{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ailab-cvc--videocrafter","slug":"ailab-cvc--videocrafter","name":"VideoCrafter","type":"model","url":"https://ailab-cvc.github.io/videocrafter2/","page_url":"https://unfragile.ai/ailab-cvc--videocrafter","categories":["video-generation","testing-quality"],"tags":["image-to-video","text-to-video","video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ailab-cvc--videocrafter__cap_0","uri":"capability://image.visual.latent.space.text.to.video.generation.with.3d.temporal.diffusion","name":"latent-space text-to-video generation with 3d temporal diffusion","description":"Generates videos from natural language prompts by encoding text into CLIP embeddings, then performing iterative denoising in a compressed latent space using a 3D UNet architecture that maintains temporal coherence across frames. The system operates in latent space rather than pixel space, enabling efficient generation of multi-second video sequences with configurable frame counts and resolutions (320×512 or 576×1024). DDIM sampling accelerates the diffusion process while preserving quality.","intents":["Generate short videos from creative text descriptions without manual animation","Create concept-driven video content combining multiple artistic styles and scene descriptions","Produce videos at different resolutions with control over temporal dynamics and motion quality"],"best_for":["Content creators and filmmakers prototyping video ideas from text","AI researchers studying diffusion-based video generation and temporal coherence","Developers building video generation pipelines that need fine-grained control over model parameters"],"limitations":["Limited to several seconds of video output per generation (typically 4-8 frames at inference time)","Requires significant VRAM (24GB+ GPU recommended for 576×1024 resolution)","Motion quality and concept handling vary by model version; VideoCrafter2 improved over v1 but still struggles with complex multi-object interactions","Latent space compression introduces artifacts in fine details; VAE reconstruction quality is bounded by training data"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","GPU with minimum 12GB VRAM (24GB+ for high-resolution models)","Pre-trained model weights (automatically downloaded or manually placed in checkpoints/)","CLIP text encoder (loaded from OpenAI or local cache)"],"input_types":["text prompts (natural language descriptions)","optional seed parameter for reproducibility","configuration parameters: num_frames, height, width, guidance_scale, num_inference_steps"],"output_types":["video file (MP4 or other format via ffmpeg)","frame sequence (PNG/JPG images)","latent tensor representation (for downstream processing)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_1","uri":"capability://image.visual.image.to.video.animation.with.text.guided.motion.synthesis","name":"image-to-video animation with text-guided motion synthesis","description":"Animates static images into dynamic videos by encoding the input image through a VAE encoder, injecting it as a conditioning signal into the diffusion process, and using text prompts to guide motion synthesis. The 3D UNet denoises latent representations while respecting the image structure in early frames and progressively generating motion-coherent subsequent frames. DynamiCrafter variant (640×1024) provides enhanced dynamics through specialized training on motion-rich datasets.","intents":["Convert still photographs or artwork into animated videos with specified motion characteristics","Create product demos or marketing videos by animating static product images with descriptive text","Extend existing images with temporally coherent motion without manual keyframing or rotoscoping"],"best_for":["Marketing and e-commerce teams creating product animation content","Digital artists and animators seeking AI-assisted motion synthesis for static assets","Developers building image-to-video pipelines for social media or streaming platforms"],"limitations":["Motion quality depends heavily on text prompt specificity; vague prompts produce generic or jittery motion","Image structure must be preserved in output, limiting radical scene transformations","DynamiCrafter (high-res variant) requires 24GB+ VRAM; standard variant limited to 320×512","Temporal consistency degrades over longer sequences; typically best for 2-4 second outputs","Cannot handle images with complex occlusions or transparent regions effectively"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA","GPU with 12GB+ VRAM (24GB+ for DynamiCrafter 640×1024)","Input image (PNG, JPG, or other standard formats)","Text prompt describing desired motion","Pre-trained I2V model weights (VideoCrafter1 or DynamiCrafter)"],"input_types":["static image file (PNG, JPG, WebP)","text prompt describing motion (e.g., 'camera pans left', 'object rotates')","optional parameters: num_frames, guidance_scale, motion_intensity"],"output_types":["video file with animated frames","frame sequence preserving input image structure","latent tensor sequence for further processing"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_10","uri":"capability://planning.reasoning.custom.model.fine.tuning.on.domain.specific.video.datasets","name":"custom model fine-tuning on domain-specific video datasets","description":"Enables fine-tuning of pre-trained VideoCrafter