🏠 Project Page | Paper | Model Weights | Demo | ComfyUI
MV-Adapter is a versatile plug-and-play adapter that adapt T2I models and their derivatives to multi-view generators.
Highlight Features: Generate multi-view images
- with 768 Resolution using SDXL
- using personalized models (e.g. DreamShaper), distilled models (e.g. LCM), or extensions (e.g. ControlNet)
- from text or image condition
- can be guided by geometry for texture generation
- [2025-05-15] Release full pipeline for text-to-texture and image-to-texture generation. [See guidelines]
- [2025-04-23] Release dataset (Objaverse-Ortho10View and Objaverse-Rand6View) and training code. [See guidelines]
- [2025-03-31] Release text/image-conditioned 3D texture generation demos on Text2Texture and Image2Texture. Feel free to try them!
- [2025-03-17] Release model weights for partial-image conditioned geometry-to-multiview generation, which can be used to generate textured 3D scenes combined with MIDI. [See guidelines]
- [2025-03-07] Release model weights for geometry-guided multi-view generation. [See guidelines]
- [2024-12-27] Release model weights, gradio demo, inference scripts and comfyui of text-/image- to multi-view generation models.
- MV-Adapter: Multi-view Consistent Image Generation Made Easy🚀
No need to download manually. Running the scripts will download model weights automatically.
Notes: Running MV-Adapter for SDXL may need higher GPU memory and more time, but produce higher-quality and higher-resolution results. On the other hand, running its SD2.1 variant needs lower computing cost, but shows a bit lower performance.
| Model | Base Model | HF Weights | Demo Link |
|---|---|---|---|
| Text-to-Multiview | SD2.1 | mvadapter_t2mv_sd21.safetensors | |
| Text-to-Multiview | SDXL | mvadapter_t2mv_sdxl.safetensors | General / Anime |
| Image-to-Multiview | SD2.1 | mvadapter_i2mv_sd21.safetensors | |
| Image-to-Multiview | SDXL | mvadapter_i2mv_sdxl.safetensors | Demo |
| Text-Geometry-to-Multiview | SDXL | mvadapter_tg2mv_sdxl.safetensors | Demo |
| Image-Geometry-to-Multiview | SDXL | mvadapter_ig2mv_sdxl.safetensors / mvadapter_ig2mv_partial_sdxl.safetensors | Demo |
| Image-to-Arbitrary-Views | SDXL |
Clone the repo first:
git clone https://github.com/huanngzh/MV-Adapter.git
cd MV-Adapter(Optional) Create a fresh conda env:
conda create -n mvadapter python=3.10
conda activate mvadapterInstall necessary packages (torch > 2):
# pytorch (select correct CUDA version)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
# other dependencies
pip install -r requirements.txtFor texture generation, you need to install CV-CUDA according to CVCUDA/CV-CUDA.
In the model zoo of MV-Adapter, running image-to-multiview generation has the highest system requirements, which requires about 14G GPU memory.
With SDXL:
python -m scripts.gradio_demo_t2mv --base_model "stabilityai/stable-diffusion-xl-base-1.0"Reminder: When switching the demo to another base model, delete the
gradio_cached_examplesdirectory, otherwise it will affect the examples results of the next demo.
With anime-themed Animagine XL 3.1:
python -m scripts.gradio_demo_t2mv --base_model "cagliostrolab/animagine-xl-3.1"With general Dreamshaper:
python -m scripts.gradio_demo_t2mv --base_model "Lykon/dreamshaper-xl-1-0" --scheduler ddpmYou can also specify a new diffusers-format text-to-image diffusion model using --base_model. Note that it should be the model name in huggingface, such as stabilityai/stable-diffusion-xl-base-1.0, or a local path refer to a text-to-image pipeline directory. Note that if you specify latent-consistency/lcm-sdxl to use latent consistency models, please add --scheduler lcm to the command.
With SDXL:
python -m scripts.gradio_demo_i2mvWe recommend that experienced users check the files in the scripts directory to adjust the parameters appropriately to try the best "card drawing" results.
Note that you can specify a diffusers-format text-to-image diffusion model as the base model using --base_model xxx. It should be the model name in huggingface, such as stabilityai/stable-diffusion-xl-base-1.0, or a local path refer to a text-to-image pipeline directory.
