{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-hunyuan-promptenhancer--promptenhancer","slug":"hunyuan-promptenhancer--promptenhancer","name":"PromptEnhancer","type":"prompt","url":"https://hunyuan-promptenhancer.github.io/","page_url":"https://unfragile.ai/hunyuan-promptenhancer--promptenhancer","categories":["prompt-engineering"],"tags":["hunyuan","hunyuan-image","image-editing","image-to-image","prompt","prompt-engineering","prompt-enhancer","text-to-image","vlm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_0","uri":"capability://text.generation.language.chain.of.thought.text.to.image.prompt.rewriting.with.intent.preservation","name":"chain-of-thought text-to-image prompt rewriting with intent preservation","description":"Accepts a raw user prompt and processes it through a full-precision transformer-based LLM (7B or 32B parameters) using chain-of-thought reasoning to decompose and restructure the prompt into a semantically richer, more detailed version suitable for image generation. The system preserves all key semantic elements (subject, action, style, layout, attributes) while expanding ambiguous descriptions into explicit, structured language that downstream image generators can better interpret. Uses multi-level fallback parsing to extract the enhanced prompt even when LLM output formatting is inconsistent.","intents":["I want to automatically improve vague user prompts before sending them to an image generation model","I need to expand short prompts into detailed, structured descriptions that preserve original intent","I want to ensure consistency in prompt quality across a batch of user-submitted image requests","I need to decompose complex visual concepts into explicit, unambiguous language for better generation results"],"best_for":["image generation platform builders integrating prompt preprocessing","teams building AI-powered creative tools with user-submitted prompts","developers optimizing image generation quality without retraining models"],"limitations":["Requires 16GB+ VRAM for 7B model, 40GB+ for 32B model in full precision — no GPU acceleration fallback documented","Inference latency ~2-5 seconds per prompt on consumer hardware due to full model loading","Intent preservation is heuristic-based — may over-expand or misinterpret highly specialized domain prompts","No built-in support for multi-language prompts; primarily optimized for English"],"requires":["Python 3.9+","PyTorch with CUDA support (for GPU inference)","Transformers library 4.30+","16GB+ VRAM for 7B model or 40GB+ for 32B model","HuggingFace model weights (7B or 32B Hunyuan LLM)"],"input_types":["text (raw user prompt, typically 5-200 tokens)"],"output_types":["text (enhanced prompt, typically 50-500 tokens)","structured metadata (optional: extracted attributes like style, composition)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_1","uri":"capability://text.generation.language.quantized.gguf.based.prompt.enhancement.with.memory.efficiency","name":"quantized gguf-based prompt enhancement with memory efficiency","description":"Implements a memory-efficient variant of text-to-image prompt enhancement using GGUF quantized models (4-bit, 8-bit) that run on consumer-grade hardware with 8-16GB VRAM instead of requiring 40GB+ for full-precision models. Uses llama.cpp backend for CPU-optimized inference with optional GPU acceleration, trading ~10-15% quality degradation for 4-6x memory reduction and 2-3x faster inference. Maintains the same chain-of-thought rewriting logic as the full-precision variant through quantization-aware model conversion.","intents":["I want to run prompt enhancement locally on consumer hardware without expensive GPU requirements","I need to deploy prompt enhancement at scale with minimal infrastructure costs","I want faster inference for real-time prompt enhancement in interactive applications","I need to run prompt enhancement on edge devices or resource-constrained environments"],"best_for":["indie developers and small teams with limited hardware budgets","edge deployment scenarios (local apps, on-device processing)","high-throughput batch processing where latency is less critical than throughput","resource-constrained cloud deployments (serverless, containers with memory limits)"],"limitations":["Quantization introduces ~10-15% quality degradation in prompt expansion detail and semantic precision","GGUF models require manual conversion from HuggingFace format — no automated pipeline provided","CPU inference is significantly slower than GPU even with optimization (5-15 seconds per prompt on CPU)","Limited to specific quantization levels (4-bit, 8-bit) — no fine-grained control over precision tradeoffs"],"requires":["Python 3.9+","llama-cpp-python library (0.2.0+)","8-16GB RAM minimum (4GB for 4-bit quantized models)","GGUF quantized model weights (pre-converted or manually quantized)","Optional: CUDA-capable GPU for acceleration"],"input_types":["text (raw user prompt)"],"output_types":["text (enhanced prompt with reduced detail vs full-precision variant)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_2","uri":"capability://image.visual.vision.language.image.to.image.editing.instruction.refinement","name":"vision-language image-to-image editing instruction refinement","description":"Accepts both an image and a text editing instruction, processes them through a vision-language model (VLM) that analyzes the visual content and instruction semantics together, then generates a refined editing instruction that is more explicit about spatial relationships, visual context, and desired modifications. The VLM grounds the editing instruction