Private creative AI
Compare local text-to-image and image-editing models by checkpoint, licence and usable GPU memory. Resolution, reference images, encoders, VAEs, ControlNet-style components and batch size all affect the complete pipeline.
Selection approach
Evaluate the full workflow rather than the transformer alone. A commercially restricted checkpoint or a pipeline that relies on heavy CPU offload may not suit an agency or production studio even when it technically loads.
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4 current models
Showing 4 models
Black Forest Labs
A compact rectified-flow model for text-to-image, image editing and multi-reference work, released under Apache 2.0.
Qwen
The December 2025 Qwen-Image update for text-to-image generation, with improved realism, natural detail and text rendering.
Stability AI
An 8B-class text-to-image model in the Stable Diffusion 3.5 family, with a large ecosystem of Diffusers and ComfyUI workflows.
Black Forest Labs
A 9-billion-parameter rectified-flow image model for text-to-image generation and multi-reference image editing.
No model matches that search and workload combination.
Model-to-hardware fit
Use the precise model version, numerical format and complete software pipeline intended for production.
Check whether it can load on one GPU or requires supported multi-GPU loading.
Allow for context, cache, batch, media encoders and concurrent users.
Measure quality, latency and stability on representative work.
Model weights are only one part of the system. Data access, runtime software, storage, monitoring and administrator handover also affect a reliable deployment.
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