{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ai-forever--ru-dalle","slug":"ai-forever--ru-dalle","name":"ru-dalle","type":"model","url":"https://rudalle.ru/","page_url":"https://unfragile.ai/ai-forever--ru-dalle","categories":["image-generation"],"tags":["dalle","image-generation","openai","python","pytorch","russian","russian-language","text-to-image","transformer"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ai-forever--ru-dalle__cap_0","uri":"capability://image.visual.russian.text.to.image.generation.with.transformer.based.latent.synthesis","name":"russian text-to-image generation with transformer-based latent synthesis","description":"Converts Russian language text prompts into images through a two-stage pipeline: a DalleTransformer encoder processes tokenized Russian text into a latent representation, which is then decoded by a Variational Autoencoder (VAE) into pixel-space images. The architecture uses transformer attention mechanisms for semantic understanding of Russian language nuances and supports multiple pre-trained model variants (Malevich, Emojich, Surrealist, Kandinsky) with parameter counts ranging from 1.3B to 12B, enabling trade-offs between generation speed and output quality.","intents":["Generate diverse images from Russian text descriptions without API dependencies","Create domain-specific imagery (emoji-style, surrealist, general) by selecting appropriate pre-trained models","Run inference locally on GPU/CPU without cloud service latency or privacy concerns","Fine-tune models on custom Russian datasets for specialized image generation tasks"],"best_for":["Russian-speaking developers building offline image generation applications","Teams requiring privacy-preserving text-to-image generation without external API calls","Researchers experimenting with DALL-E-style architectures in non-English languages"],"limitations":["Inference latency varies by model size (1.3B models ~2-5 seconds, 12B Kandinsky ~10-30 seconds on consumer GPU)","Requires significant GPU memory (minimum 8GB VRAM for 1.3B models, 24GB+ for Kandinsky)","Russian language understanding limited to training data distribution; performance degrades on out-of-domain prompts","No built-in batch processing optimization; sequential generation required for multiple images","Fixed output resolution per model; custom aspect ratios require additional post-processing"],"requires":["Python 3.7+","PyTorch 1.9+","CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","8GB+ GPU VRAM for inference","Pre-trained model weights (auto-downloaded on first use, ~2-5GB per model)"],"input_types":["text (Russian language prompts)","image (optional image prompts for guided generation)","parameters (top_k, top_p sampling controls, aspect ratio specifications)"],"output_types":["image (PIL Image objects or saved PNG/JPEG files)","latent tensors (intermediate VAE representations for advanced workflows)"],"categories":["image-visual","text-to-image-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_1","uri":"capability://image.visual.multi.model.selection.with.style.specific.pre.trained.variants","name":"multi-model selection with style-specific pre-trained variants","description":"Provides four distinct pre-trained model checkpoints (Malevich for general-purpose, Emojich for emoji-style, Surrealist for artistic, Kandinsky for high-quality) accessible via `get_rudalle_model()` API function. Each variant is independently trained on curated datasets emphasizing different visual styles, allowing users to select the appropriate model for their generation task without retraining. Model loading is abstracted through a registry pattern that handles checkpoint downloading, caching, and device placement (CPU/GPU).","intents":["Select the right model variant for specific visual style requirements (emoji vs photorealistic vs surrealist)","Trade off generation quality against inference speed by choosing smaller (1.3B) vs larger (12B) models","Programmatically load and switch between models within the same application","Ensure reproducible results by pinning specific model versions"],"best_for":["Application developers needing style-specific image generation without training custom models","Teams building multi-purpose image generation services with different aesthetic requirements","Researchers comparing generative model architectures across different training datasets"],"limitations":["Only four pre-trained variants available; custom styles require fine-tuning from scratch","Model selection is static per generation call; cannot blend or interpolate between model outputs","Each model requires separate disk storage (2-5GB per variant); total storage ~10-20GB for all models","No automatic model selection based on prompt analysis; user must manually choose variant"],"requires":["Python 3.7+","PyTorch 1.9+","Internet connection for initial model download (cached locally thereafter)","Disk space for model weights (2-5GB per model variant)"],"input_types":["string (model name: 'Malevich', 'Emojich', 'Surrealist', or 'Kandinsky')","device specification (optional: 'cuda', 'cpu')"],"output_types":["PyTorch model object (DalleTransformer instance ready for inference)"],"categories":["image-visual","model-selection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_10","uri":"capability://image.visual.videodalle.extension.for.temporal.image.sequence.generation","name":"videodalle extension for temporal image sequence generation","description":"Extends core image generation to produce sequences of images that form coherent videos through temporal consistency constraints. The VideoDALLE extension applies the generation pipeline frame-by-frame while maintaining visual continuity between frames, using techniques like optical flow guidance or latent space interpolation to ensure smooth transitions. This enables video generation from text prompts without training separate video models.","intents":["Generate short video clips from Russian text descriptions","Create smooth animated transitions between generated images","Produce temporally coherent visual sequences without training video-specific models","Extend static image generation to dynamic content creation"],"best_for":["Content creators needing short video clips from text descriptions","Applications combining image and video generation in unified interface","Researchers exploring temporal consistency in generative models"],"limitations":["VideoDALLE implementation details not documented; unclear how temporal consistency is enforced","Video generation significantly slower than image generation (frame-by-frame processing); typical 30-frame video requires minutes of computation","Output video quality and temporal coherence not benchmarked; may produce flickering or discontinuous transitions","No control over video length, frame rate, or temporal smoothness parameters","Requires additional GPU memory beyond image generation; may cause OOM on memory-constrained devices"],"requires":["Python 3.7+","PyTorch 1.9+","VideoDALLE extension module (separate from core ru-dalle)","Video codec libraries (ffmpeg or similar) for output encoding"],"input_types":["text (Russian language prompt)","video_length (optional, number of frames)","frame_rate (optional, frames per second)"],"output_types":["video file (MP4, WebM, or other codec)","image sequence (list of PIL Images)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_11","uri":"capability://automation.workflow.model.fine.tuning.on.custom.datasets.for.domain.adaptation","name":"model fine-tuning on custom datasets for domain adaptation","description":"Provides infrastructure for adapting pre-trained models to specialized domains by fine-tuning on custom Russian image-text pair datasets. The fine-tuning pipeline supports both full model training and parameter-efficient methods (LoRA, adapter layers) to reduce computational requirements. Users can supply custom datasets, configure training hyperparameters, and evaluate fine-tuned models on validation sets, enabling domain-specific image generation without training from scratch.","intents":["Adapt pre-trained models to specialized domains (e.g., medical imaging, product photography, architectural visualization)","Improve generation quality for domain-specific concepts and terminology","Create proprietary models trained on company-specific image datasets","Fine-tune models with limited computational resources using parameter-efficient methods"],"best_for":["Teams with domain-specific image generation requirements and custom training data","Organizations requiring proprietary models trained on internal datasets","Researchers studying transfer learning and domain adaptation in generative models"],"limitations":["Fine-tuning requires substantial computational resources (GPU training for hours/days); not feasible on consumer hardware","No documentation on optimal dataset size, composition, or training hyperparameters for fine-tuning","Fine-tuning infrastructure not fully documented; unclear which components support parameter-efficient methods","Risk of catastrophic forgetting; fine-tuned models may lose general-purpose capabilities if trained on narrow domains","Evaluation metrics for fine-tuned models not standardized; no built-in benchmarking tools","Custom datasets must be manually curated and annotated with Russian text descriptions"],"requires":["Python 3.7+","PyTorch 1.9+","Custom dataset of image-text pairs (minimum 1000-10000 pairs recommended)","High-end GPU (24GB+ VRAM) for full model fine-tuning","Training infrastructure (distributed training support optional but recommended)"],"input_types":["custom dataset (image-text pairs in standard format)","training hyperparameters (learning rate, batch size, epochs, etc.)","fine-tuning method (full training or parameter-efficient)"],"output_types":["fine-tuned model weights (saved checkpoint)","training metrics (loss curves, validation scores)","evaluation results (sample generations on test set)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_2","uri":"capability://image.visual.image.guided.generation.with.optional.image.prompts","name":"image-guided generation