{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-stableboost","slug":"stableboost","name":"Stableboost","type":"webapp","url":"https://stableboost.ai/","page_url":"https://unfragile.ai/stableboost","categories":["image-generation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-stableboost__cap_0","uri":"capability://image.visual.batch.image.generation.with.prompt.queuing","name":"batch image generation with prompt queuing","description":"Stableboost implements a queue-based image generation pipeline that accepts multiple prompts and generates images in batches, optimizing GPU utilization by processing multiple inference requests sequentially or in parallel depending on available VRAM. The system maintains a job queue that tracks generation status, parameters, and outputs, allowing users to submit dozens or hundreds of prompts and retrieve results asynchronously without blocking the UI.","intents":["I want to generate 50+ variations of a prompt to find the best outputs without waiting between each generation","I need to batch-process multiple prompts efficiently to maximize GPU throughput","I want to queue up generation jobs and come back later to review all results at once"],"best_for":["game developers and concept artists iterating on visual assets","product designers exploring multiple design directions rapidly","content creators building large image libraries for projects"],"limitations":["Queue processing speed depends on GPU VRAM; batching too many high-resolution prompts causes OOM errors","No built-in deduplication of similar outputs — users must manually filter results","Batch size optimization is manual; no adaptive batching based on available memory"],"requires":["GPU with minimum 6GB VRAM for standard Stable Diffusion model","Web browser with WebSocket support for real-time queue updates","Local installation or cloud deployment of Stable Diffusion backend"],"input_types":["text prompts","negative prompts","generation parameters (steps, guidance scale, seed)"],"output_types":["PNG/JPEG images","generation metadata (seed, parameters, timing)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_1","uri":"capability://image.visual.multi.parameter.variation.generation","name":"multi-parameter variation generation","description":"Stableboost enables systematic exploration of generation parameter space by allowing users to specify ranges or lists for seed, guidance scale, steps, and other Stable Diffusion parameters, then automatically generating images across all combinations or a sampled subset. This creates a structured exploration matrix where each axis represents a parameter variation, helping users understand how each setting affects output quality and style.","intents":["I want to test how different guidance scales (5-20) affect the same prompt to find the sweet spot","I need to generate variations with different seeds to see style diversity while keeping other parameters constant","I want to systematically explore the impact of step count on generation quality and speed"],"best_for":["researchers studying Stable Diffusion parameter sensitivity","artists fine-tuning model behavior for specific aesthetic goals","developers building parameter recommendation systems"],"limitations":["Combinatorial explosion: specifying ranges for 4+ parameters can generate thousands of images, consuming hours of GPU time","No built-in statistical analysis of parameter impact; users must manually compare outputs","Parameter interactions are not modeled; system treats each parameter independently"],"requires":["GPU with sufficient VRAM for extended generation sessions","Understanding of Stable Diffusion parameter semantics","Storage capacity for potentially thousands of generated images"],"input_types":["text prompt","parameter ranges (min/max or discrete lists)","sampling strategy (grid, random, or adaptive)"],"output_types":["image grid with parameter labels","CSV/JSON metadata mapping images to parameter combinations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_2","uri":"capability://image.visual.gallery.based.result.curation.and.comparison","name":"gallery-based result curation and comparison","description":"Stableboost organizes generated images in an interactive gallery interface with side-by-side comparison, filtering, and tagging capabilities. Users can mark favorite images, group results by prompt or parameters, and export curated subsets. The gallery maintains metadata for each image (generation parameters, timestamp, prompt) enabling retroactive analysis and filtering based on quality or aesthetic criteria.","intents":["I want to quickly browse through 100+ generated images and mark my top 10 favorites","I need to compare two similar images side-by-side to decide which one better matches my vision","I want to filter results by parameter values to see which settings produced the best outputs"],"best_for":["creative professionals reviewing large batches of AI-generated assets","teams collaborating on image selection and approval workflows","researchers documenting generation experiments with full parameter traceability"],"limitations":["Gallery performance degrades with >1000 images in a single session; no pagination or lazy-loading optimization mentioned","Tagging and filtering are manual; no ML-based quality scoring or aesthetic clustering","Export formats limited to image files; no integration with asset management systems (DAM)"],"requires":["Web browser with GPU-accelerated rendering for smooth image scrolling","Local storage or cloud backend to persist gallery state and metadata","Sufficient disk space for storing full-resolution images"],"input_types":["generated images from batch queue","user tags and ratings","filter criteria (parameter ranges, prompt keywords)"],"output_types":["filtered image subsets","exported image collections","metadata