{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_b-discover","slug":"b-discover","name":"B^ DISCOVER","type":"product","url":"https://bdiscover.kakaobrain.com","page_url":"https://unfragile.ai/b-discover","categories":["image-generation","testing-quality"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_b-discover__cap_0","uri":"capability://image.visual.text.to.image.generation.with.diffusion.based.synthesis","name":"text-to-image generation with diffusion-based synthesis","description":"Converts natural language text prompts into high-fidelity images using advanced diffusion models that iteratively denoise latent representations. The system processes prompts through a text encoder, maps them to a learned embedding space, and progressively refines pixel-space outputs through multiple denoising steps guided by the encoded prompt semantics. Architecture leverages attention mechanisms to align textual concepts with spatial image regions.","intents":["Generate marketing visuals and product mockups from written descriptions without hiring designers","Create concept art and mood boards for creative projects by describing visual aesthetics in natural language","Produce multiple stylistic variations of the same scene for A/B testing and creative exploration","Rapidly prototype visual ideas during brainstorming sessions without manual design work"],"best_for":["Asian-based creative professionals and marketing teams already using Kakao ecosystem services","Solo designers and small creative agencies needing fast iteration on visual concepts","Product teams prototyping UI/UX mockups and visual designs"],"limitations":["Diffusion-based generation requires 10-30 seconds per image depending on resolution and step count, making real-time interactive workflows impractical","Text prompt interpretation quality degrades with highly specific or niche visual concepts not well-represented in training data","No built-in image inpainting or outpainting capabilities — cannot selectively edit or extend existing images","Limited style consistency across multiple generated images from the same prompt — each generation is independent without cross-image coherence mechanisms","No fine-tuning or custom model training available — users cannot adapt the model to proprietary visual styles or brand guidelines"],"requires":["Active B^ DISCOVER account with sufficient credit balance","Web browser with modern JavaScript support (Chrome 90+, Firefox 88+, Safari 14+)","Internet connection with minimum 5 Mbps bandwidth for prompt submission and image download","Kakao account for ecosystem integration (recommended for Asian users)"],"input_types":["text (natural language prompts with style descriptors, composition hints, and artistic references)","numeric parameters (image dimensions, sampling steps, guidance scale for prompt adherence strength)"],"output_types":["image (PNG or JPEG format, typically 512x512 to 1024x1024 resolution)","metadata (generation parameters, seed value, inference time, model version)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_1","uri":"capability://image.visual.style.template.and.preset.application","name":"style template and preset application","description":"Provides a curated library of pre-configured style templates (e.g., oil painting, cyberpunk, watercolor, anime) that users can apply to text prompts to constrain the diffusion model's output toward specific artistic aesthetics. Templates work by embedding style descriptors and visual reference embeddings into the prompt conditioning mechanism, effectively biasing the denoising process toward learned style representations without requiring manual prompt engineering.","intents":["Apply consistent artistic styles to generated images without memorizing style-specific prompt syntax","Explore different aesthetic treatments of the same concept by swapping templates","Reduce prompt engineering complexity for non-technical users unfamiliar with style descriptor vocabulary","Maintain visual coherence across a series of generated images by reusing the same style template"],"best_for":["Non-technical creative professionals and marketing teams who need quick style application without prompt expertise","Content creators producing themed visual series (e.g., social media campaigns with consistent aesthetic)","Teams exploring multiple artistic directions for a single concept"],"limitations":["Template library is fixed and curated by Kakao Brain — users cannot create or upload custom style templates","Style templates may conflict with specific prompt descriptors, requiring manual prompt refinement to resolve aesthetic contradictions","Limited transparency into how templates modify the underlying prompt encoding — difficult to understand or predict style interactions","Template selection is UI-driven without programmatic API access — cannot automate style application in batch workflows"],"requires":["B^ DISCOVER account with active session","Familiarity with basic image generation concepts (resolution, aspect ratio)"],"input_types":["text (base prompt describing subject and composition)","categorical selection (style template choice from dropdown menu)"],"output_types":["image (PNG/JPEG with applied style template)","metadata (template identifier, style parameters used)"],"categories":["image-visual","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_10","uri":"capability://image.visual.image.editing.and.inpainting.limited.capability","name":"image editing and inpainting (limited capability)","description":"Provides basic image editing capabilities for modifying specific regions of generated images through inpainting, where users mask areas to be regenerated while preserving the rest of the image. The system uses a masked diffusion process to regenerate only the specified regions while maintaining coherence with the surrounding context. Editing is limited compared to competitors — no