| | --- |
| | 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. |
| |
|
| | **Pipeline Type**: QwenImageLayeredAutoBlocks |
| |
|
| | **Description**: Auto Modular pipeline for layered denoising tasks using QwenImage-Layered. |
| |
|
| | This pipeline uses a 4-block architecture that can be customized and extended. |
| |
|
| | ## Example Usage |
| |
|
| | [TODO] |
| |
|
| | ## Pipeline Architecture |
| |
|
| | This modular pipeline is composed of the following blocks: |
| |
|
| | 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. |
| |
|
| | ## Model Components |
| |
|
| | 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`) |
| |
|
| | ## Input/Output Specification |
| |
|
| | **Inputs:** |
| |
|
| | - `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'. |
| |
|
| | **Outputs:** |
| |
|
| | - `images` (`list`): Generated images. |
| |
|
