{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-zai-org--cogvideox-2b","slug":"zai-org--cogvideox-2b","name":"CogVideoX-2b","type":"model","url":"https://huggingface.co/zai-org/CogVideoX-2b","page_url":"https://unfragile.ai/zai-org--cogvideox-2b","categories":["video-generation"],"tags":["diffusers","safetensors","cogvideox","video-generation","thudm","text-to-video","en","arxiv:2408.06072","license:apache-2.0","diffusers:CogVideoXPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-zai-org--cogvideox-2b__cap_0","uri":"capability://image.visual.text.to.video.generation.with.diffusion.based.synthesis","name":"text-to-video generation with diffusion-based synthesis","description":"Generates short-form videos (typically 4-8 seconds) from natural language text prompts using a latent diffusion architecture. The model operates in a compressed latent space rather than pixel space, reducing computational requirements while maintaining visual quality. It uses a multi-stage denoising process conditioned on text embeddings to iteratively refine video frames from noise, enabling efficient generation on consumer hardware with 2B parameters.","intents":["Generate short video clips from text descriptions for content creation or prototyping","Create visual demonstrations or explainer videos without filming","Batch-generate multiple video variations from a single prompt","Integrate video generation into automated content pipelines"],"best_for":["Content creators and marketers needing rapid video prototyping","AI researchers experimenting with video generation architectures","Developers building video-generation features into applications","Teams with GPU access (8GB+ VRAM recommended for inference)"],"limitations":["Output limited to ~4-8 second videos at typical resolutions; longer sequences require multiple generations or post-processing","Text-to-video quality degrades with complex, multi-scene narratives or specific visual styles not well-represented in training data","Inference latency ranges 30-120 seconds per video depending on hardware and sampling steps; not suitable for real-time applications","No built-in motion control, camera movement specification, or fine-grained temporal editing — generates holistic videos from prompts only","Requires significant VRAM (8GB+ for single GPU inference); memory usage scales with video resolution and length"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (NVIDIA A100/H100 recommended, RTX 3090 minimum for reasonable speed)","Diffusers library (huggingface-hub integration)","8GB+ GPU VRAM for inference; 16GB+ for batch processing","~5GB disk space for model weights (safetensors format)"],"input_types":["text (natural language prompts, 10-200 tokens typical)","optional: negative prompts (text)","optional: seed (integer for reproducibility)"],"output_types":["video (MP4 or raw tensor format, typically 512x512 or 1024x576 resolution)","frame sequences (PIL Image tensors)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_1","uri":"capability://text.generation.language.prompt.conditioned.latent.diffusion.with.text.embedding.integration","name":"prompt-conditioned latent diffusion with text embedding integration","description":"Conditions video generation on text prompts by encoding them into embedding vectors that guide the denoising process across all timesteps. The architecture integrates a pre-trained text encoder (typically CLIP or similar) that converts natural language into a fixed-dimensional representation, which is then fused into the diffusion model's cross-attention layers. This allows fine-grained semantic control over generated video content without requiring paired video-text training data at scale.","intents":["Control video content semantically through natural language descriptions","Experiment with prompt engineering to achieve desired visual outcomes","Generate multiple video variations by modifying prompt text","Integrate semantic video generation into language-model-based pipelines"],"best_for":["Prompt engineers and creative professionals iterating on video concepts","Developers building user-facing video generation interfaces","Researchers studying text-to-video alignment and semantic understanding"],"limitations":["Text-to-video alignment quality depends on training data diversity; uncommon or highly specific visual concepts may not generate accurately","Prompt sensitivity requires careful engineering; minor wording changes can produce significantly different outputs","No explicit control over specific visual attributes (color, style, composition) — only implicit through prompt language","Negative prompts supported but less effective than positive conditioning; cannot reliably exclude unwanted elements"],"requires":["Text encoder weights (typically bundled with model, ~500MB)","Tokenizer compatible with text encoder (e.g., CLIP tokenizer)","Prompt length typically 10-200 tokens; longer prompts may be truncated"],"input_types":["text (natural language prompt)","text (optional negative prompt)"],"output_types":["video (conditioned on text semantics)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_2","uri":"capability://image.visual.multi.frame.temporal.coherence.synthesis","name":"multi-frame temporal coherence synthesis","description":"Generates temporally coherent video sequences by modeling frame-to-frame dependencies through a 3D convolutional architecture