{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-tencentarc--brushnet","slug":"tencentarc--brushnet","name":"BrushNet","type":"model","url":"https://tencentarc.github.io/BrushNet/","page_url":"https://unfragile.ai/tencentarc--brushnet","categories":["image-generation"],"tags":["diffusion","diffusion-models","eccv","eccv2024","image-inpainting","text-to-image"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-tencentarc--brushnet__cap_0","uri":"capability://image.visual.decomposed.dual.branch.diffusion.inpainting.with.masked.feature.separation","name":"decomposed dual-branch diffusion inpainting with masked feature separation","description":"Implements a specialized dual-branch architecture that separates masked image features from noisy latent features during the diffusion process, reducing the model's learning load and enabling precise inpainting. The architecture processes segmentation or random masks through dedicated branches that converge at multiple resolution levels, allowing the base diffusion model to focus on content generation within masked regions while preserving unmasked areas. This decomposition is achieved through custom UNet modifications in the diffusers library that inject BrushNet control at intermediate layers without requiring full model retraining.","intents":["I want to inpaint masked regions in images while preserving surrounding context using text guidance","I need to integrate inpainting capabilities into existing Stable Diffusion pipelines without retraining from scratch","I want to support both object-shaped segmentation masks and arbitrary random masks in the same model","I need fine-grained per-pixel control over the diffusion process for high-quality inpainting results"],"best_for":["Computer vision researchers implementing plug-and-play diffusion extensions","ML engineers building image editing applications on top of Stable Diffusion","Teams requiring production-grade inpainting without full model retraining"],"limitations":["Requires pre-trained base diffusion model (SD 1.5 or SDXL) — cannot function standalone","Inference latency depends on base model's diffusion steps (typically 50-100 steps for quality results)","Memory footprint scales with image resolution; 4K+ images may require gradient checkpointing or reduced batch sizes","Mask quality directly impacts output quality — poorly defined masks produce artifacts at boundaries"],"requires":["Python 3.9+","PyTorch 1.12.1+","CUDA 11.6+ (recommended for GPU inference)","Hugging Face diffusers library with BrushNet custom modifications","Pre-trained Stable Diffusion 1.5 or SDXL model weights"],"input_types":["PIL Image or numpy array (RGB, 512x512 or 768x768 typical)","Binary mask (single-channel, same spatial dimensions as image)","Text prompt (string, 1-77 tokens for SD 1.5, up to 256 for SDXL)","Optional: negative prompt, guidance scale, number of inference steps"],"output_types":["PIL Image (inpainted result, same dimensions as input)","Latent tensor (if returning intermediate diffusion state)"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_1","uri":"capability://image.visual.text.guided.inpainting.pipeline.with.multi.variant.model.support","name":"text-guided inpainting pipeline with multi-variant model support","description":"Provides unified inference pipelines (StableDiffusionBrushNetPipeline and StableDiffusionXLBrushNetPipeline) that orchestrate the complete inpainting workflow: text encoding via CLIP/OpenCLIP, mask preprocessing, latent encoding of the original image, iterative diffusion with BrushNet control injection, and final decoding. The pipeline abstracts away the complexity of managing multiple model components (text encoder, VAE, UNet, scheduler) and provides a simple API while supporting both SD 1.5 and SDXL base models with separate segmentation and random mask variants.","intents":["I want a simple, high-level API to perform text-guided inpainting without managing diffusion internals","I need to switch between SD 1.5 and SDXL base models without changing application code","I want to control inference parameters like guidance scale, number of steps, and random seed","I need to batch process multiple images with different masks and prompts efficiently"],"best_for":["Application developers building image editing UIs or APIs","Data scientists prototyping inpainting workflows","Teams deploying inpainting as a microservice"],"limitations":["Pipeline initialization loads all model components into memory (~7GB for SD 1.5, ~13GB for SDXL) — requires GPU with sufficient VRAM","Sequential processing of batches; no built-in distributed inference across multiple