with optional image prompts","description":"Extends text-only generation by accepting optional image prompts that condition the generation process, allowing users to guide visual output toward specific reference images. The system encodes reference images into the same latent space as text tokens, concatenating or blending these representations before passing to the VAE decoder. This enables fine-grained control over composition, style, and content without full image-to-image translation.","intents":["Generate variations of existing images with modified text descriptions","Blend visual characteristics from reference images with new semantic content from text prompts","Maintain specific visual elements (composition, color palette) while changing subject matter","Create style-transfer effects by providing artistic reference images alongside descriptive text"],"best_for":["Creative professionals needing fine-grained control over image generation output","Teams building interactive image editing tools with semantic guidance","Designers prototyping variations on existing visual concepts"],"limitations":["Image prompt influence is not independently controllable; no weight parameter to adjust reference image strength","Reference image resolution must match model training resolution; upscaling/downscaling may degrade guidance quality","Incompatible with some enhancement pipelines (ruCLIP filtering may ignore image prompt intent)","No documentation on optimal reference image characteristics (style, content, resolution)"],"requires":["Python 3.7+","PyTorch 1.9+","PIL/Pillow for image loading and preprocessing","Reference image file (PNG, JPEG, or PIL Image object)"],"input_types":["text (Russian language prompt)","image (PIL Image or file path to reference image)"],"output_types":["image (generated image influenced by both text and reference image)"],"categories":["image-visual","conditional-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_3","uri":"capability://image.visual.super.resolution.enhancement.via.realesrgan.integration","name":"super-resolution enhancement via realesrgan integration","description":"Post-processes generated images through RealESRGAN (Real-ESRGAN) super-resolution model to upscale output resolution by 2x-4x with detail enhancement. The enhancement pipeline is decoupled from core generation, allowing optional application after image synthesis. RealESRGAN uses a residual dense network trained on perceptual loss to reconstruct high-frequency details, converting low-resolution VAE outputs into sharper, higher-resolution images suitable for print or display.","intents":["Increase output image resolution from model-native size (e.g., 256x256) to higher resolutions (512x512, 1024x1024) for print or high-DPI displays","Enhance fine details and reduce compression artifacts in generated images","Create high-quality outputs without retraining larger models or increasing inference latency significantly","Apply selective super-resolution only to best-quality generated images (combined with ruCLIP filtering)"],"best_for":["Applications requiring high-resolution output (print, large displays, web galleries)","Workflows where generation speed is critical but output quality must be high","Teams combining multiple enhancement techniques (super-resolution + filtering)"],"limitations":["Super-resolution adds 2-5 seconds latency per image on consumer GPU; sequential processing required","Upscaling beyond 4x introduces hallucinated details not present in original; quality degrades at extreme scales","RealESRGAN model requires additional GPU memory (~2GB); may cause OOM on memory-constrained devices","Enhancement is lossy; cannot recover information not present in original low-resolution image","Requires separate model download and caching (~500MB for RealESRGAN weights)"],"requires":["Python 3.7+","PyTorch 1.9+","RealESRGAN model weights (auto-downloaded, ~500MB)","2GB+ GPU VRAM for super-resolution inference","PIL/Pillow for image I/O"],"input_types":["image (PIL Image or tensor from VAE decoder)","scale factor (2, 3, or 4 for upscaling multiplier)"],"output_types":["image (upscaled PIL Image with enhanced details)"],"categories":["image-visual","image-enhancement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_4","uri":"capability://image.visual.image.selection.and.ranking.via.ruclip.semantic.matching","name":"image selection and ranking via ruclip semantic matching","description":"Filters and ranks generated images by computing semantic similarity between image content and original text prompt using ruCLIP (Russian CLIP), a vision-language model trained on Russian image-text pairs. The system encodes both the prompt and each generated image into a shared embedding space, computing cosine similarity scores to identify images most aligned with user intent. This enables cherry-picking best results from batch generations without manual review.","intents":["Automatically select the highest-quality generated image from a batch without manual inspection","Rank multiple generations by semantic alignment with