/mflux/utils/lora_library.py
similarity index 100%
rename from src/mflux/weights/lora_library.py
rename to src/mflux/utils/lora_library.py
diff --git a/src/mflux/weights/quantization_util.py b/src/mflux/utils/quantization_util.py
similarity index 76%
rename from src/mflux/weights/quantization_util.py
rename to src/mflux/utils/quantization_util.py
index e4b763a..6863207 100644
--- a/src/mflux/weights/quantization_util.py
+++ b/src/mflux/utils/quantization_util.py
@@ -3,8 +3,9 @@ from typing import TYPE_CHECKING
 import mlx.nn as nn
 
 if TYPE_CHECKING:
-    from mflux.controlnet.weight_handler_controlnet import WeightHandlerControlnet
-    from mflux.weights.weight_handler import WeightHandler
+    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
 
 
 class QuantizationUtil:
@@ -75,3 +76,19 @@ class QuantizationUtil:
 
         # Only quantize layers that have to_quantized method
         return hasattr(m, "to_quantized")
+
+    @staticmethod
+    def quantize_qwen_models(
+        text_encoder: nn.Module,
+        vae: nn.Module,
+        transformer: nn.Module,
+        quantize: int,
+        weights: "QwenWeightHandler",
+    ) -> None:
+        q_level = weights.meta_data.quantization_level
+
+        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, bits=bits)
+            nn.quantize(transformer, bits=bits)
+            nn.quantize(text_encoder, bits=bits)
diff --git a/tests/arg_parser/test_lora_library_integration.py b/tests/arg_parser/test_lora_library_integration.py
index 2c5a155..0437474 100644
--- a/tests/arg_parser/test_lora_library_integration.py
+++ b/tests/arg_parser/test_lora_library_integration.py
@@ -7,7 +7,7 @@ from unittest import mock
 import pytest
 
 from mflux.ui.cli.parsers import CommandLineParser
-from mflux.weights import lora_library
+from mflux.utils import lora_library
 
 
 @pytest.fixture
diff --git a/tests/depth/test_depth_pro.py b/tests/depth/test_depth_pro.py
index 17e9be2..cafc7fc 100644
--- a/tests/depth/test_depth_pro.py
+++ b/tests/depth/test_depth_pro.py
@@ -1,10 +1,8 @@
 import os
 from pathlib import Path
 
-import numpy as np
-from PIL import Image
-
 from mflux.models.depth_pro.depth_pro import DepthPro
+from tests.image_generation.helpers.image_compare import check_images_close_enough
 
 
 class TestDepthPro:
@@ -26,10 +24,10 @@ class TestDepthPro:
             depth_result.depth_image.save(output_image_path)
 
             # Assert that the generated depth image matches the reference image
-            np.testing.assert_array_almost_equal(
-                np.array(Image.open(output_image_path)),
-                np.array(Image.open(reference_image_path)),
-                err_msg=f"Generated depth image doesn't match reference depth image. Check {output_image_path} vs {reference_image_path}",
+            check_images_close_enough(
+                output_image_path,
+                reference_image_path,
+                "Generated image doesn't match reference image.",
             )
 
         finally:
diff --git a/tests/dreambooth/config/train.json b/tests/dreambooth/config/train.json
index 1dcbbaf..43ee4b8 100644
--- a/tests/dreambooth/config/train.json
+++ b/tests/dreambooth/config/train.json
@@ -61,7 +61,7 @@
     }
   },
   "examples": {
-    "path": "../../../src/mflux/dreambooth/_example/images/",
+    "path": "../../../src/mflux/models/flux/variants/dreambooth/_example/images/",
     "images": [
         {
           "image": "01.jpeg",
diff --git a/tests/dreambooth/test_dreambooth_dataset_iterator.py b/tests/dreambooth/test_dreambooth_dataset_iterator.py
index 82b6fa0..db286e9 100644
--- a/tests/dreambooth/test_dreambooth_dataset_iterator.py
+++ b/tests/dreambooth/test_dreambooth_dataset_iterator.py
@@ -4,9 +4,9 @@ from typing import List, Set
 import mlx.core as mx
 import pytest
 
