generate_image
Create custom images using text prompts with Stable Diffusion. Customize dimensions, sampling steps, and other parameters to generate tailored visuals for your needs.
Instructions
Generate an image using Stable Diffusion
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| batch_size | No | Number of images to generate (default: 1) | |
| cfg_scale | No | CFG scale (default: 1) | |
| distilled_cfg_scale | No | Distilled CFG scale (default: 3.5) | |
| height | No | Image height (default: 1024) | |
| negative_prompt | No | Things to exclude from the image | |
| output_path | No | Custom output path for the generated image | |
| prompt | Yes | The prompt describing the desired image | |
| restore_faces | No | Enable face restoration | |
| sampler_name | No | Sampling algorithm (default: Euler) | Euler |
| scheduler_name | No | Scheduler algorithm (default: Simple) | Simple |
| seed | No | Random seed (-1 for random) | |
| steps | No | Number of sampling steps (default: 4) | |
| tiling | No | Generate tileable images | |
| width | No | Image width (default: 1024) |
Implementation Reference
- src/index.ts:251-295 (handler)Main handler for 'generate_image' tool: validates input, calls Stable Diffusion txt2img API, decodes and saves images with metadata, returns list of generated image paths.case 'generate_image': { const args = request.params.arguments; if (!isGenerateImageArgs(args)) { throw new McpError(ErrorCode.InvalidParams, 'Invalid parameters'); } const outputDir = args.output_path ? path.normalize(args.output_path.trim()) : DEFAULT_OUTPUT_DIR; await this.ensureDirectoryExists(outputDir); const payload: SDAPIPayload = { prompt: args.prompt, negative_prompt: args.negative_prompt || '', steps: args.steps || 4, width: args.width || 1024, height: args.height || 1024, cfg_scale: args.cfg_scale || 1, sampler_name: args.sampler_name || 'Euler', seed: args.seed ?? -1, n_iter: args.batch_size || 1, distilled_cfg_scale: args.distilled_cfg_scale || 3.5, scheduler: args.scheduler_name || 'Simple', tiling: !!args.tiling, restore_faces: !!args.restore_faces }; const response = await this.axiosInstance.post('/sdapi/v1/txt2img', payload); if (!response.data.images?.length) throw new Error('No images generated'); const results = []; for (const imageData of response.data.images) { const base64Data = imageData.includes(',') ? imageData.split(',')[1] : imageData; const pngInfoResponse = await this.axiosInstance.post('/sdapi/v1/png-info', { image: `data:image/png;base64,${imageData}` }); const outputPath = path.join(outputDir, `sd_${randomUUID()}.png`); const imageBuffer = Buffer.from(base64Data, 'base64'); await sharp(imageBuffer) .withMetadata({ exif: { IFD0: { ImageDescription: pngInfoResponse.data.info } } }) .toFile(outputPath); results.push({ path: outputPath, parameters: pngInfoResponse.data.info }); } return { content: [{ type: 'text', text: JSON.stringify(results) }] }; }
- src/index.ts:31-46 (schema)TypeScript interface defining the input arguments for the generate_image tool.interface GenerateImageArgs { prompt: string; negative_prompt?: string; steps?: number; width?: number; height?: number; cfg_scale?: number; sampler_name?: string; scheduler_name?: string; seed?: number; batch_size?: number; restore_faces?: boolean; tiling?: boolean; output_path?: string; distilled_cfg_scale?: number; }
- src/index.ts:148-171 (registration)Tool registration in the MCP server's tools list, including name, description, and detailed JSON schema for input validation.{ name: 'generate_image', description: 'Generate an image using Stable Diffusion', inputSchema: { type: 'object', properties: { prompt: { type: 'string', description: 'The prompt describing the desired image' }, negative_prompt: { type: 'string', description: 'Things to exclude from the image' }, steps: { type: 'number', description: 'Number of sampling steps (default: 4)', minimum: 1, maximum: 150 }, width: { type: 'number', description: 'Image width (default: 1024)', minimum: 512, maximum: 2048 }, height: { type: 'number', description: 'Image height (default: 1024)', minimum: 512, maximum: 2048 }, cfg_scale: { type: 'number', description: 'CFG scale (default: 1)', minimum: 1, maximum: 30 }, sampler_name: { type: 'string', description: 'Sampling algorithm (default: Euler)', default: 'Euler' }, scheduler_name: { type: 'string', description: 'Scheduler algorithm (default: Simple)', default: 'Simple' }, seed: { type: 'number', description: 'Random seed (-1 for random)', minimum: -1 }, batch_size: { type: 'number', description: 'Number of images to generate (default: 1)', minimum: 1, maximum: 4 }, restore_faces: { type: 'boolean', description: 'Enable face restoration' }, tiling: { type: 'boolean', description: 'Generate tileable images' }, distilled_cfg_scale: { type: 'number', description: 'Distilled CFG scale (default: 3.5)', minimum: 1, maximum: 30 }, output_path: { type: 'string', description: 'Custom output path for the generated image' } }, required: ['prompt'] } },
- src/index.ts:408-430 (helper)Runtime type guard function to validate and cast input arguments to GenerateImageArgs type before handling the tool call.function isGenerateImageArgs(value: unknown): value is GenerateImageArgs { if (typeof value !== 'object' || value === null) return false; const v = value as Record<string, unknown>; // Validate string fields if (typeof v.prompt !== 'string') return false; if (v.negative_prompt !== undefined && typeof v.negative_prompt !== 'string') return false; // Convert and validate numeric fields if (v.steps !== undefined) { const steps = Number(v.steps); if (isNaN(steps) || steps < 1 || steps > 150) return false; v.steps = steps; } if (v.batch_size !== undefined) { const batchSize = Number(v.batch_size); if (isNaN(batchSize) || batchSize < 1 || batchSize > 4) return false; v.batch_size = batchSize; } return true; }