{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-john6666--one-obsession-17-red-sdxl","slug":"john6666--one-obsession-17-red-sdxl","name":"one-obsession-17-red-sdxl","type":"model","url":"https://huggingface.co/John6666/one-obsession-17-red-sdxl","page_url":"https://unfragile.ai/john6666--one-obsession-17-red-sdxl","categories":["image-generation"],"tags":["diffusers","safetensors","text-to-image","stable-diffusion","stable-diffusion-xl","not-for-all-audiences","anime","girls","cute","balanced","hands","feet","texture","limbs","excellent lighting and shadow","details","finetune","my style","extreme light and shadow","composition"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-john6666--one-obsession-17-red-sdxl__cap_0","uri":"capability://image.visual.anime.style.text.to.image.generation.with.fine.tuned.aesthetic.control","name":"anime-style text-to-image generation with fine-tuned aesthetic control","description":"Generates images from text prompts using a fine-tuned Stable Diffusion XL model optimized for anime and illustrated character art. The model applies learned style weights across the diffusion process to consistently produce anime aesthetics with emphasis on character composition, lighting, and anatomical detail. Built on the diffusers library architecture, it integrates LoRA or full-weight fine-tuning applied to the base SDXL checkpoint, enabling style-specific image synthesis without requiring style descriptors in every prompt.","intents":["Generate anime character artwork from natural language descriptions without manual style prompting","Create consistent character designs across multiple images with the same aesthetic baseline","Produce high-quality illustrated content for games, comics, or visual novels with minimal prompt engineering","Explore anime art variations while maintaining anatomical coherence in hands, feet, and limbs"],"best_for":["anime and manga creators building visual asset pipelines","game developers prototyping character designs with consistent art direction","indie illustrators automating batch character generation","hobbyists exploring anime art generation without technical ML expertise"],"limitations":["Fine-tuning is locked to anime/illustrated style — poor performance on photorealistic or non-anime prompts","Anatomical improvements (hands, feet) are relative to base SDXL but still subject to diffusion model limitations at extreme angles","Inference speed depends on hardware; typical generation takes 20-60 seconds on consumer GPUs (RTX 3060+)","Output quality highly sensitive to prompt engineering — vague prompts produce inconsistent results despite fine-tuning","No built-in inpainting or editing capabilities — requires separate tools for post-generation modifications","Model weights are 6-8GB in safetensors format, requiring significant local storage or streaming from Hugging Face"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (minimum 6GB VRAM for inference)","diffusers library (0.21.0+)","transformers library (4.30.0+)","safetensors library for model loading","Hugging Face account for model access (open-source, no authentication required but recommended for rate limiting)"],"input_types":["text (natural language prompts, 1-500 tokens typical)","optional negative prompts (text describing unwanted attributes)","optional seed (integer for reproducibility)","optional guidance scale (float 7.0-15.0 for prompt adherence strength)"],"output_types":["image (PNG or JPEG, 1024x1024 or 1024x768 typical, configurable to 512-2048 range)"],"categories":["image-visual","generative-ai-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--one-obsession-17-red-sdxl__cap_1","uri":"capability://image.visual.local.inference.with.safetensors.model.loading.and.gpu.acceleration","name":"local inference with safetensors model loading and gpu acceleration","description":"Loads model weights from Hugging Face in safetensors format (a faster, safer alternative to pickle-based PyTorch checkpoints) and executes the full diffusion pipeline locally on GPU hardware. The architecture uses the diffusers library's pipeline abstraction, which handles tokenization, noise scheduling, UNet denoising steps, and VAE decoding in a single inference call. GPU acceleration via CUDA/ROCm enables parallel computation across diffusion steps, with memory optimization through attention slicing or token merging for lower-VRAM devices.","intents":["Run image generation entirely offline without cloud API calls or rate limits","Integrate anime image generation into local applications, games, or batch processing pipelines","Iterate rapidly on prompts with sub-minute latency on consumer hardware","Maintain privacy by keeping generated images and prompts local"],"best_for":["developers building offline-first creative tools or games","teams with privacy requirements or restricted internet access","researchers experimenting with prompt