---
title: FLUX.1-schnell-FP8
canonical_url: "https://www.modelscope.cn/models/unsloth/FLUX.1-schnell-FP8"
md_url: "https://www.modelscope.cn/models/unsloth/FLUX.1-schnell-FP8.md"
repository: unsloth/FLUX.1-schnell-FP8
last_updated: 2026-10-05
license: apache-2.0
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - black-forest-labs/FLUX.1-schnell
base_model_relation: finetune
parameters: 4.9B
tensor_type:
  - F8_E4M3
  - BF16
library_name:
  - pytorch
  - lora
  - safetensors
frameworks:
  - pytorch
language:
  - en
downloads: 759
stars: 3
tags:
  - fp8
  - unsloth
  - quantized
  - text-to-image
  - image-generation
  - flux
---

# FLUX.1-schnell-FP8

> FLUX.1-schnell-FP8 - unsloth 在 ModelScope 开源的模型。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.

unsloth/FLUX.1-schnell-FP8 是 ModelScope 魔搭社区上的 4.9B 参数text-to-image-synthesis模型，采用 apache-2.0 许可，基于 black-forest-labs/FLUX.1-schnell 构建。

- **Repository**: unsloth/FLUX.1-schnell-FP8
- **License**: apache-2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 4.9B
- **Base model**: black-forest-labs/FLUX.1-schnell
- **Tags**: fp8, unsloth, quantized, text-to-image, image-generation, flux
- **Downloads**: 759
- **Stars**: 3
- **Last updated**: 2026-10-05

Source: https://www.modelscope.cn/models/unsloth/FLUX.1-schnell-FP8

---

This is an FP8 / INT8 quantized version of [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell). <br>
- Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.151 INT8, 0.157 FP8.

<div>
  <div style="display: flex; gap: 5px; align-items: center; ">
    <a href="https://github.com/unslothai/unsloth/">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
    </a>
    <a href="https://discord.gg/unsloth">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
    </a>
    <a href="https://docs.unsloth.ai/">
      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
    </a>
  </div>
</div>

---

### Samples

Prompt: "cute sloth typing on a computer"

<table>
  <tr>
    <td align="center"><b>INT8</b></td>
    <td align="center"><b>INT8</b></td>
  </tr>
  <tr>
    <td><img src="samples/int8_1.png" width="300"></td>
    <td><img src="samples/int8_2.png" width="300"></td>
  </tr>
  <tr>
    <td align="center"><b>FP8</b></td>
    <td align="center"><b>FP8</b></td>
  </tr>
  <tr>
    <td><img src="samples/fp8_1.png" width="300"></td>
    <td><img src="samples/fp8_2.png" width="300"></td>
  </tr>
</table>

---

![FLUX.1 [schnell] Grid](./schnell_grid.jpeg)

`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](https://blackforestlabs.ai/announcing-black-forest-labs/).

# Key Features
1. Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives.
2. Trained using latent adversarial diffusion distillation, `FLUX.1 [schnell]` can generate high-quality images in only 1 to 4 steps.
3. 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](https://github.com/black-forest-labs/flux).
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
- [bfl.ml](https://docs.bfl.ml/) (currently `FLUX.1 [pro]`)
- [replicate.com](https://replicate.com/collections/flux)
- [fal.ai](https://fal.ai/models/fal-ai/flux/schnell)
- [mystic.ai](https://www.mystic.ai/black-forest-labs/flux1-schnell)

## ComfyUI
`FLUX.1 [schnell]` is also available in [Comfy UI](https://github.com/comfyanonymous/ComfyUI) for local inference with a node-based workflow.

## Diffusers
To use `FLUX.1 [schnell]` with the 🧨 diffusers python library, first install or upgrade diffusers

```shell
pip install -U diffusers
```

Then you can use `FluxPipeline` to run the model

```python
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", 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,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-schnell.png")
```

To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) 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.

Files:
- FLUX.1-schnell-INT8.pt (15.2 GB)
- FLUX.1-schnell-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.

## Samples

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

**int8**

<img src="samples/int8_1.png" width="260"> <img src="samples/int8_2.png" width="260"> <img src="samples/int8_3.png" width="260">

**fp8**

<img src="samples/fp8_1.png" width="260"> <img src="samples/fp8_2.png" width="260"> <img src="samples/fp8_3.png" width="260">
---

# 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.