{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ai-forever--kandinsky-2","slug":"ai-forever--kandinsky-2","name":"Kandinsky-2","type":"model","url":"https://github.com/ai-forever/Kandinsky-2","page_url":"https://unfragile.ai/ai-forever--kandinsky-2","categories":["image-generation"],"tags":["diffusion","image-generation","image2image","inpainting","ipython-notebook","kandinsky","outpainting","text-to-image","text2image"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ai-forever--kandinsky-2__cap_0","uri":"capability://image.visual.multilingual.text.to.image.generation.with.dual.encoder.architecture","name":"multilingual text-to-image generation with dual-encoder architecture","description":"Converts natural language text prompts into images using a two-stage pipeline: text embeddings are first processed through a diffusion prior (1B parameters in v2.1+) that maps text space to CLIP image embeddings, then fed into a latent diffusion U-Net (1.2-1.22B parameters) operating in compressed latent space. Kandinsky 2.0 uses dual text encoders (mCLIP-XLMR 560M + mT5-encoder-small 146M) while v2.1+ uses XLM-Roberta-Large-ViT-L-14 (560M). The diffusion prior acts as a bridge between modalities, enabling more coherent image generation than direct text-to-pixel approaches.","intents":["Generate photorealistic or stylized images from English, Russian, or other multilingual text descriptions","Create variations of image concepts by adjusting text prompts without retraining","Build image generation pipelines that support non-English prompts natively"],"best_for":["Developers building multilingual image generation applications","Teams requiring open-source alternatives to Stable Diffusion or DALL-E","Researchers studying diffusion priors and text-image alignment"],"limitations":["Generation speed depends on hardware; CPU inference is 10-50x slower than GPU","Memory footprint of ~8-12GB VRAM required for full model stack on GPU","Quality degrades for complex multi-object scenes or precise spatial relationships","Diffusion prior adds ~2-3 seconds latency per generation vs direct text-to-image models"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU fallback available)","8GB+ RAM for inference, 16GB+ recommended for batch processing","Hugging Face Hub API access for model weight downloads (~5-8GB total)"],"input_types":["text (string prompts in English, Russian, or multilingual)","optional negative prompts (text strings describing unwanted content)","guidance scale parameter (float, typically 10-15)"],"output_types":["PIL Image objects (RGB, 768x768 or 512x512 depending on version)","NumPy arrays (uint8, shape [height, width, 3])","Batch outputs as lists of images"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_1","uri":"capability://image.visual.image.to.image.transformation.with.text.guided.refinement","name":"image-to-image transformation with text-guided refinement","description":"Transforms existing images by encoding them into latent space via MOVQ encoder, then applying iterative diffusion steps guided by text prompts and a strength parameter (0-1) that controls how much the original image influences the output. The process uses the same diffusion prior and U-Net as text-to-image but initializes the noise schedule at a later timestep based on strength, allowing fine-grained control over preservation vs. modification. Supports both Kandinsky 2.0 (direct U-Net conditioning) and 2.1+ (diffusion prior + U-Net) architectures.","intents":["Modify existing images by applying text-based style transfers or content changes","Create image variations while preserving composition and structure","Implement iterative image refinement workflows where users progressively adjust outputs"],"best_for":["Content creators needing non-destructive image editing with AI guidance","Developers building image remix or variation generation features","Teams prototyping creative tools that blend user images with AI generation"],"limitations":["Strength parameter is coarse-grained; fine-grained control requires multiple passes","Artifacts may appear at image boundaries if input resolution doesn't match model training resolution (768x768 or 512x512)","Processing time scales linearly with image resolution; 1024x1024 inputs require custom upsampling","Semantic understanding of input image is limited to CLIP's visual encoding; complex compositions may be misinterpreted"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Input image in PIL Image format or NumPy array (uint8, RGB)","8GB+ VRAM for GPU inference","Kandinsky 2.0 or later model weights"],"input_types":["image (PIL Image or NumPy array, RGB, any resolution)","text prompt (string describing desired