{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-jamesliu1217--easycontrol_ghibli","slug":"jamesliu1217--easycontrol_ghibli","name":"EasyControl_Ghibli","type":"webapp","url":"https://huggingface.co/spaces/jamesliu1217/EasyControl_Ghibli","page_url":"https://unfragile.ai/jamesliu1217--easycontrol_ghibli","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-jamesliu1217--easycontrol_ghibli__cap_0","uri":"capability://image.visual.style.transfer.based.image.generation.with.ghibli.aesthetic","name":"style-transfer-based image generation with ghibli aesthetic","description":"Generates images in Studio Ghibli visual style by applying neural style transfer techniques to user-provided text prompts or reference images. The system likely uses a fine-tuned diffusion model or ControlNet variant trained on Ghibli film frames to enforce consistent aesthetic properties (color palette, line work, character proportions) across generated outputs. Processing occurs server-side on HuggingFace Spaces infrastructure with GPU acceleration.","intents":["I want to generate artwork that looks like it came from a Studio Ghibli film based on my text description","I need to convert my sketch or reference image into Ghibli-style artwork automatically","I want to explore how my creative ideas would look in anime/Ghibli visual language without manual art skills"],"best_for":["artists and designers exploring style transfer for concept art","indie game developers needing Ghibli-inspired visual assets","content creators prototyping animated storyboards"],"limitations":["Output quality depends on input prompt clarity — vague descriptions produce inconsistent results","Processing latency is 15-60 seconds per image due to HuggingFace Spaces CPU/GPU constraints","No fine-grained control over specific Ghibli film aesthetics (Spirited Away vs Howl's Moving Castle styles are not separately selectable)","Generated images are 512x512 or 768x768 resolution maximum, insufficient for print or high-res asset production"],"requires":["Web browser with modern JavaScript support","Internet connection with stable bandwidth for image upload/download","HuggingFace Spaces account (optional, for usage tracking)"],"input_types":["text prompt (natural language description)","image file (PNG, JPG, WebP for style reference)"],"output_types":["image (PNG or JPG, 512x512 to 768x768 pixels)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jamesliu1217--easycontrol_ghibli__cap_1","uri":"capability://tool.use.integration.interactive.web.based.image.generation.interface.with.gradio","name":"interactive web-based image generation interface with gradio","description":"Provides a Gradio-based web UI deployed on HuggingFace Spaces that abstracts the underlying model inference pipeline into simple input/output components. Users interact through text fields, image upload widgets, and parameter sliders without writing code. Gradio handles HTTP request routing, session management, and GPU queue orchestration automatically, allowing multiple concurrent users to queue generation requests.","intents":["I want to generate Ghibli-style images without installing software or writing code","I need a shareable link to let non-technical collaborators test the image generation","I want to iterate quickly on prompts and see results in real-time without API integration overhead"],"best_for":["non-technical users and stakeholders testing AI image generation","rapid prototyping and demo scenarios requiring zero setup","teams collaborating on creative assets without shared infrastructure"],"limitations":["Gradio UI is not customizable without forking the source code — limited branding or UX differentiation","Queue-based processing means users may wait 5-15 minutes during peak usage on free HuggingFace tier","No persistent session state — users cannot save generation history or manage multiple projects within the interface","Gradio's mobile responsiveness is basic, making the interface awkward on phones/tablets"],"requires":["Web browser (Chrome, Firefox, Safari, Edge)","No API key or authentication required for public HuggingFace Space"],"input_types":["text (prompt field)","image (drag-and-drop or file picker)","numeric sliders (guidance scale, steps, seed)"],"output_types":["image (displayed in browser)","downloadable PNG/JPG file"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jamesliu1217--easycontrol_ghibli__cap_2","uri":"capability://automation.workflow.gpu.accelerated.batch.image.inference.with.queue.management","name":"gpu-accelerated batch image inference with queue management","description":"Executes image generation requests on HuggingFace Spaces' shared GPU infrastructure using a queue-based scheduling system. Multiple user requests are batched and processed sequentially or in parallel depending on available VRAM. The system manages GPU memory allocation, model loading, and inference execution transparently, abstracting away CUDA/PyTorch complexity from end users.","intents":["I want to generate multiple Ghibli-style images without managing my own GPU hardware","I need reliable inference that doesn't crash due to out-of-memory errors","I want to scale from 1 user to 100 concurrent users without rewriting the backend"],"best_for":["solo developers and small teams without dedicated GPU infrastructure","projects with variable/unpredictable traffic that don't justify fixed GPU costs","rapid prototyping where infrastructure setup is a blocker"],"limitations":["Free HuggingFace Spaces tier has limited GPU hours (typically 16 hours/week) — production use requires paid tier","Queue wait times scale linearly with concurrent users — no SLA guarantees on latency","Model is loaded into GPU memory for entire session, wasting resources during idle periods","No multi-GPU or distributed inference support — single GPU bottleneck limits throughput to ~1-3 images/minute"],"requires":["HuggingFace Spaces account (free or paid)","GPU-compatible PyTorch/CUDA environment (handled by Spaces)","Model weights downloaded and cached on Spaces filesystem (automatic on first run)"],"input_types":["image tensor (PIL Image or NumPy array)","text embedding (tokenized prompt)"],"output_types":["image tensor (PIL Image or NumPy array)","inference metadata (generation time, seed, guidance scale used)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jamesliu1217--easycontrol_ghibli__cap_3","uri":"capability://image.visual.prompt.to.image.generation.with.diffusion.model.inference","name":"prompt-to-image generation with