engineering and model behavior","creators running high-volume batch generation without API costs"],"limitations":["Requires significant GPU memory (6GB minimum for 1024x1024 generation, 12GB+ recommended for batch operations)","CPU-only inference is extremely slow (5-10 minutes per image) and impractical for interactive use","Model weights must be downloaded once (~7GB), consuming bandwidth and storage","No automatic model updates — requires manual re-download when new versions are released","Inference latency is hardware-dependent; no guaranteed performance across different GPU architectures","Requires manual dependency management (PyTorch, CUDA, etc.) — no containerized distribution provided"],"requires":["NVIDIA GPU with CUDA 11.8+ (RTX 3060 or better recommended) OR AMD GPU with ROCm 5.4+","Python 3.8+","PyTorch 2.0+ compiled for your GPU architecture","diffusers 0.21.0+","transformers 4.30.0+","safetensors library","6-12GB free GPU VRAM depending on batch size and resolution","~8GB free disk space for model weights"],"input_types":["model checkpoint path (local or Hugging Face model ID string)","inference parameters (num_inference_steps: 20-50, guidance_scale: 7-15, height/width: 512-2048)"],"output_types":["PIL Image objects (in-memory) or saved PNG/JPEG files"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--one-obsession-17-red-sdxl__cap_2","uri":"capability://image.visual.prompt.to.image.synthesis.with.classifier.free.guidance.and.noise.scheduling","name":"prompt-to-image synthesis with classifier-free guidance and noise scheduling","description":"Converts text prompts into images through an iterative denoising process guided by CLIP text embeddings. The model uses classifier-free guidance (CFG), which alternates between conditional (prompt-guided) and unconditional denoising steps to steer generation toward the prompt while maintaining diversity. Noise scheduling (e.g., Euler, DPM++, DDIM) controls the rate of noise removal across 20-50 steps, with higher step counts improving quality at the cost of latency. The fine-tuned weights encode anime aesthetics learned during training, biasing the denoising trajectory toward anime outputs.","intents":["Convert natural language character descriptions into visual artwork without manual drawing","Control image composition and style through prompt engineering (e.g., 'dynamic pose, dramatic lighting, detailed face')","Generate variations of a concept by adjusting guidance scale or using different seeds","Batch-generate multiple character designs for rapid prototyping"],"best_for":["concept artists and character designers exploring design space quickly","game developers prototyping visual assets before commissioning artists","content creators generating reference images for illustration or animation","researchers studying how text-to-image models encode aesthetic preferences"],"limitations":["Prompt interpretation is non-deterministic — identical prompts with different seeds produce different outputs","Guidance scale (CFG) is a hyperparameter requiring tuning; too low (< 7) produces blurry outputs, too high (> 15) produces artifacts and oversaturation","Model struggles with complex spatial relationships (e.g., 'person sitting on chair') and text rendering","Anatomical issues (extra limbs, distorted hands) persist despite fine-tuning, especially at extreme poses","Generation quality degrades for prompts outside the anime domain (photorealism, specific real people, etc.)","Inference time scales linearly with step count; 50 steps takes ~2.5x longer than 20 steps"],"requires":["Text prompt (1-500 tokens, English language)","CLIP text encoder (included in diffusers pipeline, ~355MB)","UNet denoising model (included in checkpoint, ~2GB)","VAE decoder (included in checkpoint, ~167MB)","GPU with 6GB+ VRAM for single-image generation","Seed value (optional, for reproducibility)"],"input_types":["text prompt (string, natural language)","negative prompt (string, optional, describes unwanted attributes)","num_inference_steps (integer, 20-50 typical)","guidance_scale (float, 7-15 typical)","seed (integer, optional, for reproducibility)","height/width (integers, 512-2048, must be multiples of 8)"],"output_types":["image (PIL Image or saved file, 1024x1024 or custom resolution)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--one-obsession-17-red-sdxl__cap_3","uri":"capability://image.visual.batch.image.generation.with.seed.control.and.reproducibility","name":"batch image generation with seed control and reproducibility","description":"Generates multiple images from a single prompt or prompt list by iterating over different random seeds while keeping model weights and hyperparameters fixed. Each seed produces a unique noise initialization, resulting in different outputs from the same prompt. The diffusers library enables this through a simple loop over seed values, with optional parallelization across