in the actual image content, reducing ambiguity and enabling more precise image-to-image editing. Uses multi-modal chain-of-thought reasoning to decompose visual analysis and instruction refinement into explicit steps.","intents":["I want to improve vague image editing instructions by grounding them in actual visual content","I need to make editing instructions more explicit about spatial relationships and visual context","I want to automatically clarify ambiguous edit requests before sending them to an image-to-image model","I need to ensure editing instructions reference specific visual elements that actually exist in the image"],"best_for":["image editing platform builders integrating instruction preprocessing","teams building interactive image editing tools with natural language instructions","developers optimizing image-to-image model outputs through better instruction clarity"],"limitations":["Requires vision-language model weights (typically 7B-13B parameters) — adds 20-30GB VRAM overhead vs text-only variant","Inference latency ~3-8 seconds per image due to visual encoding and multi-modal reasoning","VLM analysis quality depends on image resolution and clarity — fails gracefully on very low-quality or corrupted images","No built-in support for batch processing of image+instruction pairs; processes one pair at a time"],"requires":["Python 3.9+","Vision-language model weights (Hunyuan V2 or compatible VLM)","30-40GB VRAM for full-precision VLM inference","Image input in standard formats (PNG, JPEG, WebP)","PyTorch with CUDA support"],"input_types":["image (PNG, JPEG, WebP format, any resolution)","text (editing instruction, typically 5-100 tokens)"],"output_types":["text (refined editing instruction with visual grounding, typically 50-300 tokens)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_3","uri":"capability://data.processing.analysis.multi.level.fallback.prompt.extraction.with.robust.parsing","name":"multi-level fallback prompt extraction with robust parsing","description":"Implements a cascading fallback mechanism for extracting enhanced prompts from LLM/VLM outputs that may have inconsistent formatting or parsing failures. Uses multiple extraction strategies in sequence: (1) structured JSON parsing if LLM outputs valid JSON, (2) regex-based pattern matching for common delimiters (e.g., 'Enhanced Prompt:'), (3) heuristic-based sentence extraction if patterns fail, (4) fallback to original prompt if all extraction attempts fail. Ensures the system always produces usable output even when LLM formatting is unpredictable, critical for production reliability.","intents":["I want robust prompt extraction that doesn't fail on inconsistent LLM output formatting","I need graceful degradation when LLM responses are malformed or unexpected","I want to maximize usable output rate in production deployments with diverse LLM behaviors","I need to handle edge cases where LLM reasoning output doesn't match expected structure"],"best_for":["production deployments requiring high reliability and uptime","systems processing diverse user prompts with unpredictable LLM outputs","teams building fault-tolerant prompt enhancement pipelines"],"limitations":["Fallback strategies may produce lower-quality prompts than ideal LLM output — quality degrades gracefully but noticeably","Heuristic extraction can misinterpret LLM reasoning steps as final output, requiring manual validation in critical applications","No configurable fallback strategy ordering — uses fixed cascade that may not match all use cases","Fallback to original prompt loses all enhancement value — only suitable as last-resort safety mechanism"],"requires":["Python 3.9+","Standard library regex module (built-in)","JSON parsing library (built-in)"],"input_types":["text (raw LLM/VLM output, any format)"],"output_types":["text (extracted enhanced prompt or original prompt as fallback)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_4","uri":"capability://text.generation.language.customizable.system.prompt.injection.for.prompt.enhancement.behavior","name":"customizable system prompt injection for prompt enhancement behavior","description":"Allows users to inject custom system prompts that control how the LLM/VLM approaches prompt enhancement, enabling fine-grained control over enhancement style, detail level, and semantic focus. System prompts can specify enhancement priorities (e.g., 'prioritize visual style over composition'), constraint rules (e.g., 'keep enhanced prompt under 100 tokens'), or domain-specific guidance (e.g., 'optimize for photorealistic rendering'). The custom system prompt is prepended to the LLM context before processing, directly influencing the chain-of-thought reasoning and output structure without requiring model retraining.","intents":["I want to customize prompt enhancement behavior for specific image generation models or styles","I need to enforce constraints like maximum prompt length or specific terminology","I want to prioritize certain aspects (composition, style, lighting) in enhancement","I need domain-specific enhancement (photorealism, illustration, 3D rendering, etc.)"],"best_for":["teams building specialized image generation pipelines with domain-specific requirements","developers optimizing for specific downstream image models (Stable Diffusion, DALL-E, Midjourney)","platforms offering white-label prompt enhancement with customizable behavior"],"limitations":["System prompt quality directly impacts enhancement quality — poorly written prompts degrade output","No validation or testing framework for custom system prompts — requires manual iteration","System prompt changes