{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-frankjoshua--novaanimexl_ilv140","slug":"frankjoshua--novaanimexl_ilv140","name":"novaAnimeXL_ilV140","type":"model","url":"https://huggingface.co/frankjoshua/novaAnimeXL_ilV140","page_url":"https://unfragile.ai/frankjoshua--novaanimexl_ilv140","categories":["image-generation"],"tags":["diffusers","safetensors","endpoints_compatible","diffusers:StableDiffusionXLPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_0","uri":"capability://image.visual.anime.style.text.to.image.generation.with.sdxl.architecture","name":"anime-style text-to-image generation with sdxl architecture","description":"Generates anime and illustration-style images from natural language text prompts using a fine-tuned Stable Diffusion XL (SDXL) base model. The model leverages the diffusers library's StableDiffusionXLPipeline, which orchestrates a multi-stage latent diffusion process: text encoding via CLIP tokenizers, UNet-based iterative denoising in latent space, and VAE decoding to RGB image space. Fine-tuning on anime datasets enables stylistic coherence and character consistency that base SDXL lacks.","intents":["Generate anime character artwork from text descriptions without manual illustration","Create consistent anime-style visual assets for game development or animation projects","Prototype anime character designs and variations rapidly from prompts","Batch-generate training data for anime classification or style-transfer models"],"best_for":["indie game developers building anime-style visual assets","animation studios prototyping character designs at scale","ML engineers fine-tuning anime-specific diffusion models","content creators generating illustrations for manga or webtoon projects"],"limitations":["Output quality highly dependent on prompt engineering and negative prompts; vague descriptions produce inconsistent results","Inference latency typically 30-60 seconds per image on consumer GPUs (RTX 3090) due to 50+ denoising steps in DDIM scheduler","Memory footprint ~7-9GB VRAM required for full model; quantization to fp16 reduces to ~5GB but may degrade quality","No built-in inpainting or editing capabilities — requires separate pipeline for image-to-image modifications","Anime style bias may produce suboptimal results for non-anime genres (photorealism, abstract art)"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ or CPU (significantly slower)","diffusers library 0.21.0+","transformers library 4.25.0+ for CLIP text encoder","4GB+ VRAM for inference (8GB+ recommended for batch generation)","safetensors library for model loading (optional but recommended for security)"],"input_types":["text (natural language prompt, 1-1000 tokens typical)","text (negative prompt for guidance, optional)","integer (random seed for reproducibility, optional)","float (guidance scale 1.0-20.0, controls prompt adherence)"],"output_types":["image (PNG or JPEG, 1024x1024 or 768x1024 default, configurable up to 2048x2048)","tensor (latent representation before VAE decoding, for downstream processing)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_1","uri":"capability://tool.use.integration.diffusers.compatible.pipeline.integration.with.safetensors.format","name":"diffusers-compatible pipeline integration with safetensors format","description":"Model weights are distributed in safetensors format and fully compatible with the HuggingFace diffusers library's StableDiffusionXLPipeline abstraction. This enables zero-configuration loading via `DiffusionPipeline.from_pretrained()` with automatic device placement, dtype inference, and scheduler selection. The safetensors format provides faster deserialization (3-5x vs pickle) and built-in integrity verification, eliminating arbitrary code execution risks during model loading.","intents":["Load and run the model with minimal boilerplate code in production environments","Integrate anime image generation into existing diffusers-based applications without custom loaders","Deploy the model safely without pickle deserialization vulnerabilities","Batch-load multiple model variants for A/B testing or ensemble generation"],"best_for":["Python developers building diffusers-based image generation pipelines","DevOps engineers deploying models in containerized environments with security constraints","Teams migrating from custom model loading code to standardized diffusers abstractions","Researchers comparing multiple anime model variants in controlled experiments"],"limitations":["Requires diffusers library as a hard dependency; no standalone model inference without it","safetensors format is read-only for this artifact; modifications require conversion back to PyTorch format","Pipeline abstraction