models on custom video datasets to adapt generation to specific domains (e.g., product videos, animation style, specific objects). The training pipeline loads pre-trained weights, freezes or unfreezes specific layers, and optimizes on custom data using standard diffusion loss. Users can customize learning rate, batch size, and training duration based on dataset size and hardware.","intents":["Adapt video generation models to domain-specific content (product videos, animation styles, etc.)","Improve generation quality for niche concepts or artistic styles underrepresented in original training data","Create proprietary models trained on company-specific video content"],"best_for":["Teams with domain-specific video datasets seeking to customize generation","Researchers studying transfer learning and fine-tuning in diffusion models","Companies building proprietary video generation models"],"limitations":["Requires substantial compute resources (24GB+ GPU, multiple days of training for meaningful improvement)","Small datasets (<1000 videos) risk overfitting; requires careful regularization","Fine-tuning entire model is expensive; layer freezing strategies must be carefully chosen","No built-in tools for dataset curation, cleaning, or augmentation; manual preprocessing required","Evaluation metrics for generation quality are subjective; no automated quality assessment","Training code and documentation are minimal; requires deep understanding of diffusion models"],"requires":["Pre-trained VideoCrafter model weights","Custom video dataset (hundreds to thousands of videos, depending on desired quality)","PyTorch with CUDA support","GPU with 24GB+ VRAM","Training scripts (provided in codebase or custom implementation)","Text annotations for videos (for text-conditioned fine-tuning)"],"input_types":["video files (MP4, WebM, or other formats)","text annotations (one per video, for conditioning)","training configuration: learning_rate, batch_size, num_epochs, layer_freeze_strategy"],"output_types":["fine-tuned model weights (checkpoint files)","training logs (loss curves, sample generations)","evaluation metrics (if validation set provided)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_11","uri":"capability://automation.workflow.inference.optimization.through.memory.efficient.attention.and.gradient.checkpointing","name":"inference optimization through memory-efficient attention and gradient checkpointing","description":"Implements memory optimization techniques including gradient checkpointing (recompute activations during backward pass to reduce memory), memory-efficient attention (e.g., Flash Attention variants), and mixed-precision training to reduce VRAM requirements and accelerate inference. These techniques enable generation at higher resolutions or longer sequences on hardware with limited VRAM.","intents":["Generate videos on consumer GPUs with 12GB VRAM instead of requiring 24GB+ enterprise hardware","Reduce inference latency through mixed-precision computation and optimized attention","Enable longer video sequences (8-16 frames) within memory constraints"],"best_for":["Developers deploying models on consumer hardware or edge devices","Teams optimizing inference cost and latency in production systems","Researchers studying efficiency trade-offs in diffusion models"],"limitations":["Gradient checkpointing adds ~20-30% latency overhead during training (not inference)","Memory-efficient attention variants may have slightly lower quality than full attention","Mixed-precision (FP16) can introduce numerical instability; requires careful tuning","Optimization techniques are model-specific; may not transfer to custom fine-tuned models","Interaction between multiple optimizations is complex; disabling one may break others"],"requires":["PyTorch with CUDA support","Optional: xFormers library for memory-efficient attention","Optional: Apex library for mixed-precision training","GPU with 12GB+ VRAM (vs. 24GB+ without optimizations)","Configuration flags to enable/disable specific optimizations"],"input_types":["model configuration with optimization flags: use_gradient_checkpointing, use_memory_efficient_attention, mixed_precision","training or inference parameters"],"output_types":["optimized model (with checkpointing/attention modifications)","performance metrics: memory usage, inference latency, quality metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_12","uri":"capability://planning.reasoning.reproducible.generation.with.seed.control.and.deterministic.sampling","name":"reproducible generation with seed control and deterministic sampling","description":"Enables reproducible video generation by fixing random seeds for noise initialization and using deterministic DDIM sampling (eta=0). Users can specify a seed parameter to generate identical videos from the same prompt, useful for debugging, A/B testing, and ensuring consistency across runs. Seed control applies to both noise initialization and random operations in the diffusion process.","intents":["Generate identical videos for debugging and quality assessment","Conduct reproducible A/B tests comparing different prompts or parameters","Ensure consistency in production systems where deterministic output is required"],"best_for":["Researchers conducting controlled experiments and benchmarking","QA teams testing generation quality across model versions","Developers