GPUs","Text encoding is fixed to CLIP tokenizer (77 tokens for SD 1.5); longer prompts are truncated","Scheduler choice (DDPM, DDIM, Euler, etc.) affects quality-speed tradeoff but requires manual tuning"],"requires":["Python 3.9+","PyTorch 1.12.1+","Hugging Face transformers library (for CLIP text encoder)","Pre-trained model weights accessible via HuggingFace Hub or local path","GPU with 8GB+ VRAM (16GB+ recommended for SDXL)"],"input_types":["image: PIL Image or numpy array (RGB, uint8)","mask: PIL Image or numpy array (grayscale, uint8 or binary)","prompt: string (text description of desired inpainted content)","negative_prompt: optional string","guidance_scale: float (typically 7.5-15.0)","num_inference_steps: int (typically 20-100)","generator: optional torch.Generator for reproducibility"],"output_types":["PIL Image (inpainted result)","Optional: list of intermediate latents if return_dict=True"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_10","uri":"capability://automation.workflow.model.weight.quantization.and.optimization.for.deployment","name":"model weight quantization and optimization for deployment","description":"Provides tools for reducing model size and inference latency through quantization (INT8, FP16) and optimization techniques. The system supports post-training quantization of BrushNet weights, mixed-precision inference (FP16 for forward pass, FP32 for critical operations), and optional pruning of less important weights. Quantized models achieve 2-4x speedup with minimal quality loss, enabling deployment on resource-constrained devices (edge GPUs, mobile) or higher throughput on servers.","intents":["I want to reduce model size for deployment on edge devices or mobile","I need to increase inference throughput on servers with limited GPU memory","I want to minimize latency for real-time interactive applications","I need to balance quality and performance for production deployments"],"best_for":["ML engineers optimizing models for production deployment","Teams building edge AI applications with resource constraints","Organizations requiring high-throughput inference on limited hardware"],"limitations":["INT8 quantization can cause 2-5% quality degradation (LPIPS) compared to FP32; requires validation on target domain","Quantization is model-specific; quantized weights cannot be transferred between different base models (SD 1.5 vs SDXL)","FP16 mixed precision may cause numerical instability in some operations; requires careful testing","Quantization tools are not standardized; different frameworks (TensorRT, ONNX, PyTorch) have different quantization approaches"],"requires":["PyTorch with quantization support (torch.quantization)","Optional: TensorRT or ONNX for advanced optimization","Calibration dataset for post-training quantization (typically 100-500 images)"],"input_types":["model: torch.nn.Module (BrushNet or full pipeline)","quantization_type: str ('int8', 'fp16', 'dynamic')","calibration_data: list of sample inputs (for post-training quantization)","target_device: str ('cpu', 'gpu', 'edge')"],"output_types":["quantized_model: torch.nn.Module (optimized weights)","quality_metrics: dict (LPIPS, FID on validation set before/after quantization)","performance_metrics: dict (latency, throughput, model_size)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_11","uri":"capability://tool.use.integration.integration.with.huggingface.diffusers.ecosystem","name":"integration with huggingface diffusers ecosystem","description":"Provides seamless integration with the HuggingFace diffusers library, enabling BrushNet to work with any diffusers-compatible scheduler, pipeline, and model. The integration includes custom BrushNet model classes (BrushNetModel) that inherit from diffusers base classes, custom pipeline classes (StableDiffusionBrushNetPipeline) that follow diffusers conventions, and compatibility with diffusers utilities (safety checker, feature extractor). This enables users to leverage the entire diffusers ecosystem (LoRA, ControlNet, other extensions) alongside BrushNet.","intents":["I want to use BrushNet with different schedulers (DDIM, Euler, DPM++) without code changes","I need to combine BrushNet with other diffusers extensions (LoRA, ControlNet, safety checkers)","I want to load/save BrushNet models using standard diffusers APIs","I need to integrate BrushNet into existing diffusers-based applications"],"best_for":["Developers already using HuggingFace diffusers in their projects","Teams building modular diffusion pipelines with multiple extensions","Researchers combining BrushNet with other diffusion techniques"],"limitations":["Integration