adds ~50-100ms overhead per inference call for device management and scheduler initialization","No built-in support for advanced features like ControlNet or multi-LoRA composition without custom pipeline extensions"],"requires":["diffusers 0.21.0+","transformers 4.25.0+","safetensors 0.3.0+","PyTorch 1.13+ or TensorFlow 2.10+ (PyTorch recommended)","HuggingFace Hub credentials for private model access (if applicable)"],"input_types":["string (HuggingFace model ID: 'frankjoshua/novaAnimeXL_ilV140')","dict (pipeline configuration overrides, optional)"],"output_types":["DiffusionPipeline object (ready for inference)","model weights in safetensors format (on disk)"],"categories":["tool-use-integration","model-loading"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_2","uri":"capability://automation.workflow.configurable.inference.scheduling.with.ddim.euler.dpm.support","name":"configurable inference scheduling with ddim/euler/dpm++ support","description":"The StableDiffusionXLPipeline supports pluggable scheduler implementations (DDIM, Euler, DPM++, Heun, etc.) that control the denoising trajectory and step count during image generation. Different schedulers trade off inference speed vs quality: DDIM enables fast 20-30 step generation with slight quality loss, while DPM++ with 50+ steps produces higher fidelity at 2-3x latency cost. The scheduler is decoupled from model weights, allowing runtime selection without reloading the model.","intents":["Generate images quickly (15-20 seconds) for interactive applications by switching to DDIM scheduler","Maximize image quality for batch processing by using DPM++ with 50+ steps","Experiment with different denoising trajectories to find optimal quality/speed tradeoff for specific use cases","Implement adaptive scheduling based on user preferences or hardware constraints"],"best_for":["Real-time applications requiring sub-30-second generation (web apps, Discord bots)","Batch processing pipelines where quality is prioritized over latency","Researchers studying the effect of scheduler choice on anime-style generation quality","Mobile or edge deployment scenarios with strict latency budgets"],"limitations":["Fewer denoising steps (DDIM <30) produces visible artifacts: color bleeding, anatomical inconsistencies, loss of fine details","DPM++ with 50+ steps requires proportional increase in VRAM usage and inference time; not suitable for real-time applications","Scheduler choice is orthogonal to model quality; poor prompts produce poor results regardless of scheduler","No adaptive scheduling built-in; developers must manually implement logic to select schedulers based on constraints"],"requires":["diffusers 0.21.0+ (scheduler implementations)","PyTorch 1.13+","Knowledge of scheduler tradeoffs (DDIM vs DPM++ vs Euler) for informed selection"],"input_types":["string (scheduler class name: 'DDIMScheduler', 'DPMPlusPlusScheduler', etc.)","integer (num_inference_steps: 20-50 typical, 1-1000 supported)"],"output_types":["image (PNG/JPEG, quality/latency determined by scheduler choice)"],"categories":["automation-workflow","performance-tuning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_3","uri":"capability://automation.workflow.guidance.scale.controlled.prompt.adherence.with.classifier.free.guidance","name":"guidance-scale controlled prompt adherence with classifier-free guidance","description":"Implements classifier-free guidance (CFG) via a guidance_scale parameter (typically 1.0-20.0) that controls how strongly the model adheres to the text prompt during denoising. At guidance_scale=1.0, the model ignores the prompt entirely (unconditional generation). At guidance_scale=7.5-15.0, the model balances prompt adherence with visual coherence. At guidance_scale>15.0, the model prioritizes prompt matching at the cost of potential artifacts or anatomical inconsistencies. This is implemented by running dual forward passes (conditioned and unconditional) and interpolating predictions.","intents":["Fine-tune the balance between prompt fidelity and image quality for specific use cases","Generate more creative/diverse outputs by lowering guidance scale (5.0-7.5)","Ensure precise prompt adherence for character design specifications by raising guidance scale (12.0-18.0)","Diagnose prompt-following issues by systematically varying guidance scale"],"best_for":["Game developers needing precise control over character appearance matching design specs","Content creators balancing creative freedom with prompt consistency","Researchers studying the relationship between guidance scale and anime-specific visual artifacts","Production pipelines requiring deterministic, reproducible outputs"],"limitations":["Guidance_scale>15.0 introduces