README.md

metadata

base_model:
  - mikeyandfriends/PixelWave_FLUX.1-dev_03
base_model_relation: quantized
library_name: diffusers
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
  - en
pipeline_tag: text-to-image

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 I currently only know how to compress models based on the Flux architecture.

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 FluxPipeline
   from dfloat11 import DFloat11Model
   pipe = FluxPipeline.from_pretrained("mikeyandfriends/PixelWave_FLUX.1-dev_03", torch_dtype=torch.bfloat16)
   pipe.enable_model_cpu_offload()
   DFloat11Model.from_pretrained('mingyi456/PixelWave_FLUX.1-dev_03-DF11', device='cpu', bfloat16_model=pipe.transformer)
   prompt = "A futuristic cityscape at sunset, with flying cars, neon lights, and reflective water canals"
   image = pipe(
       prompt,
       guidance_scale=3.5,
       num_inference_steps=30,
       max_sequence_length=256,
       generator=torch.Generator("cpu").manual_seed(0)
   ).images[0]
   image.save("PixelWave_FLUX.1-dev_03.png")
   

ComfyUI

Follow the instructions (have not tested myself) here: https://github.com/LeanModels/ComfyUI-DFloat11