10 actions in 10 pieces of software · ranked by words, meaning and measured trust · 605 ms
modelrecord
- black-forest-labs/flux-1.1-pro-ultra
FLUX1.1 [pro] in ultra and raw modes. Images are up to 4 megapixels. Use raw mode for realism.
- black-forest-labs/flux-1.1-pro-ultra
modelrecord
- black-forest-labs/flux-1.1-pro-ultra-finetuned
Inference model for FLUX 1.1 [pro] Ultra using custom `finetune_id`. Supports 4MP images and raw mode for realism
- black-forest-labs/flux-1.1-pro-ultra-finetuned
modelrecord
- black-forest-labs/flux-1.1-pro
Faster, better FLUX Pro. Text-to-image model with excellent image quality, prompt adherence, and output diversity.
- black-forest-labs/flux-1.1-pro
modelrecord
- black-forest-labs/flux-pro
State-of-the-art image generation with top of the line prompt following, visual quality, image detail and output diversity.
- black-forest-labs/flux-pro
modelrecord
- black-forest-labs/flux-fill-pro
Professional inpainting and outpainting model with state-of-the-art performance. Edit or extend images with natural, seamless results.
- black-forest-labs/flux-fill-pro
modelrecord
- black-forest-labs/flux-pro-finetuned
Inference model for FLUX.1 [pro] using custom `finetune_id`
- black-forest-labs/flux-pro-finetuned
modelrecord
- black-forest-labs/flux-2-pro
High-quality image generation and editing with support for eight reference images
- black-forest-labs/flux-2-pro
modelrecord
- black-forest-labs/flux-depth-pro
Professional depth-aware image generation. Edit images while preserving spatial relationships.
- black-forest-labs/flux-depth-pro
modelrecord
- prunaai/flux.1-dev-lora
This is a 3x faster FLUX.1 [dev] model from Black Forest Labs, optimised with pruna with minimal quality loss.
- prunaai/flux.1-dev-lora
modelrecord
- black-forest-labs/flux-canny-pro
Professional edge-guided image generation. Control structure and composition using Canny edge detection
- black-forest-labs/flux-canny-pro
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.