-from mflux.dreambooth.dataset.batch import Example
-from mflux.dreambooth.dataset.dataset import Dataset
-from mflux.dreambooth.dataset.iterator import Iterator
+from mflux.models.flux.variants.dreambooth.dataset.batch import Example
+from mflux.models.flux.variants.dreambooth.dataset.dataset import Dataset
+from mflux.models.flux.variants.dreambooth.dataset.iterator import Iterator
 
 
 def get_dataset(num_examples: int) -> Dataset:
diff --git a/tests/dreambooth/test_resume_training.py b/tests/dreambooth/test_resume_training.py
index c25ca28..d2e92f5 100644
--- a/tests/dreambooth/test_resume_training.py
+++ b/tests/dreambooth/test_resume_training.py
@@ -6,10 +6,10 @@ import numpy as np
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.dreambooth.dreambooth import DreamBooth
-from mflux.dreambooth.dreambooth_initializer import DreamBoothInitializer
-from mflux.dreambooth.state.zip_util import ZipUtil
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.dreambooth.dreambooth import DreamBooth
+from mflux.models.flux.variants.dreambooth.dreambooth_initializer import DreamBoothInitializer
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.txt2img.flux import Flux1
 
 CHECKPOINT_3 = "tests/dreambooth/tmp/_checkpoints/0000003_checkpoint.zip"
 CHECKPOINT_4 = "tests/dreambooth/tmp/_checkpoints/0000004_checkpoint.zip"
diff --git a/tests/dreambooth/test_train_and_load_weights.py b/tests/dreambooth/test_train_and_load_weights.py
index 1b4b30c..fba2584 100644
--- a/tests/dreambooth/test_train_and_load_weights.py
+++ b/tests/dreambooth/test_train_and_load_weights.py
@@ -5,10 +5,10 @@ import numpy as np
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.dreambooth.dreambooth import DreamBooth
-from mflux.dreambooth.dreambooth_initializer import DreamBoothInitializer
-from mflux.dreambooth.state.zip_util import ZipUtil
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.dreambooth.dreambooth import DreamBooth
+from mflux.models.flux.variants.dreambooth.dreambooth_initializer import DreamBoothInitializer
+from mflux.models.flux.variants.dreambooth.state.zip_util import ZipUtil
+from mflux.models.flux.variants.txt2img.flux import Flux1
 
 CHECKPOINT = "tests/dreambooth/tmp/_checkpoints/0000005_checkpoint.zip"
 OUTPUT_DIR = "tests/dreambooth/tmp/_checkpoints/0000005_checkpoint"
diff --git a/tests/image_generation/helpers/image_generation_concept_test_helper.py b/tests/image_generation/helpers/image_generation_concept_test_helper.py
index c01185c..5402375 100644
--- a/tests/image_generation/helpers/image_generation_concept_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_concept_test_helper.py
@@ -1,10 +1,10 @@
 import os
 from pathlib import Path
 
-from mflux.community.concept_attention.flux_concept import Flux1Concept
-from mflux.community.concept_attention.flux_concept_from_image import Flux1ConceptFromImage
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
+from mflux.models.flux.variants.concept_attention.flux_concept import Flux1Concept
+from mflux.models.flux.variants.concept_attention.flux_concept_from_image import Flux1ConceptFromImage
 
 from .image_compare import check_images_close_enough
 
diff --git a/tests/image_generation/helpers/image_generation_controlnet_test_helper.py b/tests/image_generation/helpers/image_generation_controlnet_test_helper.py
index 998c24f..aec0c30 100644
--- a/tests/image_generation/helpers/image_generation_controlnet_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_controlnet_test_helper.py
@@ -2,7 +2,7 @@ import os
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.controlnet.flux_controlnet import Flux1Controlnet
+from mflux.models.flux.variants.controlnet.flux_controlnet import Flux1Controlnet
 from tests.image_generation.helpers.image_generation_test_helper import ImageGeneratorTestHelper
 
 from .image_compare import check_images_close_enough
diff --git a/tests/image_generation/helpers/image_generation_depth_test_helper.py b/tests/image_generation/helpers/image_generation_depth_test_helper.py
index 5d646d2..52da724 100644
--- a/tests/image_generation/helpers/image_generation_depth_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_depth_test_helper.py
@@ -3,7 +3,7 @@ from pathlib import Path
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.flux_tools.depth.flux_depth import Flux1Depth
+from mflux.models.flux.variants.depth.flux_depth import Flux1Depth
 
 from .image_compare import check_images_close_enough
 
diff --git a/tests/image_generation/helpers/image_generation_fill_test_helper.py b/tests/image_generation/helpers/image_generation_fill_test_helper.py
index 7b61b9e..544251d 100644
--- a/tests/image_generation/helpers/image_generation_fill_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_fill_test_helper.py
@@ -2,7 +2,7 @@ import os
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.flux_tools.fill.flux_fill import Flux1Fill
+from mflux.models.flux.variants.fill.flux_fill import Flux1Fill
 from mflux.ui import defaults as ui_defaults
 from tests.image_generation.helpers.image_generation_test_helper import ImageGeneratorTestHelper
 
diff --git a/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py b/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py
index 6b389ed..c244198 100644
--- a/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py
@@ -1,10 +1,10 @@
 import os
 from pathlib import Path
 