is limited to diffusers API conventions; custom diffusion implementations may not be compatible","Some diffusers features (e.g., IP-Adapter, certain LoRA variants) may not work seamlessly with BrushNet without additional integration work","Scheduler compatibility depends on diffusers version; older versions may lack support for newer schedulers","Custom modifications to BrushNet may break diffusers compatibility; requires careful API adherence"],"requires":["HuggingFace diffusers library (0.21.0+)","PyTorch 1.12.1+","Transformers library for CLIP text encoder"],"input_types":["model_id: str (HuggingFace model identifier or local path)","scheduler: diffusers.SchedulerMixin (e.g., DDIMScheduler, EulerDiscreteScheduler)","safety_checker: optional diffusers safety checker","feature_extractor: optional diffusers feature extractor"],"output_types":["pipeline: StableDiffusionBrushNetPipeline (fully initialized and ready for inference)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_2","uri":"capability://data.processing.analysis.mask.aware.latent.encoding.and.feature.extraction","name":"mask-aware latent encoding and feature extraction","description":"Preprocesses input images and masks into latent space representations that preserve spatial information about masked vs unmasked regions. The system encodes the original image through the VAE encoder, then applies mask-aware feature extraction that separates masked image features from the noisy latent representation. This preprocessing step is critical for the dual-branch architecture, as it ensures the BrushNet model receives properly formatted input that distinguishes between regions to inpaint and regions to preserve, using spatial masking operations at the latent level (typically 8x downsampled from image space).","intents":["I need to convert high-resolution images and masks into latent representations for efficient diffusion processing","I want to ensure masked regions are properly isolated in latent space before diffusion begins","I need to handle variable image sizes and aspect ratios while maintaining mask alignment","I want to extract features from both masked and unmasked regions for the dual-branch architecture"],"best_for":["ML engineers optimizing inference performance by working in latent space","Researchers studying diffusion model behavior in latent representations","Teams building custom inpainting pipelines with specialized preprocessing needs"],"limitations":["VAE encoding introduces quantization artifacts due to 8x spatial downsampling — fine details in masks may be lost","Mask must be resized to match latent dimensions (typically 64x64 for 512x512 images); interpolation can introduce boundary artifacts","Latent space representation is model-specific (SD 1.5 VAE differs from SDXL VAE) — cannot transfer latents between models","Requires careful normalization of mask values (0-1 range) to avoid numerical instability in subsequent diffusion steps"],"requires":["Pre-trained VAE encoder (included in SD 1.5 or SDXL model)","PyTorch with CUDA support for efficient tensor operations","Input image dimensions must be multiples of 8 (e.g., 512x512, 768x768)"],"input_types":["image: PIL Image or torch.Tensor (RGB, normalized to [-1, 1])","mask: PIL Image or torch.Tensor (grayscale, normalized to [0, 1])","generator: optional torch.Generator for reproducible noise sampling"],"output_types":["latents: torch.Tensor (shape: [batch, 4, height//8, width//8])","masked_latents: torch.Tensor (latents with mask applied)","mask_latent: torch.Tensor (downsampled mask in latent space)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_3","uri":"capability://image.visual.multi.resolution.dense.per.pixel.control.injection","name":"multi-resolution dense per-pixel control injection","description":"Injects BrushNet control signals at multiple UNet resolution levels (typically 4 scales: 64x64, 32x32, 16x16, 8x8) to provide fine-grained guidance over the diffusion process. The control mechanism works by modifying the UNet's cross-attention and self-attention layers with BrushNet-specific conditioning that incorporates mask information and masked image features at each resolution. This multi-scale injection ensures that both coarse structure (from low-resolution features) and fine details (from high-resolution features) are properly controlled, enabling precise inpainting without affecting unmasked regions.","intents":["I want to guide diffusion at multiple scales to ensure both structural coherence and fine detail quality","I need to prevent the diffusion process from modifying unmasked regions while inpainting masked areas","I