JSON/CSV for external tools"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_3","uri":"capability://automation.workflow.real.time.generation.progress.tracking.and.cancellation","name":"real-time generation progress tracking and cancellation","description":"Stableboost provides live progress indicators for each image in the generation queue, showing step-by-step completion percentage and estimated time remaining. Users can cancel individual generation jobs or the entire queue without losing previously completed images. The system uses WebSocket or polling to update the UI in real-time, and maintains a persistent queue state so users can pause and resume generation sessions.","intents":["I want to see real-time progress on my 50-image batch so I know when results will be ready","I need to cancel a generation job that's taking too long without losing the other images I've already generated","I want to pause my generation queue, come back in an hour, and resume where I left off"],"best_for":["users with limited GPU time or cloud credits who need visibility into generation costs","iterative workflows where users want to adjust prompts based on partial results","long-running batch jobs that span multiple sessions"],"limitations":["Progress estimates are based on average step time; actual duration varies with prompt complexity and model state","No granular control over queue priority; all jobs are processed FIFO","Cancellation is not atomic; images already in VRAM may complete before cancellation takes effect"],"requires":["WebSocket or HTTP polling support in browser","Backend queue service with state persistence","GPU monitoring to provide accurate time estimates"],"input_types":["queue state (job IDs, parameters)","user cancellation/pause commands"],"output_types":["real-time progress JSON (step count, ETA, status)","completion notifications"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_4","uri":"capability://image.visual.model.and.checkpoint.management.with.quick.switching","name":"model and checkpoint management with quick switching","description":"Stableboost abstracts Stable Diffusion model loading and switching, allowing users to select from multiple installed checkpoints (base models, fine-tuned variants, LoRA adapters) through a UI dropdown without restarting the backend. The system manages model memory efficiently by unloading unused models and caching frequently-used ones, reducing the overhead of switching between different model variants during exploration.","intents":["I want to quickly switch between my base Stable Diffusion model and a fine-tuned anime variant to compare outputs","I need to apply different LoRA adapters to the same prompt to see which one produces the best style","I want to test the same prompt across 3 different model checkpoints without manually restarting the backend"],"best_for":["artists experimenting with multiple model variants and LoRA combinations","researchers comparing model outputs across different checkpoints","teams with shared GPU infrastructure needing quick model switching"],"limitations":["Model switching requires VRAM to hold both old and new models during transition; can cause OOM on GPUs with <8GB","No automatic model selection based on prompt content; users must manually choose appropriate models","LoRA composition is sequential; no support for blending multiple LoRAs with weighted combinations"],"requires":["Multiple model checkpoints pre-downloaded and indexed","GPU with sufficient VRAM for largest model variant","Model metadata file (name, type, LoRA compatibility)"],"input_types":["model selection (dropdown or search)","LoRA adapter selection and parameters"],"output_types":["images generated with selected model","model metadata in generation output"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_5","uri":"capability://text.generation.language.prompt.templating.and.variable.substitution","name":"prompt templating and variable substitution","description":"Stableboost supports prompt templates with variable placeholders that can be substituted with lists of values, enabling systematic prompt variation without manual editing. Users can define templates like 'a {style} painting of a {subject}' and provide lists for {style} and {subject}, which generates the Cartesian product of all combinations. This reduces prompt engineering overhead and ensures consistency across variations.","intents":["I want to generate images for 'a [style] painting of a [subject]' where [style] is one of 10 art styles and [subject] is one of 5 objects","I need to quickly create variations of a prompt by swapping out key nouns and adjectives without retyping the entire prompt","I want to test how different subject descriptions affect the output while keeping the style and composition constant"],"best_for":["content creators building large image libraries with systematic variations","designers exploring style/subject combinations efficiently","teams standardizing prompt formats across projects"],"limitations":["Combinatorial explosion: 10 styles × 5 subjects = 50 prompts; no built-in sampling to reduce generation count","Variable substitution is text-based; no semantic understanding of prompt meaning or token count impact","No conditional logic; cannot skip certain combinations (e.g., 'avoid anime style with photorealistic subject')"],"requires":["Template syntax understanding (placeholder format)","Lists of values for each variable","Sufficient GPU time for all generated combinations"],"input_types":["prompt template string with {variable} placeholders","lists of values for each variable"],"output_types":["expanded prompts (one per combination)","images generated from expanded prompts"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_6","uri":"capability://image.visual.seed.management.and.reproducibility.control","name":"seed