require redeployment or runtime configuration — not dynamically updatable in all deployment scenarios","Conflicting system prompt instructions can confuse the LLM, producing inconsistent or lower-quality enhancements"],"requires":["Python 3.9+","Access to HunyuanPromptEnhancer or PromptEnhancerImg2Img class initialization","Understanding of LLM prompt engineering best practices"],"input_types":["text (custom system prompt, typically 50-500 tokens)"],"output_types":["behavior modification (affects all subsequent prompt enhancement outputs)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_5","uri":"capability://automation.workflow.batch.processing.with.production.deployment.optimization","name":"batch processing with production deployment optimization","description":"Provides infrastructure for processing multiple prompts or image+instruction pairs in batches with optimizations for production deployments: (1) batch inference to amortize model loading overhead, (2) configurable batch sizes to balance memory usage and throughput, (3) optional GPU memory management (gradient checkpointing, mixed precision) to fit larger batches on constrained hardware, (4) progress tracking and error logging for monitoring batch jobs. Enables efficient processing of hundreds or thousands of prompts without reloading the model between each inference.","intents":["I want to process large volumes of prompts efficiently without reloading the model each time","I need to optimize GPU memory usage when processing batches on constrained hardware","I want to monitor and log batch processing jobs for production reliability","I need to balance throughput and latency for batch prompt enhancement pipelines"],"best_for":["teams processing large prompt datasets (1000+ prompts) for image generation","production systems requiring high throughput and efficient resource utilization","batch processing pipelines (e.g., nightly enhancement of user-submitted prompts)"],"limitations":["Batch processing introduces latency variance — individual prompts may wait for batch completion","Memory overhead scales with batch size — requires careful tuning for specific hardware","No distributed batch processing across multiple GPUs or machines — single-machine only","Error handling is batch-level, not per-prompt — one failure can affect entire batch depending on configuration"],"requires":["Python 3.9+","PyTorch with CUDA support","Sufficient VRAM to hold model + batch data (varies by batch size and model)"],"input_types":["list of text prompts or list of (image, instruction) tuples"],"output_types":["list of enhanced prompts or list of refined instructions"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_6","uri":"capability://automation.workflow.hardware.aware.model.selection.and.deployment.scaling","name":"hardware-aware model selection and deployment scaling","description":"Provides guidance and automated selection of appropriate model variants (7B vs 32B full-precision, GGUF quantized, VLM) based on available hardware (VRAM, CPU cores, GPU type) and performance requirements (latency, throughput, quality). Includes documentation of hardware requirements for each variant and scaling recommendations for production deployments. Enables users to make informed decisions about model selection without trial-and-error, and provides pathways for scaling from development to production.","intents":["I want to select the right model variant for my available hardware","I need to understand VRAM and compute requirements before deployment","I want to scale from development (consumer GPU) to production (enterprise hardware)","I need guidance on hardware-to-model-variant mapping for cost optimization"],"best_for":["developers evaluating PromptEnhancer for their hardware setup","teams planning production deployments and infrastructure requirements","organizations optimizing cost-to-performance tradeoffs"],"limitations":["Hardware requirements are documented but not automatically detected — requires manual configuration","Scaling recommendations are general guidance, not automatically optimized for specific use cases","No cost calculator or ROI analysis provided — users must estimate infrastructure costs independently","Hardware compatibility varies by CUDA version and GPU architecture — not all combinations are tested"],"requires":["Understanding of hardware specifications (VRAM, GPU type, CPU cores)","Access to documentation or DeepWiki for model selection guidance"],"input_types":["hardware specifications (VRAM, GPU type, CPU cores, inference latency/throughput requirements)"],"output_types":["model variant recommendation (7B, 32B, GGUF, VLM) with configuration parameters"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_7","uri":"capability://text.generation.language.intent.preserving.semantic.decomposition.and.restructuring","name":"intent-preserving semantic decomposition and restructuring","description":"Implements semantic analysis and restructuring logic that decomposes user prompts into constituent semantic elements (subject, action, style, composition, attributes, lighting, etc.), analyzes each element for clarity and completeness, then restructures them into a more explicit and detailed prompt that preserves the original intent while improving clarity. Uses LLM chain-of-thought reasoning to make decomposition and restructuring steps explicit and interpretable. The restructured prompt maintains semantic equivalence to the original while being more suitable for image generation models.","intents":["I want to ensure prompt enhancement preserves the user's original intent and creative vision","I need to decompose