debugging generation failures and model behavior"],"limitations":["Deterministic sampling (eta=0) reduces diversity; stochastic sampling (eta>0) produces different outputs per run","Seed reproducibility depends on PyTorch version and CUDA version; may not be reproducible across different environments","Different hardware (GPU models) may produce slightly different results due to floating-point precision differences","Reproducibility is per-model; changing model weights invalidates previous seeds","Seed control does not guarantee reproducibility across different inference frameworks or implementations"],"requires":["Seed parameter (integer, typically 0-2^32-1)","DDIM sampler with eta=0 for deterministic sampling","PyTorch with reproducibility flags enabled (torch.manual_seed, torch.cuda.manual_seed)","Consistent hardware and software environment"],"input_types":["seed parameter (integer)","eta parameter (0.0 for deterministic, >0 for stochastic)","all other generation parameters (prompt, resolution, etc.)"],"output_types":["video file (identical for same seed and parameters)","generation metadata (seed used, parameters)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_2","uri":"capability://data.processing.analysis.variational.autoencoder.latent.space.compression.and.reconstruction","name":"variational autoencoder latent space compression and reconstruction","description":"Compresses video frames into a low-dimensional latent representation using an AutoencoderKL (VAE) architecture, enabling efficient diffusion in compressed space. The encoder maps images to latent codes with configurable compression ratios (typically 4-8x spatial reduction), and the decoder reconstructs high-quality frames from latent tensors. This compression reduces memory requirements and accelerates diffusion sampling while maintaining visual quality through careful VAE training.","intents":["Reduce computational cost of diffusion by operating in compressed latent space instead of pixel space","Enable higher-resolution video generation within memory constraints of consumer GPUs","Decouple video generation from pixel-space details, allowing focus on semantic content and motion"],"best_for":["Researchers optimizing diffusion model efficiency and memory footprint","Developers deploying video generation on resource-constrained hardware","Teams fine-tuning models on custom datasets and needing to understand VAE bottlenecks"],"limitations":["VAE reconstruction introduces artifacts and detail loss; compression ratio inversely affects quality","Latent space artifacts can propagate through diffusion process, creating visual anomalies in output","VAE training is separate from diffusion model training; pre-trained VAE quality is fixed","Extreme compression ratios (>8x) produce noticeable quality degradation in fine details","Latent space is not interpretable; difficult to debug or manipulate specific visual features"],"requires":["Pre-trained AutoencoderKL weights","PyTorch with CUDA support","Input frames or latent tensors compatible with VAE input dimensions","Knowledge of VAE compression ratio for proper scaling of latent operations"],"input_types":["video frames (tensor format, normalized to [-1, 1] or [0, 1])","latent tensors (for decoder-only operations)","configuration: compression_ratio, latent_channels"],"output_types":["latent tensor representation (for diffusion input)","reconstructed video frames (from latent tensors)","compression statistics (for analysis)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_3","uri":"capability://text.generation.language.clip.text.embedding.and.semantic.prompt.conditioning","name":"clip text embedding and semantic prompt conditioning","description":"Encodes natural language text prompts into semantic embeddings using OpenAI's CLIP text encoder, which are then injected into the diffusion process as conditioning signals. The embeddings capture semantic meaning and artistic concepts, allowing the 3D UNet to generate videos aligned with textual descriptions. Guidance scale parameter controls the strength of text conditioning, enabling trade-offs between prompt adherence and generation diversity.","intents":["Guide video generation toward specific semantic content and artistic styles via natural language","Control generation diversity and prompt adherence through guidance scale tuning","Enable concept composition by combining multiple descriptive phrases in single prompts"],"best_for":["Content creators writing detailed prompts to control video generation output","Researchers studying semantic alignment between text and video in diffusion models","Developers building prompt engineering tools or interfaces for video generation"],"limitations":["CLIP embeddings have limited semantic resolution; fine-grained details in prompts may be lost","Guidance scale >15 often produces artifacts or unrealistic visual distortions","Prompt understanding varies by training data; uncommon concepts or niche styles may not be recognized","Negation and complex logical relationships in prompts are poorly understood","CLIP encoder is frozen; cannot be fine-tuned for domain-specific vocabulary without retraining entire pipeline"],"requires":["CLIP text encoder (loaded from OpenAI or local cache)","Text prompt as input string","PyTorch with CUDA for embedding computation","Knowledge of effective prompt engineering for diffusion