unsloth/FLUX.1-dev-FP8

This is an FP8 / INT8 quantized version of FLUX.1-dev.

• Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.125 INT8, 0.134 FP8.

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Samples

Prompt: "cute sloth typing on a computer"

INT8	INT8
FP8	FP8

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FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.

Key Features

1. Cutting-edge output quality, second only to our state-of-the-art model FLUX.1 [pro].
2. Competitive prompt following, matching the performance of closed source alternatives .
3. Trained using guidance distillation, making FLUX.1 [dev] more efficient.
4. Open weights to drive new scientific research, and empower artists to develop innovative workflows.
5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the FLUX.1 [dev] Non-Commercial License.

Usage

We provide a reference implementation of FLUX.1 [dev], as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of FLUX.1 [dev] are encouraged to use this as a starting point.

API Endpoints

The FLUX.1 models are also available via API from the following sources

• bfl.ml (currently FLUX.1 [pro])
• replicate.com
• fal.ai
• mystic.ai

ComfyUI

FLUX.1 [dev] is also available in Comfy UI for local inference with a node-based workflow.

Diffusers

To use FLUX.1 [dev] with the 🧨 diffusers python library, first install or upgrade diffusers

pip install -U diffusers

Then you can use FluxPipeline to run the model

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=3.5,
    num_inference_steps=50,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-dev.png")

To learn more check out the diffusers documentation

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Limitations

• This model is not intended or able to provide factual information.
• As a statistical model this checkpoint might amplify existing societal biases.
• The model may fail to generate output that matches the prompts.
• Prompt following is heavily influenced by the prompting-style.

Out-of-Scope Use

The model and its derivatives may not be used

• In any way that violates any applicable national, federal, state, local or international law or regulation.
• For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
• To generate or disseminate verifiably false information and/or content with the purpose of harming others.
• To generate or disseminate personal identifiable information that can be used to harm an individual.
• To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
• To create non-consensual nudity or illegal pornographic content.
• For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
• Generating or facilitating large-scale disinformation campaigns.

License

This model falls under the FLUX.1 [dev] Non-Commercial License.

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Quantized transformer checkpoints (this repo)

This repo adds pre-quantized diffusion transformer checkpoints for black-forest-labs/FLUX.1-dev, built with torchao dynamic activation quantization from the dense bf16 transformer. The official model card above is unchanged from the source repo.

Files:

• FLUX.1-dev-INT8.pt (15.2 GB)
• FLUX.1-dev-FP8.pt (11.9 GB)

Details:

• int8: Int8DynamicActivationInt8WeightConfig (per-token activation, per-channel weight, torch._int_mm).
• fp8: Float8DynamicActivationFloat8WeightConfig with PerRow granularity (e4m3, torch._scaled_mm). The loader must floor the dynamic activation scale (activation_value_lb=1e-12 on torchao 0.13+) so all-zero activation token rows cannot produce a zero scale.
• Loading a checkpoint is bit-identical to quantizing the dense bf16 transformer on the fly; the checkpoint skips the dense load and quantize step.
• Validated against same-seed dense bf16 renders (SSIM, LPIPS-vgg, CLIP delta, non-finite and black-frame checks) on torch 2.12.1 and torchao 0.17.

Note: Derived from black-forest-labs/FLUX.1-dev and distributed under the FLUX.1-dev Non-Commercial License. The weights are modified only by the quantization described above. Not an official Black Forest Labs product and not endorsed by Black Forest Labs.

Samples

Prompt: "cute sloth typing on a computer" (1024x1024, family default steps/guidance, seeds 0-2).

int8

fp8