---
base_model:
- mikeyandfriends/PixelWave_FLUX.1-schnell_04
base_model_relation: quantized
library_name: diffusers
language:
- en
pipeline_tag: text-to-image
license: apache-2.0
---

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-schnell_04", torch_dtype=torch.bfloat16)
    pipe.enable_model_cpu_offload()
    DFloat11Model.from_pretrained('mingyi456/PixelWave_FLUX.1-schnell_04-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=8,
        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