that processes spatial and temporal dimensions jointly. The model learns to predict plausible motion and object continuity across frames during the denoising process, ensuring that generated videos exhibit smooth transitions and consistent object identities rather than flickering or discontinuous motion. This is achieved through temporal attention mechanisms and 3D convolutions that operate on stacked frame representations.","intents":["Generate videos with smooth, natural motion and object continuity","Avoid temporal artifacts like flickering, jittering, or sudden object jumps","Create videos where objects maintain consistent appearance across frames","Ensure generated motion aligns with physical plausibility"],"best_for":["Content creators requiring broadcast-quality temporal smoothness","Developers building video generation features where motion quality is critical","Researchers studying temporal consistency in generative models"],"limitations":["Temporal coherence quality degrades with longer video sequences (>8 seconds); accumulation of prediction errors over time leads to drift","Complex multi-object interactions or occlusions may result in inconsistent object tracking across frames","Motion synthesis is learned from training data distribution; unusual or highly dynamic motions may appear unnatural","No explicit control over motion speed, direction, or camera movement — only implicit through prompt language","Temporal resolution fixed at model training resolution; cannot easily adjust frame rate or temporal sampling"],"requires":["3D convolutional layers in model architecture (memory-intensive; requires 8GB+ VRAM)","Temporal attention mechanisms (adds ~20-30% inference latency vs. spatial-only models)"],"input_types":["text prompt (guides motion semantics)"],"output_types":["video with temporally coherent frames"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_3","uri":"capability://image.visual.efficient.latent.space.video.generation.with.vae.compression","name":"efficient latent-space video generation with vae compression","description":"Operates in a compressed latent space rather than pixel space by using a pre-trained Video Autoencoder (VAE) that encodes high-resolution videos into low-dimensional latent representations. The diffusion process occurs in this compressed space, reducing memory requirements and computational cost by 4-8x compared to pixel-space generation. After denoising, a VAE decoder reconstructs the video from latent tensors back to pixel space, enabling efficient inference on consumer hardware while maintaining visual quality through learned compression.","intents":["Generate videos on hardware with limited VRAM (8GB GPUs)","Reduce inference latency for real-time or near-real-time applications","Batch-generate multiple videos efficiently","Deploy video generation models in resource-constrained environments"],"best_for":["Developers with consumer-grade GPUs (RTX 3090, RTX 4090, A100)","Teams needing cost-effective video generation at scale","Researchers studying efficient generative model architectures"],"limitations":["Latent-space compression introduces quantization artifacts; fine details may be lost compared to pixel-space generation","VAE decoder quality is fixed by pre-training; cannot improve reconstruction fidelity without retraining the VAE","Compression ratio is fixed (typically 4-8x); cannot trade off between speed and quality at inference time","Latent-space operations may limit interpretability compared to pixel-space approaches"],"requires":["Pre-trained Video VAE weights (~1-2GB)","Latent-space compatible with diffusion model (typically 4-8x spatial compression)","VAE encoder/decoder implementations (included in Diffusers library)"],"input_types":["text prompt"],"output_types":["video (reconstructed from latent space)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_4","uri":"capability://image.visual.batch.video.generation.with.deterministic.seeding","name":"batch video generation with deterministic seeding","description":"Supports generating multiple video variations from the same prompt by controlling the random noise initialization through seed parameters. The model uses deterministic random number generation seeded by user-provided integers, enabling reproducible outputs and systematic exploration of the generation space. This allows developers to generate video ensembles for quality assessment, A/B testing, or creating multiple content variations without re-running the full model.","intents":["Generate multiple video variations from a single prompt for quality assessment","Create reproducible outputs for testing and debugging","Perform A/B testing on different prompts or model configurations","Batch-generate content variations for content libraries"],"best_for":["Content creators needing multiple takes of the same concept","Developers building quality assurance pipelines for video generation","Researchers studying output diversity and model robustness"],"limitations":["Seed-based reproducibility only works within the same hardware/software configuration; different GPUs or library versions may produce slightly different outputs","Batch generation requires sequential inference (no built-in parallelization); generating N videos takes ~N times the single-video latency","Seed space is large (2^32 or 2^64) but not all seeds produce equally diverse outputs; some seeds may