{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-yangling0818--rpg-diffusionmaster","slug":"yangling0818--rpg-diffusionmaster","name":"RPG-DiffusionMaster","type":"repo","url":"https://proceedings.mlr.press/v235/yang24ai.html","page_url":"https://unfragile.ai/yangling0818--rpg-diffusionmaster","categories":["image-generation"],"tags":["image-editting","large-language-models","multimodal-large-language-models","text-to-image"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-yangling0818--rpg-diffusionmaster__cap_0","uri":"capability://text.generation.language.mllm.guided.prompt.recaptioning.and.enhancement","name":"mllm-guided prompt recaptioning and enhancement","description":"Leverages multimodal large language models (GPT-4 or local models via mllm.py) to analyze and refine user-provided text prompts, enriching them with additional detail, clarity, and structural information before passing to the diffusion pipeline. The system uses templated prompt engineering to guide MLLMs toward consistent, parseable outputs that enhance semantic richness while maintaining user intent.","intents":["I want to automatically enhance my vague text prompts with more descriptive details before image generation","I need to clarify ambiguous prompt descriptions to improve image quality and consistency","I want to leverage GPT-4's understanding to add visual context that diffusion models respond better to"],"best_for":["developers building text-to-image systems who want better prompt quality without manual refinement","teams creating image generation APIs that need automatic prompt enhancement","researchers exploring MLLM-diffusion integration patterns"],"limitations":["Cloud-based MLLM calls (GPT-4) add latency and incur API costs per generation","Local MLLM option requires significant VRAM and model download overhead","Prompt template brittleness — changes to MLLM behavior or output format may break parsing","No guarantee that recaptioning improves all prompt types equally; some simple prompts may be over-elaborated"],"requires":["OpenAI API key for GPT-4 option, or local model weights (e.g., LLaVA) for offline operation","Python 3.8+","transformers library for local MLLM inference"],"input_types":["text (user prompt string)"],"output_types":["text (enhanced prompt string)","structured data (parameter dictionary with split ratios and regional prompts)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_1","uri":"capability://planning.reasoning.spatial.region.planning.via.mllm.generated.layout.decomposition","name":"spatial region planning via mllm-generated layout decomposition","description":"Decomposes image generation into spatially-aware regions by using MLLMs to analyze the recaptioned prompt and generate region-specific sub-prompts along with split ratios that define how the image canvas should be divided. The planning phase (via mllm.py's get_params_dict()) parses MLLM output into structured region definitions, enabling precise control over object placement and attribute binding across different image areas without retraining the diffusion model.","intents":["I need to generate images with multiple distinct objects in specific spatial locations (e.g., 'cat on left, dog on right')","I want to ensure different attributes apply to different regions without the model conflating them","I need to control image composition programmatically without manual region mask creation"],"best_for":["developers building multi-entity image generation systems","teams creating layout-aware text-to-image APIs","researchers exploring spatial reasoning in diffusion models"],"limitations":["MLLM spatial reasoning is heuristic-based and may fail on complex multi-entity scenes with overlapping or ambiguous spatial relationships","Split ratio generation is deterministic per MLLM but not guaranteed to match user intent for unusual layouts","No explicit validation that generated regions align with prompt semantics — relies on MLLM quality","Rectangular region decomposition limits expressiveness for non-rectangular object shapes or complex compositions"],"requires":["Recaptioned prompt from prior phase","MLLM backend (GPT-4 API key or local model weights)","Python 3.8+"],"input_types":["text (recaptioned prompt)"],"output_types":["structured data (dictionary with split_ratio array and regional prompt strings)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_10","uri":"capability://image.visual.batch.image.generation.with.consistent.regional.decomposition.across.multiple.prompts","name":"batch image generation with consistent regional decomposition across multiple prompts","description":"Supports generating multiple images from different prompts while maintaining consistent regional decomposition strategies (e.g., same split ratios, same region count) across the batch. The MLLM planning phase can be run once and reused, or run per-prompt with constraints to maintain consistency, enabling efficient batch processing without per-image planning overhead.","intents":["I want to generate multiple images with the same spatial layout but different content per region","I need to create image batches efficiently without replanning regions for each prompt","I want to maintain visual consistency across a batch of generated