complex prompts into explicit semantic elements for clarity","I want to expand ambiguous descriptions while maintaining semantic fidelity","I need to validate that enhanced prompts don't introduce unintended semantic changes"],"best_for":["creative platforms where preserving user intent is critical","systems requiring semantic validation of prompt transformations","teams building interpretable prompt enhancement with explainable reasoning"],"limitations":["Intent preservation is heuristic-based — no formal semantic equivalence guarantee","Decomposition may fail on highly abstract or poetic prompts that don't fit standard semantic categories","Over-expansion of ambiguous elements can introduce unintended semantic drift","No built-in mechanism to validate intent preservation — requires manual review or downstream feedback"],"requires":["Python 3.9+","LLM with sufficient reasoning capability (7B+ parameters recommended)"],"input_types":["text (user prompt with any level of detail or ambiguity)"],"output_types":["text (semantically restructured prompt with preserved intent)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hunyuan-promptenhancer--promptenhancer__cap_8","uri":"capability://tool.use.integration.multi.model.variant.support.with.unified.api","name":"multi-model variant support with unified api","description":"Provides a unified Python API that abstracts over four distinct model variants (HunyuanPromptEnhancer for full-precision T2I, PromptEnhancerGGUF for quantized T2I, PromptEnhancerImg2Img for vision-language I2I, PromptEnhancerV2 for alternative VLM), allowing users to switch between variants without changing application code. Each variant implements the same core interface (initialization, prediction) but with different backend implementations and performance characteristics. Enables flexible deployment where the same application code can run on different hardware or use different models.","intents":["I want to switch between model variants without rewriting application code","I need to support multiple deployment scenarios (consumer GPU, edge device, cloud) with the same codebase","I want to experiment with different model variants to find the best quality-performance tradeoff","I need to migrate from one model variant to another as hardware or requirements change"],"best_for":["teams building flexible prompt enhancement systems that support multiple deployment scenarios","developers experimenting with different model variants","platforms offering multiple enhancement quality tiers"],"limitations":["API abstraction is not perfect — some variant-specific parameters may not be available through unified interface","Quality and performance vary significantly between variants — unified API doesn't guarantee consistent output","Each variant requires separate model weights — no automatic variant selection or fallback","Documentation for variant-specific behavior is scattered across multiple API references"],"requires":["Python 3.9+","Appropriate model weights for selected variant","Variant-specific dependencies (Transformers for full-precision, llama-cpp-python for GGUF, etc.)"],"input_types":["text (prompt) or (image, instruction) tuple depending on variant"],"output_types":["text (enhanced prompt or refined instruction)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch with CUDA support (for GPU inference)","Transformers library 4.30+","16GB+ VRAM for 7B model or 40GB+ for 32B model","HuggingFace model weights (7B or 32B Hunyuan LLM)","llama-cpp-python library (0.2.0+)","8-16GB RAM minimum (4GB for 4-bit quantized models)","GGUF quantized model weights (pre-converted or manually quantized)","Optional: CUDA-capable GPU for acceleration","Vision-language model weights (Hunyuan V2 or compatible VLM)"],"failure_modes":["Requires 16GB+ VRAM for 7B model, 40GB+ for 32B model in full precision — no GPU acceleration fallback documented","Inference latency ~2-5 seconds per prompt on consumer hardware due to full model loading","Intent preservation is heuristic-based — may over-expand or misinterpret highly specialized domain prompts","No built-in support for multi-language prompts; primarily optimized for English","Quantization introduces ~10-15% quality degradation in prompt expansion detail and semantic precision","GGUF models require manual conversion from HuggingFace format — no automated pipeline provided","CPU inference is significantly slower than GPU even with optimization (5-15 seconds per prompt on CPU)","Limited to specific quantization levels (4-bit, 8-bit) — no fine-grained control over precision tradeoffs","Requires vision-language model weights (typically 7B-13B parameters) — adds 20-30GB VRAM overhead vs text-only variant","Inference latency ~3-8 seconds per image due to visual encoding and multi-modal reasoning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.29817307425372214,"quality":0.43,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2026-01-26T06:47:25Z"},"community":{"stars":3672,"forks":320,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hunyuan-promptenhancer--promptenhancer","compare_url":"https://unfragile.ai/compare?artifact=hunyuan-promptenhancer--promptenhancer"}},"signature":"Icx7SiNI2Z6pYrS6EVpuVk564mPlGtswqeV5qf1qwOHffBkFPTgXNhXdGHCwnBe3WaGOMEyU3/jIoreBydmaDQ==","signedAt":"2026-06-21T07:26:42.257Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hunyuan-promptenhancer--promptenhancer","artifact":"https://unfragile.ai/hunyuan-promptenhancer--promptenhancer","verify":"https://unfragile.ai/api/v1/verify?slug=hunyuan-promptenhancer--promptenhancer","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"}}