outpainting (extending image boundaries) or advanced selection tools.","intents":["Fix or modify specific elements in generated images without regenerating the entire image","Refine details or correct errors in localized regions","Experiment with variations of specific image components while preserving the overall composition"],"best_for":["Users needing minor edits or refinements to generated images","Teams iterating on designs with feedback-driven modifications"],"limitations":["Inpainting capability is limited or unavailable — editorial summary indicates lack of inpainting as a key missing feature","No outpainting support — cannot extend image boundaries or add new content outside original dimensions","Masking tools are basic — no advanced selection capabilities (e.g., intelligent object selection, feathering)","Inpainted regions may have visible seams or coherence issues at mask boundaries","Inpainting requires additional credits and may have longer latency than standard generation"],"requires":["B^ DISCOVER account","Generated image to edit","Basic image editing interface familiarity"],"input_types":["image (generated image to edit)","mask (user-drawn or selected region to regenerate)","text (optional prompt for inpainted region)"],"output_types":["image (edited image with inpainted region)"],"categories":["image-visual","image-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_2","uri":"capability://image.visual.image.quality.and.detail.refinement.controls","name":"image quality and detail refinement controls","description":"Exposes numerical parameters (sampling steps, guidance scale, seed values) that allow users to trade off generation speed against output quality and prompt adherence. Higher step counts increase denoising iterations for finer detail, while guidance scale controls how strongly the diffusion process is conditioned on the text prompt versus unconditional generation. Seed values enable deterministic reproduction of specific outputs for iteration and refinement.","intents":["Fine-tune image quality by adjusting sampling steps when default settings produce insufficient detail","Control the balance between creative freedom and prompt fidelity using guidance scale parameters","Reproduce and iterate on specific generated images by fixing seed values across multiple generations","Optimize generation time for time-sensitive workflows by reducing sampling steps"],"best_for":["Advanced users and creative technologists who understand diffusion model mechanics","Teams requiring reproducible outputs for design iteration and client feedback cycles","Professionals optimizing for specific quality-to-latency tradeoffs in production workflows"],"limitations":["Parameter tuning requires domain knowledge of diffusion model behavior — no guidance for optimal settings per use case","Guidance scale values outside recommended ranges (typically 7-20) produce degraded outputs or visual artifacts without clear feedback","Seed reproducibility is not guaranteed across model versions or infrastructure updates — Kakao Brain may update the underlying model, breaking seed-based workflows","No batch parameter sweeping or automated optimization — users must manually test parameter combinations","Sampling step increases add linear latency (approximately 1-2 seconds per additional 10 steps) without diminishing returns beyond ~50 steps"],"requires":["B^ DISCOVER account with advanced settings access (may require paid tier)","Understanding of diffusion model concepts (guidance, sampling, conditioning)"],"input_types":["numeric (sampling steps: 20-100, guidance scale: 1-20, seed: 0-2^32-1)"],"output_types":["image (PNG/JPEG with specified quality parameters)","metadata (parameter values used, inference time, quality metrics if available)"],"categories":["image-visual","parameter-tuning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_3","uri":"capability://image.visual.multi.image.batch.generation.with.prompt.variation","name":"multi-image batch generation with prompt variation","description":"Enables users to generate multiple image variations from a single prompt or to apply systematic prompt variations (e.g., different subjects, styles, compositions) across a batch of generation requests. The system queues requests and processes them sequentially or in parallel depending on account tier, returning a gallery of results that can be compared side-by-side. Variation modes include random seed variation (same prompt, different outputs) and parameterized prompt templates (e.g., 'A [SUBJECT] in [STYLE]' with substitution lists).","intents":["Generate multiple aesthetic options for a single concept to present to stakeholders or clients","Create themed image series by systematically varying subjects, styles, or compositions across a prompt template","Explore the output distribution of the model by generating many variations and analyzing common patterns","Reduce per-image cost by batching requests and leveraging volume discounts or batch pricing"],"best_for":["Creative teams and agencies producing multiple design options for client approval","Content creators generating themed image series for social media or marketing campaigns","Researchers and analysts studying model behavior and output distributions"],"limitations":["Batch generation is sequential or limited-parallel depending on account tier — no guaranteed concurrent processing, leading to unpredictable total completion times","No built-in comparison or ranking tools — users must manually review and select preferred outputs from potentially hundreds of images","Prompt templating is UI-driven without programmatic API — cannot automate complex variation patterns or integrate with external tools","Batch results are not persisted long-term — images may be deleted after 30 days or require manual export to permanent storage","No cost estimation before batch submission — users cannot predict total