models"],"input_types":["text prompt (string, typically 10-100 tokens)","guidance_scale parameter (float, typically 7.5-15.0)","optional: negative prompts (for classifier-free guidance)"],"output_types":["text embedding tensor (shape: [1, 77, 768] for CLIP ViT-L)","conditioning signal for diffusion UNet","embedding statistics (for debugging)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_4","uri":"capability://planning.reasoning.ddim.accelerated.diffusion.sampling.with.configurable.inference.steps","name":"ddim accelerated diffusion sampling with configurable inference steps","description":"Implements Denoising Diffusion Implicit Models (DDIM) sampling to accelerate the diffusion process by skipping intermediate timesteps while maintaining quality. Instead of the standard 1000-step DDPM schedule, DDIM enables generation in 20-50 steps with minimal quality loss. The sampler is configurable for different speed-quality trade-offs, allowing inference time optimization based on deployment constraints.","intents":["Reduce video generation latency from minutes to seconds for interactive applications","Balance generation quality against inference time constraints in production deployments","Enable real-time or near-real-time video generation for interactive user interfaces"],"best_for":["Developers building interactive video generation web applications or APIs","Teams deploying models on edge devices or resource-constrained environments","Researchers studying diffusion sampling efficiency and quality-speed trade-offs"],"limitations":["Fewer inference steps (20-30) produce lower quality output; diminishing returns below 20 steps","Quality degradation is non-linear; some prompts degrade gracefully while others fail catastrophically at low step counts","Optimal step count varies by model and prompt; requires empirical tuning","DDIM determinism enables reproducibility but reduces diversity compared to stochastic DDPM","Guidance scale effectiveness changes with step count; parameters must be re-tuned for different step configurations"],"requires":["Pre-trained diffusion model (VideoCrafter1 or VideoCrafter2)","DDIM sampler implementation (included in codebase)","Configuration: num_inference_steps (typically 20-50), guidance_scale","PyTorch with CUDA for efficient sampling"],"input_types":["latent tensor (from VAE encoder or noise initialization)","text embedding (from CLIP encoder)","num_inference_steps (integer, 20-50 recommended)","guidance_scale (float, 7.5-15.0 typical)","eta parameter (controls stochasticity, 0.0 for deterministic DDIM)"],"output_types":["denoised latent tensor (ready for VAE decoder)","intermediate latent states (for visualization or analysis)","timing metrics (inference time per step)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_5","uri":"capability://image.visual.multi.resolution.video.generation.with.configurable.frame.counts","name":"multi-resolution video generation with configurable frame counts","description":"Supports generation of videos at multiple resolutions (320×512, 576×1024) and frame counts (4-16 frames typical) through model variants and configuration parameters. The 3D UNet architecture scales to different spatial and temporal dimensions, and the VAE encoder/decoder handles corresponding latent space sizes. Users can trade off resolution, frame count, and inference time based on quality requirements and hardware constraints.","intents":["Generate videos at resolution and duration appropriate for specific use cases (social media, cinema, etc.)","Optimize inference time by selecting lower resolution when quality requirements permit","Create videos with specific aspect ratios and frame counts for platform-specific requirements"],"best_for":["Content creators targeting different platforms with varying resolution/duration requirements","Developers deploying models on heterogeneous hardware with different VRAM constraints","Teams optimizing inference pipelines for cost and latency"],"limitations":["Higher resolutions (576×1024) require 24GB+ VRAM; not feasible on consumer GPUs","More frames increase memory usage quadratically; 16-frame generation at high resolution may be infeasible","Model variants are separate; cannot dynamically scale single model to different resolutions","Quality varies significantly between resolution variants; 576×1024 model not simply upsampled 320×512","Aspect ratio is fixed per model; cannot generate arbitrary aspect ratios without retraining"],"requires":["Model variant matching desired resolution (VideoCrafter1-320×512, VideoCrafter1-576×1024, DynamiCrafter-640×1024)","GPU VRAM matching resolution: 12GB for 320×512, 24GB+ for 576×1024","Configuration parameters: height, width, num_frames","PyTorch with CUDA support"],"input_types":["text prompt or image (depending on T2V or I2V mode)","height, width parameters (must match model training resolution)","num_frames parameter (typically 4-16, model-dependent)","guidance_scale, num_inference_steps"],"output_types":["video file at specified resolution and frame count","frame sequence (PNG/JPG)","metadata: actual resolution, frame rate, duration"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_6","uri":"capability://tool.use.integration.gradio.web.interface.for.interactive.video.generation","name":"gradio web interface for interactive video generation","description":"Provides a browser-based UI built with Gradio