Flux
by Various
Official inference repo for FLUX.1 models
Apps
Flux
Added 1 June 2026
Overview
Official inference implementation for FLUX.1 text-to-image models from Black Forest Labs. Provides Python code to run FLUX.1 variants locally or via API, generating images from text prompts with configurable parameters.
Best for
Best for
Python developers building image generation features who want direct model control and local deployment options
Use cases
- Generate images from text descriptions in Python applications
- Fine-tune or customize FLUX.1 model behavior for specific domains
- Batch process image generation at scale with local compute
Notes
Official inference implementation for FLUX.1 text-to-image models from Black Forest Labs. Provides Python code to run FLUX.1 variants locally or via API, generating images from text prompts with configurable parameters.
25,590 stars on GitHub. Last updated 2025-07-31. Licensed Apache-2.0.
Use cases
- Generate images from text descriptions in Python applications
- Fine-tune or customize FLUX.1 model behavior for specific domains
- Batch process image generation at scale with local compute
Pros
- Official repository with direct access to model weights and inference code
- Supports multiple FLUX.1 variants with documented configuration options
- Active community (25k+ stars) with examples and troubleshooting resources
Cons
- Requires Python environment setup and GPU resources for reasonable inference speed
- Documentation focuses on technical implementation rather than beginner workflows
- Model weights are large, demanding significant storage and bandwidth
Indexed from awesome-generative-ai and enriched against its public facts.
Pros
- Official repository with direct access to model weights and inference code
- Supports multiple FLUX.1 variants with documented configuration options
- Active community (25k+ stars) with examples and troubleshooting resources
Cons
- Requires Python environment setup and GPU resources for reasonable inference speed
- Documentation focuses on technical implementation rather than beginner workflows
- Model weights are large, demanding significant storage and bandwidth
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