produce similar videos"],"requires":["Seed parameter (integer, typically 0-2^32-1)","Sufficient GPU memory for sequential inference (no multi-GPU batching built-in)"],"input_types":["text prompt","seed (integer, optional)"],"output_types":["multiple videos (one per seed)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_5","uri":"capability://image.visual.configurable.sampling.algorithms.with.noise.scheduling","name":"configurable sampling algorithms with noise scheduling","description":"Supports multiple denoising sampling strategies (e.g., DDPM, DDIM, Euler, DPM++) with configurable noise schedules that control the diffusion process trajectory. Different samplers trade off between inference speed and output quality; faster samplers (DDIM, Euler) use fewer denoising steps but may produce lower-quality outputs, while slower samplers (DDPM) use more steps for higher quality. Noise schedules determine how noise is progressively removed during denoising, affecting the balance between diversity and fidelity.","intents":["Trade off between inference speed and video quality based on use case","Optimize for latency-critical applications using fast samplers","Maximize quality for offline content generation using slow samplers","Experiment with different denoising trajectories for research"],"best_for":["Developers optimizing for specific latency/quality trade-offs","Researchers studying diffusion sampling strategies","Teams with variable computational budgets"],"limitations":["Faster samplers (DDIM, Euler) may produce lower-quality or less diverse outputs; quality degradation is non-linear with step count","Optimal sampling configuration is prompt-dependent; no universal best configuration","Noise schedule selection requires domain knowledge; poor choices can result in mode collapse or high variance outputs","Inference latency scales roughly linearly with number of denoising steps; doubling steps ~doubles latency"],"requires":["Sampler implementation (included in Diffusers library)","Noise schedule configuration (linear, cosine, or custom schedules)","Denoising step count (typically 20-50 steps; more steps = higher quality but slower)"],"input_types":["text prompt","sampler type (string: 'ddpm', 'ddim', 'euler', etc.)","num_inference_steps (integer, 20-50 typical)","noise_schedule (string or custom schedule)"],"output_types":["video (quality/speed determined by sampler configuration)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_6","uri":"capability://data.processing.analysis.safetensors.format.model.distribution.with.integrity.verification","name":"safetensors format model distribution with integrity verification","description":"Distributes model weights in safetensors format, a secure serialization format that enables fast loading, memory-safe deserialization, and built-in integrity verification. Safetensors files include checksums that verify model weights haven't been corrupted or tampered with during download or storage. This format is significantly faster to load than PyTorch's pickle format and reduces security risks associated with arbitrary code execution during deserialization.","intents":["Load model weights quickly without waiting for pickle deserialization","Verify model integrity and detect corruption or tampering","Deploy models securely without pickle-based code execution risks","Integrate models into production systems with confidence"],"best_for":["Production deployments requiring security and reliability","Teams with strict security policies against pickle deserialization","Developers optimizing model loading latency"],"limitations":["Safetensors format is read-only; cannot modify weights in-place without re-serializing","Requires safetensors library (small dependency, but adds to installation footprint)","Integrity verification only detects corruption; does not prevent adversarial weight modifications"],"requires":["safetensors library (pip install safetensors)","Model weights in safetensors format (~5GB for CogVideoX-2b)"],"input_types":["safetensors file path"],"output_types":["loaded model weights (PyTorch tensors)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_7","uri":"capability://tool.use.integration.hugging.face.diffusers.pipeline.integration.with.standardized.api","name":"hugging face diffusers pipeline integration with standardized api","description":"Implements the CogVideoXPipeline class within the Hugging Face Diffusers ecosystem, providing a standardized interface for video generation that follows Diffusers conventions. This integration enables seamless composition with other Diffusers components (schedulers, safety checkers, memory optimizations) and allows developers to use familiar patterns from image generation (StableDiffusion, etc.) for video. The pipeline abstracts away low-level diffusion mechanics, exposing a simple `__call__` method that handles tokenization, noise scheduling, denoising, and VAE decoding.","intents":["Use video generation with the same API patterns as image generation models","Compose video generation with other Diffusers components (schedulers, safety checkers)","Leverage Diffusers ecosystem tools (memory optimization, quantization, etc.)","Integrate video generation into existing Diffusers-based applications"],"best_for":["Developers already familiar with Diffusers (StableDiffusion, etc.)","Teams building multi-modal generation pipelines","Researchers studying