management and reproducibility control","description":"Stableboost provides explicit seed management allowing users to fix seeds for reproducible outputs or randomize them for diversity. Users can specify a seed range, generate images with the same seed across different prompts/parameters to isolate the effect of those changes, or use random seeds for exploration. The system displays the seed used for each image in metadata, enabling retroactive reproduction of specific outputs.","intents":["I want to generate the same image twice with identical parameters to verify reproducibility","I need to test how changing the guidance scale affects the output while keeping the seed constant","I want to generate 50 variations of a prompt with different random seeds to see style diversity"],"best_for":["researchers validating Stable Diffusion determinism","artists creating consistent character designs across multiple images","developers building reproducible image generation pipelines"],"limitations":["Seed reproducibility depends on identical GPU hardware and driver versions; different GPUs may produce different outputs with same seed","No seed interpolation or morphing; cannot smoothly transition between two seed-based outputs","Seed space is 32-bit; no guidance on optimal seed selection or distribution"],"requires":["Seed value (integer or 'random')","Identical model checkpoint and parameters for reproducibility","Same GPU hardware for guaranteed determinism"],"input_types":["seed value (fixed or range)","randomization strategy (fixed, random, sequential)"],"output_types":["images with seed metadata","seed-to-image mapping for reproducibility"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-stableboost__cap_7","uri":"capability://text.generation.language.negative.prompt.management.and.weighting","name":"negative prompt management and weighting","description":"Stableboost supports negative prompts (concepts to avoid) with optional weighting to control their influence on generation. Users can specify multiple negative prompts and adjust their relative strength, allowing fine-grained control over what the model should NOT generate. The system may support syntax for weighted negative prompts (e.g., '(bad quality:0.7), (blurry:0.5)') enabling nuanced exclusion of undesired attributes.","intents":["I want to avoid 'blurry, low quality' artifacts in my generations without completely excluding those concepts","I need to prevent the model from generating certain styles or subjects that appear in my base prompt","I want to weight negative prompts differently to prioritize avoiding some artifacts over others"],"best_for":["artists refining generation quality by iteratively adding negative prompts","teams standardizing quality baselines with shared negative prompt libraries","researchers studying how negative prompts affect model behavior"],"limitations":["Negative prompt effectiveness varies with base prompt; some undesired concepts are difficult to suppress","Weighted negative prompts may conflict, producing unpredictable results when weights sum to >1.0","No built-in library of common negative prompts; users must discover effective ones through trial and error"],"requires":["Understanding of negative prompt syntax and semantics","Stable Diffusion model version supporting negative prompts (most modern versions)"],"input_types":["negative prompt string","weights for each negative prompt component"],"output_types":["images generated with negative prompt guidance","metadata showing negative prompts used"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"low","permissions":["GPU with minimum 6GB VRAM for standard Stable Diffusion model","Web browser with WebSocket support for real-time queue updates","Local installation or cloud deployment of Stable Diffusion backend","GPU with sufficient VRAM for extended generation sessions","Understanding of Stable Diffusion parameter semantics","Storage capacity for potentially thousands of generated images","Web browser with GPU-accelerated rendering for smooth image scrolling","Local storage or cloud backend to persist gallery state and metadata","Sufficient disk space for storing full-resolution images","WebSocket or HTTP polling support in browser"],"failure_modes":["Queue processing speed depends on GPU VRAM; batching too many high-resolution prompts causes OOM errors","No built-in deduplication of similar outputs — users must manually filter results","Batch size optimization is manual; no adaptive batching based on available memory","Combinatorial explosion: specifying ranges for 4+ parameters can generate thousands of images, consuming hours of GPU time","No built-in statistical analysis of parameter impact; users must manually compare outputs","Parameter interactions are not modeled; system treats each parameter independently","Gallery performance degrades with >1000 images in a single session; no pagination or lazy-loading optimization mentioned","Tagging and filtering are manual; no ML-based quality scoring or aesthetic clustering","Export formats limited to image files; no integration with asset management systems (DAM)","Progress estimates are based on average step time; actual duration varies with prompt complexity and model state","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-04-22T08:05:10.921Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stableboost","compare_url":"https://unfragile.ai/compare?artifact=stableboost"}},"signature":"rIOVsLLQPFFn6+HUihkkg2xoa4UmcaMMJlt2bQh7vxJXZ7IChoD+rDY77NlexBN9cdENWKPvNiGyOhIZpQR4Bg==","signedAt":"2026-06-20T14:36:44.347Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stableboost","artifact":"https://unfragile.ai/stableboost","verify":"https://unfragile.ai/api/v1/verify?slug=stableboost","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"}}