images"],"best_for":["developers building batch image generation services","teams creating product catalogs with consistent layouts","researchers exploring consistency in multi-image generation"],"limitations":["Batch processing requires careful memory management; VRAM usage scales with batch size","Consistent regional decomposition may be suboptimal for diverse prompts; one-size-fits-all layouts may not suit all content","No built-in batching optimization in diffusers; users must implement batching logic externally","Batch generation latency is not perfectly linear due to GPU memory overhead and scheduling"],"requires":["Multiple prompts or prompt variants","Sufficient VRAM for batch inference (typically 2-4x single-image VRAM)","Python 3.8+","PyTorch with batch processing support"],"input_types":["list of text (multiple prompts)","optional: shared regional decomposition parameters"],"output_types":["list of images (PIL Images)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_2","uri":"capability://image.visual.regional.diffusion.pipeline.with.per.region.prompt.injection","name":"regional diffusion pipeline with per-region prompt injection","description":"Implements two specialized diffusion pipeline classes (RegionalDiffusionPipeline for SD v1.4/1.5/2.0/2.1 and RegionalDiffusionXLPipeline for SDXL) that extend the standard diffusers library pipelines to support region-specific prompt conditioning. During the diffusion sampling loop, different prompts are applied to different spatial regions of the latent representation, enabling fine-grained control over content generation in each region while maintaining global coherence through a base prompt and cross-region attention mechanisms.","intents":["I want to generate images where different regions respond to different text prompts without model retraining","I need to apply region-specific guidance scales or sampling parameters to control generation intensity per area","I want to use regional diffusion with both SD and SDXL models without reimplementing the pipeline logic"],"best_for":["developers building production text-to-image systems requiring spatial control","teams migrating from single-prompt to multi-region diffusion workflows","researchers exploring region-aware conditioning in diffusion models"],"limitations":["Regional masking adds ~15-30% computational overhead per sampling step due to region-specific attention computation","Requires explicit region split ratio definition — no automatic region detection from image content","Cross-region bleeding may occur at boundaries if guidance scales differ significantly between adjacent regions","Only supports rectangular region decomposition; complex non-rectangular shapes require post-processing or mask-based workarounds","Latent-space region application may not perfectly align with pixel-space semantics due to VAE decoder artifacts"],"requires":["Stable Diffusion model weights (v1.4/1.5/2.0/2.1 or SDXL)","diffusers library (>=0.21.0)","PyTorch 1.13+","CUDA-capable GPU with >=8GB VRAM for inference","Regional prompts and split ratios from planning phase"],"input_types":["structured data (regional prompts, split ratios, base prompt)","numeric parameters (guidance_scale, num_inference_steps, seed)"],"output_types":["image (PIL Image or tensor, typically 512x512 or 1024x1024)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_3","uri":"capability://tool.use.integration.multi.model.mllm.backend.abstraction.with.unified.interface","name":"multi-model mllm backend abstraction with unified interface","description":"Provides a unified Python interface (mllm.py) that abstracts over multiple MLLM backends — GPT-4 (via OpenAI API) and local models (via transformers/ollama) — allowing users to swap backends without changing downstream code. The abstraction handles API communication, response parsing, and parameter extraction, exposing a single get_params_dict() function that returns consistent structured outputs regardless of backend choice.","intents":["I want to use GPT-4 for high-quality planning but fall back to a local model for cost savings or offline operation","I need to experiment with different MLLM backends without rewriting integration code","I want to deploy RPG with different MLLM options depending on infrastructure constraints"],"best_for":["developers building flexible image generation systems with backend optionality","teams managing costs by switching between cloud and local MLLM inference","researchers comparing MLLM quality impact on diffusion output"],"limitations":["Output format consistency depends on prompt template quality — different MLLMs may produce unparseable outputs if templates don't generalize","Local MLLM option requires significant disk space (4-13GB) and VRAM (8-24GB) depending on model size","API latency for GPT-4 adds 2-10 seconds per generation; local models add 5-30 seconds depending on hardware","No built-in fallback mechanism if MLLM fails to produce valid structured output — requires external error handling","Parameter extraction (get_params_dict) is regex/string-based and brittle to MLLM output format variations"],"requires":["OpenAI API key (for GPT-4 backend) or local model weights + transformers library (for local backend)","Python 3.8+","requests library for API calls"],"input_types":["text (prompt string)","string (backend identifier: 'gpt4' or 'local')","optional: model path (for local backend)"],"output_types":["structured data (parameter dictionary with split_ratio, regional_prompts, etc.)