visible artifacts: color saturation, anatomical distortions, unnatural lighting","Dual forward passes at high guidance scales increase inference latency by ~30-50% compared to guidance_scale=1.0","Guidance scale effectiveness is prompt-dependent; poorly written prompts produce poor results at any guidance scale","No automatic guidance scale selection; developers must manually tune per use case or implement heuristics"],"requires":["diffusers 0.21.0+","Understanding of classifier-free guidance mechanics for informed parameter selection","Empirical testing to find optimal guidance_scale for specific prompts/use cases"],"input_types":["float (guidance_scale: 1.0-20.0 typical, 0.0-100.0 supported)"],"output_types":["image (PNG/JPEG, visual characteristics determined by guidance scale)"],"categories":["automation-workflow","parameter-tuning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_4","uri":"capability://automation.workflow.reproducible.generation.via.seed.based.random.initialization","name":"reproducible generation via seed-based random initialization","description":"Supports optional seed parameter for deterministic image generation by controlling the random noise initialization in the latent diffusion process. When seed is provided, the same prompt+seed combination produces identical images across runs and hardware (within floating-point precision). This is implemented by seeding PyTorch's random number generator before latent initialization. Without a seed, generation is non-deterministic, enabling diversity in batch generation.","intents":["Reproduce specific generated images for debugging, iteration, or client approval workflows","Generate consistent character variations by fixing seed and varying only prompt details","Create deterministic test cases for image generation pipelines","Enable A/B testing by generating paired images with identical seeds but different prompts/guidance scales"],"best_for":["Production pipelines requiring reproducible outputs for quality assurance","Game development teams iterating on character designs with version control","Researchers conducting controlled experiments on prompt/parameter effects","Client-facing applications where users expect to regenerate approved images"],"limitations":["Reproducibility is approximate due to floating-point non-determinism across GPU architectures (NVIDIA vs AMD vs CPU); results may vary slightly","Seed-based generation reduces diversity in batch pipelines; requires seed variation strategy for diverse outputs","No built-in seed management; developers must implement seed tracking/versioning for reproducibility workflows","Seed values are arbitrary integers; no semantic mapping between seeds and visual characteristics"],"requires":["diffusers 0.21.0+","PyTorch 1.13+","Optional: CUDA determinism flags for strict reproducibility (`torch.use_deterministic_algorithms(True)`)"],"input_types":["integer (seed: 0-2^32-1, optional)"],"output_types":["image (PNG/JPEG, identical across runs for same seed+prompt)"],"categories":["automation-workflow","reproducibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_5","uri":"capability://automation.workflow.batch.image.generation.with.memory.efficient.processing","name":"batch image generation with memory-efficient processing","description":"Supports batch inference via num_images_per_prompt parameter, generating multiple images from a single prompt in a single forward pass. The implementation reuses the text encoding and scheduler state across batch items, reducing redundant computation. Memory usage scales linearly with batch size; typical batch_size=4 requires ~8-9GB VRAM. For larger batches, developers can implement sequential batching (generate 4 images, unload, generate next 4) to trade latency for memory efficiency.","intents":["Generate multiple character variations from a single prompt for design exploration","Create training datasets by batch-generating 100+ images with prompt variations","Parallelize image generation across multiple prompts using batch processing","Optimize throughput for production pipelines by batching requests"],"best_for":["Content creators generating multiple design variations in a single session","ML engineers creating anime-style training datasets at scale","Game studios batch-generating asset variations for different character skins","Production pipelines with queued generation requests"],"limitations":["Memory usage scales linearly with batch size; batch_size=8 requires ~16GB VRAM, limiting practical batch sizes on consumer hardware","Latency scales sublinearly with batch size (e.g., batch_size=4 takes ~1.3x the time of batch_size=1, not 4x), but still significant for large batches","No built-in batching across