diffusion model inference","description":"Converts natural language text prompts into images by tokenizing the prompt, encoding it into a latent embedding space, and iteratively denoising a random noise tensor through a pre-trained diffusion model conditioned on the prompt embedding. The model likely uses a UNet-based architecture with cross-attention layers to inject prompt semantics. Inference runs for 20-50 denoising steps, each step reducing noise while reinforcing Ghibli aesthetic features learned during fine-tuning.","intents":["I want to describe a scene in words and have it rendered as Ghibli-style artwork","I need to generate multiple variations of the same concept by adjusting the prompt","I want to control generation randomness using a seed parameter for reproducibility"],"best_for":["concept artists and storyboard creators working in iterative design loops","game developers generating environment and character concept art","content creators producing social media visuals or promotional artwork"],"limitations":["Prompt engineering is required — vague or contradictory prompts produce low-quality outputs; users need to learn effective prompt syntax","Inference is non-deterministic even with fixed seed due to floating-point precision variations across hardware","Model struggles with text rendering, precise object counts, and complex spatial relationships (e.g., 'three characters standing in a line')","Fine-tuning on Ghibli data may bias outputs toward specific character archetypes or environments, limiting diversity"],"requires":["Text tokenizer compatible with model (typically CLIP or similar)","Diffusion model weights (likely Stable Diffusion or custom variant)","GPU with minimum 6GB VRAM for inference"],"input_types":["text prompt (natural language, 1-500 characters)","numeric parameters: guidance scale (1-20), inference steps (20-100), seed (0-2^32)"],"output_types":["image (512x512 or 768x768 pixels, PNG/JPG format)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jamesliu1217--easycontrol_ghibli__cap_4","uri":"capability://image.visual.image.to.image.style.transfer.with.reference.conditioning","name":"image-to-image style transfer with reference conditioning","description":"Accepts a user-provided reference image and applies Ghibli aesthetic transformation by encoding the reference image into latent space, then running diffusion denoising conditioned on both the image embedding and an optional text prompt. The process preserves structural and compositional elements from the reference while replacing textures, colors, and stylistic details with Ghibli-characteristic features. Uses ControlNet or similar conditioning mechanism to anchor the generation to the reference image structure.","intents":["I have a photograph or sketch and want to convert it to Ghibli-style artwork while preserving composition","I want to reimagine my original artwork in Ghibli's visual language","I need to batch-convert multiple reference images to a consistent style"],"best_for":["artists and illustrators wanting to explore style variations of existing work","photographers creating stylized versions of photos for social media","game developers converting concept art into in-game asset style"],"limitations":["Output quality heavily depends on reference image clarity and composition — low-resolution or cluttered inputs produce poor results","Structural fidelity is not guaranteed; the model may reinterpret composition if Ghibli aesthetic conflicts with input structure","Processing time is 30-90 seconds due to dual encoding (image + optional text) and iterative denoising","No control over which structural elements are preserved vs. reinterpreted — users cannot specify 'keep character poses but change background'"],"requires":["Reference image file (PNG, JPG, WebP, 512x512 to 1024x1024 pixels)","Optional text prompt to guide style transfer direction","GPU with 8GB+ VRAM for dual-stream inference"],"input_types":["image (reference photo or sketch)","text prompt (optional, to guide style direction)"],"output_types":["image (same resolution as input, PNG/JPG format)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Internet connection with stable bandwidth for image upload/download","HuggingFace Spaces account (optional, for usage tracking)","Web browser (Chrome, Firefox, Safari, Edge)","No API key or authentication required for public HuggingFace Space","HuggingFace Spaces account (free or paid)","GPU-compatible PyTorch/CUDA environment (handled by Spaces)","Model weights downloaded and cached on Spaces filesystem (automatic on first run)","Text tokenizer compatible with model (typically CLIP or similar)","Diffusion model weights (likely Stable Diffusion or custom variant)"],"failure_modes":["Output quality depends on input prompt clarity — vague descriptions produce inconsistent results","Processing latency is 15-60 seconds per image due to HuggingFace Spaces CPU/GPU constraints","No fine-grained control over specific Ghibli film aesthetics (Spirited Away vs Howl's Moving Castle styles are not separately selectable)","Generated images are 512x512 or 768x768 resolution maximum, insufficient for print or high-res asset production","Gradio UI is not customizable without forking the source code — limited branding or UX differentiation","Queue-based processing means users may wait 5-15 minutes during peak usage on free HuggingFace tier","No persistent session state — users cannot save generation history or manage multiple projects within the interface","Gradio's mobile responsiveness is basic, making the interface awkward on phones/tablets","Free HuggingFace Spaces tier has limited GPU hours (typically 16 hours/week) — production use requires paid tier","Queue wait times scale linearly with concurrent users — no SLA guarantees on latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.36,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:48.012Z","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=jamesliu1217--easycontrol_ghibli","compare_url":"https://unfragile.ai/compare?artifact=jamesliu1217--easycontrol_ghibli"}},"signature":"JrGDTBrS991WpRjB4Bj4k8t+kmKaHW+yTs/+Ys4XrFaDXCl20OIRsRvRGHMoYmFeIXSgWFzBv9xpG2gN0pusBQ==","signedAt":"2026-06-22T07:16:25.589Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jamesliu1217--easycontrol_ghibli","artifact":"https://unfragile.ai/jamesliu1217--easycontrol_ghibli","verify":"https://unfragile.ai/api/v1/verify?slug=jamesliu1217--easycontrol_ghibli","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"}}