| { |
| "tools": [ |
| { |
| "id": "youtube_video_collector", |
| "name": "ADAM Video Dataset Collector", |
| "description": "Previews and downloads supplied YouTube videos or playlists, normalizes MP4 files, extracts traceable frames, and writes provenance and credits.", |
| "category": "Dataset", |
| "entry_function": "collect_youtube_dataset", |
| "arguments": ["dataset_name", "urls", "output_root", "max_videos", "max_duration_seconds", "max_total_duration_seconds", "max_total_size_mb", "preferred_resolution", "download_audio", "skip_beginning_seconds", "skip_ending_seconds", "mode", "frames_per_second", "max_accepted_frames", "remove_blurry_frames", "remove_black_frames", "remove_near_duplicates", "duplicate_threshold", "keep_mp4", "separate_source_folders", "mix_accepted_frames", "generate_captions", "generate_credits", "save_exact_timestamps", "dry_run", "permission_status", "retry_limit"], |
| "required_arguments": ["dataset_name", "urls"], |
| "capabilities": ["manual_urls", "playlist_limit", "metadata_preview", "mp4_normalization", "frame_provenance", "progress", "pause", "cancel"], |
| "requires_confirmation": true, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.youtube_video_collector", |
| "function": "collect_youtube_dataset" |
| } |
| }, |
| { |
| "id": "dataset_collector", |
| "name": "Dataset Collector", |
| "description": "Collects image references and produces a reviewable dataset manifest.", |
| "category": "Dataset", |
| "entry_function": "collect_dataset", |
| "arguments": ["subject", "image_count", "collection_mode", "project_name", "output_dir"], |
| "required_arguments": ["subject", "image_count", "project_name"], |
| "capabilities": ["fresh_collection", "named_output"], |
| "requires_confirmation": true, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.real_dataset_collector", |
| "function": "collect_dataset" |
| } |
| }, |
| { |
| "id": "dataset_preparer", |
| "name": "Dataset Preparation", |
| "description": "Validates, filters, deduplicates, and prepares collected images.", |
| "category": "Dataset", |
| "entry_function": "prepare_dataset", |
| "arguments": ["project_name"], |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": true, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.demo_backends", |
| "function": "prepare_dataset" |
| } |
| }, |
| { |
| "id": "caption_generator", |
| "name": "Caption Generator", |
| "description": "Creates editable training captions from a prepared dataset.", |
| "category": "Dataset", |
| "entry_function": "generate_captions", |
| "arguments": ["subject", "project_name"], |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": true, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.demo_backends", |
| "function": "generate_captions" |
| } |
| }, |
| { |
| "id": "lora_trainer", |
| "name": "LoRA Trainer", |
| "description": "Launches and monitors a registered SD/SDXL LoRA training backend.", |
| "category": "Training", |
| "entry_function": "train_lora", |
| "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "base_model", "resume_from", "preview_enabled", "preview_every", "preview_prompt", "preview_seed"], |
| "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "base_model"], |
| "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel"], |
| "requires_confirmation": true, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.lora_adapter", |
| "function": "train_lora" |
| } |
| }, |
| { |
| "id": "preview_generator", |
| "name": "Preview Generator", |
| "description": "Produces review previews from the latest registered model output.", |
| "category": "Output", |
| "entry_function": "generate_previews", |
| "arguments": [ |
| "subject", |
| "project_name", |
| "preview_count", |
| "model_name", |
| "checkpoint", |
| "prompt", |
| "seed" |
| ], |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": true, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.demo_backends", |
| "function": "generate_previews" |
| } |
| }, |
| { |
| "id": "ddpm_generator", |
| "name": "DDPM Generator", |
| "description": "Generates reproducible image batches from completed models in the connected DDPM project.", |
| "category": "Output", |
| "entry_function": "generate_ddpm_images", |
| "arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio", "reference_image", "reference_strength", "width", "height", "preview_interval"], |
| "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"], |
| "capabilities": ["image_generation", "seed", "sampler", "batch", "aspect_ratio", "reference_image", "live_preview", "progress", "cancel"], |
| "model_trainers": ["ddpm"], |
| "generation_options": { |
| "samplers": ["DDIM", "DDPM"], |
| "aspect_ratios": ["1:1 (Square)", "16:9 (Widescreen)", "9:16 (Portrait)", "4:3 (Classic)", "3:4 (Portrait Classic)", "3:2 (Photo)", "2:3 (Portrait Photo)"], |
| "step_min": 5, |
| "step_max": 500, |
| "step_default": 50 |
| }, |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.ddpm_generator", |
| "function": "generate_ddpm_images" |
| } |
| }, |
| { |
| "id": "flow_generator", |
| "name": "Flow Matching Generator", |