original prompt","Filter out off-topic or low-quality outputs before applying expensive post-processing (super-resolution)","Provide confidence scores for generated images to inform downstream workflows"],"best_for":["Batch generation workflows where multiple candidates are produced and best must be selected","Quality-critical applications where semantic alignment with prompt is paramount","Cost-sensitive pipelines combining generation with expensive enhancement (super-resolution)"],"limitations":["ruCLIP semantic understanding limited to Russian language and training data distribution; may fail on novel concepts or non-Russian prompts","Similarity scores are relative, not absolute; no threshold to distinguish 'good' from 'bad' images, only ranking","Requires additional inference pass per image; adds latency proportional to batch size (~500ms per image on GPU)","ruCLIP model requires separate download and GPU memory (~2GB); may cause OOM on memory-constrained devices","Cannot distinguish between different valid interpretations of ambiguous prompts; may penalize creative variations"],"requires":["Python 3.7+","PyTorch 1.9+","ruCLIP model weights (auto-downloaded, ~2GB)","2GB+ GPU VRAM for CLIP inference","PIL/Pillow for image I/O"],"input_types":["text (Russian language prompt used for generation)","list of images (PIL Images or file paths to generated images)"],"output_types":["ranked list of images with similarity scores (float 0-1)","top-k images (configurable, typically 1-5)"],"categories":["image-visual","ranking-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_5","uri":"capability://image.visual.configurable.sampling.with.top.k.and.top.p.nucleus.controls","name":"configurable sampling with top-k and top-p nucleus controls","description":"Provides fine-grained control over generation randomness through top-k (select from k most likely tokens) and top-p (nucleus sampling, select from smallest set of tokens with cumulative probability ≥ p) parameters passed to the DalleTransformer decoder. These sampling strategies control the trade-off between diversity (high k/p) and coherence (low k/p) during autoregressive token generation, allowing users to tune output variability without retraining models.","intents":["Generate diverse image variations from the same prompt by increasing top-k/top-p values","Produce more consistent, deterministic outputs by decreasing sampling parameters","Fine-tune generation behavior for specific use cases (creative exploration vs product photography)","Reproduce specific outputs by fixing random seed and sampling parameters"],"best_for":["Interactive applications where users want to explore multiple interpretations of a prompt","Deterministic workflows requiring reproducible outputs for testing or comparison","Fine-tuning generation behavior without retraining models"],"limitations":["Sampling parameters affect only token-level diversity; cannot control high-level semantic variation (e.g., object count, composition)","No guidance on optimal parameter values for specific use cases; requires empirical tuning","Extreme values (very high k/p) may produce incoherent outputs; very low values may produce repetitive results","Parameters are global; cannot vary sampling strategy per token or per generation stage","No automatic parameter selection based on prompt analysis"],"requires":["Python 3.7+","PyTorch 1.9+","Understanding of sampling strategies (top-k, top-p) for effective tuning"],"input_types":["top_k (integer, typically 256-2048)","top_p (float, typically 0.8-0.99)","temperature (float, typically 0.5-1.5, controls softmax sharpness)"],"output_types":["image (generated with specified sampling strategy)"],"categories":["image-visual","generation-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_6","uri":"capability://image.visual.custom.aspect.ratio.support.with.flexible.output.dimensions","name":"custom aspect ratio support with flexible output dimensions","description":"Allows generation of images in non-square aspect ratios (e.g., 16:9, 4:3, 1:2) by adjusting VAE decoder input dimensions and applying aspect-ratio-aware padding or cropping during latent space processing. The system supports multiple predefined aspect ratios and custom dimensions, enabling users to generate images optimized for specific display contexts (mobile, widescreen, portrait) without training aspect-ratio-specific models.","intents":["Generate images in specific aspect ratios for web, mobile, or print layouts without manual cropping","Create portrait-oriented images for mobile apps or vertical displays","Produce widescreen images for cinema or panoramic displays","Maintain aspect ratio consistency across batch generations"],"best_for":["Web and mobile application developers needing images in specific dimensions","Content creators producing images for multiple platforms with different aspect ratio requirements","Design teams standardizing on specific output dimensions"],"limitations":["Aspect ratio support requires VAE decoder modification; not all pre-trained models support all aspect