{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"comfyui-cli","slug":"comfyui-cli","name":"ComfyUI CLI","type":"cli","url":"https://github.com/comfyanonymous/ComfyUI","page_url":"https://unfragile.ai/comfyui-cli","categories":["image-generation","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"comfyui-cli__cap_0","uri":"capability://automation.workflow.graph.based.workflow.execution.with.smart.caching","name":"graph-based workflow execution with smart caching","description":"ComfyUI represents image generation pipelines as directed acyclic graphs where nodes represent atomic operations (model loading, sampling, conditioning, etc.). The execution engine traverses this graph, executing only nodes whose inputs have changed since the last run, leveraging a smart caching system that tracks node outputs and invalidates downstream dependencies. This architecture enables iterative refinement of complex multi-stage pipelines without re-executing unchanged operations, dramatically reducing inference latency for workflow modifications.","intents":["I want to iterate on image generation parameters without re-running the entire pipeline from scratch","I need to design complex multi-stage diffusion workflows with conditional branching and feedback loops","I want to understand which parts of my pipeline are actually being re-executed when I change a parameter"],"best_for":["researchers and artists building complex generative workflows","teams prototyping multi-model pipelines with expensive inference steps","developers building custom image generation applications with iterative refinement"],"limitations":["Graph-based execution adds complexity compared to linear pipelines; requires understanding node dependencies and data flow","Caching system requires sufficient VRAM/disk to store intermediate node outputs; memory pressure can force cache invalidation","No built-in support for dynamic graph modification during execution; workflows must be fully defined before execution begins"],"requires":["Python 3.9+","PyTorch with CUDA/ROCm support or CPU fallback","Sufficient VRAM (8GB minimum for SD1.5, 24GB+ recommended for larger models)"],"input_types":["workflow JSON (graph definition with node connections)","model files (safetensors, ckpt, diffusers format)","image files (PNG, JPG for conditioning)","text prompts"],"output_types":["generated images (PNG with metadata)","execution logs with timing per node","cached intermediate tensors"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_1","uri":"capability://tool.use.integration.node.based.extensible.architecture.with.custom.node.registration","name":"node-based extensible architecture with custom node registration","description":"ComfyUI provides a plugin system where custom nodes are registered via Python classes implementing a standard interface (INPUT_TYPES, RETURN_TYPES, execute methods). The extension system dynamically discovers and loads custom nodes from designated directories, allowing third-party developers to add new operations without modifying core code. Each node declares its input/output types using a type system (comfy_types/node_typing.py) that enables automatic validation, UI generation, and workflow serialization.","intents":["I want to add custom image processing operations to my workflow without forking ComfyUI","I need to integrate external APIs or models into the generation pipeline as reusable nodes","I want to create domain-specific nodes for my use case (e.g., face enhancement, style transfer) that other users can install"],"best_for":["extension developers building specialized nodes for specific domains","teams integrating third-party models or APIs into ComfyUI workflows","researchers prototyping new diffusion techniques as reusable components"],"limitations":["Custom nodes must follow ComfyUI's node interface contract; incompatible with arbitrary Python code","No built-in dependency management for custom nodes; version conflicts between extensions can cause runtime errors","Type system is Python-based; requires understanding of ComfyUI's type annotations and validation rules"],"requires":["Python 3.9+","Understanding of ComfyUI node interface (INPUT_TYPES, RETURN_TYPES, execute)","Custom node directory configured in ComfyUI paths"],"input_types":["Python class definitions implementing node interface","Type declarations (strings, ints, floats, images, conditioning, etc.)"],"output_types":["Registered node available in workflow editor","Dynamically generated UI inputs based on INPUT_TYPES","Serializable node configuration in workflow JSON"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_10","uri":"capability://image.visual.video.and.animation.generation.with.frame.interpolation.and.temporal.consistency","name":"video and animation generation with frame interpolation and temporal consistency","description":"ComfyUI supports video generation through specialized nodes for frame-by-frame generation, temporal consistency enforcement, and frame interpolation. The system can generate videos by iteratively sampling frames with temporal conditioning that maintains consistency across frames, or by generating keyframes and interpolating between them. Supports video models