| --- |
| library_name: diffusers |
| tags: |
| - modular-diffusers |
| - diffusers |
| - qwenimage-layered |
| - text-to-image |
| - modular-diffusers |
| - diffusers |
| - qwenimage-layered |
| - text-to-image |
| --- |
| This is a modular diffusion pipeline built with 🧨 Diffusers' modular pipeline framework. |
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| **Pipeline Type**: QwenImageLayeredAutoBlocks |
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| **Description**: Auto Modular pipeline for layered denoising tasks using QwenImage-Layered. |
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| This pipeline uses a 4-block architecture that can be customized and extended. |
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| ## Example Usage |
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| [TODO] |
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| ## Pipeline Architecture |
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| This modular pipeline is composed of the following blocks: |
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| 1. **text_encoder** (`QwenImageLayeredTextEncoderStep`) |
| - QwenImage-Layered Text encoder step that encode the text prompt, will generate a prompt based on image if not provided. |
| 2. **vae_encoder** (`QwenImageLayeredVaeEncoderStep`) |
| - Vae encoder step that encode the image inputs into their latent representations. |
| 3. **denoise** (`QwenImageLayeredCoreDenoiseStep`) |
| - Core denoising workflow for QwenImage-Layered img2img task. |
| 4. **decode** (`QwenImageLayeredDecoderStep`) |
| - Decode unpacked latents (B, C, layers+1, H, W) into layer images. |
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| ## Model Components |
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| 1. image_resize_processor (`VaeImageProcessor`) |
| 2. text_encoder (`Qwen2_5_VLForConditionalGeneration`) |
| 3. processor (`Qwen2VLProcessor`) |
| 4. tokenizer (`Qwen2Tokenizer`): The tokenizer to use |
| 5. guider (`ClassifierFreeGuidance`) |
| 6. image_processor (`VaeImageProcessor`) |
| 7. vae (`AutoencoderKLQwenImage`) |
| 8. pachifier (`QwenImageLayeredPachifier`) |
| 9. scheduler (`FlowMatchEulerDiscreteScheduler`) |
| 10. transformer (`QwenImageTransformer2DModel`) |
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| ## Input/Output Specification |
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| **Inputs:** |
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| - `image` (`Image | list`): Reference image(s) for denoising. Can be a single image or list of images. |
| - `resolution` (`int`, *optional*, defaults to `640`): The target area to resize the image to, can be 1024 or 640 |
| - `prompt` (`str`, *optional*): The prompt or prompts to guide image generation. |
| - `use_en_prompt` (`bool`, *optional*, defaults to `False`): Whether to use English prompt template |
| - `negative_prompt` (`str`, *optional*): The prompt or prompts not to guide the image generation. |
| - `max_sequence_length` (`int`, *optional*, defaults to `1024`): Maximum sequence length for prompt encoding. |
| - `generator` (`Generator`, *optional*): Torch generator for deterministic generation. |
| - `num_images_per_prompt` (`int`, *optional*, defaults to `1`): The number of images to generate per prompt. |
| - `latents` (`Tensor`, *optional*): Pre-generated noisy latents for image generation. |
| - `layers` (`int`, *optional*, defaults to `4`): Number of layers to extract from the image |
| - `num_inference_steps` (`int`, *optional*, defaults to `50`): The number of denoising steps. |
| - `sigmas` (`list`, *optional*): Custom sigmas for the denoising process. |
| - `attention_kwargs` (`dict`, *optional*): Additional kwargs for attention processors. |
| - `**denoiser_input_fields` (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. |
| - `output_type` (`str`, *optional*, defaults to `pil`): Output format: 'pil', 'np', 'pt'. |
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| **Outputs:** |
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| - `images` (`list`): Generated images. |
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