README.md

---
library_name: diffusers
tags:
- quantization
---

# Usage with Diffusers

To use this quantized FLUX.1 [dev] checkpoint, you need to install the 🧨 diffusers and bitsandbytes library:

```
pip install -U diffusers
pip install -U bitsandbytes
```

After installing the required library, you can run the following script: 

```python
from diffusers import AutoModel, FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-bnb-4bit",
    torch_dtype=torch.bfloat16
)
pipe.to("cuda")

prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."

pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}

image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]

image.save("flux.png")
```


# How to generate this quantized checkpoint ? 

This checkpoint was created with the following script using "black-forest-labs/FLUX.1-dev" checkpoint:

```python

import torch
from diffusers import AutoModel, FluxPipeline
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from diffusers.quantizers import PipelineQuantizationConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig

pipeline_quant_config = PipelineQuantizationConfig(
    quant_mapping={
        "transformer": DiffusersBitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16),
        "text_encoder_2": TransformersBitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16),
    }
)

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    quantization_config=pipeline_quant_config,
    torch_dtype=torch.bfloat16
)

pipe.save_pretrained("FLUX.1-dev-bnb-4bit")
```