want to inject spatial control without modifying the base UNet architecture","I need to balance inpainting quality with computational efficiency across different image resolutions"],"best_for":["Researchers studying multi-scale diffusion control mechanisms","ML engineers implementing custom diffusion guidance strategies","Teams requiring fine-grained control over inpainting quality and boundary preservation"],"limitations":["Multi-scale injection increases inference latency by ~15-25% compared to single-scale approaches due to additional feature processing","Requires careful tuning of control weights at each resolution level — improper weighting can cause artifacts or loss of detail","Control injection modifies attention patterns, which may affect generation diversity or introduce mode collapse at certain guidance scales","Not compatible with some advanced UNet modifications (e.g., LoRA, other control mechanisms) without careful integration"],"requires":["Modified UNet implementation with BrushNet control injection points","Mask and masked image features preprocessed at multiple resolutions","PyTorch with support for custom attention module modifications"],"input_types":["unet: diffusers.UNet2DConditionModel (modified with BrushNet control)","masked_image_latents: torch.Tensor (multi-scale features)","mask: torch.Tensor (binary or soft mask)","timestep: int (current diffusion step)","encoder_hidden_states: torch.Tensor (text embeddings from CLIP)"],"output_types":["noise_pred: torch.Tensor (predicted noise with BrushNet control applied)","intermediate_features: dict (optional, for debugging or analysis)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_4","uri":"capability://image.visual.segmentation.and.random.mask.variant.support","name":"segmentation and random mask variant support","description":"Provides separate model variants optimized for two distinct mask types: segmentation masks (clean, object-shaped boundaries) and random masks (arbitrary, potentially irregular shapes). Each variant is trained with different mask distributions and augmentation strategies to handle the specific characteristics of its target mask type. The system automatically selects the appropriate variant based on mask properties or allows explicit selection, enabling optimal inpainting quality for different use cases without requiring users to understand the underlying mask type differences.","intents":["I want to inpaint objects defined by clean segmentation masks (e.g., from semantic segmentation models)","I need to handle arbitrary, user-drawn masks with irregular boundaries","I want the model to automatically adapt to different mask types without manual configuration","I need to support both precise object replacement and freeform content generation"],"best_for":["Computer vision applications using semantic segmentation for object removal/replacement","Interactive image editing tools where users draw arbitrary masks","Production systems requiring robust handling of diverse mask types"],"limitations":["Using wrong variant for mask type (e.g., segmentation variant on random mask) degrades quality by ~10-15% LPIPS","Segmentation variant assumes clean boundaries; noisy or anti-aliased mask edges may produce artifacts","Random mask variant may over-smooth boundaries on clean segmentation masks, losing precision","No automatic mask type detection — requires explicit selection or heuristic-based inference"],"requires":["Separate pre-trained model weights for segmentation and random mask variants","Mask preprocessing to ensure proper format (binary or soft mask, normalized to [0, 1])"],"input_types":["image: PIL Image or torch.Tensor","mask: PIL Image or torch.Tensor (binary or soft mask)","mask_type: str ('segmentation' or 'random', optional for auto-detection)","prompt: string"],"output_types":["PIL Image (inpainted result)","metadata: dict (mask type used, confidence if auto-detected)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_5","uri":"capability://automation.workflow.training.pipeline.with.dataset.preparation.and.augmentation","name":"training pipeline with dataset preparation and augmentation","description":"Provides end-to-end training infrastructure for fine-tuning BrushNet on custom datasets, including dataset loading, mask generation/augmentation, and training loop management. The training system supports both SD 1.5 and SDXL base models with separate training scripts, implements mask augmentation strategies (random mask generation, boundary noise, dilation/erosion), and uses mixed-precision training with gradient accumulation for memory efficiency. Training can be