huanngzh/MV-Adapter

Health v2 · maintenance only

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1285 stars · 95 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MV-Adapter is a plug-and-play adapter that turns pre-trained text-to-image diffusion models (e.g., SDXL, SD2.1) into multi-view consistent image generators, supporting text-, image-, and geometry-conditioned generation as well as 3D texture generation. It is the official PyTorch implementation of the ICCV 2025 paper, with model weights, Gradio demos, training code, and a ComfyUI integration.

Use cases

• generate multi-view consistent images from a single image
• generate multi-view images from text prompts
• generate textures for 3D models from text or images
• adapt personalized or distilled T2I models like DreamShaper or LCM to multi-view generation
• use ControlNet with multi-view generation
• train a custom multi-view generation adapter on my own dataset
• generate multi-view images for 3D scene generation pipelines

When to choose

• you need multi-view consistent image generation built on existing Stable Diffusion models
• you want to preserve the quality and priors of a base T2I model instead of full fine-tuning
• you need geometry-guided texture generation for 3D assets
• you want a ComfyUI or Gradio workflow for multi-view generation

When to avoid

• you need a standalone text-to-image model rather than a multi-view adapter
• you lack a GPU or need low-VRAM inference for the SDXL-based pipelines
• you need production-grade 3D reconstruction rather than multi-view images
• you need a non-Python or non-diffusers-based integration

Facets

library · maturity active

image-processing machine-learning deep-learning llm-inference artificial-intelligence computer-vision image-processing graphics python cross-platform diffusion-models stable-diffusion multi-view-generation texture-generation 3d sdxl comfyui iccv-2025 research 3d-generation gpu

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