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_4","uri":"capability://planning.reasoning.itercomp.iterative.refinement.with.multi.step.region.optimization","name":"itercomp iterative refinement with multi-step region optimization","description":"Implements an iterative composition refinement loop (IterComp) that generates an initial image, analyzes it with an MLLM to identify composition issues, and regenerates with refined regional prompts and split ratios. Each iteration feeds the previous image back to the MLLM for visual analysis, enabling multi-step optimization of spatial layout, object placement, and attribute binding without manual intervention or retraining.","intents":["I want to automatically improve image composition through multiple generation passes without manual prompt editing","I need to fix spatial issues (e.g., objects in wrong positions) by analyzing generated images and refining regions","I want to achieve better attribute-object binding by iteratively adjusting regional prompts based on visual feedback"],"best_for":["developers building interactive image generation systems with refinement loops","teams creating high-quality image generation pipelines where composition matters","researchers exploring feedback loops between vision and language models"],"limitations":["Each iteration requires a full diffusion pass + MLLM inference, multiplying total latency by iteration count (typically 3-5x slower than single-pass generation)","MLLM visual analysis may not identify all composition issues; convergence is not guaranteed","Iterative refinement can lead to prompt drift if MLLM suggestions accumulate errors across iterations","No explicit stopping criterion — requires manual iteration count or external convergence detection","Computational cost scales linearly with iterations; impractical for real-time or high-throughput scenarios"],"requires":["Initial image from regional diffusion pipeline","MLLM backend with vision capabilities (GPT-4V or local multimodal model)","Python 3.8+","Iteration count parameter (typically 2-5)"],"input_types":["image (PIL Image from prior generation)","text (original user prompt)","structured data (current regional prompts and split ratios)"],"output_types":["image (refined PIL Image)","structured data (updated regional prompts and split ratios)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_5","uri":"capability://image.visual.controlnet.integration.for.structural.guidance.and.edge.aware.generation","name":"controlnet integration for structural guidance and edge-aware generation","description":"Integrates ControlNet models (edge detection, pose, depth, etc.) as optional auxiliary conditioning inputs to the regional diffusion pipeline, allowing users to provide structural constraints (edge maps, pose skeletons, depth maps) that guide generation while regional prompts control semantic content. The integration preserves regional decomposition while adding structural priors, enabling generation that respects both spatial layout and visual structure.","intents":["I want to generate images that follow a specific edge structure or composition while respecting regional prompts","I need to generate images with specific poses or depth layouts without manual annotation","I want to combine semantic control (regional prompts) with structural control (ControlNet) for precise generation"],"best_for":["developers building structured image generation systems (e.g., character pose control)","teams creating design tools that combine semantic and structural constraints","researchers exploring multi-modal conditioning in diffusion models"],"limitations":["ControlNet adds ~20-40% computational overhead per sampling step due to auxiliary UNet inference","Requires pre-computed or user-provided control images (edge maps, pose skeletons, etc.) — no automatic generation","ControlNet conditioning may conflict with regional prompts if structural and semantic constraints are misaligned","Limited to ControlNet architectures compatible with SD/SDXL (not all control types are equally effective)","Control strength tuning is manual and prompt-dependent; no automatic parameter optimization"],"requires":["ControlNet model weights (e.g., canny edge, pose, depth)","diffusers library with ControlNet support (>=0.21.0)","Control image (edge map, pose skeleton, depth map, etc.) matching input resolution","PyTorch 1.13+, CUDA-capable GPU with >=10GB VRAM"],"input_types":["image (control image: edge map, pose skeleton, depth map, etc.)","structured data (regional prompts, split ratios)","numeric parameters (control_guidance_scale, typically 0.5-1.0)"],"output_types":["image (PIL Image respecting both regional prompts and control structure)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_6","uri":"capability://text.generation.language.template.based.prompt.engineering.for.consistent.mllm.output.parsing","name":"template-based prompt engineering for consistent mllm output parsing","description":"Uses hand-crafted prompt templates (embedded in mllm.py and