Tasks and inputs
stable-diffusion-v1-5/stable-diffusion-inpainting is listed for Text-to-image. It accepts text and produces image. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
Hugging Face repository for Text-to-image.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
stable-diffusion-v1-5/stable-diffusion-inpainting is a Hugging Face repository returned by the public Text-to-image task feed. Its recorded download count is 148,169 and it has 116 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Text-to-image. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists diffusers as its library metadata. Its recorded tags include diffusers, stable-diffusion, stable-diffusion-diffusers, text-to-image, arxiv:2207.12598, arxiv:2112.10752, arxiv:2103.00020, arxiv:2205.11487. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Keep prompts, resolution and generation settings comparable. Check composition, prompt adherence, legible text, consistency and failure rates. Record the exact checkpoint and any adapter used.
Explore stable-diffusion-v1-5 models and the text-to-image collection. Use the comparison tool to inspect a second candidate.
Compare the model on the work you need to complete.
stable-diffusion-v1-5/stable-diffusion-inpainting is listed for Text-to-image. It accepts text and produces image. Use the publisher’s input format and preprocessing for your chosen deployment.
Choose the output size and quality your design needs. Include drafts and revisions when budgeting an accepted image.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Alibaba
Qwen-Image-2.1-Turbo creates and edits images, including reference-guided and transparency workflows; hosted billing is separate from local deployment.
Alibaba
Qwen-Image-2.1-Pro creates and edits images, including reference-guided and transparency workflows; hosted billing is separate from local deployment.
Meta
Muse Image 1.0 generates and edits images through conversational or single-shot endpoints, with flat per-image Meta Model API billing.
krea
Krea 2 Raw: a base checkpoint for further training, not the recommended everyday inference release.
krea
Krea 2 Turbo: the post-trained and distilled Krea 2 checkpoint for image generation.
OpenAI
GPT-5.4 Image 2 from OpenAI: Image model for prompt-driven generation, editing, and visual design workflows.