ratios","Extreme aspect ratios (very wide or very tall) may produce distorted or incoherent outputs due to training data bias toward square images","Custom dimensions must be multiples of VAE latent space stride (typically 8 or 16 pixels); arbitrary dimensions not supported","No automatic aspect ratio selection based on prompt content; user must manually specify","Aspect ratio changes may affect generation quality compared to native square outputs"],"requires":["Python 3.7+","PyTorch 1.9+","Knowledge of supported aspect ratios for specific model variant"],"input_types":["aspect_ratio (string: 'square', 'portrait', 'landscape', etc. or tuple: (width, height))","custom dimensions (optional: width and height in pixels, must be multiples of VAE stride)"],"output_types":["image (generated in specified aspect ratio)"],"categories":["image-visual","generation-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_7","uri":"capability://data.processing.analysis.tokenizer.with.russian.language.support.and.cyrillic.encoding","name":"tokenizer with russian language support and cyrillic encoding","description":"Implements a specialized tokenizer that converts Russian language text into discrete tokens compatible with the DalleTransformer encoder. The tokenizer handles Cyrillic character encoding, Russian morphology, and language-specific preprocessing (punctuation normalization, case handling) to create token sequences that preserve semantic meaning for the transformer. Tokens are mapped to learned embeddings in the transformer's vocabulary space, enabling the model to understand Russian language nuances.","intents":["Convert Russian text prompts into token sequences for transformer processing","Handle Russian-specific text preprocessing (case normalization, punctuation, special characters)","Ensure consistent tokenization across different Russian text variations (e.g., uppercase/lowercase)","Support Russian language morphology and grammar in token representation"],"best_for":["Russian-language image generation applications","Multilingual systems requiring language-specific tokenization","Researchers studying language-specific effects on image generation"],"limitations":["Tokenizer vocabulary is fixed to training data; out-of-vocabulary Russian words may be split into subword tokens or treated as unknown","No support for non-Cyrillic scripts or mixed-language prompts; English words in Russian prompts may be mishandled","Tokenizer design not documented; unclear how Russian morphology is handled vs English tokenizers","Maximum token sequence length fixed (typically 256-512 tokens); long Russian prompts may be truncated","No dynamic vocabulary expansion; cannot adapt to new Russian terminology without retraining"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-trained tokenizer weights (auto-loaded with model)"],"input_types":["text (Russian language string)"],"output_types":["token sequence (list of integers)","token embeddings (tensor of shape [sequence_length, embedding_dim])"],"categories":["data-processing-analysis","text-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_8","uri":"capability://image.visual.variational.autoencoder.vae.decoding.from.latent.to.pixel.space","name":"variational autoencoder (vae) decoding from latent to pixel space","description":"Implements a Variational Autoencoder that maps latent representations (produced by DalleTransformer) into high-dimensional pixel space, reconstructing images from compressed latent codes. The VAE decoder uses transposed convolutions and upsampling layers to progressively reconstruct image details from low-resolution latent features, enabling efficient generation without pixel-space autoregression. The decoder is trained jointly with the encoder to minimize reconstruction loss, enabling lossy compression of image information into latent space.","intents":["Convert transformer-generated latent codes into viewable images","Enable efficient image generation by operating in compressed latent space rather than pixel space","Support image enhancement pipelines by providing intermediate latent representations","Decouple image generation (transformer) from image reconstruction (VAE), enabling modular architecture"],"best_for":["Efficient image generation systems where latent space compression is critical for speed","Modular architectures separating semantic generation (transformer) from visual reconstruction (VAE)","Applications requiring intermediate latent representations for enhancement or filtering"],"limitations":["VAE decoder introduces reconstruction loss; generated images may lack fine details present in training data","Latent space dimensionality and compression ratio fixed during training; cannot adjust quality/speed trade-off at inference","VAE decoder is model-specific; cannot swap decoders between different model variants without retraining","Decoder output resolution fixed to training resolution (typically 256x256); higher resolutions require separate super-resolution model","No control over reconstruction quality; cannot trade off speed for fidelity at