performed on standard datasets (Places, CelebA-HQ) or custom image collections, with support for distributed training across multiple GPUs.","intents":["I want to fine-tune BrushNet on domain-specific images (e.g., medical images, product photos)","I need to train separate models for segmentation vs random masks with appropriate augmentation","I want to optimize training for limited GPU memory using gradient accumulation and mixed precision","I need to evaluate training progress with standard metrics (LPIPS, FID, SSIM)"],"best_for":["ML engineers building domain-specific inpainting models","Research teams experimenting with BrushNet variants","Organizations with custom datasets requiring specialized inpainting models"],"limitations":["Training requires 24-48 hours on single A100 GPU for convergence; multi-GPU training setup is non-trivial","Requires large-scale image datasets (100k+ images recommended) for stable training; smaller datasets may overfit","Mask augmentation strategies must be carefully tuned for target use case; poor augmentation leads to poor generalization","Training is computationally expensive; requires significant GPU resources and electricity costs"],"requires":["Python 3.9+","PyTorch 1.12.1+ with CUDA support","GPU with 24GB+ VRAM (A100 or equivalent recommended)","Image dataset (local files or HuggingFace datasets)","Optional: distributed training setup (torch.distributed, accelerate library)"],"input_types":["dataset_path: str (directory containing images or HuggingFace dataset identifier)","base_model: str ('sd1.5' or 'sdxl')","mask_type: str ('segmentation' or 'random')","batch_size: int (typically 4-16 depending on GPU memory)","learning_rate: float (typically 1e-4 to 1e-5)","num_epochs: int (typically 10-50)"],"output_types":["model_checkpoint: torch.nn.Module (trained BrushNet weights)","training_logs: dict (loss curves, metric values)","evaluation_metrics: dict (LPIPS, FID, SSIM on validation set)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_6","uri":"capability://data.processing.analysis.evaluation.metrics.computation.lpips.fid.ssim","name":"evaluation metrics computation (lpips, fid, ssim)","description":"Computes standard image quality metrics for evaluating inpainting results: LPIPS (learned perceptual image patch similarity) for perceptual quality, FID (Fréchet Inception Distance) for distribution matching, and SSIM (structural similarity) for pixel-level fidelity. The evaluation system loads pre-trained feature extractors (InceptionV3 for FID, AlexNet for LPIPS) and compares generated inpainted images against ground truth or reference images. Results are aggregated across test sets and reported with statistical summaries (mean, std, percentiles).","intents":["I want to quantitatively evaluate inpainting quality using standard computer vision metrics","I need to compare different BrushNet variants or base models objectively","I want to track model performance improvements during training or fine-tuning","I need to benchmark against other inpainting methods using comparable metrics"],"best_for":["Researchers publishing inpainting papers with quantitative results","ML engineers comparing model variants during development","Teams establishing quality baselines for production models"],"limitations":["LPIPS and FID require pre-trained feature extractors (InceptionV3, AlexNet) which add ~2-5 seconds per image evaluation overhead","Metrics are sensitive to image preprocessing (normalization, resizing) — must match training/evaluation setup exactly","LPIPS and FID are not perfectly correlated with human perception; high metric scores don't guarantee visual quality","Evaluation requires ground truth images; cannot evaluate on real-world inpainting tasks without reference images"],"requires":["PyTorch with torchvision (for pre-trained feature extractors)","Pre-trained InceptionV3 model (auto-downloaded from torchvision)","Test dataset with ground truth images and masks","GPU recommended for efficient metric computation (can run on CPU but slower)"],"input_types":["generated_images: list of PIL Images or torch.Tensors (inpainted results)","ground_truth_images: list of PIL Images or torch.Tensors (reference images)","masks: list of PIL Images or torch.Tensors (optional, for masked metrics)","batch_size: int (for efficient GPU processing)"],"output_types":["metrics: dict with keys 'lpips', 'fid', 'ssim', each containing mean and std","per_image_metrics: list of dicts (individual image scores for detailed analysis)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_7","uri":"capability://image.visual.gradio.web.interface.for.interactive.inpainting","name":"gradio