metadata
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
base_model_relation: quantized
tags:
- quantization
Visual comparison of Flux-dev model outputs using BF16 and torchao int8_weight_only quantization
BF16
torchao int8_weight_only
Usage with Diffusers
To use this quantized FLUX.1 [dev] checkpoint, you need to install the 🧨 diffusers and torchao library:
pip install -U diffusers
pip install -U torchao
After installing the required library, you can run the following script:
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
"diffusers/FLUX.1-dev-torchao-int8",
torch_dtype=torch.bfloat16,
use_safetensors=False,
device_map="balanced"
)
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:
import torch
from diffusers import FluxPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers import TorchAoConfig as DiffusersTorchAoConfig
from transformers import TorchAoConfig as TransformersTorchAoConfig
pipeline_quant_config = PipelineQuantizationConfig(
quant_mapping={
"transformer": DiffusersTorchAoConfig("int8_weight_only"),
"text_encoder_2": TransformersTorchAoConfig("int8_weight_only"),
}
)
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
quantization_config=pipeline_quant_config,
torch_dtype=torch.bfloat16,
device_map="balanced"
)
# safe_serialization set to `False` as we can't save torchao quantized model to safetensors format
pipe.save_pretrained("FLUX.1-dev-torchao-int8", safe_serialization=False)