inference time"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-trained VAE weights (auto-loaded with model)"],"input_types":["latent tensor (shape [batch_size, latent_channels, latent_height, latent_width])"],"output_types":["image tensor (shape [batch_size, 3, height, width], values in [0, 1] or [0, 255])","PIL Image (converted from tensor)"],"categories":["image-visual","latent-space-decoding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--ru-dalle__cap_9","uri":"capability://automation.workflow.batch.generation.with.sequential.processing.and.result.aggregation","name":"batch generation with sequential processing and result aggregation","description":"Supports generating multiple images from the same or different prompts by iterating through input prompts and applying the generation pipeline sequentially. The system accumulates generated images in memory or writes them to disk, providing options for batch result aggregation, filtering, and ranking. While individual generation steps are sequential (no parallelization within a single batch), the API abstracts batch handling to simplify multi-image workflows.","intents":["Generate multiple image variations from a single prompt for comparison and selection","Process lists of prompts in a single API call without manual looping","Aggregate results with optional filtering (ruCLIP ranking) and enhancement (super-resolution)","Create image galleries or datasets from batch generations"],"best_for":["Batch processing workflows where multiple images are needed from single or multiple prompts","Quality-critical applications combining generation with filtering and enhancement","Data collection pipelines creating image datasets from prompt lists"],"limitations":["Sequential processing without parallelization; batch generation time scales linearly with batch size","No built-in progress tracking or cancellation; long batches cannot be interrupted mid-execution","Memory accumulation for large batches; all results held in memory before aggregation (may cause OOM)","No distributed processing support; batches cannot be parallelized across multiple GPUs or machines","Batch size not automatically optimized; users must manually tune for memory constraints"],"requires":["Python 3.7+","PyTorch 1.9+","Sufficient GPU memory for sequential inference (8GB+ for 1.3B models)"],"input_types":["list of prompts (list of strings)","batch_size (optional, for memory management)","num_images_per_prompt (optional, number of variations per prompt)"],"output_types":["list of images (PIL Images or file paths)","results dataframe (optional, with metadata and similarity scores if filtering applied)"],"categories":["automation-workflow","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+","CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","8GB+ GPU VRAM for inference","Pre-trained model weights (auto-downloaded on first use, ~2-5GB per model)","Internet connection for initial model download (cached locally thereafter)","Disk space for model weights (2-5GB per model variant)","VideoDALLE extension module (separate from core ru-dalle)","Video codec libraries (ffmpeg or similar) for output encoding","Custom dataset of image-text pairs (minimum 1000-10000 pairs recommended)"],"failure_modes":["Inference latency varies by model size (1.3B models ~2-5 seconds, 12B Kandinsky ~10-30 seconds on consumer GPU)","Requires significant GPU memory (minimum 8GB VRAM for 1.3B models, 24GB+ for Kandinsky)","Russian language understanding limited to training data distribution; performance degrades on out-of-domain prompts","No built-in batch processing optimization; sequential generation required for multiple images","Fixed output resolution per model; custom aspect ratios require additional post-processing","Only four pre-trained variants available; custom styles require fine-tuning from scratch","Model selection is static per generation call; cannot blend or interpolate between model outputs","Each model requires separate disk storage (2-5GB per variant); total storage ~10-20GB for all models","No automatic model selection based on prompt analysis; user must manually choose variant","VideoDALLE implementation details not documented; unclear how temporal consistency is enforced","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.25769334338571004,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"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":"2026-05-03T13:58:44.860Z","last_commit":"2023-01-10T14:07:02Z"},"community":{"stars":1647,"forks":243,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ai-forever--ru-dalle","compare_url":"https://unfragile.ai/compare?artifact=ai-forever--ru-dalle"}},"signature":"zitlEc6QHnSPGZo8nS4gLwIgUHanoV4AFsmqOZZ3SBPIjN/wiFCGSBLIc7QnY3SjwPGKguNKA2cxINesAMTvBQ==","signedAt":"2026-06-20T08:36:37.583Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-forever--ru-dalle","artifact":"https://unfragile.ai/ai-forever--ru-dalle","verify":"https://unfragile.ai/api/v1/verify?slug=ai-forever--ru-dalle","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"}}