-from mflux.community.in_context.flux_in_context_fill import Flux1InContextFill
-from mflux.community.in_context.utils.in_context_loras import prepare_ic_edit_loras
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
+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 .image_compare import check_images_close_enough
 
diff --git a/tests/image_generation/helpers/image_generation_in_context_test_helper.py b/tests/image_generation/helpers/image_generation_in_context_test_helper.py
index df20d0f..09269b6 100644
--- a/tests/image_generation/helpers/image_generation_in_context_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_in_context_test_helper.py
@@ -1,10 +1,10 @@
 import os
 from pathlib import Path
 
-from mflux.community.in_context.flux_in_context_dev import Flux1InContextDev
-from mflux.community.in_context.utils.in_context_loras import LORA_REPO_ID, get_lora_filename
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
+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 .image_compare import check_images_close_enough
 
diff --git a/tests/image_generation/helpers/image_generation_kontext_test_helper.py b/tests/image_generation/helpers/image_generation_kontext_test_helper.py
index c3b6cdc..195094b 100644
--- a/tests/image_generation/helpers/image_generation_kontext_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_kontext_test_helper.py
@@ -3,7 +3,7 @@ from pathlib import Path
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.kontext.flux_kontext import Flux1Kontext
+from mflux.models.flux.variants.kontext.flux_kontext import Flux1Kontext
 
 from .image_compare import check_images_close_enough
 
diff --git a/tests/image_generation/helpers/image_generation_redux_test_helper.py b/tests/image_generation/helpers/image_generation_redux_test_helper.py
index 16f2dab..b3ae0f5 100644
--- a/tests/image_generation/helpers/image_generation_redux_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_redux_test_helper.py
@@ -3,7 +3,7 @@ from pathlib import Path
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.flux_tools.redux.flux_redux import Flux1Redux
+from mflux.models.flux.variants.redux.flux_redux import Flux1Redux
 
 from .image_compare import check_images_close_enough
 
diff --git a/tests/image_generation/helpers/image_generation_test_helper.py b/tests/image_generation/helpers/image_generation_test_helper.py
index a1731df..c0be630 100644
--- a/tests/image_generation/helpers/image_generation_test_helper.py
+++ b/tests/image_generation/helpers/image_generation_test_helper.py
@@ -1,9 +1,11 @@
 import os
 from pathlib import Path
+from typing import Type, Union
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.txt2img.flux import Flux1
+from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage
 
 from .image_compare import check_images_close_enough
 
@@ -13,16 +15,22 @@ class ImageGeneratorTestHelper:
     def assert_matches_reference_image(
         reference_image_path: str,
         output_image_path: str,
+        model_class: Type[Union[Flux1, QwenImage]],
         model_config: ModelConfig,
         prompt: str,
         steps: int,
         seed: int,
+        quantize=8,
         height: int | None = None,
         width: int | None = None,
         image_path: str | None = None,
         image_strength: float | None = None,
         lora_paths: list[str] | None = None,
         lora_scales: list[float] | None = None,
+        lora_names: list[str] | None = None,
+        lora_repo_id: str | None = None,
+        negative_prompt: str | None = None,
+        guidance: float | None = None,
     ):
         # resolve paths
         reference_image_path = ImageGeneratorTestHelper.resolve_path(reference_image_path)
@@ -31,24 +39,44 @@ class ImageGeneratorTestHelper:
 
         try:
             # given
-            flux = Flux1(
-                model_config=model_config,
-                quantize=8,
-                lora_paths=lora_paths,
-                lora_scales=lora_scales,
-            )
+            model_kwargs = {
+                "model_config": model_config,
+                "quantize": quantize,
+                "lora_paths": lora_paths,
+                "lora_scales": lora_scales,
+            }
+
+            # Add HuggingFace LoRA parameters if provided
+            if lora_names is not None:
+                model_kwargs["lora_names"] = lora_names
+            if lora_repo_id is not None:
+                model_kwargs["lora_repo_id"] = lora_repo_id
+
+            model = model_class(**model_kwargs)
+            config_kwargs = {
+                "num_inference_steps": steps,
+                "image_path": ImageGeneratorTestHelper.resolve_path(image_path),
+                "image_strength": image_strength,
+                "height": height,
+                "width": width,
+            }
+
+            # Add guidance if provided
+            if guidance is not None:
+                config_kwargs["guidance"] = guidance
+
+            generate_kwargs = {
+                "seed": seed,
+                "prompt": prompt,
+                "config": Config(**config_kwargs),
+            }
+
+            # Add negative_prompt for Qwen models
+            if model_class == QwenImage and negative_prompt is not None:
+                generate_kwargs["negative_prompt"] = negative_prompt
+
             # when
-            image = flux.generate_image(
-                seed=seed,
-                prompt=prompt,
-                config=Config(
-                    num_inference_steps=steps,
-                    image_path=ImageGeneratorTestHelper.resolve_path(image_path),
-                    image_strength=image_strength,
-                    height=height,
-                    width=width,
-                ),
-            )
+            image = model.generate_image(**generate_kwargs)
             image.save(path=output_image_path, overwrite=True)
 
