MCP Server Replicate

# Template Documentation ## Overview This document provides comprehensive documentation for all templates available in the MCP Server for Replicate. Templates are organized into several categories: 1. Model Parameters 2. Common Configurations 3. Prompt Templates ## Model Parameters ### SDXL Parameters The SDXL template provides parameters optimized for Stable Diffusion XL models. ```python { "prompt": "your detailed prompt", "negative_prompt": "elements to avoid", "width": 1024, # 512-2048, multiple of 8 "height": 1024, # 512-2048, multiple of 8 "num_inference_steps": 50, # 1-150 "guidance_scale": 7.5, # 1-20 "prompt_strength": 1.0, # 0-1 "refine": "expert_ensemble_refiner", # or "no_refiner", "base_image_refiner" "scheduler": "K_EULER", # or "DDIM", "DPM_MULTISTEP", "PNDM", "KLMS" "num_outputs": 1, # 1-4 "high_noise_frac": 0.8, # 0-1 "seed": null, # null or integer "apply_watermark": true } ``` ### SD 1.5 Parameters The SD 1.5 template provides parameters optimized for Stable Diffusion 1.5 models. ```python { "prompt": "your detailed prompt", "negative_prompt": "elements to avoid", "width": 512, # 256-1024, multiple of 8 "height": 512, # 256-1024, multiple of 8 "num_inference_steps": 50, # 1-150 "guidance_scale": 7.5, # 1-20 "scheduler": "K_EULER", # or "DDIM", "DPM_MULTISTEP", "PNDM", "KLMS" "num_outputs": 1, # 1-4 "seed": null, # null or integer "apply_watermark": true } ``` ### ControlNet Parameters The ControlNet template provides parameters for controlled image generation. ```python { "control_image": "image_url_or_base64", "control_mode": "balanced", # or "prompt", "control" "control_scale": 0.9, # 0-2 "begin_control_step": 0.0, # 0-1 "end_control_step": 1.0, # 0-1 "detection_resolution": 512, # 256-1024, multiple of 8 "image_resolution": 512, # 256-1024, multiple of 8 "guess_mode": false, "preprocessor": "canny" # or other preprocessors } ``` ## Common Configurations ### Quality Presets Pre-configured quality settings for different use cases: - `draft`: Fast iterations (20 steps) - `balanced`: General use (30 steps) - `quality`: High quality (50 steps) - `extreme`: Maximum quality (150 steps) ### Style Presets Pre-configured style settings: - `photorealistic`: Highly detailed photo style - `cinematic`: Movie-like dramatic style - `anime`: Anime/manga style - `digital_art`: Modern digital art style - `oil_painting`: Classical painting style ### Aspect Ratio Presets Common aspect ratios with optimal resolutions: - `square`: 1:1 (1024x1024) - `portrait`: 2:3 (832x1216) - `landscape`: 3:2 (1216x832) - `wide`: 16:9 (1344x768) - `mobile`: 9:16 (768x1344) ### Negative Prompt Presets Quality control negative prompts: - `quality_control`: Basic quality control - `strict_quality`: Comprehensive quality control - `photo_quality`: Photo-specific quality control - `artistic_quality`: Art-specific quality control ## Prompt Templates ### Text-to-Image #### Detailed Scene Template ``` {subject} in {setting}, {lighting} lighting, {mood} atmosphere, {style} style, {details} ``` Example: ``` "a young explorer in ancient temple ruins, dramatic golden hour lighting, mysterious atmosphere, cinematic style, vines growing on weathered stone, dust particles in light beams" ``` #### Character Portrait Template ``` {gender} {character_type}, {appearance}, {clothing}, {expression}, {pose}, {style} style, {background} ``` #### Landscape Template ``` {environment} landscape, {time_of_day}, {weather}, {features}, {style} style, {mood} mood ``` ### Image-to-Image #### Style Transfer Template ``` Transform into {style} style, {quality} quality, maintain {preserve} from original ``` #### Variation Template ``` Similar to original but with {changes}, {style} style, {quality} quality ``` ### ControlNet #### Pose-Guided Template ``` {subject} in {pose_description}, {clothing}, {style} style, {background} ``` #### Depth-Guided Template ``` {subject} with {depth_elements}, {perspective}, {style} style ``` ## Best Practices 1. **Parameter Selection** - Start with preset configurations - Adjust parameters gradually - Use appropriate aspect ratios for your use case 2. **Prompt Engineering** - Use detailed, specific descriptions - Include style and quality indicators - Use negative prompts for quality control 3. **ControlNet Usage** - Match detection and output resolutions - Use appropriate preprocessors for your use case - Adjust control scale based on desired influence 4. **Quality Optimization** - Use higher step counts for final outputs - Adjust guidance scale for creativity vs. accuracy - Use refiners for enhanced quality ## Version History - v1.1.0: Added comprehensive parameter descriptions and validation - v1.0.0: Initial release with basic parameters