like Flux Video and WAN (World Animation Network) with specialized sampling strategies for temporal coherence.","intents":["I want to generate videos with temporal consistency across frames","I need to interpolate between keyframes to create smooth animations","I want to use specialized video models (Flux Video, WAN) for high-quality video generation"],"best_for":["creators generating short videos and animations","teams building video synthesis applications","researchers exploring temporal consistency and video diffusion models"],"limitations":["Video generation is significantly slower than image generation; requires substantial VRAM for frame batching","Temporal consistency is not guaranteed; long videos may accumulate drift or artifacts","Video models are less mature than image models; quality varies significantly by model and parameters"],"requires":["Video model (Flux Video, WAN, etc.)","Sufficient VRAM for frame batching (24GB+ recommended)","Frame rate and duration parameters"],"input_types":["prompt or conditioning for video generation","keyframes (optional, for interpolation-based generation)","temporal consistency parameters","frame rate and duration"],"output_types":["video file (MP4, WebM, etc.)","frame sequence (PNG or tensor)","temporal consistency metrics"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_11","uri":"capability://automation.workflow.blueprint.and.subgraph.system.for.workflow.composition.and.reusability","name":"blueprint and subgraph system for workflow composition and reusability","description":"ComfyUI implements a blueprint system that allows users to encapsulate complex subgraphs as reusable components with defined inputs and outputs. Blueprints are essentially workflows-within-workflows that can be instantiated multiple times with different parameters, enabling modular workflow design and code reuse. The system supports nested blueprints, parameter passing, and automatic input/output exposure.","intents":["I want to create reusable workflow components that I can instantiate multiple times with different parameters","I need to organize complex workflows into logical subgraphs for better readability and maintainability","I want to share workflow templates with other users without exposing internal implementation details"],"best_for":["teams building complex, modular workflows","users creating workflow templates for specific use cases","researchers building reusable components for diffusion pipelines"],"limitations":["Blueprint nesting adds complexity; deeply nested blueprints can be difficult to debug","Parameter passing between blueprints requires careful type matching; type errors can be hard to diagnose","No built-in blueprint versioning or dependency management; blueprint updates can break dependent workflows"],"requires":["Understanding of blueprint syntax and parameter passing","Workflow JSON with blueprint definitions","Input/output type declarations for blueprint interface"],"input_types":["blueprint definition (workflow JSON with designated inputs/outputs)","blueprint instantiation parameters"],"output_types":["instantiated subgraph with parameters bound","blueprint interface documentation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_12","uri":"capability://automation.workflow.cli.argument.parsing.and.headless.execution.for.automation","name":"cli argument parsing and headless execution for automation","description":"ComfyUI provides a comprehensive CLI interface (cli_args.py, main.py) that allows headless execution of workflows without the web UI. The CLI supports specifying model paths, VRAM optimization flags, execution parameters, and workflow input overrides. The system can run in server mode (with API) or direct execution mode, enabling integration into automated pipelines and batch processing systems.","intents":["I want to run ComfyUI workflows from the command line without the web UI for automation and scripting","I need to configure VRAM optimization and device placement via CLI arguments for different hardware","I want to override workflow parameters from the command line for batch processing"],"best_for":["DevOps engineers deploying ComfyUI in containerized environments","researchers building automated image generation pipelines","teams integrating ComfyUI into larger automation systems"],"limitations":["CLI interface is less discoverable than web UI; requires documentation reading","Parameter overrides are limited to simple types; complex workflow modifications require JSON editing","Headless execution provides no visual feedback; errors may be harder to diagnose"],"requires":["Python 3.9+","ComfyUI installation","Workflow JSON file","Model files in configured paths"],"input_types":["CLI arguments (model paths, VRAM settings, execution parameters)","workflow JSON file","parameter overrides (key=value pairs)"],"output_types":["generated images","execution logs","exit code (0 for success, non-zero for failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_13","uri":"capability://data.processing.analysis.dynamic.quantization.and.mixed.precision.inference.for.memory.optimization","name":"dynamic quantization and mixed-precision inference for memory optimization","description":"ComfyUI