             # then
diff --git a/tests/image_generation/test_generate_image.py b/tests/image_generation/test_generate_image.py
index b67c15b..982e0af 100644
--- a/tests/image_generation/test_generate_image.py
+++ b/tests/image_generation/test_generate_image.py
@@ -1,4 +1,6 @@
 from mflux.config.model_config import ModelConfig
+from mflux.models.flux.variants.txt2img.flux import Flux1
+from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage
 from tests.image_generation.helpers.image_generation_test_helper import ImageGeneratorTestHelper
 
 
@@ -7,6 +9,7 @@ class TestImageGenerator:
         ImageGeneratorTestHelper.assert_matches_reference_image(
             reference_image_path="reference_schnell.png",
             output_image_path="output_schnell.png",
+            model_class=Flux1,
             model_config=ModelConfig.schnell(),
             steps=2,
             seed=42,
@@ -19,6 +22,7 @@ class TestImageGenerator:
         ImageGeneratorTestHelper.assert_matches_reference_image(
             reference_image_path="reference_dev.png",
             output_image_path="output_dev.png",
+            model_class=Flux1,
             model_config=ModelConfig.dev(),
             steps=15,
             seed=42,
@@ -31,6 +35,7 @@ class TestImageGenerator:
         ImageGeneratorTestHelper.assert_matches_reference_image(
             reference_image_path="reference_dev_lora.png",
             output_image_path="output_dev_lora.png",
+            model_class=Flux1,
             model_config=ModelConfig.dev(),
             steps=15,
             seed=42,
@@ -45,6 +50,7 @@ class TestImageGenerator:
         ImageGeneratorTestHelper.assert_matches_reference_image(
             reference_image_path="reference_dev_lora_multiple.png",
             output_image_path="output_dev_lora_multiple.png",
+            model_class=Flux1,
             model_config=ModelConfig.dev(),
             steps=15,
             seed=42,
@@ -59,6 +65,7 @@ class TestImageGenerator:
         ImageGeneratorTestHelper.assert_matches_reference_image(
             reference_image_path="reference_dev_image_to_image.png",
             output_image_path="output_dev_image_to_image.png",
+            model_class=Flux1,
             image_strength=0.4,
             model_config=ModelConfig.dev(),
             steps=8,
@@ -68,3 +75,53 @@ class TestImageGenerator:
             image_path="reference_dev_lora.png",
             prompt="Luxury food photograph of a burger",
         )
+
+    def test_qwen_image_generation_text_to_image(self):
+        ImageGeneratorTestHelper.assert_matches_reference_image(
+            reference_image_path="reference_qwen_txt2img.png",
+            output_image_path="output_qwen_txt2img.png",
+            model_class=QwenImage,
+            model_config=ModelConfig.qwen_image(),
+            quantize=6,
+            steps=20,
+            seed=42,
+            height=341,
+            width=768,
+            prompt="Luxury food photograph",
+            negative_prompt="ugly, blurry, low quality",
+        )
+
+    def test_qwen_image_generation_image_to_image(self):
+        ImageGeneratorTestHelper.assert_matches_reference_image(
+            reference_image_path="reference_qwen_img2img.png",
+            output_image_path="output_qwen_img2img.png",
+            model_class=QwenImage,
+            model_config=ModelConfig.qwen_image(),
+            quantize=6,
+            steps=20,
+            seed=44,
+            height=341,
+            width=768,
+            image_path="reference_dev_image_to_image.png",
+            image_strength=0.4,
+            prompt="Luxury food photograph of a burger",
+            negative_prompt="ugly, blurry, low quality",
+        )
+
+    def test_qwen_image_generation_lora(self):
+        ImageGeneratorTestHelper.assert_matches_reference_image(
+            reference_image_path="reference_qwen_lora.png",
+            output_image_path="output_qwen_lora.png",
+            model_class=QwenImage,
+            model_config=ModelConfig.qwen_image(),
+            guidance=1.0,
+            quantize=6,
+            steps=4,
+            seed=42,
+            height=341,
+            width=768,
+            prompt="Luxury food photograph",
+            negative_prompt="ugly, blurry, low quality",
+            lora_repo_id="lightx2v/Qwen-Image-Lightning",
+            lora_names=["Qwen-Image-Lightning-4steps-V2.0.safetensors"],
+        )
diff --git a/tests/model_config/test_model_config.py b/tests/model_config/test_model_config.py
index 7345552..315b72a 100644
--- a/tests/model_config/test_model_config.py
+++ b/tests/model_config/test_model_config.py
@@ -224,3 +224,30 @@ def test_model_config_error():
 def test_invalid_base_model_error():
     with pytest.raises(InvalidBaseModel):
         ModelConfig.from_name("acme-lab/some-model-who-knows-what-its-based-on", base_model="something_unknown")
+
+
+def test_qwen_image():
+    model = ModelConfig.from_name("qwen")
+    assert model.model_name == "Qwen/Qwen-Image"
+    assert model.max_sequence_length is None
+    assert model.num_train_steps is None
+    assert model.supports_guidance is None
+    assert model.requires_sigma_shift is None
+
+
+def test_qwen_image_alias():
+    model = ModelConfig.from_name("qwen-image")
+    assert model.model_name == "Qwen/Qwen-Image"
+    assert model.max_sequence_length is None
+    assert model.num_train_steps is None
+    assert model.supports_guidance is None
+    assert model.requires_sigma_shift is None
+
+
+def test_qwen_image_full_name():
+    model = ModelConfig.from_name("Qwen/Qwen-Image")
+    assert model.model_name == "Qwen/Qwen-Image"
+    assert model.max_sequence_length is None
+    assert model.num_train_steps is None
+    assert model.supports_guidance is None
+    assert model.requires_sigma_shift is None
diff --git a/tests/model_saving/test_model_saving.py b/tests/model_saving/test_model_saving.py
index 6f7f7e0..accf641 100644
--- a/tests/model_saving/test_model_saving.py
+++ b/tests/model_saving/test_model_saving.py
@@ -6,9 +6,9 @@ import numpy as np
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.txt2img.flux import Flux1
+from mflux.models.flux.weights.weight_handler import WeightHandler
 from mflux.utils.version_util import VersionUtil
-from mflux.weights.weight_handler import WeightHandler
 