different prompts; requires manual loop implementation for multi-prompt batching","Batch generation produces diverse outputs; reproducibility requires per-image seed management"],"requires":["diffusers 0.21.0+","8GB+ VRAM for batch_size>2 (16GB+ recommended for batch_size>4)","PyTorch 1.13+"],"input_types":["integer (num_images_per_prompt: 1-16 typical, 1-1000 supported)"],"output_types":["list of images (PNG/JPEG, length = num_images_per_prompt)"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_6","uri":"capability://automation.workflow.negative.prompt.guidance.for.artifact.suppression","name":"negative prompt guidance for artifact suppression","description":"Supports negative_prompt parameter to guide the model away from undesired visual characteristics (e.g., 'blurry, low quality, deformed hands'). Negative prompts are encoded separately and used in the classifier-free guidance calculation to suppress predicted noise in undesired directions. This is implemented as a second text encoding pass and interpolation in the guidance step. Effective negative prompts require domain knowledge of common anime generation artifacts (anatomical distortions, color bleeding, etc.).","intents":["Suppress common anime generation artifacts (deformed hands, anatomical inconsistencies) without retraining","Enforce quality standards by excluding low-quality visual characteristics","Improve consistency across batch generation by using consistent negative prompts","Fine-tune image aesthetics by iteratively refining negative prompt lists"],"best_for":["Game developers enforcing character design standards via negative prompts","Content creators iteratively improving image quality through negative prompt refinement","Production pipelines with standardized negative prompt templates","Researchers studying the effect of negative prompts on anime-specific artifacts"],"limitations":["Negative prompt effectiveness is highly dependent on prompt engineering; generic negative prompts ('bad quality') are ineffective","Negative prompts increase inference latency by ~15-20% due to additional text encoding and guidance computation","No automatic negative prompt generation; developers must manually curate negative prompts based on observed artifacts","Negative prompts can conflict with positive prompts, producing unexpected results (e.g., 'no hands' conflicts with 'holding sword')"],"requires":["diffusers 0.21.0+","Domain knowledge of common anime generation artifacts for effective negative prompt design","Empirical testing to validate negative prompt effectiveness"],"input_types":["string (negative_prompt: natural language description of undesired characteristics)"],"output_types":["image (PNG/JPEG, visual characteristics suppressed per negative prompt)"],"categories":["automation-workflow","quality-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_7","uri":"capability://tool.use.integration.huggingface.hub.integration.with.automatic.model.caching","name":"huggingface hub integration with automatic model caching","description":"Model is hosted on HuggingFace Hub with automatic caching via the `huggingface_hub` library. First inference downloads model weights (~6-7GB) to local cache directory (~/.cache/huggingface/hub/), subsequent inferences load from cache. The Hub integration provides version control, model cards with usage examples, and community discussions. Caching is transparent to users; the diffusers pipeline handles download/cache logic automatically.","intents":["Download and cache the model automatically without manual weight management","Access model documentation, usage examples, and community discussions via HuggingFace Hub","Version-control model weights and track training iterations","Share models with collaborators via Hub links without manual file transfer"],"best_for":["Developers using HuggingFace ecosystem (transformers, diffusers, datasets)","Teams collaborating on model development via HuggingFace Hub","Researchers sharing models with reproducible links","Production deployments using HuggingFace model registry"],"limitations":["Initial download requires 6-7GB bandwidth and ~5-10 minutes on typical internet connections","Cache directory can grow large (6-7GB per model); requires manual cleanup for storage-constrained environments","HuggingFace Hub outages prevent model downloads; no fallback mechanism for offline deployment","Model versioning via Hub is manual; no automatic version pinning or rollback"],"requires":["huggingface_hub 0.16.0+","diffusers 0.21.0+","Internet connection for initial model download","6-7GB disk space for model cache","Optional: HuggingFace API token for private model access"],"input_types":["string (model ID: 