{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lucidrains--imagen-pytorch","slug":"lucidrains--imagen-pytorch","name":"imagen-pytorch","type":"framework","url":"https://github.com/lucidrains/imagen-pytorch","page_url":"https://unfragile.ai/lucidrains--imagen-pytorch","categories":["image-generation"],"tags":["artificial-intelligence","deep-learning","imagination-machine","text-to-image","text-to-video"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lucidrains--imagen-pytorch__cap_0","uri":"capability://image.visual.cascading.text.to.image.generation.with.progressive.resolution.refinement","name":"cascading text-to-image generation with progressive resolution refinement","description":"Generates images from text descriptions using a multi-stage cascading diffusion architecture where a base UNet first generates low-resolution (64x64) images from noise conditioned on T5 text embeddings, then successive super-resolution UNets (SRUnet256, SRUnet1024) progressively upscale and refine details. Each stage conditions on both text embeddings and outputs from previous stages, enabling efficient high-quality synthesis without requiring a single massive model.","intents":["Generate high-quality images from natural language text descriptions","Control image generation quality and detail through multi-stage refinement","Reduce memory footprint by decomposing generation into specialized resolution-specific models","Achieve state-of-the-art text-image alignment using T5 conditioning"],"best_for":["researchers implementing diffusion-based image synthesis","developers building text-to-image applications requiring fine-grained control over generation stages","teams with GPU memory constraints needing modular architecture"],"limitations":["Inference requires sequential execution through all cascading stages, adding latency compared to single-stage models","T5 text encoder must be loaded separately; no built-in lightweight text encoding alternatives","Memory overhead from maintaining multiple UNet models in VRAM during inference","Cascading approach requires careful tuning of guidance scales across stages for optimal results"],"requires":["PyTorch 1.10+","CUDA-capable GPU with minimum 8GB VRAM for base model, 16GB+ for full cascade","Pretrained T5 text encoder (transformers library)","Python 3.7+"],"input_types":["text (natural language descriptions)","text embeddings (precomputed T5 embeddings as tensors)"],"output_types":["image tensors (torch.Tensor, shape [batch, 3, height, width])","PIL Image objects","numpy arrays"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_1","uri":"capability://image.visual.classifier.free.guidance.with.dynamic.thresholding.for.text.alignment.control","name":"classifier-free guidance with dynamic thresholding for text alignment control","description":"Implements classifier-free guidance mechanism that allows steering image generation toward text descriptions without requiring a separate classifier, using unconditional predictions as a baseline. Incorporates dynamic thresholding that adaptively clips predicted noise based on percentiles rather than fixed values, preventing saturation artifacts and improving sample quality across diverse prompts without manual hyperparameter tuning per prompt.","intents":["Improve text-image alignment by controlling guidance strength during inference","Prevent oversaturation and artifacts in generated images through adaptive thresholding","Generate diverse outputs from same prompt by varying guidance scale","Achieve better quality without retraining by adjusting guidance at inference time"],"best_for":["practitioners tuning generation quality without retraining","applications requiring variable text-image fidelity across different prompts","researchers studying guidance mechanisms in diffusion models"],"limitations":["Guidance scale is a manual hyperparameter requiring empirical tuning (typically 3-15 range)","Dynamic thresholding adds ~5-10% computational overhead per denoising step","Excessive guidance (>20) can produce artifacts or mode collapse toward average images","Requires unconditional model training branch, doubling training data requirements"],"requires":["PyTorch 1.10+","Both conditional and unconditional model checkpoints","T5 text encoder for embedding generation"],"input_types":["text descriptions (strings)","guidance scale parameter (float, typically 1.0-15.0)","dynamic thresholding percentile (float, typically 0.95)"],"output_types":["image tensors with improved text alignment","PIL Image objects"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_10","uri":"capability://automation.workflow.command.line.interface.for.training.and.inference.without.code","name":"command-line interface for training and inference without code","description":"Provides CLI tool enabling training and inference through configuration files and command-line arguments without writing Python code. Supports YAML/JSON configuration for model architecture, training hyperparameters, and data paths. CLI handles model instantiation, training loop execution, and inference with automatic device detection and distributed training coordination.","intents":["Train Imagen models without writing custom Python code","Run inference through simple command-line commands","Reproduce experiments using configuration files","Enable non-programmers to use the framework"],"best_for":["practitioners without Python expertise","researchers reproducing published results","teams standardizing training procedures through configs"],"limitations":["CLI abstracts implementation details; difficult to