RPG.py) to guide MLLMs toward generating structured, parseable outputs with consistent formatting. Templates specify the desired output format (e.g., 'split_ratio: [0.3, 0.7]', 'region_1_prompt: ...'), enabling reliable extraction of parameters via regex or string parsing without requiring MLLM function calling or JSON schema enforcement.","intents":["I want to reliably extract structured parameters from MLLM outputs without JSON schema validation","I need to guide MLLMs toward consistent output formats that my downstream code can parse","I want to minimize parsing errors and handle MLLM output variability gracefully"],"best_for":["developers integrating MLLMs into pipelines without function calling support","teams building systems that must work with multiple MLLM backends with varying output formats","researchers exploring prompt engineering for structured generation"],"limitations":["Template-based parsing is brittle — minor MLLM output format deviations break parsing logic","No built-in validation that extracted parameters are semantically correct (e.g., split ratios sum to 1.0)","Templates must be manually tuned per MLLM backend; generalization across models is limited","Regex-based extraction is error-prone for complex nested structures; no formal grammar validation","Template changes require code modifications and testing; no dynamic template adaptation"],"requires":["MLLM backend (GPT-4 or local model)","Python 3.8+","re library for regex-based parsing"],"input_types":["text (MLLM response string)"],"output_types":["structured data (extracted parameter dictionary)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_7","uri":"capability://image.visual.multi.entity.image.generation.with.independent.attribute.binding.per.region","name":"multi-entity image generation with independent attribute binding per region","description":"Enables generation of images containing multiple distinct entities (e.g., 'a red cat and a blue dog') by decomposing the scene into per-entity regions with independent prompts that specify entity-specific attributes. Each region's prompt is isolated from others, preventing attribute confusion where properties intended for one entity bleed into another. The regional diffusion pipeline applies region-specific guidance to enforce attribute binding without cross-region interference.","intents":["I want to generate images with multiple objects where each object has distinct, non-conflicting attributes","I need to prevent attribute confusion (e.g., ensuring 'red' applies only to the cat, not the dog)","I want to control which entities appear in which image regions without manual mask creation"],"best_for":["developers building product image generation systems with multiple SKUs per image","teams creating scene composition tools for design or game development","researchers exploring entity-aware text-to-image generation"],"limitations":["Attribute binding quality depends on region isolation — overlapping or adjacent regions may still exhibit attribute bleeding","Requires explicit entity-to-region mapping; no automatic entity detection or assignment","Complex multi-entity scenes (>4 entities) may exceed MLLM planning capacity or require manual region definition","Entity interactions (e.g., 'cat sitting on dog') are difficult to express in isolated regional prompts","Region boundaries may create visible seams if entities span multiple regions or if guidance scales differ significantly"],"requires":["MLLM backend for entity-aware region planning","Regional diffusion pipeline with per-region prompt injection","Python 3.8+","Entity descriptions in input prompt (e.g., 'red cat on left, blue dog on right')"],"input_types":["text (prompt with multiple entity descriptions)"],"output_types":["image (PIL Image with multiple distinct entities)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_8","uri":"capability://image.visual.training.free.diffusion.model.adaptation.without.fine.tuning","name":"training-free diffusion model adaptation without fine-tuning","description":"Achieves spatial control and multi-region generation without modifying or fine-tuning the underlying diffusion model weights. Instead, it adapts pre-trained SD/SDXL models by modifying the inference-time conditioning mechanism (regional prompt injection into the UNet forward pass) and using MLLM-guided planning to structure the generation process. This enables high-quality generation with off-the-shelf models without the computational cost or data requirements of fine-tuning.","intents":["I want to add spatial control to existing Stable Diffusion models without retraining","I need to use multiple SDXL checkpoints with regional generation without fine-tuning each one","I want to leverage pre-trained model quality while adding new capabilities at inference time"],"best_for":["developers deploying existing SD/SDXL models with minimal infrastructure changes","teams with limited compute budgets who can't afford fine-tuning","researchers exploring inference-time adaptation techniques"],"limitations":["Inference-time adaptation adds computational overhead (~15-30% per sampling step) compared to standard diffusion","Regional control quality is limited by the base model's semantic understanding — weak models produce weak