Instructions to use YuCollection/FLUX.1-schnell-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YuCollection/FLUX.1-schnell-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("YuCollection/FLUX.1-schnell-Diffusers", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - text-to-image | |
| - image-generation | |
| - flux | |
| # FLUX.1 [schnell] | |
| > **Note:** This repository is an **archived mirror** and is **not** the original upstream source. | |
| > The original model, weights, and documentation are developed and maintained by **Black Forest Labs**. | |
| > | |
| > All hosted model weights are **unmodified**. | |
| > | |
| > The model is released under the **Apache License, Version 2.0**, which permits use, modification, and redistribution under its terms. | |
| > | |
| > *This repository is not affiliated with or endorsed by Black Forest Labs.* | |
| <p align="center"> | |
| <a href="https://blackforestlabs.ai/announcing-black-forest-labs/" target="_blank"> | |
| <img src="https://img.shields.io/badge/Announcement-Black%20Forest%20Labs-black?style=flat-square"> | |
| </a> | |
| <a href="https://huggingface.co/black-forest-labs/FLUX.1-schnell" target="_blank"> | |
| <img src="https://img.shields.io/badge/Models-Hugging%20Face-yellow?style=flat-square"> | |
| </a> | |
| <a href="https://github.com/black-forest-labs/flux" target="_blank"> | |
| <img src="https://img.shields.io/badge/Source-GitHub-lightgrey?style=flat-square"> | |
| </a> | |
| </p> | |
| `FLUX.1 [schnell]` is a 12-billion-parameter rectified flow transformer capable of generating high-quality images from text descriptions. | |
| # 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 | |
| A full reference implementation of `FLUX.1 [schnell]`, including sampling code, is available in the official GitHub repository: | |
| https://github.com/black-forest-labs/flux | |
| Developers and creators should rely on the upstream GitHub repository when building applications, tools, or fine-tuning pipelines. | |
| ## 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. |