customize beyond configuration options","Configuration files can become complex for advanced use cases","Limited error messages; debugging requires understanding underlying code","Performance tuning requires modifying configs and rerunning; no interactive optimization"],"requires":["Python 3.7+","imagen-pytorch installed","YAML/JSON configuration file"],"input_types":["configuration file (YAML/JSON)","command-line arguments (strings)","training data directory path"],"output_types":["trained model checkpoints","generated images (inference mode)","training logs"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_11","uri":"capability://data.processing.analysis.flexible.data.loading.with.image.preprocessing.and.augmentation","name":"flexible data loading with image preprocessing and augmentation","description":"Implements data loading pipeline supporting various image formats (PNG, JPEG, WebP) with automatic preprocessing (resizing, normalization, center cropping). Supports augmentation strategies (random crops, flips, color jittering) applied during training. 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Supports resuming training from checkpoints with automatic state restoration, enabling long training runs to be interrupted and resumed without loss of progress. Checkpoints include version information for compatibility checking.","intents":["Save training progress at regular intervals for fault tolerance","Resume training from checkpoints without restarting from scratch","Maintain separate checkpoints for model, optimizer, and EMA weights","Track training metadata for reproducibility and analysis"],"best_for":["practitioners training large models requiring multi-day runs","teams with unreliable hardware needing fault tolerance","researchers requiring reproducible training trajectories"],"limitations":["Checkpoint files are large (50GB+ for full cascade); requires substantial storage","Saving checkpoints blocks training; frequent saves reduce throughput by 5-10%","Resuming from checkpoint requires exact same hardware/software configuration","No automatic checkpoint cleanup; manual deletion required to manage disk space"],"requires":["PyTorch 1.10+","Sufficient disk space (50GB+ per checkpoint)","Consistent hardware/software between save and resume"],"input_types":["checkpoint file path (string)","model, optimizer, scheduler objects"],"output_types":["checkpoint files (.pt format)","metadata files (JSON with training state)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_13","uri":"capability://automation.workflow.mixed.precision.training.with.automatic.loss.scaling","name":"mixed precision training with automatic loss scaling","description":"Supports mixed precision training (fp16/bf16) through Hugging Face Accelerate integration, automatically casting computations to lower precision while maintaining numerical stability through loss scaling. Reduces memory usage by 30-50% and accelerates training on GPUs with tensor cores (A100, RTX 30-series). Automatic loss scaling prevents gradient underflow in lower precision.","intents":["Reduce memory usage to fit larger models or batch sizes on same GPU","Accelerate training on modern GPUs with tensor core support","Maintain numerical stability in lower precision through automatic loss scaling","Enable training of larger models on consumer-grade GPUs"],"best_for":["practitioners with GPU memory constraints","teams training on modern GPUs with tensor core support (A100, RTX 30-series, H100)","researchers optimizing training efficiency"],"limitations":["Mixed precision can cause numerical instability in some edge cases; requires careful monitoring","Automatic loss scaling adds ~5% computational overhead","Not all operations benefit equally from lower precision; some remain bottlenecks","Requires GPU with tensor core support; older GPUs see minimal speedup"],"requires":["PyTorch 1.10+","Hugging Face Accelerate library","GPU with tensor core support (optional but recommended)","CUDA 11.0+"],"input_types":["mixed precision flag (string: 'fp16', 'bf16', or 'no')"],"output_types":["trained models with same precision as input data"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_2","uri":"capability://text.generation.language.t5.based.text.embedding.conditioning.with.pretrained.transformer.integration","name":"t5-based text embedding conditioning with pretrained transformer integration","description":"Encodes text descriptions into high-dimensional embeddings using pretrained T5 transformer models (typically T5-base or T5-large), which are then used to condition all diffusion stages. The implementation integrates with Hugging Face transformers library to automatically download and cache pretrained weights, supporting flexible T5 model selection and custom text preprocessing pipelines.","intents":["Convert natural language prompts into semantic embeddings for diffusion conditioning","Leverage pretrained language understanding without fine-tuning","Support variable-length text inputs with automatic padding/truncation","Enable semantic understanding of complex, multi-clause descriptions"],"best_for":["developers building text-to-image systems with semantic understanding requirements","researchers studying text conditioning in diffusion models","applications requiring multilingual or domain-specific text understanding"],"limitations":["T5 model loading adds 5-30 seconds to first inference (cached after initial load)","T5-large requires 3GB+ VRAM; T5-base requires ~1GB","Fixed maximum sequence length (typically 256 tokens) requires prompt truncation for longer inputs","Text