'frankjoshua/novaAnimeXL_ilV140')"],"output_types":["model weights (cached locally in safetensors format)"],"categories":["tool-use-integration","model-distribution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-frankjoshua--novaanimexl_ilv140__cap_8","uri":"capability://automation.workflow.multi.resolution.image.generation.with.aspect.ratio.control","name":"multi-resolution image generation with aspect ratio control","description":"Supports configurable height and width parameters (e.g., 768x1024, 1024x1024, 1024x768) for generating images at different aspect ratios. SDXL was trained on images up to 1024x1024, so outputs at this resolution or below maintain quality. Larger resolutions (up to 2048x2048) are supported but may produce artifacts or require additional fine-tuning. Resolution is specified at inference time without model reloading; the VAE decoder adapts to the specified dimensions.","intents":["Generate portrait-oriented character artwork (768x1024) for mobile game assets","Create landscape-oriented backgrounds (1024x768) for game environments","Generate square images (1024x1024) for social media or NFT projects","Experiment with different aspect ratios to find optimal composition for specific use cases"],"best_for":["Game developers generating assets for specific screen dimensions","Content creators optimizing images for different platforms (mobile, desktop, social media)","Researchers studying the effect of aspect ratio on anime character generation","Production pipelines with standardized resolution requirements"],"limitations":["Resolutions >1024x1024 may produce artifacts or anatomical inconsistencies due to training data distribution","Memory usage scales with resolution; 2048x2048 requires ~16GB VRAM vs ~8GB for 1024x1024","Inference latency scales with resolution; 2048x2048 takes ~2-3x longer than 1024x1024","Aspect ratios far from training distribution (e.g., 512x2048) produce poor quality; optimal range is 0.5-2.0"],"requires":["diffusers 0.21.0+","PyTorch 1.13+","VRAM proportional to resolution (8GB for 1024x1024, 16GB for 2048x2048)"],"input_types":["integer (height: 512-2048, multiples of 8)","integer (width: 512-2048, multiples of 8)"],"output_types":["image (PNG/JPEG, dimensions = height x width)"],"categories":["automation-workflow","asset-generation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ or CPU (significantly slower)","diffusers library 0.21.0+","transformers library 4.25.0+ for CLIP text encoder","4GB+ VRAM for inference (8GB+ recommended for batch generation)","safetensors library for model loading (optional but recommended for security)","diffusers 0.21.0+","transformers 4.25.0+","safetensors 0.3.0+","PyTorch 1.13+ or TensorFlow 2.10+ (PyTorch recommended)"],"failure_modes":["Output quality highly dependent on prompt engineering and negative prompts; vague descriptions produce inconsistent results","Inference latency typically 30-60 seconds per image on consumer GPUs (RTX 3090) due to 50+ denoising steps in DDIM scheduler","Memory footprint ~7-9GB VRAM required for full model; quantization to fp16 reduces to ~5GB but may degrade quality","No built-in inpainting or editing capabilities — requires separate pipeline for image-to-image modifications","Anime style bias may produce suboptimal results for non-anime genres (photorealism, abstract art)","Requires diffusers library as a hard dependency; no standalone model inference without it","safetensors format is read-only for this artifact; modifications require conversion back to PyTorch format","Pipeline abstraction adds ~50-100ms overhead per inference call for device management and scheduler initialization","No built-in support for advanced features like ControlNet or multi-LoRA composition without custom pipeline extensions","Fewer denoising steps (DDIM <30) produces visible artifacts: color bleeding, anatomical inconsistencies, loss of fine details","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.585034437093888,"quality":0.28,"ecosystem":0.45,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:49.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":453383,"model_likes":2}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=frankjoshua--novaanimexl_ilv140","compare_url":"https://unfragile.ai/compare?artifact=frankjoshua--novaanimexl_ilv140"}},"signature":"zkg2FGp8VVVV8YlJfubZbP0Weva+E5a2/hQAhNpfX6DuyotYhKRbT75/28lmoVL6bPlIIQBWadrTAPJFHP5uAQ==","signedAt":"2026-06-21T21:39:08.071Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/frankjoshua--novaanimexl_ilv140","artifact":"https://unfragile.ai/frankjoshua--novaanimexl_ilv140","verify":"https://unfragile.ai/api/v1/verify?slug=frankjoshua--novaanimexl_ilv140","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"}}