framework enabling users to input text prompts or images, configure generation parameters (resolution, frames, guidance scale), and preview generated videos without command-line interaction. The interface handles model loading, inference orchestration, and result display through a responsive web application. Supports both T2V and I2V modes with mode-specific input fields.","intents":["Enable non-technical users to generate videos through intuitive web interface","Prototype and experiment with different prompts and parameters interactively","Share video generation capability with collaborators via shareable web link"],"best_for":["Non-technical content creators and designers experimenting with video generation","Teams prototyping video generation features before integration into production systems","Researchers demonstrating capabilities and gathering user feedback on generation quality"],"limitations":["Single-user interface; concurrent requests may queue or timeout on limited hardware","No persistent storage of generated videos; outputs are temporary unless manually downloaded","Limited parameter customization compared to CLI; advanced options (custom schedulers, etc.) not exposed","Inference latency directly impacts user experience; slow generation (2-5 minutes) creates poor UX","No authentication or access control; requires manual deployment security configuration"],"requires":["Python 3.8+","Gradio library (pip install gradio)","PyTorch with CUDA","GPU with 12GB+ VRAM","Pre-trained model weights","Optional: ngrok or similar for public sharing"],"input_types":["text prompt (text input field)","image file (for I2V mode, file upload)","generation parameters: resolution, num_frames, guidance_scale, num_inference_steps (sliders/dropdowns)"],"output_types":["generated video (displayed in web UI, downloadable)","generation metadata (inference time, parameters used)","error messages (if generation fails)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_7","uri":"capability://automation.workflow.command.line.batch.processing.with.shell.scripts","name":"command-line batch processing with shell scripts","description":"Provides shell scripts (run_text2video.sh, run_image2video.sh) enabling batch video generation from command line with configurable parameters. Scripts handle model loading, inference orchestration, and output file management. Users can specify multiple prompts or images in configuration files and generate videos in batch mode, useful for production pipelines and non-interactive workflows.","intents":["Generate multiple videos in batch without manual UI interaction for each","Integrate video generation into automated production pipelines and CI/CD workflows","Process large datasets of prompts or images systematically"],"best_for":["DevOps engineers integrating video generation into production systems","Researchers processing large datasets and benchmarking generation quality","Teams building automated content creation pipelines"],"limitations":["Limited error handling and recovery; single failure may halt entire batch","No built-in progress tracking or monitoring; difficult to track status of large batches","Parameter configuration through shell variables is error-prone; no schema validation","Output file naming and organization must be manually managed","No built-in logging or result aggregation; requires external tools for analysis"],"requires":["Bash shell (Linux/macOS) or WSL (Windows)","Python 3.8+ with VideoCrafter dependencies installed","GPU with 12GB+ VRAM","Pre-trained model weights","Input files: text prompts or images in specified directory structure"],"input_types":["shell script parameters: model_name, prompt_file, output_dir, resolution, num_frames","text file with prompts (one per line for T2V)","image directory (for I2V batch processing)"],"output_types":["video files (MP4 or other format) in output directory","log files (if logging configured)","metadata files (generation parameters, timing)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_8","uri":"capability://tool.use.integration.cog.containerized.deployment.for.api.integration","name":"cog containerized deployment for api integration","description":"Packages VideoCrafter as a Cog container (Replicate-compatible format) enabling deployment as a containerized API service. The predict.py interface defines input/output schemas and inference logic, allowing VideoCrafter to be deployed on Replicate, Banana, or other container-based inference platforms. Cog handles dependency management, GPU allocation, and HTTP API generation automatically.","intents":["Deploy video generation as a scalable API service without building custom web server","Integrate VideoCrafter into third-party platforms (Replicate, Banana) for monetization or sharing","Enable serverless or on-demand inference with automatic scaling"],"best_for":["Developers deploying models on Replicate or similar container-based platforms","Teams building SaaS products around video generation","Researchers sharing models with broader community via standardized API"],"limitations":["Cog abstraction adds latency overhead (~200-500ms per request for container startup/shutdown)","Limited to Cog-compatible platforms; cannot deploy to arbitrary Kubernetes clusters without adaptation","Input/output schema must be defined in predict.py; complex workflows require custom wrapper logic","Cold start