embeddings are frozen (not fine-tuned during image generation training), limiting adaptation to domain-specific vocabulary"],"requires":["transformers library (Hugging Face) 4.0+","PyTorch 1.10+","Internet connection for initial model download (or local model cache)","2-3GB disk space for T5 model weights"],"input_types":["text strings (natural language descriptions)","list of text strings (batch processing)"],"output_types":["text embeddings (torch.Tensor, shape [batch, sequence_length, embedding_dim])","text embeddings with attention masks"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_3","uri":"capability://image.visual.multi.stage.unet.architecture.with.resolution.specific.variants","name":"multi-stage unet architecture with resolution-specific variants","description":"Provides modular UNet implementations optimized for different resolution stages: BaseUnet64 for initial 64x64 generation, SRUnet256 and SRUnet1024 for progressive super-resolution, and Unet3D for video generation. Each variant uses attention mechanisms, residual connections, and adaptive group normalization, with configurable channel depths and attention head counts. The modular design allows independent training, selective stage execution, and memory-efficient inference by loading only required stages.","intents":["Generate images at specific resolutions using specialized architectures","Train individual resolution stages independently with different learning rates and schedules","Reduce inference latency by skipping unnecessary super-resolution stages","Extend to video generation using 3D convolutions without architectural redesign"],"best_for":["researchers experimenting with diffusion model architectures","practitioners building production systems with memory constraints","teams implementing custom resolution pipelines (e.g., 32x32 → 128x128 → 512x512)"],"limitations":["Each UNet stage must be trained separately, requiring multiple training runs and careful checkpoint management","Super-resolution UNets require high-resolution training data, increasing dataset size requirements","Attention mechanisms in UNets add ~30-40% computational overhead compared to pure convolution","NullUnet placeholder for skipped stages still requires instantiation, adding code complexity"],"requires":["PyTorch 1.10+","einops library for tensor operations","CUDA for GPU acceleration (CPU inference is extremely slow)","8GB+ VRAM per stage during training"],"input_types":["noisy image tensors (torch.Tensor, shape [batch, 3, height, width])","timestep embeddings (torch.Tensor, shape [batch])","text embeddings (torch.Tensor, shape [batch, seq_len, embedding_dim])","optional: previous stage outputs for super-resolution"],"output_types":["predicted noise tensors (torch.Tensor, same shape as input)","intermediate feature maps (for debugging/analysis)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_4","uri":"capability://image.visual.gaussian.vs.elucidated.diffusion.process.selection.with.configurable.noise.schedules","name":"gaussian vs. elucidated diffusion process selection with configurable noise schedules","description":"Provides two diffusion implementations: standard Gaussian diffusion (DDPM) and Elucidated diffusion (from Karras et al.), both supporting configurable noise schedules (linear, cosine, sigmoid). The framework abstracts the diffusion process through a unified interface, allowing runtime selection between implementations and custom schedule parameters. Elucidated variant uses improved parameterization for better sample quality and faster convergence.","intents":["Choose between standard DDPM and improved Elucidated diffusion based on quality/speed tradeoffs","Customize noise schedules to match specific training data characteristics","Experiment with different diffusion parameterizations without code changes","Achieve faster convergence and better sample quality through Elucidated approach"],"best_for":["researchers comparing diffusion formulations","practitioners optimizing for specific quality/speed targets","teams implementing custom diffusion variants"],"limitations":["Elucidated diffusion requires different hyperparameter ranges (typically lower learning rates)","Switching between implementations requires retraining (checkpoints not compatible)","Custom noise schedules require empirical validation; poor choices degrade convergence","No automatic schedule selection; practitioners must manually choose based on dataset"],"requires":["PyTorch 1.10+","Understanding of diffusion process mathematics for effective schedule tuning"],"input_types":["diffusion type selection (string: 'gaussian' or 'elucidated')","noise schedule parameters (dict with schedule type and parameters)"],"output_types":["diffusion process object with forward/reverse methods","noise predictions for training loss computation"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_5","uri":"capability://automation.workflow.imagentrainer.with.gradient.accumulation.ema.and.multi.gpu.distributed.training","name":"imagentrainer with gradient accumulation, ema, and multi-gpu distributed training","description":"Unified training interface handling gradient accumulation for effective larger batch sizes, exponential moving average (EMA) weight updates for improved model stability, checkpoint saving/loading, and distributed training via Hugging Face Accelerate library. Supports multi-GPU training with automatic device placement, mixed precision (fp16/bf16), and learning rate scheduling. Trainer manages training loop, loss computation, and model updates across all cascading stages.","intents":["Train