RealESRGAN x4plus anime 6B is a six-block ESRGAN model for anime images, with a native 4x scale. Uses the official PyTorch .pth checkpoint through stable-diffusion.cpp, without conversion. Upscale-only: not a text-to-image model.
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Repository: localaiLicense: apache-2.0
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on-demand deployment. #### Key Enhancements: * **Visual Agent**: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks. * **Visual Coding Boost**: Generates Draw.io/HTML/CSS/JS from images/videos. * **Advanced Spatial Perception**: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI. * **Long Context & Video Understanding**: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing. * **Enhanced Multimodal Reasoning**: Excels in STEM/Math—causal analysis and logical, evidence-based answers. * **Upgraded Visual Recognition**: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc. * **Expanded OCR**: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing. * **Text Understanding on par with pure LLMs**: Seamless text–vision fusion for lossless, unified comprehension. #### Model Architecture Updates: 1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning. 2. **DeepStack**: Fuses multi‑level ViT features to capture fine-grained details and sharpen image–text alignment. 3. **Text–Timestamp Alignment:** Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling. This is the weight repository for Qwen3-VL-30B-A3B-Instruct.
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A text-to-image model that uses Stable Diffusion 1.5 to generate images from text prompts. This model is DreamShaper model by Lykon.
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Repository: localaiLicense: other

Qwen-Image 2.1 Anime FUSAL runtime assembly with a Q5_0 denoiser, Heretic Q4_K_M text encoder, BF16 VAE, and separate rank-80 FUSAL adapter. Downloads total about 10.4 GiB; allow at least that much combined RAM/VRAM plus runtime overhead. This assembly has mixed licensing: the Qwen-derived denoiser, VAE, and FUSAL adapter are under the Qwen Research License and are limited to non-commercial research or evaluation unless separately authorized; the Heretic encoder is Apache-2.0. Review the repository notices for NSFW content and third-party-rights responsibilities. Use 30 steps, Euler sampling, and CFG 1.0. The adapter is applied at runtime, so prompts must use `fusal style. [TASK: TEXT_TO_IMAGE] DESCRIPTION`, replacing DESCRIPTION with the image description. This practical encoder omits vision weights, so this entry is text-to-image only and does not support reference-image editing.
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