Turns natural-language directions into end-to-end video editing workflows: LLM-powered planning, media search/organization, ASR rough-cut, and reusable Style Skills for consistent storytelling. Integrates agent Skills (OpenClaw/Claude Code) and optional AIGC transitions.
Generates production-ready App Store and Google Play screenshots from app metadata and style preferences using AI. Scaffolds a Next.js project, composes ad-style slides with localized/RTL support, and exports PNGs at all required Apple and Google resolutions.
Transforms articulated 3D asset creation into a programmatic, LLM-driven code-generation workflow that produces objects with semantic parts, robust geometry, and physical joints. Includes CLI generation, a local viewer, and pipelines for large-scale dataset contribution.
An open text-to-image generation model built on an 8B Diffusion Transformer that focuses on layout-sensitive, text-heavy, and instruction-following image synthesis. Notable for accurate text rendering, structured/compositional generation (posters, comics), and ability to run on consumer 24GB GPUs when paired with prompt enhancement.
A 1.4M image–text style dataset for text-to-image generation and style transfer, produced by mapping 170K curated style prompts to 400K content prompts via Qwen-Image to yield strong intra-style consistency. Designed for training and evaluating style-aware generative models; license: other.
Generates expressive, scene-aware speech from XML-style prompts and supports zero-shot voice cloning from 10–20s references. Produces emotional acting, ambient SFX, multilingual output, and continuous long-form narration; requires large model weights and gated Gemma text-encoder access.
Contains ~1,973 distilled roleplay conversations with character-perspective chain-of-thought traces (<think> blocks) for fine-tuning persona-focused chat models. Includes teacher provenance, safety/review flags, and filters for NSFW/borderline samples — suited for SFT and character retention tests.
Image-to-video (I2V) diffusion model merge tuned for prompt-conditioned motion and evolution. Uses layer-scaled weight merges (not straight averaging) with BF16 and FP8 checkpoint options; prompt engineering is required for predictable motion and audio. Avoid large distilled Loras for best results.
Transforms pretrained latent-diffusion priors into pixel-space diffusion models by removing the VAE and training shallow pixel layers on LDM-generated synthetic images — enabling fast convergence, native 4K output, and low-data training on 8 GPUs.
Collection of 1,000 AI-generated dreamcore aesthetic images (2K JPEGs, numbered 001–1000) intended for creative prototyping and visual research. Images were produced with GPT Image 2 and released under an MIT license.
Generates music, sound effects, and general audio from text prompts using a medium-size Stable Audio 3 diffusion model — a balance of generation quality and inference cost suitable for prototyping, demo assets, and creative sound design workflows.
Dataset of 5,000 reconstructed chain-of-thought samples produced by trace‑inversion from Claude‑opus‑4.7 summaries — packaged for SFT/DPO fine‑tuning. Key features: reconstructed CoT traces, multilingual prompts, gzip .jsonl format. Best used for reasoning distillation and model-level supervision; synthetic traces may need extra verification.