 PATH = "tests/4bit/"
 
diff --git a/tests/model_saving/test_model_saving_lora.py b/tests/model_saving/test_model_saving_lora.py
index 3851f81..f8e8e7b 100644
--- a/tests/model_saving/test_model_saving_lora.py
+++ b/tests/model_saving/test_model_saving_lora.py
@@ -6,7 +6,7 @@ import numpy as np
 
 from mflux.config.config import Config
 from mflux.config.model_config import ModelConfig
-from mflux.flux.flux import Flux1
+from mflux.models.flux.variants.txt2img.flux import Flux1
 
 PATH = "tests/4bit/"
 
diff --git a/tests/resources/reference_qwen_img2img.png b/tests/resources/reference_qwen_img2img.png
new file mode 100644
index 0000000..5e79517
Binary files /dev/null and b/tests/resources/reference_qwen_img2img.png differ
diff --git a/tests/resources/reference_qwen_lora.png b/tests/resources/reference_qwen_lora.png
new file mode 100644
index 0000000..63910f5
Binary files /dev/null and b/tests/resources/reference_qwen_lora.png differ
diff --git a/tests/resources/reference_qwen_txt2img.png b/tests/resources/reference_qwen_txt2img.png
new file mode 100644
index 0000000..0742dea
Binary files /dev/null and b/tests/resources/reference_qwen_txt2img.png differ
diff --git a/tests/weights/test_lora_library.py b/tests/weights/test_lora_library.py
index 716d4d0..e43d7a0 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.weights import lora_library
+from mflux.utils import lora_library
 
 
 @pytest.fixture(autouse=True)
diff --git a/uv.lock b/uv.lock
index ee99516..90f2d61 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,37 +1,80 @@
 version = 1
-revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.13' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
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     { name = "platformdirs" },
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@@ -746,6 +793,8 @@ dev = [
 
 [package.metadata]
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 [[package]]
@@ -2140,62 +2345,79 @@ name = "torch"
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