credit consumption for large batches"],"requires":["B^ DISCOVER account with sufficient credit balance for all requested generations","Paid tier account for batch generation (free tier may have single-image-only restrictions)"],"input_types":["text (base prompt or prompt template with variation placeholders)","numeric (batch size: 1-100, number of variations per prompt)"],"output_types":["image gallery (PNG/JPEG collection, typically 512x512 to 1024x1024 per image)","metadata (generation parameters per image, batch completion time, total credits consumed)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_4","uri":"capability://image.visual.image.resolution.and.aspect.ratio.customization","name":"image resolution and aspect ratio customization","description":"Allows users to specify output image dimensions (e.g., 512x512, 768x1024, 1024x1024) and aspect ratios (square, portrait, landscape, custom) before generation. The diffusion model is conditioned on the target resolution, adjusting the denoising process to generate coherent outputs at the specified dimensions. Different resolutions incur different computational costs and credit consumption, with higher resolutions requiring more sampling steps or longer inference time.","intents":["Generate images in specific dimensions matching target use cases (e.g., social media posts, print materials, UI mockups)","Create portrait or landscape compositions optimized for specific display contexts without manual cropping","Control generation cost by selecting lower resolutions for rapid prototyping and higher resolutions for final outputs","Maintain aspect ratio consistency across a series of generated images for cohesive visual presentation"],"best_for":["Marketing and content teams producing images for specific platforms (Instagram, LinkedIn, print)","UI/UX designers generating mockups in exact dimensions matching design specifications","Teams optimizing for cost-efficiency by using lower resolutions for iteration and higher resolutions for finals"],"limitations":["Non-standard aspect ratios may produce distorted or compositionally awkward outputs if the model was primarily trained on square images","Higher resolutions (1024x1024 and above) consume significantly more credits and require longer inference times (30-60 seconds), making rapid iteration impractical","No dynamic resolution scaling — users must manually select resolution before generation, with no preview of how composition changes across resolutions","Aspect ratio constraints may conflict with prompt semantics (e.g., 'tall building' in square format produces awkward framing)","Maximum resolution may be limited by account tier or infrastructure constraints — premium resolutions may require paid tier"],"requires":["B^ DISCOVER account","Understanding of target use case dimensions (e.g., Instagram: 1080x1080, LinkedIn: 1200x627)"],"input_types":["categorical (aspect ratio: square, portrait, landscape, custom)","numeric (width and height in pixels, typically 512-1024 range)"],"output_types":["image (PNG/JPEG at specified dimensions)","metadata (actual resolution generated, aspect ratio used, credit cost)"],"categories":["image-visual","output-formatting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_5","uri":"capability://image.visual.image.download.and.export.with.metadata.preservation","name":"image download and export with metadata preservation","description":"Provides multiple export options for generated images including direct download (PNG/JPEG), cloud storage integration (Kakao Cloud, potentially others), and social media sharing (KakaoStory, KakaoTalk). Downloaded images include embedded metadata (generation parameters, seed, timestamp) in EXIF or custom headers, enabling users to reproduce outputs or track generation history. Export workflow is optimized for Kakao ecosystem with one-click sharing to Kakao services.","intents":["Download generated images in standard formats for use in design tools, websites, or print materials","Export images with embedded generation parameters for reproducibility and documentation","Share images directly to social media or messaging platforms without manual upload steps","Archive generated images in cloud storage for long-term access and team collaboration"],"best_for":["Creative professionals and designers integrating B^ DISCOVER outputs into existing design workflows","Content creators publishing to Kakao ecosystem platforms (KakaoStory, KakaoTalk)","Teams requiring image archival and version control with generation metadata"],"limitations":["Metadata preservation is not guaranteed across all export formats — JPEG compression may strip EXIF data depending on export settings","Cloud storage integration is limited to Kakao ecosystem services — no native support for AWS S3, Google Drive, or other third-party storage","Downloaded images may include watermarks or attribution requirements depending on license terms (not specified in available documentation)","No batch export or automated archival — users must manually download or share each image individually","Export history is not persisted — users cannot retrieve previously downloaded images if they clear their browser cache or switch devices"],"requires":["B^ DISCOVER account with active session","Web browser with file download capability","Kakao account for ecosystem sharing (optional, required only for KakaoStory/KakaoTalk export)"],"input_types":["generated image (from B^ DISCOVER gallery)","categorical (export format: PNG, JPEG, WebP; destination: local download, Kakao Cloud, social media)"],"output_types":["image file (PNG/JPEG with metadata)","social media post (if sharing to KakaoStory/KakaoTalk)","cloud storage reference (if exporting to Kakao Cloud)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_6","uri":"capability://text.generation.language.prompt.optimization.and.suggestion.system","name":"prompt optimization and