multiple GPUs or sequential processing on a single device. Reproducibility is guaranteed: the same seed + prompt + hyperparameters always produce identical outputs, enabling version control and debugging.","intents":["Generate 10-100 character variations from a single prompt to explore design space","Create reproducible datasets for training or evaluation by fixing seeds","Parallelize generation across multiple GPUs to reduce total wall-clock time","Version-control generated images by storing seed values instead of image files"],"best_for":["game studios generating large character asset libraries","researchers building datasets for model evaluation or fine-tuning","content creators producing high-volume character designs for animation","developers building generative tools with reproducible outputs"],"limitations":["Batch processing requires proportional GPU memory; generating 10 images sequentially uses same memory as 1 image but takes 10x longer","Multi-GPU parallelization requires manual distribution logic; diffusers does not provide built-in distributed inference","Seed reproducibility only holds within the same model version and PyTorch/CUDA versions; minor library updates can break reproducibility","Storage of seed values is negligible, but storing 100 1024x1024 images requires ~100GB disk space","No built-in deduplication — similar seeds may produce visually similar outputs, requiring manual filtering"],"requires":["List of seed values (integers, typically 0-2^32)","Prompt text (string, shared across all seeds)","GPU with 6GB+ VRAM for sequential generation, or multiple GPUs for parallelization","Python script or wrapper to iterate over seeds and save outputs","Storage for output images (~1GB per 10 images at 1024x1024)"],"input_types":["prompt (string)","seed_list (list of integers)","num_inference_steps (integer)","guidance_scale (float)","height/width (integers)"],"output_types":["list of images (PIL Images or saved files)","optional metadata file (JSON with seed, prompt, hyperparameters per image)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--one-obsession-17-red-sdxl__cap_4","uri":"capability://image.visual.memory.efficient.inference.with.attention.slicing.and.token.merging","name":"memory-efficient inference with attention slicing and token merging","description":"Reduces GPU memory consumption during inference by decomposing the attention mechanism into smaller chunks (attention slicing) or merging redundant tokens before attention computation (token merging). Attention slicing computes attention over spatial dimensions in slices rather than all-at-once, reducing peak memory from O(H*W*H*W) to O(H*W) at the cost of ~10-20% latency increase. Token merging (ToMe) reduces the number of tokens in the sequence before attention, further lowering memory without quality loss. These optimizations are exposed via diffusers pipeline methods (enable_attention_slicing(), enable_token_merging()) and can be combined for maximum memory savings.","intents":["Generate 1024x1024 images on GPUs with 4-6GB VRAM (e.g., RTX 3060) instead of requiring 12GB+","Batch-generate multiple images on consumer hardware without running out of memory","Trade off latency for memory when hardware is constrained","Enable inference on mobile or edge devices with limited GPU memory"],"best_for":["indie developers with limited hardware budgets","researchers running experiments on shared GPU clusters with memory constraints","mobile app developers integrating image generation on-device","teams deploying inference on cost-optimized cloud instances (e.g., AWS g4dn.xlarge)"],"limitations":["Attention slicing introduces ~10-20% latency overhead (e.g., 30 seconds → 35-40 seconds per image)","Token merging may reduce output quality slightly, especially for fine details or text rendering","Memory savings are non-linear; enabling both slicing and merging provides diminishing returns","These optimizations are most effective for high-resolution (1024x1024+) generation; low-res (512x512) may not benefit significantly","Requires explicit API calls to enable; not automatic or configurable via hyperparameters","Incompatible with some advanced features (e.g., ControlNet, IP-Adapter) depending on implementation"],"requires":["diffusers 0.21.0+ with attention slicing support","PyTorch 2.0+ (for optimal memory efficiency)","GPU with 4GB+ VRAM (minimum; 6GB+ recommended for 1024x1024)","Python script to call enable_attention_slicing() and/or enable_token_merging() on pipeline"],"input_types":["pipeline object (diffusers StableDiffusionXLPipeline)","optional token_merge_ratio (float, 0.0-1.0, controls merge aggressiveness)"],"output_types":["modified pipeline object with optimizations applied"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--one-obsession-17-red-sdxl__cap_5","uri":"capability://tool.use.integration.model.distribution.and.versioning.via.hugging.face.hub","name":"model