This is an FP8 / INT8 quantized version of FLUX.1-schnell.
Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.151 INT8, 0.157 FP8.
Samples
Prompt: "cute sloth typing on a computer"
INT8
INT8
FP8
FP8
FLUX.1 [schnell] Grid
FLUX.1 [schnell] 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
Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives.
Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps.
Released under the apache-2.0 licence, the model can be used for personal, scientific, and commercial purposes.
Usage
We provide a reference implementation of FLUX.1 [schnell], as well as sampling code, in a dedicated github repository.
Developers and creatives looking to build on top of FLUX.1 [schnell] are encouraged to use this as a starting point.
API Endpoints
The FLUX.1 models are also available via API from the following sources
FLUX.1 [schnell] is also available in Comfy UI for local inference with a node-based workflow.
Diffusers
To use FLUX.1 [schnell] with the 🧨 diffusers python library, first install or upgrade diffusers
pip install -U diffusers
Then you can use FluxPipeline to run the model
python
1import torch
2from diffusers import FluxPipeline
34pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)5pipe.enable_model_cpu_offload()#save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power67prompt ="A cat holding a sign that says hello world"8image = pipe(9 prompt,10 guidance_scale=0.0,11 num_inference_steps=4,12 max_sequence_length=256,13 generator=torch.Generator("cpu").manual_seed(0)14).images[0]15image.save("flux-schnell.png")
To learn more check out the diffusers documentation
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.
Quantized transformer checkpoints (this repo)
This repo adds pre-quantized diffusion transformer checkpoints for black-forest-labs/FLUX.1-schnell, built with torchao
dynamic activation quantization from the dense bf16 transformer. The official model card above
is unchanged from the source repo.
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.
Samples
Prompt: "cute sloth typing on a computer" (1024x1024, family default steps/guidance, seeds 0-2).
int8
fp8
Pre-cast fp8 text encoder (this repo)
FLUX.1-schnell-text_encoder_2-FP8.pt (5.9 GB) is the pipeline's text_encoder_2 (T5EncoderModel,
T5-XXL, the text_encoder_2 subfolder of black-forest-labs/FLUX.1-schnell) with the layerwise fp8
storage cast Unsloth Studio applies at load time, saved pre-cast:
Loading it is bit-identical to downloading the dense encoder and casting on load
(verified tensor for tensor: 220 tensors, 144 cast to fp8 storage; T5's fp32-kept wo
projections stay dense per the runtime cast).
Cuts the T5 download from 9.5 GB to 5.9 GB. The T5 shards are byte-identical across
FLUX.1-schnell, FLUX.1-dev and FLUX.1-Krea-dev (verified sha256), so this one artifact
serves all three bases. The small CLIP-L text encoder stays dense.
Plain-tensor state dict: loads with torch.load(weights_only=True). Metadata records
scheme fp8, component text_encoder_2, base black-forest-labs/FLUX.1-schnell.
![FLUX.1 [schnell] Grid](./schnell_grid.jpeg)