implements dynamic quantization strategies that automatically convert model weights to lower precision (FP16, INT8, NF4) based on available VRAM and user preferences. The system supports mixed-precision execution where different layers run at different precisions, and can dynamically switch precision during execution based on memory pressure. Quantization is applied transparently without requiring model retraining.","intents":["I want to run large models (SDXL, Flux) on limited VRAM by automatically quantizing weights","I need to balance quality and speed by using lower precision for less critical layers","I want to understand the quality/speed tradeoff of different quantization schemes"],"best_for":["users with limited VRAM (8-16GB) running large models","teams optimizing inference latency on resource-constrained hardware","researchers exploring quantization techniques for diffusion models"],"limitations":["Quantization reduces model quality; INT8 and NF4 may produce visible artifacts","Quantization overhead varies by hardware; some GPUs have poor INT8 support","Mixed-precision execution adds complexity; some layer combinations are unstable"],"requires":["Model file in supported format","Quantization library (bitsandbytes for NF4, etc.)","Understanding of quantization tradeoffs"],"input_types":["model file","quantization scheme (FP32, FP16, INT8, NF4)","quantization parameters (bit width, layer selection, etc.)"],"output_types":["quantized model","memory usage statistics","quality metrics (optional)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_2","uri":"capability://data.processing.analysis.unified.model.loading.and.memory.management.with.automatic.device.placement","name":"unified model loading and memory management with automatic device placement","description":"ComfyUI implements intelligent model loading (model_management.py, model_detection.py) that automatically detects model architecture, quantization format, and optimal device placement (CUDA/ROCm/CPU) based on available VRAM and model size. The system supports multiple quantization schemes (fp32, fp16, int8, NF4) and can dynamically offload models between VRAM and system RAM or disk based on memory pressure, using a priority-based eviction strategy to keep frequently-used models resident.","intents":["I want to run multiple large models (base model + LoRA + ControlNet) on limited VRAM without manual memory management","I need to automatically detect and load models in their optimal format (quantized vs full precision) based on my hardware","I want to understand which models are currently in VRAM and how much memory each operation will consume"],"best_for":["users with limited VRAM (8-16GB) running large models like Flux or SDXL","teams deploying ComfyUI on heterogeneous hardware (mix of GPUs and CPUs)","researchers experimenting with model combinations without manual memory optimization"],"limitations":["Automatic device placement adds overhead; manual placement may be faster for fixed workloads","Disk offloading is significantly slower than VRAM; workflows with frequent model swaps will have high latency","Quantization reduces model quality; INT8 or NF4 quantization may produce visible artifacts compared to FP32"],"requires":["Python 3.9+","PyTorch with CUDA/ROCm support","Model files in supported formats (safetensors, ckpt, diffusers)","Sufficient disk space for model offloading (models can be 2-20GB each)"],"input_types":["model file paths (safetensors, ckpt, diffusers format)","quantization preferences (fp32, fp16, int8, NF4)","device constraints (force CPU, prefer GPU, etc.)"],"output_types":["loaded model in optimal format and device","memory usage statistics per model","device placement decisions (VRAM vs RAM vs disk)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_3","uri":"capability://image.visual.multi.model.conditioning.and.guidance.system.with.controlnet.t2i.adapter.support","name":"multi-model conditioning and guidance system with controlnet/t2i-adapter support","description":"ComfyUI implements a sophisticated conditioning system that combines multiple control signals (text embeddings, image conditioning, ControlNet spatial guidance, T2I-Adapter features) into a unified conditioning tensor that guides the diffusion process. The system supports weighted combination of multiple conditioning inputs, negative conditioning for guidance inversion, and advanced guidance methods (CFG, DPM++ guidance) that modulate the denoising trajectory based on combined conditioning signals.","intents":["I want to guide image generation using both text prompts and spatial control signals (edge maps, pose, depth) simultaneously","I need to apply multiple ControlNets with different weights to achieve fine-grained control over composition and style","I want to use advanced guidance methods that combine multiple conditioning signals for better quality"],"best_for":["artists and designers requiring precise spatial control over generated images","researchers exploring multi-modal conditioning and guidance techniques","teams building applications that need both semantic (text) and spatial (image) control"],"limitations":["Multiple ControlNets increase inference