Imagen models efficiently on multi-GPU setups without manual distributed code","Stabilize training through EMA weight averaging without separate model maintenance","Accumulate gradients to simulate larger batch sizes on memory-constrained hardware","Save and resume training from checkpoints with full optimizer/scheduler state"],"best_for":["researchers training large diffusion models on multi-GPU clusters","practitioners with limited GPU memory needing gradient accumulation","teams requiring production-grade training infrastructure with checkpointing"],"limitations":["Gradient accumulation adds memory overhead for storing intermediate gradients (~10-15% extra)","EMA updates add ~5-10% computational overhead per training step","Distributed training requires careful batch size tuning; naive scaling often reduces convergence speed","Checkpoint files are large (model weights + optimizer state + EMA weights); requires 50GB+ storage for full cascade"],"requires":["PyTorch 1.10+","Hugging Face Accelerate library","CUDA-capable GPU(s) with minimum 8GB VRAM per GPU","For multi-GPU: NCCL backend and proper network connectivity"],"input_types":["training dataset (PyTorch DataLoader)","Imagen model instance","training hyperparameters (dict: learning_rate, num_epochs, accumulation_steps, etc.)"],"output_types":["trained model checkpoints (PyTorch .pt files)","training logs (loss curves, metrics)","EMA model weights"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_6","uri":"capability://image.visual.unconditional.image.generation.with.optional.text.conditioning","name":"unconditional image generation with optional text conditioning","description":"Supports training and inference without text conditioning by using null/empty embeddings, enabling unconditional image generation or hybrid modes where text is optional. Architecture remains identical; conditioning is simply disabled by passing zero embeddings. This allows training on unpaired image data and generating diverse samples without text guidance.","intents":["Train on image datasets without text annotations","Generate diverse images without text constraints","Use as baseline for ablation studies on text conditioning impact","Implement optional text guidance where text is provided only for some samples"],"best_for":["researchers studying conditioning mechanisms through ablation","practitioners with image-only datasets lacking text annotations","applications where text guidance is optional or user-provided"],"limitations":["Unconditional models require separate training; cannot reuse text-conditioned checkpoints","Generated images lack semantic control; diversity is limited to model capacity","Hybrid conditioning (sometimes text, sometimes not) requires careful training to prevent mode collapse","No guidance mechanism for unconditional generation; quality depends entirely on model capacity"],"requires":["PyTorch 1.10+","Image dataset (no text annotations required)"],"input_types":["image tensors only (no text embeddings)","optional: null/empty embeddings for explicit unconditional mode"],"output_types":["image tensors (torch.Tensor, shape [batch, 3, height, width])"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_7","uri":"capability://image.visual.image.inpainting.with.masked.region.filling","name":"image inpainting with masked region filling","description":"Implements inpainting capability where masked regions of images are filled/regenerated while preserving unmasked areas. During training, random masks are applied to images; during inference, the model conditions on both text and the unmasked image regions to generate coherent completions. Masks are incorporated into the diffusion process through concatenation with noisy images, enabling the model to learn spatial context awareness.","intents":["Fill masked regions of images with text-guided content","Perform object removal by masking and regenerating regions","Implement interactive image editing where users specify regions to modify","Generate variations of images while preserving specific areas"],"best_for":["developers building interactive image editing tools","researchers studying spatial conditioning in diffusion models","applications requiring object removal or region replacement"],"limitations":["Inpainting requires separate training with mask augmentation; cannot use standard text-to-image checkpoints","Mask boundaries often show visible seams; post-processing may be needed for seamless results","Model must learn to respect mask boundaries; poor training can cause bleeding into unmasked regions","Inference requires mask input at same resolution as generated image, adding preprocessing overhead"],"requires":["PyTorch 1.10+","Training data with corresponding masks or mask generation strategy","Imagen model trained with inpainting objective"],"input_types":["text descriptions (strings)","image tensors with regions to preserve (torch.Tensor)","binary masks indicating regions to inpaint (torch.Tensor, shape [batch, 1, height, width])"],"output_types":["inpainted image tensors (torch.Tensor, shape [batch, 3, height, width])"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_8","uri":"capability://image.visual.video.generation.with.3d.unet.and.temporal.consistency","name":"video generation with 3d unet and temporal consistency","description":"Extends image generation to video using Unet3D architecture with 3D convolutions and temporal attention mechanisms. Generates video frames autoregressively or in parallel, conditioning on text embeddings and maintaining temporal coherence through shared weights across