modifications)","strength parameter (float 0.0-1.0, where 1.0 = complete regeneration, 0.0 = no change)","optional negative prompt (text string)"],"output_types":["PIL Image (RGB, 768x768 or 512x512)","NumPy array (uint8, shape [height, width, 3])"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_10","uri":"capability://planning.reasoning.guidance.scale.parameter.tuning.for.semantic.fidelity.tradeoff","name":"guidance scale parameter tuning for semantic-fidelity tradeoff","description":"Classifier-free guidance (CFG) is implemented by computing both conditional (text-guided) and unconditional predictions, then scaling the difference: output = unconditional + guidance_scale * (conditional - unconditional). Higher guidance scales (10-15) increase semantic alignment with text prompts but reduce image diversity and may introduce artifacts. Lower scales (5-8) produce more diverse but less prompt-aligned images. Guidance scale is a hyperparameter exposed in all generation methods.","intents":["Control the tradeoff between semantic alignment with text prompts and image diversity","Tune generation quality for specific use cases (e.g., high guidance for precise control, low for creative variation)","Implement adaptive guidance strategies that adjust scale based on prompt complexity or user feedback"],"best_for":["Content creators fine-tuning generation quality for specific aesthetic goals","Developers building interactive image generation interfaces with guidance control","Researchers studying the relationship between guidance scale and semantic alignment"],"limitations":["Guidance scale is a coarse hyperparameter; no fine-grained control over which text tokens are emphasized","Very high guidance scales (>20) often produce artifacts, oversaturation, or unrealistic textures","Optimal guidance scale varies by prompt; no automatic tuning mechanism","Guidance scale affects generation speed minimally but increases memory usage slightly"],"requires":["Python 3.8+","Kandinsky model instance from get_kandinsky2() factory","Understanding of diffusion guidance mechanics for effective tuning"],"input_types":["guidance_scale parameter (float, typically 5-20, default 10)"],"output_types":["PIL Image (RGB, 768x768 or 512x512)","NumPy array (uint8, shape [height, width, 3])"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_11","uri":"capability://data.processing.analysis.movq.encoder.decoder.for.latent.space.reconstruction","name":"movq encoder-decoder for latent space reconstruction","description":"MOVQ (Multiscale Orthogonal Vector Quantization) is a 67M parameter encoder-decoder that compresses images into latent space for efficient diffusion processing. Unlike standard VAE, MOVQ uses vector quantization to discretize latent codes, improving reconstruction fidelity and reducing artifacts. Introduced in Kandinsky 2.1 as a replacement for VAE. The encoder downsamples images by 8x; the decoder upsamples latent codes back to pixel space with minimal quality loss.","intents":["Efficiently encode images into latent space for image-to-image and inpainting tasks","Reconstruct images from latent codes with minimal quality loss compared to VAE","Enable high-quality image editing in latent space without pixel-level artifacts"],"best_for":["Developers implementing image-to-image or inpainting features requiring high reconstruction quality","Researchers studying vector quantization in generative models","Teams optimizing latent space quality for downstream tasks"],"limitations":["MOVQ encoder-decoder is not exposed via high-level API; requires accessing internal model objects","Reconstruction quality depends on input image resolution; best results at 512x512 or 768x768","Vector quantization introduces quantization artifacts for very high-frequency details","Encoder-decoder adds ~500ms latency per image for encoding/decoding operations"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Kandinsky 2.1 or later (MOVQ not used in v2.0)","Access to Kandinsky source code for model introspection"],"input_types":["image (PIL Image or NumPy array, RGB, 512x512 or 768x768 recommended)"],"output_types":["latent codes (PyTorch tensor, shape [1, latent_channels, latent_height, latent_width])","reconstructed image (PIL Image or NumPy array after decoding)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_12","uri":"capability://text.generation.language.multilingual.text.encoding.with.dual.encoder.architecture.v2.0.only","name":"multilingual text encoding with dual-encoder architecture (v2.0 only)","description":"Kandinsky 2.0 uses two text encoders in parallel: mCLIP-XLMR (560M parameters) for multilingual semantic understanding and mT5-encoder-small (146M parameters) for linguistic