With SDXL:
python -m scripts.inference_t2mv_sdxl --text "an astronaut riding a horse" \
--seed 42 \
--output output.pngWith personalized models:
anime-themed Animagine XL 3.1
python -m scripts.inference_t2mv_sdxl --base_model "cagliostrolab/animagine-xl-3.1" \
--text "1girl, izayoi sakuya, touhou, solo, maid headdress, maid, apron, short sleeves, dress, closed mouth, white apron, serious face, upper body, masterpiece, best quality, very aesthetic, absurdres" \
--seed 0 \
--output output.pnggeneral Dreamshaper
python -m scripts.inference_t2mv_sdxl --base_model "Lykon/dreamshaper-xl-1-0" \
--scheduler ddpm \
--text "the warrior Aragorn from Lord of the Rings, film grain, 8k hd" \
--seed 0 \
--output output.pngrealistic real-dream-sdxl
python -m scripts.inference_t2mv_sdxl --base_model "stablediffusionapi/real-dream-sdxl" \
--scheduler ddpm \
--text "macro shot, parrot, colorful, dark shot, film grain, extremely detailed" \
--seed 42 \
--output output.pngWith LCM:
python -m scripts.inference_t2mv_sdxl --unet_model "latent-consistency/lcm-sdxl" \
--scheduler lcm \
--text "Samurai koala bear" \
--num_inference_steps 8 \
--seed 42 \
--output output.pngWith LoRA:
stylized lora 3d_render_style_xl
python -m scripts.inference_t2mv_sdxl --lora_model "goofyai/3d_render_style_xl/3d_render_style_xl.safetensors" \
--text "3d style, a fox with flowers around it" \
--seed 20 \
--lora_scale 1.0 \
--output output.pngWith ControlNet:
Scribble to Multiview with controlnet-scribble-sdxl-1.0
python -m scripts.inference_scribble2mv_sdxl --text "A 3D model of Finn the Human from the animated television series Adventure Time. He is wearing his iconic blue shirt and green backpack and has a neutral expression on his face. He is standing in a relaxed pose with his left foot slightly forward and his right foot back. His arms are at his sides and his head is turned slightly to the right. The model is made up of simple shapes and has a stylized, cartoon-like appearance. It is textured to resemble the character's appearance in the show." \
--seed 0 \
--output output.png \
--guidance_scale 5.0 \
--controlnet_images "assets/demo/scribble2mv/color_0000.webp" "assets/demo/scribble2mv/color_0001.webp" "assets/demo/scribble2mv/color_0002.webp" "assets/demo/scribble2mv/color_0003.webp" "assets/demo/scribble2mv/color_0004.webp" "assets/demo/scribble2mv/color_0005.webp" \
--controlnet_conditioning_scale 0.7With SD2.1:
SD2.1 has lower demand for computing resources and higher inference speed, but a bit lower performance than SDXL. In our tests, ddpm scheduler works better than other schedulers here.
python -m scripts.inference_t2mv_sd --text "a corgi puppy" \
--seed 42 --scheduler ddpm \
--output output.pngWith SDXL:
python -m scripts.inference_i2mv_sdxl \
--image assets/demo/i2mv/A_decorative_figurine_of_a_young_anime-style_girl.png \
--text "A decorative figurine of a young anime-style girl" \
--seed 21 --output output.png --remove_bgWith LCM:
python -m scripts.inference_i2mv_sdxl \
--unet_model "latent-consistency/lcm-sdxl" \
--scheduler lcm \
--image assets/demo/i2mv/A_juvenile_emperor_penguin_chick.png \
--text "A juvenile emperor penguin chick" \
--num_inference_steps 8 \
--seed 0 --output output.png --remove_bgWith SD2.1: (lower demand for computing resources and higher inference speed)
In our tests, ddpm scheduler works better than other schedulers here.
python -m scripts.inference_i2mv_sd \
--image assets/demo/i2mv/A_decorative_figurine_of_a_young_anime-style_girl.png \
--text "A decorative figurine of a young anime-style girl" \
--output output.png --remove_bg --scheduler ddpmImportantly, when using geometry-condition generation, please make sure that the orientation of the mesh you provide is consistent with the following example. Otherwise, you need to adjust the angles in the scripts when rendering the view.
With SDXL:
python -m scripts.inference_tg2mv_sdxl \
--mesh assets/demo/tg2mv/ac9d4e4f44f34775ad46878ba8fbfd86.glb \
--text "Mater, a rusty and beat-up tow truck from the 2006 Disney/Pixar animated film 'Cars', with a rusty brown exterior, big blue eyes."python -m scripts.inference_tg2mv_sdxl \
--mesh assets/demo/tg2mv/b5f0f0f33e3644d1ba73576ceb486d42.glb \
--text "Optimus Prime, a character from Transformers, with blue, red and gray colors, and has a flame-like pattern on the body"With SDXL:
python -m scripts.inference_ig2mv_sdxl \
--image assets/demo/ig2mv/1ccd5c1563ea4f5fb8152eac59dabd5c.jpeg \
--mesh assets/demo/ig2mv/1ccd5c1563ea4f5fb8152eac59dabd5c.glb \
--output output.png --remove_bgWith SDXL:
python -m scripts.inference_ig2mv_partial_sdxl \
--image assets/demo/ig2mv/cartoon_style_table.png \
--mesh assets/demo/ig2mv/cartoon_style_table.glb \
--output output.pngExample input:
Example output:
The above command will save a *_transform.json file in the output dir, which contains transformation information like this:
{
"offset": [
0.7446051140100826,
-0.3421213991056582,
0.1360104325533671
],
"scale": 1.0086087120792668
}You can use it to transform your mesh into the canonical space, map the generated multi-view images onto the mesh, and then re-transform the mesh back to the original spatial position.