latency; each additional control signal adds ~10-20% overhead","Conditioning weight tuning is empirical; no principled method for optimal weight selection across different control types","ControlNet quality varies significantly by model version and training data; some control types (e.g., depth) are less reliable than others"],"requires":["Text encoder model (CLIP or equivalent)","ControlNet or T2I-Adapter model files for spatial control","Base diffusion model supporting conditioning injection","Understanding of conditioning tensor shapes and weight ranges"],"input_types":["text prompts (positive and negative)","control images (edge maps, pose, depth, etc.)","conditioning weights (0.0-1.0 per control signal)","guidance scale (CFG scale, typically 7.5-15.0)"],"output_types":["combined conditioning tensor","guidance-modified noise predictions","generated images influenced by all conditioning signals"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_4","uri":"capability://image.visual.sampling.algorithm.abstraction.with.scheduler.and.sampler.composition","name":"sampling algorithm abstraction with scheduler and sampler composition","description":"ComfyUI abstracts diffusion sampling into composable components: schedulers (noise schedules like linear, cosine, karras) that define the denoising trajectory, samplers (DPM++, Euler, Heun, etc.) that implement specific integration methods, and custom sampler nodes that allow users to define arbitrary sampling loops. The system decouples noise scheduling from sampling algorithm, enabling users to combine any scheduler with any sampler and implement novel sampling strategies without modifying core code.","intents":["I want to experiment with different sampling algorithms and noise schedules without recompiling or modifying core code","I need to implement a custom sampling loop that combines multiple models or applies dynamic guidance during denoising","I want to understand how different schedulers and samplers affect image quality and generation speed"],"best_for":["researchers exploring novel sampling algorithms and noise schedules","practitioners optimizing sampling for specific hardware or quality requirements","developers building custom sampling strategies for specialized use cases"],"limitations":["Custom sampler nodes require understanding of diffusion mathematics and PyTorch; not accessible to non-technical users","Sampling algorithm choice significantly affects quality and speed; no automatic selection method","Some sampler/scheduler combinations are unstable or produce artifacts; requires empirical testing"],"requires":["Understanding of diffusion sampling theory","PyTorch knowledge for custom sampler implementation","Noise schedule parameters (sigma values, timesteps, etc.)"],"input_types":["latent tensor (noise or image encoding)","conditioning tensor","scheduler name and parameters","sampler name and parameters","guidance scale and other sampling hyperparameters"],"output_types":["denoised latent tensor","intermediate denoising steps (optional)","sampling trajectory metadata"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_5","uri":"capability://data.processing.analysis.lora.and.model.patching.system.for.parameter.efficient.fine.tuning","name":"lora and model patching system for parameter-efficient fine-tuning","description":"ComfyUI implements a model patching system that applies Low-Rank Adaptation (LoRA) weights to base models by injecting learned low-rank updates into specific layers (typically attention and MLP layers). The system supports multiple LoRA files simultaneously with per-LoRA strength scaling, automatic layer matching across different model architectures, and efficient in-place weight modification that avoids duplicating the base model in memory.","intents":["I want to apply multiple LoRA fine-tunings to a base model with independent strength control for each","I need to combine style LoRAs, character LoRAs, and other specialized adaptations in a single generation","I want to understand which layers are being modified by LoRA and how much the weights are changing"],"best_for":["users applying community-created LoRA models for style transfer or character consistency","teams fine-tuning models for specific domains without storing full model copies","researchers exploring parameter-efficient adaptation techniques"],"limitations":["LoRA quality depends heavily on training data and rank; poorly-trained LoRAs can degrade image quality","Multiple LoRAs can conflict if they modify the same layers; no automatic conflict detection","LoRA strength tuning is empirical; no principled method for optimal strength selection"],"requires":["Base model (Stable Diffusion, SDXL, Flux, etc.)","LoRA files in safetensors or ckpt format","LoRA strength parameters (typically 0.0-1.0 per LoRA)"],"input_types":["base model","LoRA file paths","per-LoRA strength values","optional layer name mappings for architecture compatibility"],"output_types":["patched model with LoRA weights applied","layer-by-layer weight modification statistics"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_6","uri":"capability://image.visual.vae.encoding.decoding.with.latent.format.abstraction","name":"vae