suggestion system","description":"Provides real-time suggestions and auto-completion for prompt text based on learned patterns from successful generations and user behavior. The system analyzes partial prompts and recommends style descriptors, composition keywords, and artistic references that are likely to produce high-quality outputs. Suggestions are ranked by popularity, aesthetic quality scores, and relevance to the current prompt context.","intents":["Improve prompt quality without manual research or trial-and-error by leveraging system suggestions","Discover effective style descriptors and artistic references from the community's successful prompts","Reduce prompt engineering time for users unfamiliar with effective prompt syntax","Explore creative directions by browsing suggested keywords and aesthetic combinations"],"best_for":["Novice users and non-technical creators unfamiliar with effective prompt engineering","Teams seeking to improve output quality without extensive trial-and-error","Content creators exploring new aesthetic directions and style combinations"],"limitations":["Suggestions are based on historical data and may reinforce popular but potentially clichéd aesthetic choices","Suggestion quality depends on the diversity and quality of the training data — niche or specialized concepts may receive poor suggestions","No transparency into how suggestions are ranked — users cannot understand why certain keywords are recommended over others","Suggestions are UI-driven without programmatic API — cannot integrate with external prompt engineering tools or workflows","Suggestion system may not understand context-specific requirements (e.g., brand guidelines, target audience) and may recommend aesthetically inappropriate options"],"requires":["B^ DISCOVER account with active session","Typing at least 3-5 characters in the prompt field to trigger suggestions"],"input_types":["text (partial prompt, typically 3+ characters)"],"output_types":["text suggestions (ranked list of recommended keywords, style descriptors, artistic references)","metadata (suggestion confidence scores, popularity metrics)"],"categories":["text-generation-language","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_7","uri":"capability://memory.knowledge.generation.history.and.project.management","name":"generation history and project management","description":"Maintains a searchable history of all user-generated images with associated metadata (prompts, parameters, timestamps, quality ratings). Users can organize generations into projects, create collections, and tag images for easy retrieval. The system provides filtering and search capabilities to find specific outputs by prompt keywords, style, generation date, or user-defined tags. Project organization enables team collaboration with shared access to generation history.","intents":["Track and retrieve previously generated images without manual file management","Organize generations into projects for specific clients, campaigns, or creative initiatives","Search for successful prompts and parameters to reproduce or iterate on previous outputs","Share generation history and project collections with team members for collaborative feedback"],"best_for":["Creative teams and agencies managing multiple projects and client deliverables","Individual creators maintaining a portfolio of generated images and successful prompts","Teams requiring audit trails and version control for design iterations"],"limitations":["History retention is limited by account tier or storage quota — older generations may be automatically deleted after 30-90 days","Search functionality is limited to metadata and tags — no content-based image search (e.g., 'find images similar to this one')","Project sharing requires manual invitation or permission management — no granular access controls (e.g., read-only vs. edit permissions)","No version control or branching — users cannot track how a design evolved across multiple iterations","Export of project history is not supported — users cannot backup or migrate their generation history to external systems"],"requires":["B^ DISCOVER account with project management features (may require paid tier)","Sufficient storage quota for history retention (typically 1000-10000 images depending on tier)"],"input_types":["categorical (project selection, tag creation, filter criteria)","text (search queries, project names, collection descriptions)"],"output_types":["image gallery (filtered/searched results)","metadata (generation parameters, timestamps, quality ratings)","project summary (image count, date range, team members)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_8","uri":"capability://automation.workflow.credit.and.usage.tracking.with.transparent.billing","name":"credit and usage tracking with transparent billing","description":"Provides real-time tracking of credit consumption per generation, with transparent pricing displayed before image generation. The system shows credit cost based on resolution, sampling steps, and other parameters, enabling users to estimate total costs before committing to batch operations. Usage dashboards display historical credit consumption, cost trends, and remaining balance. Billing is integrated with Kakao's payment system, supporting multiple payment methods.","intents":["Understand and control generation costs by viewing per-image credit consumption before generation","Budget for creative projects by estimating total credit requirements based on generation parameters","Monitor usage patterns and identify cost optimization opportunities (e.g., lower resolution for iteration)","Track team spending and allocate credits across team members or projects"],"best_for":["Teams and agencies managing budgets for AI-generated content production","Individual creators optimizing for cost-efficiency while maintaining output quality","Finance teams tracking and controlling