web interface for interactive inpainting","description":"Provides a browser-based interactive interface for real-time inpainting using Gradio, enabling users to upload images, draw masks, enter text prompts, and adjust inference parameters (guidance scale, steps) without coding. The interface handles image upload, mask drawing with canvas tools, prompt input, and displays results with latency information. The Gradio app wraps the inference pipeline and can be deployed locally or on cloud platforms (HuggingFace Spaces, Gradio Cloud) for easy sharing and collaboration.","intents":["I want to test BrushNet inpainting without writing code","I need to share inpainting capabilities with non-technical users","I want to quickly iterate on prompts and masks to find optimal results","I need to deploy inpainting as a web service for team collaboration"],"best_for":["Non-technical users exploring inpainting capabilities","Product teams prototyping image editing features","Researchers sharing models with collaborators"],"limitations":["Gradio interface adds ~500ms-1s overhead per request due to HTTP serialization and image encoding/decoding","Mask drawing tools are basic (brush, eraser) — complex masks are tedious to create; better suited for simple object removal","Single-user inference; concurrent requests queue sequentially on single GPU","No persistent state or history — each session starts fresh, no undo/redo functionality"],"requires":["Python 3.9+","Gradio library (pip install gradio)","Pre-trained BrushNet model weights","GPU with 8GB+ VRAM for reasonable inference speed","Modern web browser for interface access"],"input_types":["image: uploaded image file (PNG, JPG, WebP)","mask: drawn on canvas or uploaded as image","prompt: text input field","negative_prompt: optional text input","guidance_scale: slider (typically 1-20)","num_steps: slider (typically 20-100)"],"output_types":["inpainted_image: PIL Image displayed in browser","inference_time: float (seconds, displayed to user)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_8","uri":"capability://image.visual.instruction.guided.editing.with.text.based.spatial.control","name":"instruction-guided editing with text-based spatial control","description":"Extends basic text-guided inpainting with instruction-based editing that interprets natural language instructions to automatically generate masks and guide inpainting. The system parses instructions like 'remove the person on the left' or 'replace the sky with clouds' to identify regions of interest and apply appropriate inpainting. This capability combines text understanding with spatial reasoning, potentially using auxiliary models (object detection, segmentation) to convert instructions into masks before applying BrushNet inpainting.","intents":["I want to edit images using natural language instructions without manually drawing masks","I need to automatically identify and inpaint specific objects mentioned in text","I want to support complex editing tasks like 'replace X with Y' using semantic understanding","I need to reduce user effort by automating mask generation from text descriptions"],"best_for":["End-user image editing applications with natural language interfaces","Teams building AI-powered photo editing tools","Accessibility-focused applications where drawing masks is difficult"],"limitations":["Instruction parsing requires additional models (object detection, segmentation) which add latency (~1-2 seconds per instruction)","Spatial understanding is limited to objects detectable by auxiliary models; abstract concepts ('make it more vibrant') cannot be spatially grounded","Instruction ambiguity can lead to incorrect mask generation; 'remove the person' may fail if multiple people are present","Requires careful prompt engineering for auxiliary models to work reliably; performance degrades on out-of-distribution images"],"requires":["Pre-trained object detection or segmentation model (e.g., YOLO, SAM, Mask R-CNN)","Natural language processing for instruction parsing (rule-based or LLM-based)","BrushNet inpainting pipeline for final generation"],"input_types":["image: PIL Image or torch.Tensor","instruction: string (natural language editing instruction)","optional: reference_image (for style transfer or content guidance)"],"output_types":["edited_image: PIL Image (result of instruction-guided inpainting)","mask_used: PIL Image (generated mask for transparency/debugging)","instruction_confidence: float (confidence in instruction interpretation)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencentarc--brushnet__cap_9","uri":"capability://automation.workflow.batch.processing.with.multi.image.inpainting","name":"batch