encoding/decoding with latent format abstraction","description":"ComfyUI abstracts VAE (Variational Autoencoder) operations through a latent format system (latent_formats.py) that handles encoding images to latent space and decoding latents back to images. The system supports multiple VAE variants (SD1.5, SDXL, Flux VAEs with different scaling factors) and latent formats (standard, tiled for memory efficiency, scaled variants), automatically selecting the appropriate VAE based on the base model and handling format conversions transparently.","intents":["I want to encode images to latent space for inpainting or image-to-image generation without manual VAE selection","I need to use tiled VAE encoding for high-resolution images that exceed VRAM limits","I want to understand the latent space representation and how different VAE choices affect generation quality"],"best_for":["users working with image-to-image and inpainting workflows","teams generating high-resolution images (2K+) with limited VRAM","researchers exploring latent space properties and VAE variants"],"limitations":["VAE encoding/decoding adds latency; tiled VAE is slower than standard VAE","Different VAE variants produce different latent distributions; switching VAEs can affect generation quality","Latent format conversions may introduce artifacts if not handled carefully"],"requires":["VAE model file (typically included with base model)","Input image in RGB format","Understanding of latent space scaling factors"],"input_types":["image (PNG, JPG, RGB tensor)","VAE model","latent format specification (standard, tiled, scaled)"],"output_types":["latent tensor (typically 4D: batch, channels, height, width)","decoded image (RGB tensor or PNG)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_7","uri":"capability://tool.use.integration.http.and.websocket.api.for.remote.workflow.execution.and.real.time.monitoring","name":"http and websocket api for remote workflow execution and real-time monitoring","description":"ComfyUI exposes a REST/WebSocket API (server.py) that allows remote clients to submit workflows, monitor execution progress in real-time, and retrieve results. The API uses JSON workflow serialization, WebSocket connections for live progress updates (execution status, intermediate images, memory usage), and supports batch job submission with queuing. The system maintains execution history and allows clients to cancel running jobs or modify queued jobs.","intents":["I want to integrate ComfyUI into my application via API without running it locally","I need real-time progress updates and intermediate results as the workflow executes","I want to submit batch jobs and monitor their execution status remotely"],"best_for":["developers building web applications or services that use ComfyUI as a backend","teams deploying ComfyUI on remote servers with multiple concurrent users","researchers building automated pipelines that orchestrate ComfyUI workflows"],"limitations":["API latency depends on network; remote execution adds overhead compared to local execution","WebSocket connections require persistent network; disconnections can interrupt long-running workflows","No built-in authentication or rate limiting; requires external security layer for production deployment"],"requires":["ComfyUI server running with API enabled","HTTP client library (requests, fetch, etc.)","WebSocket support for real-time updates","Network connectivity to ComfyUI server"],"input_types":["workflow JSON (graph definition)","model parameters and paths","image inputs (base64 encoded or file paths)"],"output_types":["execution status (queued, running, completed, failed)","intermediate images during execution","final generated images","execution logs and timing statistics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_8","uri":"capability://text.generation.language.text.encoding.with.clip.and.alternative.text.encoders","name":"text encoding with clip and alternative text encoders","description":"ComfyUI implements text encoding through a pluggable encoder system that supports CLIP (OpenAI's vision-language model) as the primary text encoder, with support for alternative encoders like T5 (used in Flux) and other transformer-based models. The system handles tokenization, embedding generation, and prompt weighting (e.g., (prompt:1.5) syntax) that allows users to emphasize specific words or phrases in the generated output.","intents":["I want to encode text prompts into embeddings that guide image generation with fine-grained control over word emphasis","I need to use alternative text encoders (T5, etc.) for models that don't use CLIP","I want to understand how prompt weighting affects the generated image"],"best_for":["users crafting detailed prompts with emphasis and weighting","teams using models with non-CLIP text encoders (Flux, etc.)","researchers exploring text-to-image alignment and prompt engineering"],"limitations":["Prompt weighting syntax varies by encoder; CLIP and T5 have different weight ranges and effects","Text encoder quality significantly affects generation quality; CLIP has known biases and limitations","Token limit (typically 77 tokens for CLIP) truncates long prompts; requires careful