frames. Supports variable frame counts and frame rates through flexible temporal dimension handling.","intents":["Generate short video clips from text descriptions","Maintain temporal consistency across generated frames","Control video length and frame rate through configuration","Extend text-to-image model to video domain with minimal architectural changes"],"best_for":["researchers exploring text-to-video generation","practitioners building video synthesis applications","teams extending image models to temporal domain"],"limitations":["Video generation requires 3-5x more VRAM than image generation due to temporal dimension","Inference is significantly slower; generating 16 frames takes 5-10x longer than single image","Temporal consistency is difficult to enforce; models often produce flickering or jittery motion","Training requires video datasets with text annotations; much smaller than image datasets","Unet3D requires separate training; cannot reuse image-only checkpoints"],"requires":["PyTorch 1.10+","Video dataset with text annotations","16GB+ VRAM for reasonable batch sizes","Unet3D model weights (separate from image models)"],"input_types":["text descriptions (strings)","frame count parameter (int, typically 8-32)","frame rate parameter (float, typically 8-30 fps)"],"output_types":["video tensors (torch.Tensor, shape [batch, frames, 3, height, width])","video files (mp4, gif formats with post-processing)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--imagen-pytorch__cap_9","uri":"capability://image.visual.super.resolution.with.progressive.upscaling.through.cascaded.stages","name":"super-resolution with progressive upscaling through cascaded stages","description":"Implements progressive super-resolution where images are upscaled through multiple stages (64→256→1024) using specialized SRUnet models. Each stage conditions on text embeddings and the output from the previous stage, enabling fine-grained detail addition at each resolution level. Stages can be trained independently or jointly, and inference can skip stages for faster generation at intermediate resolutions.","intents":["Upscale low-resolution images to high resolution with text-guided detail addition","Train super-resolution stages independently for modular development","Skip super-resolution stages for faster inference when high resolution is not needed","Add semantic details guided by text descriptions during upscaling"],"best_for":["practitioners building high-resolution image generation systems","researchers studying progressive refinement in diffusion models","applications requiring variable output resolutions"],"limitations":["Each super-resolution stage requires separate training with high-resolution data","Training data must include high-resolution images; dataset size increases with resolution","Super-resolution stages add significant inference latency; 1024x1024 generation takes 3-5x longer than 64x64","Cascading approach can accumulate errors from previous stages if not carefully trained"],"requires":["PyTorch 1.10+","High-resolution training data (256x256, 1024x1024 images)","Pretrained base model for conditioning","16GB+ VRAM for training super-resolution stages"],"input_types":["text descriptions (strings)","low-resolution image tensors from previous stage","text embeddings (torch.Tensor)"],"output_types":["high-resolution image tensors (torch.Tensor, shape [batch, 3, 256/1024, 256/1024])"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.10+","CUDA-capable GPU with minimum 8GB VRAM for base model, 16GB+ for full cascade","Pretrained T5 text encoder (transformers library)","Python 3.7+","Both conditional and unconditional model checkpoints","T5 text encoder for embedding generation","imagen-pytorch installed","YAML/JSON configuration file","Pillow library for image processing","torchvision for augmentation transforms"],"failure_modes":["Inference requires sequential execution through all cascading stages, adding latency compared to single-stage models","T5 text encoder must be loaded separately; no built-in lightweight text encoding alternatives","Memory overhead from maintaining multiple UNet models in VRAM during inference","Cascading approach requires careful tuning of guidance scales across stages for optimal results","Guidance scale is a manual hyperparameter requiring empirical tuning (typically 3-15 range)","Dynamic thresholding adds ~5-10% computational overhead per denoising step","Excessive guidance (>20) can produce artifacts or mode collapse toward average images","Requires unconditional model training branch, doubling training data requirements","CLI abstracts implementation details; difficult to customize beyond configuration options","Configuration files can become complex for advanced use cases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6412993394445313,"quality":0.35,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.061Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2024-10-07T13:00:53Z"},"community":{"stars":8407,"forks":800,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lucidrains--imagen-pytorch","compare_url":"https://unfragile.ai/compare?artifact=lucidrains--imagen-pytorch"}},"signature":"UOVj0dyLeZQ3PTKSLoeiLAfWc+LyTsVvqyKrOu0Og9Bgo18wadtdQ8V9uD4q+98ThmzDudXYYOdS1Oid+qv0BQ==","signedAt":"2026-06-20T00:44:07.410Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lucidrains--imagen-pytorch","artifact":"https://unfragile.ai/lucidrains--imagen-pytorch","verify":"https://unfragile.ai/api/v1/verify?slug=lucidrains--imagen-pytorch","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"}}