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View details ↗DISCOVER & COMPARE
Explore AI models 85001
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View details ↗Minimax-h3-Turbo
lightx2v> 🌐 **Create online with LightX2V Studio** — generate videos with audio from text, images, or reference materials. No local installation or GPU setup required. For local inference and reproducibility, see the MiniMax-H3 Turbo GitHub repository, LightX2V MiniMax-H3 examples, and model specifications. - **Text-to-Audio/Video:** Generate video with audio from text prompts. - **Image-to-Audio/Video:** Generate video with audio from an input image. - **Omni-Reference Audio/Video:** Generate video with audio guided by reference materials.
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View details ↗Qwen-Image-2.1-Lightning
lightx2vA **4-step distilled LoRA for Qwen-Image-2.1**, supporting text-to-image generation and image editing. Training was performed entirely on **16 Huawei Ascend 910B NPUs**, using DMD in LightX2V. For inference on heterogeneous hardware platforms, refer to the LightX2V platform scripts. Both examples use the training noise schedule: four steps with dynamic exponential shifting and no terminal stretching. Training and LightX2V inference keep latents in FP32; these BF16 Diffusers examples can produce different numerical results.
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View details ↗Qwen-Image-2.1-PE-I2I-GGUF-Q8_0
lightx2v基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen-Image-2.1-PE-T2I-GGUF-Q8_0
lightx2v基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen-Image-2512-Lightning
lightx2vThis model suite supports two mainstream usage frameworks, with detailed guides provided below: 1. Qwen-Image-Lightning Framework For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository 2. LightX2V Framework The models are fully compatible with the LightX2V lightweight video/image generation inference framework. For step-by-step usage examples, configuration templates, and performance optimization tips, see: LightX2V Qwen Image Documentation
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View details ↗Qwen-Image-Edit-2511-Lightning
lightx2vQwen-Image-Edit-2511-Lightning is a collection of optimized models tailored for image editing tasks, leveraging step distillation and quantization techniques to deliver high-efficiency inference performance. This repository hosts three core model files with distinct characteristics: This model suite supports two mainstream usage frameworks, with detailed guides provided below: For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository
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View details ↗Qwen-Image-Edit-Causal
lightx2v基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen-Image-Lightning
lightx2vQwen-Image-Lightning provides distilled LoRA weights for Qwen-Image, enabling image generation with fewer inference steps. **Create Online — LightX2V Studio** · **GitHub — Qwen-Image-Lightning** · **GitHub — LightX2V Framework** Studio supports **text-to-image** and **image-to-image** creation directly in your browser, without local installation or GPU setup.
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View details ↗Wan-NVFP4
lightx2v基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wan2.1-Distill-Loras
lightx2v基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wan2.1-Distill-Models
lightx2v*Distillation-accelerated versions of Wan2.1 - Dramatically faster while maintaining exceptional quality* - **4-step inference** (vs traditional 50+ steps) - Up to **2x faster** than ComfyUI - Real-time video generation capability - Multiple resolutions (480P/720P) - Various precision formats (BF16/FP8/INT8) - I2V and T2V support
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View details ↗Wan2.2-Distill-Loras
lightx2v*LoRA weights extracted from Wan2.2 distilled models - Flexible deployment with excellent generation quality* - **Base Model + LoRA**: Can be combined with base models - **Offline Merging**: Pre-merge LoRA into models - **Online Loading**: Dynamically load LoRA during inference - **Multiple Frameworks**: Supports LightX2V and ComfyUI - **High Noise**: More creative, diverse outputs - **Low Noise**: More faithful to input, stable outputs - Rank 64 LoRA, compact size
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View details ↗Wan2.2-Distill-Models
lightx2v*Distillation-accelerated version of Wan2.2 - Dramatically faster speed with excellent quality* > 🌐 **Create online with LightX2V Studio** — > an AI avatar and multimodal content production platform built on LightX2V. > Use Wan2.2 video generation in your browser, with no local setup required. > See the online usage guide below. --- - 2026.04.12: We are excited to release the Wan2.2-I2V-A14B-4step-720p-high and Wan2.2-I2V-A14B-4step-720p-low models. Compared to previous iterations, this version was trained on a high-quality 720p dataset and features an optimized low-noise training algorithm. These enhancements significantly boost the model's performance in fine-grained detail rendering and visual texture.
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View details ↗Z-Image-Turbo-Quantized
lightx2v基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗depth-anything-base-hf
LiheYoungDepth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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View details ↗depth-anything-large-hf
LiheYoungDepth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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View details ↗depth-anything-small-hf
LiheYoungDepth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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View details ↗depth_anything_vitb14
LiheYoung基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗depth_anything_vitl14
LiheYoung基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗depth_anything_vits14
LiheYoung基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗a-fixed-output-model
lijie1228基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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