metadata
library_name: diffusers
license: apache-2.0
int8-wo version of Flux.1-Schnell.
from diffusers import FluxTransformer2DModel
from torchao.quantization import quantize_, int8_weight_only
import torch
ckpt_id = "black-forest-labs/FLUX.1-schnell"
transformer = FluxTransformer2DModel.from_pretrained(
ckpt_id, subfolder="transformer", torch_dtype=torch.bfloat16
)
quantize_(transformer, int8_weight_only())
output_dir = "./flux-schnell-int8wo"
transformer.save_pretrained(output_dir, safe_serialization=False)
save_to = "sayakpaul/flux.1-schell-int8wo-improved"
transformer.push_to_hub(save_to, safe_serialization=False)
Install diffusers, huggingface_hub, ao from the source.
Inference:
import torch
from diffusers import FluxTransformer2DModel, DiffusionPipeline
dtype, device = torch.bfloat16, "cuda"
ckpt_id = "black-forest-labs/FLUX.1-schnell"
model = FluxTransformer2DModel.from_pretrained(
"sayakpaul/flux.1-schell-int8wo-improved", torch_dtype=dtype, use_safetensors=False
)
pipeline = DiffusionPipeline.from_pretrained(ckpt_id, transformer=model, torch_dtype=dtype).to("cuda")
image = pipeline(
"cat", guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256
).images[0]
image.save("flux_schnell_int8.png")