distribution and versioning via hugging face hub","description":"The model is hosted on Hugging Face Hub, enabling one-click downloads, automatic versioning, and integration with the diffusers library's model loading API. The Hub provides safetensors format weights, model cards with usage instructions, and version history. The diffusers library's from_pretrained() method automatically downloads the model, caches it locally, and loads it into memory with a single function call. Hub integration enables easy model swapping (e.g., switching between different fine-tuned checkpoints) without manual weight management or URL handling.","intents":["Download and use the model with a single Python function call (from_pretrained('John6666/one-obsession-17-red-sdxl'))","Access model documentation, usage examples, and community discussions on the Hub","Automatically cache model weights locally to avoid re-downloading","Switch between different model versions or checkpoints without code changes"],"best_for":["developers building applications that need easy model swapping","researchers comparing multiple fine-tuned checkpoints","teams using version control for model selection (e.g., storing model ID in config files)","open-source projects distributing models without hosting infrastructure"],"limitations":["First download requires internet connectivity and ~7GB bandwidth; subsequent runs use local cache","Hub rate limiting may apply for high-volume downloads (e.g., 100+ concurrent users)","Model cache location is OS-dependent (~/.cache/huggingface on Linux/Mac, %USERPROFILE%\\.cache\\huggingface on Windows); requires manual cleanup if disk space is limited","No built-in model versioning in from_pretrained(); switching versions requires specifying revision parameter or re-downloading","Hub availability is a dependency; offline environments cannot download models (though cached models work offline)","Model card is user-maintained; no guarantee of accuracy or completeness"],"requires":["Internet connection for initial model download","~8GB free disk space for model cache","huggingface-hub library (installed as dependency of diffusers)","Python 3.8+","Optional: Hugging Face account for private model access (not required for public models)"],"input_types":["model ID string ('John6666/one-obsession-17-red-sdxl')","optional revision parameter (branch, tag, or commit hash for version selection)"],"output_types":["loaded model object (diffusers pipeline or checkpoint dict)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA 11.8+ or compatible GPU (minimum 6GB VRAM for inference)","diffusers library (0.21.0+)","transformers library (4.30.0+)","safetensors library for model loading","Hugging Face account for model access (open-source, no authentication required but recommended for rate limiting)","NVIDIA GPU with CUDA 11.8+ (RTX 3060 or better recommended) OR AMD GPU with ROCm 5.4+","PyTorch 2.0+ compiled for your GPU architecture","diffusers 0.21.0+","transformers 4.30.0+"],"failure_modes":["Fine-tuning is locked to anime/illustrated style — poor performance on photorealistic or non-anime prompts","Anatomical improvements (hands, feet) are relative to base SDXL but still subject to diffusion model limitations at extreme angles","Inference speed depends on hardware; typical generation takes 20-60 seconds on consumer GPUs (RTX 3060+)","Output quality highly sensitive to prompt engineering — vague prompts produce inconsistent results despite fine-tuning","No built-in inpainting or editing capabilities — requires separate tools for post-generation modifications","Model weights are 6-8GB in safetensors format, requiring significant local storage or streaming from Hugging Face","Requires significant GPU memory (6GB minimum for 1024x1024 generation, 12GB+ recommended for batch operations)","CPU-only inference is extremely slow (5-10 minutes per image) and impractical for interactive use","Model weights must be downloaded once (~7GB), consuming bandwidth and storage","No automatic model updates — requires manual re-download when new versions are released","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5543345409481532,"quality":0.22,"ecosystem":0.5000000000000001,"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":291468,"model_likes":3}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=john6666--one-obsession-17-red-sdxl","compare_url":"https://unfragile.ai/compare?artifact=john6666--one-obsession-17-red-sdxl"}},"signature":"D5Y5HzMKk5CoKssmgHrROOC+dX7npVoqlSTPfNNPz/avVJQeaSUXfKRbSdYFXztxqRSJyWlnAEP44wMGWr/4DA==","signedAt":"2026-06-21T21:36:52.508Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/john6666--one-obsession-17-red-sdxl","artifact":"https://unfragile.ai/john6666--one-obsession-17-red-sdxl","verify":"https://unfragile.ai/api/v1/verify?slug=john6666--one-obsession-17-red-sdxl","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"}}