base_model: black-forest-labs/FLUX.1-dev
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
pipeline_tag: text-to-image
tags:
- flux
- text-to-image
- quantized
- fp8
- e4m3
- diffusers
base_model_relation: quantized
FLUX.1-dev-e4m3
FP8 (e4m3) dynamically-quantized FLUX.1-dev,
saved as a complete FluxPipeline.
What was changed
Every double and single transformer block of the FluxTransformer2DModel is
quantized to e4m3_e4m3_dynamic — float8_e4m3fn weights with dynamically
scaled float8_e4m3fn activations. The rest of the pipeline is unchanged: the
transformer's non-block tensors, the CLIP text encoder and the VAE stay in
fp16, and the T5 text encoder stays in bf16. The transformer shrinks from
~23.8 GB to ~12.0 GB.
This is the same recipe as
wavespeed/FLUX.1-dev-int8
with an fp8 rather than int8 numeric format. FP8 matmul needs Hopper (H100/H200)
or newer; on Ada and older the weights dequantize instead and you lose the speedup.
Quantization was done with WaveSpeed's xelerate.ao.quantize. Weights are
stored as pickled .bin shards, so loading requires use_safetensors=False.
Usage
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
"wavespeed/FLUX.1-dev-e4m3",
torch_dtype=torch.float16,
use_safetensors=False,
).to("cuda")
License
Derived from FLUX.1-dev, so the FLUX.1 [dev] Non-Commercial License applies to these weights and to anything generated with them. Not for commercial use.