Please check ComfyUI-MVAdapter Repo for details.
Text to Multiview Generation
Text to Multiview Generation with LoRA
Image to Multiview Generation
Prepare Models
Download pre-trained RealESRGAN for upscaling images and LaMa for view in-painting.
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth -O ./checkpoints/RealESRGAN_x2plus.pth
wget https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt -O ./checkpoints/big-lama.ptUsage
All in one script
Text-conditioned texture generation:
python -m scripts.texture_t2tex \
--mesh assets/demo/tg2mv/ac9d4e4f44f34775ad46878ba8fbfd86.glb \
--text "Mater, a rusty and beat-up tow truck from the 2006 Disney/Pixar animated film 'Cars', with a rusty brown exterior, big blue eyes." \
--save_dir outputs --save_name t2tex_sampleImage-conditioned texture generation:
python -m scripts.texture_i2tex \
--image assets/demo/ig2mv/1ccd5c1563ea4f5fb8152eac59dabd5c.jpeg \
--mesh assets/demo/ig2mv/1ccd5c1563ea4f5fb8152eac59dabd5c.glb \
--save_dir outputs --save_name i2tex_sample \
--remove_bgIt will save the textured model into <save_dir>/<save_name>_shaded.glb.
Our training dataset, rendered from Objaverse, can be downloaded from Objaverse-Ortho10View and Objaverse-Rand6View.
- Objaverse-Ortho10View contains 10 orthographic views of 1024x1024 resolution, and is used as ground truth.
- Objaverse-Rand6View contains 6 randomly distributed views, and is used as reference image conditions.
Please refer to their dataset cards to extract the data files, and organize them into the following structures:
data
├── texture_ortho10view_easylight_objaverse # Objaverse-Ortho10View
│ ├── 00
│ │ ├── 00a4d2b0c4c240289ed456e87d8b9e02
│ ...
├── texture_rand_easylight_objaverse # Objaverse-Rand6View
│ ├── 00
│ │ ├── 00a4d2b0c4c240289ed456e87d8b9e02
│ ...
├── objaverse_list_6w.json # objaverse ids
└── objaverse_short_captions.json # id to captionsThe key training code can be found in mvadapter/systems:
MVAdapterTextSDXLSysteminmvadapter_text_sdxl.pyis used for text or text+geometry conditioned multi-view generation.MVAdapterImageSDXLSysteminmvadapter_image_sdxl.pyis used for image or image+geometry conditioned multi-view generation.
The specific training commands are as follows:
For text to 6 view generation:
python launch.py --config configs/view-guidance/mvadapter_t2mv_sdxl.yaml --train --gpu 0,1,2,3,4,5,6,7For single image to 6 view generation:
python launch.py --config configs/view-guidance/mvadapter_i2mv_sdxl.yaml --train --gpu 0,1,2,3,4,5,6,7For single image to 2/3/4/6 view generation:
python launch.py --config configs/view-guidance/mvadapter_i2mv_sdxl_aug_quantity.yaml --train --gpu 0,1,2,3,4,5,6,7For text + geometry to 6 view generation:
python launch.py --config configs/geometry-guidance/mvadapter_tg2mv_sdxl.yaml --train --gpu 0,1,2,3,4,5,6,7For single image + geometry to 6 view generation:
python launch.py --config configs/geometry-guidance/mvadapter_ig2mv_sdxl.yaml --train --gpu 0,1,2,3,4,5,6,7For single partial image + geometry to 6 view generation (used for texture generation conditioned on occluded image, for example, used in MIDI-3D):
python launch.py --config configs/geometry-guidance/mvadapter_ig2mv_partialimg_sdxl.yaml --train --gpu 0,1,2,3,4,5,6,7@article{huang2024mvadapter,
title={MV-Adapter: Multi-view Consistent Image Generation Made Easy},
author={Huang, Zehuan and Guo, Yuanchen and Wang, Haoran and Yi, Ran and Ma, Lizhuang and Cao, Yan-Pei and Sheng, Lu},
journal={arXiv preprint arXiv:2412.03632},
year={2024}
}