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Found 13 Skills
AI coding agent skill for AlayaRenderer — a generative world rendering framework with inverse rendering (RGB→G-buffers) and game editing (G-buffers+text→stylized video) using fine-tuned video diffusion models.
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
PyTorch deep learning development with transformers, diffusion models, and GPU optimization.
Apply the Bass Diffusion Model (1969) to forecast innovation adoption using innovation and imitation coefficients. Use this skill when the user needs to forecast new product adoption curves, estimate market penetration timing, calibrate launch strategy based on diffusion dynamics, or when they ask 'how fast will this spread', 'when does adoption take off', or 'what is the expected S-curve'.
Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
Return public original model architecture diagrams for user-specified LLM, VLM, MoE, diffusion, OCR, and SGLang/sgl-cookbook model families. Use when the user asks for a model structure chart, architecture diagram, or rendered image link for a specific model such as DeepSeek, GLM, Qwen, Kimi, MiniMax, Step, Hunyuan, or Qwen3-VL.
Used for generating synthetic brain MRI volumes with NV-Generate-CTMR rflow-mr-brain. Not for production training data.
Optimize, rewrite, and craft image generation and editing prompts tailored specifically for Alibaba's Qwen-Image-2.1 diffusion model. Use this skill whenever the user wants to generate images with Qwen 2.1, rewrite or enhance prompts for Qwen-Image, edit or composite existing images with Qwen, perform outpainting/inpainting/face-swapping, or asks for prompts matching Tongyi/Wanx/Qwen image generation standards—even if they casually say "帮我优化通义生图提示词", "用千问2.1出图", or "Qwen改图".
Expertise in See-through, a framework for single-image layer decomposition of anime characters into manipulatable 2.5D PSD files using diffusion models.
Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.