diffusion model composition"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per generation due to Python function call overhead","Limited customization within the pipeline; advanced use cases may require subclassing or direct model access","Diffusers API stability is not guaranteed; breaking changes in major versions may require code updates"],"requires":["diffusers library (pip install diffusers>=0.24.0)","transformers library (for text encoder)","torch library (PyTorch backend)"],"input_types":["text prompt (string)","optional: negative_prompt (string)","optional: num_inference_steps (int)","optional: guidance_scale (float)","optional: seed (int)"],"output_types":["PIL Image list or video tensor"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zai-org--cogvideox-2b__cap_8","uri":"capability://image.visual.classifier.free.guidance.with.guidance.scale.control","name":"classifier-free guidance with guidance scale control","description":"Implements classifier-free guidance (CFG) to strengthen the influence of text conditioning on video generation. During denoising, the model predicts noise for both conditioned (with text) and unconditioned (without text) scenarios; the final prediction is a weighted combination that amplifies the text influence. The guidance_scale parameter controls this weighting: higher values (e.g., 7.5) produce videos more closely aligned to the prompt but with reduced diversity, while lower values (e.g., 1.0) produce more diverse but less prompt-aligned outputs.","intents":["Strengthen text-to-video alignment by increasing guidance scale","Increase output diversity by reducing guidance scale","Fine-tune the trade-off between prompt adherence and creative variation","Control the 'strength' of semantic conditioning"],"best_for":["Content creators fine-tuning prompt adherence vs. diversity","Developers building interactive video generation interfaces","Researchers studying conditional generation trade-offs"],"limitations":["Higher guidance scales (>10) can produce artifacts, oversaturation, or unrealistic outputs due to over-optimization toward the prompt","Guidance scale effectiveness is prompt-dependent; optimal values vary by prompt","Guidance computation requires two forward passes (conditioned + unconditioned), doubling inference latency","No principled way to select optimal guidance scale; requires empirical tuning"],"requires":["guidance_scale parameter (float, typically 1.0-15.0; default ~7.5)","Unconditioned model predictions (requires additional forward pass)"],"input_types":["text prompt","guidance_scale (float, 1.0-15.0 typical)"],"output_types":["video (with guidance-adjusted conditioning)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (NVIDIA A100/H100 recommended, RTX 3090 minimum for reasonable speed)","Diffusers library (huggingface-hub integration)","8GB+ GPU VRAM for inference; 16GB+ for batch processing","~5GB disk space for model weights (safetensors format)","Text encoder weights (typically bundled with model, ~500MB)","Tokenizer compatible with text encoder (e.g., CLIP tokenizer)","Prompt length typically 10-200 tokens; longer prompts may be truncated","3D convolutional layers in model architecture (memory-intensive; requires 8GB+ VRAM)","Temporal attention mechanisms (adds ~20-30% inference latency vs. spatial-only models)"],"failure_modes":["Output limited to ~4-8 second videos at typical resolutions; longer sequences require multiple generations or post-processing","Text-to-video quality degrades with complex, multi-scene narratives or specific visual styles not well-represented in training data","Inference latency ranges 30-120 seconds per video depending on hardware and sampling steps; not suitable for real-time applications","No built-in motion control, camera movement specification, or fine-grained temporal editing — generates holistic videos from prompts only","Requires significant VRAM (8GB+ for single GPU inference); memory usage scales with video resolution and length","Text-to-video alignment quality depends on training data diversity; uncommon or highly specific visual concepts may not generate accurately","Prompt sensitivity requires careful engineering; minor wording changes can produce significantly different outputs","No explicit control over specific visual attributes (color, style, composition) — only implicit through prompt language","Negative prompts supported but less effective than positive conditioning; cannot reliably exclude unwanted elements","Temporal coherence quality degrades with longer video sequences (>8 seconds); accumulation of prediction errors over time leads to drift","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4659537181168624,"quality":0.28,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:52.093Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":21431,"model_likes":362}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=zai-org--cogvideox-2b","compare_url":"https://unfragile.ai/compare?artifact=zai-org--cogvideox-2b"}},"signature":"v365yhlr+PL9ta0fQlWJDfrHqsvNFdWjUwkKgcqCBoMu+uMeQ8FWVfrFDe3ZenSjTRXTH4ir/97c0t58YKx3Cg==","signedAt":"2026-06-20T18:52:44.051Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zai-org--cogvideox-2b","artifact":"https://unfragile.ai/zai-org--cogvideox-2b","verify":"https://unfragile.ai/api/v1/verify?slug=zai-org--cogvideox-2b","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"}}