AI tool spending"],"limitations":["Credit pricing is opaque relative to Midjourney's straightforward subscription model — unclear how credits map to actual computational costs","No usage alerts or spending caps — users can accidentally consume large credit balances without warning","Credit expiration policies are not clearly documented — users may lose unused credits if they expire","No credit sharing or team pooling — each user has individual credit balance, making team budgeting complex","Refund policies for failed or unsatisfactory generations are unclear — users may not be able to recover credits for poor outputs"],"requires":["B^ DISCOVER account with payment method configured","Kakao account linked to payment system (credit card, Kakao Pay, etc.)"],"input_types":["numeric (generation parameters affecting cost: resolution, sampling steps)","categorical (batch size, generation mode)"],"output_types":["numeric (credit cost estimate, remaining balance, historical usage)","dashboard (usage trends, cost breakdown by resolution/style, billing history)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_b-discover__cap_9","uri":"capability://memory.knowledge.community.prompt.sharing.and.discovery","name":"community prompt sharing and discovery","description":"Enables users to share successful prompts and generated images with the community, and to discover and reuse prompts from other users. The platform maintains a searchable prompt library indexed by keywords, styles, and aesthetic tags. Community ratings and usage statistics help surface high-quality prompts. Shared prompts can be forked and modified, creating derivative prompts that build on community knowledge.","intents":["Discover effective prompts and aesthetic combinations from the community without manual trial-and-error","Share successful prompts to contribute to community knowledge and build reputation","Reuse and adapt community prompts for similar creative tasks","Learn prompt engineering best practices by studying highly-rated community prompts"],"best_for":["Novice users learning effective prompt engineering from community examples","Creative professionals seeking inspiration and aesthetic references from peers","Communities of practice building shared knowledge around effective prompts and styles"],"limitations":["Community size is significantly smaller than Midjourney or Stable Diffusion — fewer prompts available and less diverse aesthetic coverage","Prompt quality is not curated or moderated — low-quality or misleading prompts may be highly rated due to gaming or lack of critical evaluation","No attribution or licensing system — users may reuse prompts without crediting original creators","Prompt sharing is limited to the B^ DISCOVER platform — no integration with external prompt databases or communities","No version control for shared prompts — users cannot track how prompts evolve or see alternative versions"],"requires":["B^ DISCOVER account","Community participation enabled (may require opt-in)"],"input_types":["text (prompt text, description, tags)","image (generated image to share alongside prompt)","categorical (style category, aesthetic tags)"],"output_types":["prompt library entry (searchable, with metadata and community ratings)","community engagement metrics (views, likes, forks, comments)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active B^ DISCOVER account with sufficient credit balance","Web browser with modern JavaScript support (Chrome 90+, Firefox 88+, Safari 14+)","Internet connection with minimum 5 Mbps bandwidth for prompt submission and image download","Kakao account for ecosystem integration (recommended for Asian users)","B^ DISCOVER account with active session","Familiarity with basic image generation concepts (resolution, aspect ratio)","B^ DISCOVER account","Generated image to edit","Basic image editing interface familiarity","B^ DISCOVER account with advanced settings access (may require paid tier)"],"failure_modes":["Diffusion-based generation requires 10-30 seconds per image depending on resolution and step count, making real-time interactive workflows impractical","Text prompt interpretation quality degrades with highly specific or niche visual concepts not well-represented in training data","No built-in image inpainting or outpainting capabilities — cannot selectively edit or extend existing images","Limited style consistency across multiple generated images from the same prompt — each generation is independent without cross-image coherence mechanisms","No fine-tuning or custom model training available — users cannot adapt the model to proprietary visual styles or brand guidelines","Template library is fixed and curated by Kakao Brain — users cannot create or upload custom style templates","Style templates may conflict with specific prompt descriptors, requiring manual prompt refinement to resolve aesthetic contradictions","Limited transparency into how templates modify the underlying prompt encoding — difficult to understand or predict style interactions","Template selection is UI-driven without programmatic API access — cannot automate style application in batch workflows","Inpainting capability is limited or unavailable — editorial summary indicates lack of inpainting as a key missing feature","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"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":"2026-05-24T12:16:29.134Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=b-discover","compare_url":"https://unfragile.ai/compare?artifact=b-discover"}},"signature":"5DxiAjPbtA6ff09HutsWDM7m3j4ZDohXVlnIlxNF42fJfSNTADeEfx7pkpVWulBHwD+JdgzkB305pXS5nIkMCg==","signedAt":"2026-06-22T13:10:21.558Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/b-discover","artifact":"https://unfragile.ai/b-discover","verify":"https://unfragile.ai/api/v1/verify?slug=b-discover","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"}}