structure. Both encoders process the same text prompt independently, producing separate embeddings that are concatenated and fed into the U-Net. This dual-encoder approach enables strong multilingual support without requiring separate models per language. Kandinsky 2.1+ replaces this with a single XLM-Roberta-Large-ViT-L-14 encoder (560M).","intents":["Generate images from text prompts in Russian, English, or other multilingual languages","Leverage semantic and linguistic information from dual encoders for improved text understanding","Support code-switching or mixed-language prompts"],"best_for":["Developers building image generation services for non-English-speaking users","Teams requiring robust multilingual support without language-specific model variants","Researchers studying multilingual text encoding in generative models"],"limitations":["Dual-encoder architecture is only in Kandinsky 2.0; v2.1+ use single encoder","Multilingual support quality varies by language; best for Russian and English, weaker for low-resource languages","Dual encoders add ~200ms latency compared to single-encoder v2.1+","No explicit control over which encoder influences which image regions"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Kandinsky 2.0 model weights (not v2.1 or v2.2)","4GB+ VRAM for encoder inference"],"input_types":["text prompt (string in English, Russian, or other supported languages)"],"output_types":["concatenated text embeddings (PyTorch tensor, shape [1, seq_len, embedding_dim])"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_13","uri":"capability://safety.moderation.negative.prompts.for.content.exclusion.and.quality.improvement","name":"negative prompts for content exclusion and quality improvement","description":"Negative prompts are text descriptions of unwanted content (e.g., 'blurry, low quality, distorted'). During generation, the model computes predictions for both positive and negative prompts, then uses the difference to steer generation away from negative content. Implemented via classifier-free guidance: output = conditional_positive + guidance_scale * (conditional_positive - conditional_negative). Negative prompts are optional but widely used to improve quality by excluding common artifacts.","intents":["Exclude unwanted visual elements or styles from generated images","Improve image quality by specifying what should NOT appear (e.g., 'no watermarks, no text')","Fine-tune generation toward desired aesthetics by combining positive and negative prompts"],"best_for":["Content creators fine-tuning generation quality without retraining models","Developers building user-facing image generation interfaces with quality controls","Teams implementing content filtering or safety constraints via prompting"],"limitations":["Negative prompts are less effective than positive prompts; exclusion is weaker than inclusion","Very specific negative prompts may conflict with positive prompts, reducing quality","No guarantee that negative content will be excluded; effectiveness depends on model training","Negative prompts add ~50% latency (requires computing unconditional prediction) but no memory overhead"],"requires":["Python 3.8+","Kandinsky model instance from get_kandinsky2() factory","Understanding of effective negative prompt engineering"],"input_types":["negative_prompt parameter (string or list of strings, optional)"],"output_types":["PIL Image (RGB, 768x768 or 512x512)","NumPy array (uint8, shape [height, width, 3])"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_2","uri":"capability://image.visual.masked.image.inpainting.with.diffusion.guided.completion","name":"masked image inpainting with diffusion-guided completion","description":"Fills masked regions of images by encoding the full image into latent space, zeroing out latent features corresponding to masked pixels, then running diffusion with text guidance to reconstruct masked areas while preserving unmasked context. The process uses the diffusion prior (v2.1+) or direct U-Net conditioning (v2.0) to guide generation toward text-aligned completions. Mask can be binary (0/255) or soft (grayscale 0-255) for graduated blending at boundaries.","intents":["Remove unwanted objects or people from images while maintaining background coherence","Fill in missing image regions with AI-generated content matching text descriptions","Implement object removal or content replacement workflows in image editing applications"],"best_for":["Image editing tool developers adding AI-powered inpainting features","Content creators needing object removal or image restoration","Teams building generative image manipulation interfaces"],"limitations":["Inpainting quality degrades for large masked regions (>50% of image); small masks (<10%) work best","Boundary artifacts may appear where masked and unmasked regions meet unless soft masks are used","Semantic consistency across mask boundaries depends on text prompt specificity","No explicit control over inpainting style; relies on prompt engineering for desired aesthetic"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Input image and mask as PIL Images or NumPy arrays","8GB+ VRAM for GPU inference","Kandinsky 2.0 or later (inpainting supported in all versions)"],"input_types":["image (PIL Image or NumPy array, RGB, any resolution)","mask (PIL Image or NumPy array, grayscale uint8, same resolution as image)","text prompt (string describing desired inpainting content)","optional negative prompt (text string)"],"output_types":["PIL Image (RGB, 768x768 or 512x512)","NumPy array (uint8, shape [height, width, 3])"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_3","uri":"capability://image.visual.image.mixing.with.multi.image.concept.blending","name":"image mixing with multi-image concept blending","description":"Combines multiple images and text prompts by encoding each image into CLIP embeddings via the image encoder (ViT-L/14 in v2.1, ViT-bigG-14 in v2.2), interpolating or averaging embeddings, then using the diffusion prior to map the blended embedding to a coherent image. Supported in Kandinsky 2.1+ only. Allows weighted blending of image concepts (e.g., 0.7*image1 + 0.3*image2) with text guidance to steer the final output toward desired attributes.","intents":["Create hybrid images by blending visual concepts from multiple source images","Generate variations that combine aesthetic elements from different reference images","Implement image interpolation or morphing workflows with semantic guidance"],"best_for":["Creative directors blending visual concepts for design exploration","Developers building image remix or fusion features","Researchers studying image embedding interpolation and concept blending"],"limitations":["Only available in Kandinsky 2.1+; not supported in v2.0","Blending quality depends on semantic similarity of input images; dissimilar images produce incoherent results","No explicit control over which visual attributes are blended; relies on CLIP embedding space geometry","Requires all input images to be processed through CLIP encoder, adding ~1-2 seconds per image"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Kandinsky 2.1 or later (v2.0 does not support image mixing)","8GB+ VRAM for GPU inference","Multiple input images (2-4 recommended for stable blending)"],"input_types":["images (list of PIL Images or NumPy arrays, RGB, any resolution)","weights (list of floats summing to 1.0, one per image)","text prompt (string describing desired output characteristics)","optional negative prompt (text string)"],"output_types":["PIL Image (RGB, 768x768 or 512x512)","NumPy array (uint8, shape [height, width, 3])"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_4","uri":"capability://image.visual.controlnet.guided.image.generation.with.spatial.conditioning","name":"controlnet-guided image generation with spatial conditioning","description":"Kandinsky 2.2 integrates ControlNet architecture to enable spatial conditioning of image generation via depth maps, edge maps, or other control signals. The control signal is encoded into a separate conditioning pathway that guides the diffusion U-Net without replacing text embeddings, allowing precise spatial control while maintaining semantic alignment with text prompts. Currently supports depth-based control; architecture extensible to other control modalities.","intents":["Generate images with specific spatial layouts or compositions by providing depth or edge maps","Maintain consistent camera viewpoints or 3D structure across multiple generated images","Implement pose-guided or structure-guided image generation for character or object creation"],"best_for":["3D artists and game developers needing spatially-controlled image generation","Teams building structure-aware image synthesis pipelines","Researchers exploring conditional diffusion with spatial priors"],"limitations":["Only available in Kandinsky 2.2; not in v2.0 or v2.1","Currently supports depth control only; other modalities (pose, canny edges) not yet implemented","Control signal quality directly impacts output; noisy or inconsistent depth maps produce artifacts","ControlNet adds ~500ms-1s latency per generation compared to unconditional text-to-image"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Kandinsky 2.2 model weights","8GB+ VRAM for GPU inference","Depth map input (NumPy array or PIL Image, grayscale uint8, same resolution as desired output)"],"input_types":["text