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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-to-image
+- image-generation
+- flux
+---
+
+![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.

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vae/config.json

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+  "_diffusers_version": "0.30.0.dev0",
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+  "use_quant_conv": false
+}

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