processing with multi-image inpainting","description":"Enables efficient processing of multiple images with different masks and prompts in a single batch, optimizing GPU utilization and reducing per-image overhead. The batch processor handles variable image sizes through padding/resizing, manages memory efficiently with dynamic batching, and provides progress tracking and error handling for robust production use. Results are returned with metadata (processing time, success/failure status) for each image.","intents":["I want to inpaint hundreds of images efficiently without sequential processing","I need to optimize GPU utilization for production inpainting workloads","I want to process images with different sizes and aspect ratios in a single batch","I need robust error handling and progress tracking for long-running batch jobs"],"best_for":["Production systems processing large image collections","Data preprocessing pipelines requiring bulk inpainting","Teams building batch image editing services"],"limitations":["Variable image sizes require padding to common dimensions, wasting GPU memory on smaller images","Batch size is limited by GPU VRAM; larger batches require larger GPUs or smaller images","Error in one image can fail entire batch unless error handling is implemented; requires careful exception management","Batch processing adds complexity compared to single-image inference; debugging is harder with multiple concurrent operations"],"requires":["PyTorch with CUDA for efficient batched tensor operations","GPU with sufficient VRAM for batch size (typically 8GB+ for batch_size=4 at 512x512)","Image dataset accessible as list or iterable"],"input_types":["images: list of PIL Images or torch.Tensors","masks: list of PIL Images or torch.Tensors (same length as images)","prompts: list of strings (same length as images)","batch_size: int (typically 2-8 depending on GPU memory)","num_workers: int (for parallel data loading)"],"output_types":["results: list of dicts, each containing {'image': PIL Image, 'time': float, 'success': bool, 'error': str or None}","summary: dict with aggregate statistics (total_time, success_rate, avg_time_per_image)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch 1.12.1+","CUDA 11.6+ (recommended for GPU inference)","Hugging Face diffusers library with BrushNet custom modifications","Pre-trained Stable Diffusion 1.5 or SDXL model weights","Hugging Face transformers library (for CLIP text encoder)","Pre-trained model weights accessible via HuggingFace Hub or local path","GPU with 8GB+ VRAM (16GB+ recommended for SDXL)","PyTorch with quantization support (torch.quantization)","Optional: TensorRT or ONNX for advanced optimization"],"failure_modes":["Requires pre-trained base diffusion model (SD 1.5 or SDXL) — cannot function standalone","Inference latency depends on base model's diffusion steps (typically 50-100 steps for quality results)","Memory footprint scales with image resolution; 4K+ images may require gradient checkpointing or reduced batch sizes","Mask quality directly impacts output quality — poorly defined masks produce artifacts at boundaries","Pipeline initialization loads all model components into memory (~7GB for SD 1.5, ~13GB for SDXL) — requires GPU with sufficient VRAM","Sequential processing of batches; no built-in distributed inference across multiple GPUs","Text encoding is fixed to CLIP tokenizer (77 tokens for SD 1.5); longer prompts are truncated","Scheduler choice (DDPM, DDIM, Euler, etc.) affects quality-speed tradeoff but requires manual tuning","INT8 quantization can cause 2-5% quality degradation (LPIPS) compared to FP32; requires validation on target domain","Quantization is model-specific; quantized weights cannot be transferred between different base models (SD 1.5 vs SDXL)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.26014640627127944,"quality":0.49,"ecosystem":0.5800000000000001,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2024-12-17T13:49:54Z"},"community":{"stars":1729,"forks":144,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tencentarc--brushnet","compare_url":"https://unfragile.ai/compare?artifact=tencentarc--brushnet"}},"signature":"IUFoPCY8YVmrfx+TTjdTD7/pROAcB1BtNR9MAWS8OXKL3zxysPmNKQWdpykNPPKo1MbHQUR7kKa96uaWhM0zAg==","signedAt":"2026-06-20T00:20:39.942Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tencentarc--brushnet","artifact":"https://unfragile.ai/tencentarc--brushnet","verify":"https://unfragile.ai/api/v1/verify?slug=tencentarc--brushnet","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"}}