prompt (string describing desired image content)","depth map (NumPy array or PIL Image, grayscale uint8, 512x512 or 768x768)","optional negative prompt (text string)","control strength parameter (float, typically 0.5-1.0)"],"output_types":["PIL Image (RGB, 768x768 or 512x512)","NumPy array (uint8, shape [height, width, 3])"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_5","uri":"capability://tool.use.integration.factory.based.model.instantiation.with.device.and.version.management","name":"factory-based model instantiation with device and version management","description":"The get_kandinsky2() factory function provides a unified entry point for loading Kandinsky models with automatic device placement (CPU/CUDA), version selection (2.0, 2.1, 2.2), and task-specific configuration. The factory handles model weight downloading from Hugging Face Hub, caching, and memory-efficient loading. Abstracts version differences so users can switch between Kandinsky versions with a single parameter change without rewriting generation code.","intents":["Load Kandinsky models with automatic device detection and fallback to CPU if CUDA unavailable","Switch between model versions (2.0, 2.1, 2.2) without changing application code","Manage model caching and weight downloads transparently from Hugging Face Hub"],"best_for":["Developers building production image generation services requiring flexible model selection","Teams evaluating different Kandinsky versions for quality/speed tradeoffs","Researchers comparing architectural changes across model versions"],"limitations":["Initial model load requires downloading 5-8GB of weights; subsequent loads use cache","No built-in model quantization or pruning; full precision models required for best quality","Device placement is automatic; manual GPU selection not supported (uses first available CUDA device)","Factory does not support loading multiple model versions simultaneously; requires separate instantiation and memory management"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ (CPU fallback available)","Hugging Face Hub API access and internet connectivity for initial weight download","5-8GB disk space for model cache","Kandinsky 2 package installed via pip"],"input_types":["task parameter (string: 'text2img', 'img2img', 'inpainting', 'mix_images', 'controlnet')","model_version parameter (string: '2.0', '2.1', '2.2')","device parameter (string: 'cuda' or 'cpu', optional; auto-detected if omitted)"],"output_types":["Kandinsky2 model object with methods: generate_images(), generate_images_with_guidance(), etc.","Model object exposes task-specific APIs (e.g., img2img_batch() for batch processing)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_6","uri":"capability://automation.workflow.batch.image.generation.with.memory.efficient.processing","name":"batch image generation with memory-efficient processing","description":"Supports generating multiple images from a single prompt or multiple prompts in a single batch operation, with configurable batch size to fit available VRAM. Internally manages tensor allocation and GPU memory to prevent out-of-memory errors. Batch processing is more efficient than sequential generation due to amortized model loading and reduced overhead per image.","intents":["Generate multiple image variations from a single prompt efficiently","Process multiple prompts in parallel without reloading models between generations","Implement high-throughput image generation pipelines for content creation or data augmentation"],"best_for":["Content creators generating image variations for design exploration","Teams building data augmentation pipelines for training datasets","Developers implementing batch image processing APIs or services"],"limitations":["Batch size is limited by available VRAM; typical batch size 1-4 on 8GB VRAM, 4-8 on 16GB+","Batch processing adds minimal latency savings (10-20%) compared to sequential generation due to diffusion step overhead","No built-in load balancing across multiple GPUs; single-GPU batching only","Memory usage scales linearly with batch size; no gradient checkpointing or other memory optimization techniques"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","8GB+ VRAM for batch_size=1, 16GB+ for batch_size=4+","Kandinsky model instance from get_kandinsky2() factory"],"input_types":["prompts (list of strings or single string repeated)","batch_size parameter (int, 1-8 typical)","num_images_per_prompt parameter (int, number of variations per prompt)","optional negative_prompts (list of strings)"],"output_types":["list of PIL Images (length = batch_size * num_images_per_prompt)","list of NumPy arrays (uint8, shape [height, width, 