Z Image Turbo DF11

--- license: apache-2.0 language: - en - zh pipeline_tag: text-to-image tags: - comfyui - diffusion-single-file base_model: - Tongyi-MAI/Z-Image-Turbo base_model_relation: quantized --- For more information (including how to compress models yourself), check out https://huggingface.co/DFloat11 and https://github.com/LeanModels/DFloat11 Feel free to request for other models for compression as well, although models whose architecture I am unfamiliar with might be slightly tricky for me. ### How to Use #### ComfyUI Install my own fork of the DF11 ComfyUI custom node: https://github.com/mingyi456/ComfyUI-DFloat11-Extended. After installing the DF11 custom node, use the provided workflow [json](z_image_turbo_bf16-DF11-workflow.json), or simply replace the "Load Diffusion Model" node of an existing workflow with the "Load Diffusion Model" node. If you run into any issues, feel free to leave a comment. The workflow is also embedded in the below [png](z_image_turbo_bf16-DF11-workflow.png) image.  #### `diffusers` Refer to this [model](https://huggingface.co/mingyi456/Z-Image-Turbo-DF11) instead. ### Compression Details This is the `pattern_dict` for compressing Z-Image-based models in ComfyUI: ```python pattern_dict_comfyui = { r"noise_refiner\.\d+": ( "attention.qkv", "attention.out", "feed_forward.w1", "feed_forward.w2", "feed_forward.w3", "adaLN_modulation.0" ), r"context_refiner\.\d+": ( "attention.qkv", "attention.out", "feed_forward.w1", "feed_forward.w2", "feed_forward.w3", ), r"layers\.\d+": ( "attention.qkv", "attention.out", "feed_forward.w1", "feed_forward.w2", "feed_forward.w3", "adaLN_modulation.0" ) } ```

Source context: 92 downloads · 25 likes · Pipeline text-to-image · Library diffusion-single-file · Repo mingyi456/Z-Image-Turbo-DF11-ComfyUI

Source context: 118 downloads · 23 likes · Pipeline text-to-image · Library diffusion-single-file · Repo mingyi456/Z-Image-Turbo-DF11-ComfyUI