| | --- |
| | 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)*: |
| |
|
| | ```bash |
| | 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: |
| | ```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 |