regional results","No fine-tuning means the model hasn't learned to optimize for regional generation; results may be suboptimal vs. fine-tuned alternatives","Requires careful prompt engineering to work around base model limitations","Spatial control is approximate and heuristic-based; no guarantee of perfect region isolation"],"requires":["Pre-trained Stable Diffusion model (v1.4/1.5/2.0/2.1 or SDXL)","diffusers library (>=0.21.0)","PyTorch 1.13+","CUDA-capable GPU with >=8GB VRAM"],"input_types":["structured data (regional prompts, split ratios)","numeric parameters (guidance_scale, num_inference_steps)"],"output_types":["image (PIL Image)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yangling0818--rpg-diffusionmaster__cap_9","uri":"capability://image.visual.unified.image.generation.api.supporting.multiple.stable.diffusion.architectures","name":"unified image generation api supporting multiple stable diffusion architectures","description":"Provides a single Python API (RPG.py) that abstracts over multiple Stable Diffusion architectures (v1.4/1.5/2.0/2.1 and SDXL) with different pipeline implementations (RegionalDiffusionPipeline and RegionalDiffusionXLPipeline) but identical user-facing interfaces. Users specify model architecture once and the framework automatically selects the correct pipeline, enabling seamless model switching without code changes.","intents":["I want to experiment with different SD versions without rewriting generation code","I need to deploy with SDXL for quality but fall back to SD v1.5 for speed without code changes","I want to support multiple model architectures in a single application"],"best_for":["developers building flexible image generation systems supporting multiple model versions","teams migrating from SD v1.5 to SDXL without refactoring","researchers comparing output quality across SD architectures"],"limitations":["API abstraction hides architecture-specific parameters (e.g., SDXL uses different latent dimensions); some tuning may be needed per architecture","Pipeline selection is automatic but requires explicit model identifier; no auto-detection of model type","Performance characteristics differ significantly between architectures (SDXL is ~2x slower); users must manage expectations","Not all features are equally supported across architectures (e.g., some ControlNet types may work better with SDXL)"],"requires":["Stable Diffusion model weights (v1.4/1.5/2.0/2.1 or SDXL)","diffusers library (>=0.21.0)","PyTorch 1.13+","CUDA-capable GPU with >=8GB VRAM (SDXL requires >=10GB)"],"input_types":["string (model identifier: 'sd15', 'sd21', 'sdxl')","structured data (regional prompts, split ratios)","numeric parameters (guidance_scale, num_inference_steps)"],"output_types":["image (PIL Image)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key for GPT-4 option, or local model weights (e.g., LLaVA) for offline operation","Python 3.8+","transformers library for local MLLM inference","Recaptioned prompt from prior phase","MLLM backend (GPT-4 API key or local model weights)","Multiple prompts or prompt variants","Sufficient VRAM for batch inference (typically 2-4x single-image VRAM)","PyTorch with batch processing support","Stable Diffusion model weights (v1.4/1.5/2.0/2.1 or SDXL)","diffusers library (>=0.21.0)"],"failure_modes":["Cloud-based MLLM calls (GPT-4) add latency and incur API costs per generation","Local MLLM option requires significant VRAM and model download overhead","Prompt template brittleness — changes to MLLM behavior or output format may break parsing","No guarantee that recaptioning improves all prompt types equally; some simple prompts may be over-elaborated","MLLM spatial reasoning is heuristic-based and may fail on complex multi-entity scenes with overlapping or ambiguous spatial relationships","Split ratio generation is deterministic per MLLM but not guaranteed to match user intent for unusual layouts","No explicit validation that generated regions align with prompt semantics — relies on MLLM quality","Rectangular region decomposition limits expressiveness for non-rectangular object shapes or complex compositions","Batch processing requires careful memory management; VRAM usage scales with batch size","Consistent regional decomposition may be suboptimal for diverse prompts; one-size-fits-all layouts may not suit all content","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4666578234378947,"quality":0.37,"ecosystem":0.52,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"inactive","updated_at":"2026-05-05T11:48:10.236Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2025-02-01T13:28:48Z"},"community":{"stars":1844,"forks":101,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yangling0818--rpg-diffusionmaster","compare_url":"https://unfragile.ai/compare?artifact=yangling0818--rpg-diffusionmaster"}},"signature":"iBVAXUU6sC4VZyPDo318x0JCIXBywpsTLhhWb9udhSWacrA4kLe3m7xKGUPxKuY+McmWGYQuIIGIiuR3itdbDg==","signedAt":"2026-06-15T18:35:28.704Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yangling0818--rpg-diffusionmaster","artifact":"https://unfragile.ai/yangling0818--rpg-diffusionmaster","verify":"https://unfragile.ai/api/v1/verify?slug=yangling0818--rpg-diffusionmaster","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"}}