3])"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_7","uri":"capability://data.processing.analysis.clip.based.image.encoding.for.semantic.understanding","name":"clip-based image encoding for semantic understanding","description":"Encodes images into CLIP embedding space (ViT-L/14 in v2.1, ViT-bigG-14 in v2.2) to extract semantic features for image mixing, similarity comparison, or downstream tasks. The image encoder is frozen (not fine-tuned) and used as a feature extractor. Embeddings are 768-dimensional (ViT-L) or 1280-dimensional (ViT-bigG), enabling semantic operations in embedding space without pixel-level processing.","intents":["Extract semantic embeddings from images for similarity search or clustering","Enable image mixing by interpolating embeddings in CLIP space","Build image-to-image retrieval systems using CLIP embeddings as features"],"best_for":["Developers building image similarity or retrieval systems","Teams implementing image clustering or categorization pipelines","Researchers studying CLIP embedding space geometry and image-text alignment"],"limitations":["CLIP embeddings capture semantic content but lose fine-grained visual details (texture, exact colors)","Embedding quality depends on CLIP's training data; may have biases or gaps for specialized domains","ViT-bigG-14 (v2.2) is 1.8B parameters; encoding large image batches requires significant VRAM","No built-in similarity metrics; users must implement cosine similarity or other distance functions"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","Kandinsky 2.1 or later (image encoder not exposed in v2.0)","4GB+ VRAM for ViT-L, 8GB+ for ViT-bigG"],"input_types":["image (PIL Image or NumPy array, RGB, any resolution)"],"output_types":["NumPy array (float32, shape [768] for ViT-L or [1280] for ViT-bigG)","PyTorch tensor (float32, shape [1, 768] or [1, 1280])"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_8","uri":"capability://automation.workflow.diffusion.prior.training.and.fine.tuning.infrastructure","name":"diffusion prior training and fine-tuning infrastructure","description":"Kandinsky 2.1+ includes a trainable diffusion prior (1B parameters) that maps text embeddings to CLIP image embeddings. The prior can be fine-tuned on custom datasets to improve alignment between text and generated images for specific domains (e.g., product photography, character art). Training uses standard diffusion loss (MSE between predicted and actual noise) with text conditioning. Requires custom training code; not exposed via high-level API.","intents":["Fine-tune diffusion prior on domain-specific datasets to improve generation quality","Adapt Kandinsky to specialized image domains (e.g., medical imaging, architectural visualization)","Research diffusion prior architectures and training dynamics"],"best_for":["ML researchers and engineers with PyTorch expertise","Teams with large domain-specific image datasets wanting to customize Kandinsky","Organizations building proprietary image generation models based on Kandinsky"],"limitations":["No high-level training API; requires writing custom PyTorch training loops","Training requires large datasets (10k+ images recommended) and significant compute (8x A100 GPUs typical)","Fine-tuning the prior alone does not improve generation quality without also fine-tuning the U-Net","No distributed training utilities; multi-GPU training requires manual synchronization","Training code examples are minimal; requires deep understanding of diffusion models"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+","32GB+ VRAM for single-GPU training, 256GB+ for multi-GPU","Custom training dataset with images and text captions","Kandinsky source code (not just pip package) for access to model classes"],"input_types":["training dataset (images + text captions, any format)","hyperparameters (learning rate, batch size, num_epochs, etc.)","optional validation dataset for monitoring training"],"output_types":["fine-tuned diffusion prior weights (PyTorch checkpoint, ~4GB)","training logs (loss curves, validation metrics)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-forever--kandinsky-2__cap_9","uri":"capability://image.visual.latent.diffusion.u.net.with.cross.attention.text.conditioning","name":"latent diffusion u-net with cross-attention text conditioning","description":"The core image generation component is a 1.2-1.22B parameter U-Net operating in latent space (encoded by MOVQ, 67M parameters). The U-Net uses cross-attention layers to condition on text embeddings (from dual encoders in v2.0, or from diffusion prior in v2.1+). Iterative denoising over 50-100 diffusion steps produces the final image. 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