latency significant for infrequent requests; model loading happens per request","GPU allocation and cost management delegated to platform; limited control over resource optimization"],"requires":["Cog CLI tool (pip install cog)","Docker (for local testing)","predict.py file defining input/output schema","Replicate account or alternative Cog-compatible platform","Pre-trained model weights (downloaded during container build)"],"input_types":["JSON payload with: prompt (string), image (optional, base64 or URL), resolution, num_frames, guidance_scale","HTTP POST request to deployed API endpoint"],"output_types":["video file (returned as URL or base64-encoded data)","JSON metadata (generation parameters, timing)","HTTP response with appropriate status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ailab-cvc--videocrafter__cap_9","uri":"capability://planning.reasoning.3d.unet.temporal.spatial.denoising.with.frame.coherence","name":"3d unet temporal-spatial denoising with frame coherence","description":"Core diffusion model architecture using 3D convolutions and attention mechanisms to denoise video latents while maintaining temporal coherence across frames. The UNet operates on 4D tensors (batch, channels, time, spatial) with 3D convolutions that process temporal and spatial dimensions jointly, enabling the model to learn motion patterns and frame-to-frame consistency. Attention layers capture long-range temporal dependencies and semantic relationships.","intents":["Generate temporally coherent videos where motion is smooth and consistent across frames","Learn and reproduce motion patterns from training data","Maintain semantic consistency while introducing controlled variation across frames"],"best_for":["Researchers studying temporal coherence in diffusion models","Developers fine-tuning models on custom video datasets","Teams analyzing failure modes and improving generation quality"],"limitations":["3D convolutions are computationally expensive; memory usage scales with temporal dimension","Temporal coherence quality degrades with longer sequences (>16 frames); attention becomes intractable","Motion patterns learned from training data; cannot generate novel motion types not in training set","Attention mechanism has quadratic complexity in sequence length; long videos require memory-efficient attention variants","Architecture is fixed; cannot dynamically adjust temporal receptive field without retraining"],"requires":["Pre-trained 3D UNet weights","PyTorch with CUDA support","Sufficient VRAM for 3D convolution operations (24GB+ for high-resolution)","Understanding of diffusion model architecture for debugging or fine-tuning"],"input_types":["noisy latent tensor (shape: [batch, channels, frames, height, width])","timestep embedding (indicating noise level)","text conditioning embedding (from CLIP)","optional: image conditioning (for I2V mode)"],"output_types":["denoised latent tensor (same shape as input)","intermediate feature maps (for visualization or analysis)","attention maps (for interpretability)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA support","GPU with minimum 12GB VRAM (24GB+ for high-resolution models)","Pre-trained model weights (automatically downloaded or manually placed in checkpoints/)","CLIP text encoder (loaded from OpenAI or local cache)","PyTorch 1.13+ with CUDA","GPU with 12GB+ VRAM (24GB+ for DynamiCrafter 640×1024)","Input image (PNG, JPG, or other standard formats)","Text prompt describing desired motion","Pre-trained I2V model weights (VideoCrafter1 or DynamiCrafter)"],"failure_modes":["Limited to several seconds of video output per generation (typically 4-8 frames at inference time)","Requires significant VRAM (24GB+ GPU recommended for 576×1024 resolution)","Motion quality and concept handling vary by model version; VideoCrafter2 improved over v1 but still struggles with complex multi-object interactions","Latent space compression introduces artifacts in fine details; VAE reconstruction quality is bounded by training data","Motion quality depends heavily on text prompt specificity; vague prompts produce generic or jittery motion","Image structure must be preserved in output, limiting radical scene transformations","DynamiCrafter (high-res variant) requires 24GB+ VRAM; standard variant limited to 320×512","Temporal consistency degrades over longer sequences; typically best for 2-4 second outputs","Cannot handle images with complex occlusions or transparent regions effectively","Requires substantial compute resources (24GB+ GPU, multiple days of training for meaningful improvement)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3142909141489101,"quality":0.35,"ecosystem":0.5900000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:59:47.981Z","last_commit":"2026-01-09T15:01:22Z"},"community":{"stars":5053,"forks":411,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ailab-cvc--videocrafter","compare_url":"https://unfragile.ai/compare?artifact=ailab-cvc--videocrafter"}},"signature":"G0CVaMvmsLqiweZQAV2KUGFED/peAp4ci+x6/onafC4fEYqGtwOKXDK0KixDtROaSV62J8IYgK0Z8Izs4BFoCQ==","signedAt":"2026-06-21T00:16:42.166Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ailab-cvc--videocrafter","artifact":"https://unfragile.ai/ailab-cvc--videocrafter","verify":"https://unfragile.ai/api/v1/verify?slug=ailab-cvc--videocrafter","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"}}