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"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"
}
}
]
}
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