Flux better than Stable Diffusion
FLUX by Black Forest Labs provides three models for text-to-image and image-to-image transformations, requires local installation, supports interactive image generation, and integrates with Hugging Face Diffusers library.
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The GitHub repository for FLUX, developed by Black Forest Labs, provides minimal inference code for text-to-image and image-to-image transformations using Flux latent rectified flow transformers. It offers three models: `FLUX.1 [pro]`, a base model accessible via API; `FLUX.1 [dev]`, a guidance-distilled variant; and `FLUX.1 [schnell]`, a guidance and step-distilled variant. Installation can be done locally by cloning the repository and setting up a Python virtual environment. Users can interactively sample or generate single images by executing specific Python commands. Additionally, the models are integrated with the Hugging Face Diffusers library, facilitating their use in various applications. Access to the pro model is available through an API, which requires user registration and an API key. For further details, users can refer to the FLUX GitHub repository.
- FLUX offers three distinct models for different use cases.
- Installation requires cloning the repository and setting up a Python virtual environment.
- Users can generate images through interactive sampling or single sample commands.
- The models are compatible with the Hugging Face Diffusers library.
- API access for the pro model necessitates registration and an API key.
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3 commentsiirc, a lot of the devs who left Stable Diffusion went on to found/join Black Forest Labs.
FWIW I haven’t tried it for that reason alone. Curious to hear from people plugged into leaderboard competitions — how does this rank objectively? I feel like image models are super hard to evaluate though, to be fair. All I can find is instructions, but no centralized results
Eg https://huggingface.co/docs/diffusers/en/conceptual/evaluati...
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