prompt engineering"],"requires":["Text encoder model (CLIP, T5, etc.)","Tokenizer for the encoder","Text prompt input"],"input_types":["text prompt (string with optional weighting syntax)","text encoder model","tokenizer configuration"],"output_types":["embedding tensor (typically 768-1280 dimensions for CLIP)","tokenized prompt with token IDs"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__cap_9","uri":"capability://image.visual.image.and.mask.processing.with.batch.operations","name":"image and mask processing with batch operations","description":"ComfyUI provides a comprehensive set of image processing nodes that handle resizing, cropping, blending, masking, and batch operations on images and masks. The system supports efficient batch processing where multiple images are processed simultaneously, and provides mask-aware operations that preserve transparency and alpha channels. Operations include interpolation methods (bilinear, nearest, lanczos), color space conversions, and compositing with alpha blending.","intents":["I want to preprocess images (resize, crop, normalize) before feeding them to the diffusion model","I need to create and manipulate masks for inpainting or selective generation","I want to batch-process multiple images through the same pipeline efficiently"],"best_for":["users building image preprocessing pipelines","teams processing batches of images for consistency","researchers exploring image manipulation and compositing techniques"],"limitations":["Batch processing requires all images to have the same dimensions; requires padding or resizing","Some interpolation methods (lanczos) are slower than others; quality vs speed tradeoff","Mask operations assume specific formats (grayscale, alpha channel); format mismatches can cause errors"],"requires":["Input images in supported formats (PNG, JPG, tensor)","Optional mask images for mask operations","Interpolation method specification"],"input_types":["image tensor or file path","mask tensor or file path","operation parameters (size, crop region, blend mode, etc.)"],"output_types":["processed image tensor","processed mask tensor","batch of processed images"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comfyui-cli__headline","uri":"capability://image.visual.modular.image.generation.framework","name":"modular image generation framework","description":"ComfyUI is a powerful and modular framework for designing complex image generation workflows using a node-based interface, allowing users to create custom pipelines for image, video, and audio generation without coding.","intents":["best modular image generation tool","image generation framework for custom workflows","top node-based image generation software","advanced image generation pipeline builder","image generation GUI for Stable Diffusion"],"best_for":["developers seeking customizable image generation solutions"],"limitations":["requires understanding of node-based workflows"],"requires":["Python environment"],"input_types":["image prompts","video inputs"],"output_types":["images","videos","audio"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch with CUDA/ROCm support or CPU fallback","Sufficient VRAM (8GB minimum for SD1.5, 24GB+ recommended for larger models)","Understanding of ComfyUI node interface (INPUT_TYPES, RETURN_TYPES, execute)","Custom node directory configured in ComfyUI paths","Video model (Flux Video, WAN, etc.)","Sufficient VRAM for frame batching (24GB+ recommended)","Frame rate and duration parameters","Understanding of blueprint syntax and parameter passing","Workflow JSON with blueprint definitions"],"failure_modes":["Graph-based execution adds complexity compared to linear pipelines; requires understanding node dependencies and data flow","Caching system requires sufficient VRAM/disk to store intermediate node outputs; memory pressure can force cache invalidation","No built-in support for dynamic graph modification during execution; workflows must be fully defined before execution begins","Custom nodes must follow ComfyUI's node interface contract; incompatible with arbitrary Python code","No built-in dependency management for custom nodes; version conflicts between extensions can cause runtime errors","Type system is Python-based; requires understanding of ComfyUI's type annotations and validation rules","Video generation is significantly slower than image generation; requires substantial VRAM for frame batching","Temporal consistency is not guaranteed; long videos may accumulate drift or artifacts","Video models are less mature than image models; quality varies significantly by model and parameters","Blueprint nesting adds complexity; deeply nested blueprints can be difficult to debug","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=comfyui-cli","compare_url":"https://unfragile.ai/compare?artifact=comfyui-cli"}},"signature":"kBxUg3rA7s53x3yOz09zWtg79NneF73cJyx2X5ropFlZvI5dQfbtlkdvE+W+V/KEazmwWsVt+VDErusE+EpJCg==","signedAt":"2026-06-23T14:09:52.297Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/comfyui-cli","artifact":"https://unfragile.ai/comfyui-cli","verify":"https://unfragile.ai/api/v1/verify?slug=comfyui-cli","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}