mingyi456/Chroma1-HD-DF11

For more information (including how to compress models yourself), check out https://huggingface.co/DFloat11 and https://github.com/LeanModels/DFloat11

This is my first time using DF11 to compress a model outside the Flux architecture. The process for compressing Flux-based models is much more straightforward as compared to other architectures because the compression code requires a pattern_dict as input, but the original example code only provides it for Flux, which meant I had to learn the notation myself and modify it to fit other models. At least Chroma is just a pruned version of Flux, so it was relatively simple to derive the correct pattern_dict this time. Do let me know if you run into any problems.

This is the pattern_dict I used for compression:

pattern_dict = {
    "transformer_blocks\.\d+": (
        "attn.to_q",
        "attn.to_k",
        "attn.to_v",
        "attn.add_k_proj",
        "attn.add_v_proj",
        "attn.add_q_proj",
        "attn.to_out.0",
        "attn.to_add_out",
        "ff.net.0.proj",
        "ff.net.2",
        "ff_context.net.0.proj",
        "ff_context.net.2",
    ),
    "single_transformer_blocks\.\d+": (
        "proj_mlp",
        "proj_out",
        "attn.to_q",
        "attn.to_k",
        "attn.to_v",
    ),
}

How to Use

diffusers

1. Install the DFloat11 pip package (installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed):

   pip install dfloat11[cuda12]
   # or if you have CUDA version 11:
   # pip install dfloat11[cuda11]
   

2. To use the DFloat11 model, run the following example code in Python:

   import torch
   from diffusers import ChromaPipeline, ChromaTransformer2DModel
   from dfloat11 import DFloat11Model
   from transformers.modeling_utils import no_init_weights
   with no_init_weights():
       transformer = ChromaTransformer2DModel.from_config(
           ChromaTransformer2DModel.load_config(
               "lodestones/Chroma1-HD",
               subfolder="transformer"
           ),
           torch_dtype=torch.bfloat16
       ).to(torch.bfloat16)
      
   pipe = ChromaPipeline.from_pretrained(
       "lodestones/Chroma1-HD",
       transformer=transformer,
       torch_dtype=torch.bfloat16
   )
   DFloat11Model.from_pretrained("mingyi456/Chroma1-HD-DF11", device='cpu', bfloat16_model=pipe.transformer)
   pipe.enable_model_cpu_offload()
   prompt = "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
   negative_prompt = "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
   image = pipe(
       prompt,
       negative_prompt=negative_prompt,
       generator=torch.Generator("cpu").manual_seed(0)
   ).images[0]
   image.save("Chroma1-HD.png")
   

ComfyUI

Follow the instructions (have not tested myself) here: https://github.com/LeanModels/ComfyUI-DFloat11 Currently, this model will not work with ComfyUI out of the box, because the custom node currently only supports Flux models. It should be possible to modify the code to successfully load this model as well, but it requires another pattern_dict that is of a completely different form compared to the one used to compress the model. If you are interested in running this model in ComfyUI, please try to contact the developer to request support.