Image to Stable Diffusion prompt. Upload a photo to generate positive prompt, negative prompt, style modifiers, and LoRA suggestions.
Choose the type of analysis you want to perform on your image.
Select the AI vision model for analysis.
PNG, JPG or GIF files supported. You can upload multiple images.
Stable Diffusion Prompt Generator analyzes images and creates optimized prompts specifically formatted for Stable Diffusion AI image generation. Stable Diffusion uses a unique prompt structure with positive prompts (what you want) and negative prompts (what to avoid), along with style modifiers, quality boosters, and LoRA (Low-Rank Adaptation) model recommendations. This tool understands Stable Diffusion's prompt syntax, including how to structure positive and negative prompts, which quality tags work best, and which LoRA models might enhance your results. Stable Diffusion's prompt system is more technical than other AI generators - it responds to specific keywords, understands weight modifiers, and can use embeddings and LoRA models to achieve precise styles. This tool translates your reference image into Stable Diffusion's language, generating prompts that include appropriate quality boosters, style modifiers, and negative prompts to avoid common issues like distorted faces, extra limbs, or unwanted artifacts.
Upload your reference image and the AI analyzes visual elements including subject matter, artistic style, color palette, composition, and technical aspects. It then creates a Stable Diffusion prompt with a positive prompt (describing what you want) and a negative prompt (listing what to avoid). The positive prompt includes main subject description, style modifiers that Stable Diffusion recognizes, quality boosters like 'highly detailed' or 'masterpiece', and relevant technical terms. The negative prompt includes common issues to avoid like 'blurry', 'low quality', 'deformed', and other artifacts. The tool also suggests relevant LoRA models and embeddings that could enhance the results. Stable Diffusion prompts use specific formatting with pipes (|) separating positive and negative prompts, and the tool formats everything correctly. It understands which keywords Stable Diffusion responds to best and structures the prompt for optimal results.
Upload the image and you get the full Stable Diffusion package: a positive prompt describing subject and style with quality boosters, a separate negative prompt listing artifacts to suppress, and suggestions for LoRA models or embeddings that fit the aesthetic. Paste each part into its field in AUTOMATIC1111, ComfyUI, or whatever frontend you run.
It is the list of things you are telling the model to avoid: blurry, deformed hands, extra limbs, low quality, watermark. Stable Diffusion checkpoints, especially older ones, genuinely produce fewer artifacts with a decent negative prompt. The generator writes one matched to your image type, anatomy guards for portraits, artifact and noise terms for landscapes.
Yes, when the image's style maps to a recognizable LoRA category (anime styles, photorealistic portraits, specific art aesthetics) the output names the type of LoRA or embedding worth loading. Treat these as starting points: the LoRA ecosystem moves fast and exact availability depends on what you have installed or can pull from Civitai.
Expect style and subject fidelity, not duplication. Results depend heavily on which checkpoint you run, your sampler and CFG settings, and the seed, all outside the prompt's control. The prompt also loses precision when the reference is dark, noisy, or cluttered. For near-copies, use the prompt together with img2img at low denoising.
The descriptive core works across SD 1.5, SDXL, and most fine-tunes; that part is just accurate visual description. The keyword-style quality boosters matter more on 1.5-era checkpoints, while SDXL responds well to plain descriptive language too. Keep the negative prompt either way and trim tags your specific model ignores.
CLIP interrogators output keyword soup: accurate-ish tags with no structure. This produces an ordered positive prompt, a matched negative prompt, and model suggestions in one pass, formatted with the pipe convention frontends expect. You might still run both, but this output is usable immediately without an editing pass.
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