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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-to-image |
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- image-generation |
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- flux |
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--- |
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![FLUX.1 [schnell] Grid](./schnell_grid.jpeg) |
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`FLUX.1 [schnell]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. |
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For more information, please read our [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/). |
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# Key Features |
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1. Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives. |
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2. Trained using latent adversarial diffusion distillation, `FLUX.1 [schnell]` can generate high-quality images in only 1 to 4 steps. |
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3. Released under the `apache-2.0` licence, the model can be used for personal, scientific, and commercial purposes. |
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# Usage |
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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). |
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Developers and creatives looking to build on top of `FLUX.1 [schnell]` are encouraged to use this as a starting point. |
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## API Endpoints |
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The FLUX.1 models are also available via API from the following sources |
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- [bfl.ml](https://docs.bfl.ml/) (currently `FLUX.1 [pro]`) |
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- [replicate.com](https://replicate.com/collections/flux) |
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- [fal.ai](https://fal.ai/models/fal-ai/flux/schnell) |
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- [mystic.ai](https://www.mystic.ai/black-forest-labs/flux1-schnell) |
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## ComfyUI |
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`FLUX.1 [schnell]` is also available in [Comfy UI](https://github.com/comfyanonymous/ComfyUI) for local inference with a node-based workflow. |
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## Diffusers |
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To use `FLUX.1 [schnell]` with the 🧨 diffusers python library, first install or upgrade diffusers |
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```shell |
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pip install -U diffusers |
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``` |
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Then you can use `FluxPipeline` to run the model |
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```python |
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import torch |
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from diffusers import FluxPipeline |
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) |
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pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power |
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prompt = "A cat holding a sign that says hello world" |
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image = pipe( |
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prompt, |
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guidance_scale=0.0, |
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num_inference_steps=4, |
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max_sequence_length=256, |
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generator=torch.Generator("cpu").manual_seed(0) |
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).images[0] |
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image.save("flux-schnell.png") |
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``` |
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To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation |
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--- |
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# Limitations |
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- This model is not intended or able to provide factual information. |
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- As a statistical model this checkpoint might amplify existing societal biases. |
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- The model may fail to generate output that matches the prompts. |
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- Prompt following is heavily influenced by the prompting-style. |
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# Out-of-Scope Use |
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The model and its derivatives may not be used |
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- In any way that violates any applicable national, federal, state, local or international law or regulation. |
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- 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. |
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- To generate or disseminate verifiably false information and/or content with the purpose of harming others. |
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- To generate or disseminate personal identifiable information that can be used to harm an individual. |
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- To harass, abuse, threaten, stalk, or bully individuals or groups of individuals. |
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- To create non-consensual nudity or illegal pornographic content. |
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- For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation. |
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- Generating or facilitating large-scale disinformation campaigns. |