| "description": "Generates reproducible image batches from completed models in the connected Flow Matching project.", |
| "category": "Output", |
| "entry_function": "generate_flow_images", |
| "arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio", "preview_interval"], |
| "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"], |
| "capabilities": ["image_generation", "seed", "ode_method", "batch", "aspect_ratio", "live_preview", "progress", "cancel"], |
| "model_trainers": ["flow"], |
| "generation_options": { |
| "samplers": ["Heun", "Euler"], |
| "aspect_ratios": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"], |
| "step_min": 1, |
| "step_max": 200, |
| "step_default": 20 |
| }, |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.flow_generator", |
| "function": "generate_flow_images" |
| } |
| }, |
| { |
| "id": "lora_generator", |
| "name": "LoRA Generator", |
| "description": "Generates prompted SDXL image batches with completed LoRAs from the connected LoRA Trainer project.", |
| "category": "Output", |
| "entry_function": "generate_lora_images", |
| "arguments": ["model_name", "model_path", "prompt", "negative_prompt", "base_model_path", "image_count", "steps", "seed", "sampler", "aspect_ratio", "width", "height", "cfg_scale", "lora_strength", "reference_image", "denoise_strength", "prompt_weighting", "preview_interval"], |
| "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"], |
| "capabilities": ["image_generation", "text_prompt", "seed", "sampler", "batch", "aspect_ratio", "reference_image", "live_preview", "progress", "cancel"], |
| "model_trainers": ["lora"], |
| "generation_options": { |
| "samplers": ["DPM++ 2M", "DPM++ SDE", "Euler", "Euler a", "DDIM"], |
| "aspect_ratios": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"], |
| "step_min": 1, |
| "step_max": 150, |
| "step_default": 30 |
| }, |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.lora_generator", |
| "function": "generate_lora_images" |
| } |
| }, |
| { |
| "id": "showcase_video_renderer", |
| "name": "Showcase Video Renderer", |
| "description": "Composes images generated by the current job into ADAM's finished showcase MP4 interface.", |
| "category": "Output", |
| "entry_function": "render_showcase_video", |
| "arguments": ["title", "display_seconds", "resolution", "models"], |
| "required_arguments": ["title", "display_seconds", "resolution", "models"], |
| "capabilities": ["video_generation", "progress", "cancel"], |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.showcase", |
| "function": "render_showcase_video" |
| } |
| }, |
| { |
| "id": "completion_notifier", |
| "name": "Completion Notification", |
| "description": "Records pipeline completion and makes the output easy to open.", |
| "category": "System", |
| "entry_function": "notify_complete", |
| "arguments": ["project_name"], |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": true, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.demo_backends", |
| "function": "notify_complete" |
| } |
| }, |
| { |
| "id": "system_monitor", |
| "name": "System Monitor", |
| "description": "Reports CPU, RAM, GPU, VRAM, temperature, and active processes.", |
| "category": "System", |
| "entry_function": "inspect_system", |
| "arguments": ["project_name"], |
| "requires_confirmation": false, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.demo_backends", |
| "function": "inspect_system" |
| } |
| }, |
| { |
| "id": "ddpm_trainer", |
| "name": "DDPM Trainer", |
| "description": "Launches and monitors the connected DDPM trainer with safe, explicit run settings.", |
| "category": "Training", |
| "entry_function": "train_ddpm", |
| "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from", "resolution", "batch_size", "learning_rate", "gradient_accumulation_steps", "dataloader_num_workers", "mixed_precision", "save_every", "preview_steps", "training_intensity", "preview_enabled", "preview_every", "preview_prompt", "preview_seed"], |
| "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir"], |
| "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel"], |
| "requires_confirmation": true, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.ddpm_adapter", |
| "function": "train_ddpm" |
| } |
| }, |
| { |
| "id": "flow_trainer", |
| "name": "Flow Matching Trainer", |
| "description": "Launches and monitors the connected Rectified Flow image trainer.", |
| "category": "Training", |
| "entry_function": "train_flow", |
| "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from", "resolution", "batch_size", "learning_rate", "gradient_accumulation", "workers", "mixed_precision", "save_every", "preview_every", "preview_steps", "gradient_checkpointing", "preview_enabled", "preview_prompt", "preview_seed"], |
| "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir"], |
| "capabilities": ["fresh_training", "resume_training", "progress", "cancel"], |
| "requires_confirmation": true, |
| "enabled": true, |
| "demo": false, |
| "backend": { |
| "type": "python", |
| "module": "adam.tools.flow_adapter", |
| "function": "train_flow" |
| } |
| } |
| ] |
| } |
|
|
