This extension integrates MV-Adapter into ComfyUI, allowing users to generate multi-view consistent images from text prompts or single images directly within the ComfyUI interface.
ComfyUI/custom_nodes/ directory.pip install -r requirements.txt.We provide the example workflows in workflows directory.
Note that our code depends on diffusers, and will automatically download the model weights from huggingface to the hf cache path at the first time. The ckpt_name in the node corresponds to the model name in huggingface, such as stabilityai/stable-diffusion-xl-base-1.0.
We also provide the nodes Ldm**Loader to support loading text-to-image models in ldm format. Please see the workflow files with the suffix _ldm.json.
If your GPU resources are limited, we recommend using the following configuration:
upcast_fp32 to False.
enable_vae_slicing in the Diffusers Model Makeup node to True.
However, since SDXL is used as the base model, it still requires about 13G to 14G GPU memory.

workflows/t2mv_sdxl_diffusers.json for loading diffusers-format modelsworkflows/t2mv_sdxl_ldm.json for loading ldm-format models
workflows/t2mv_sdxl_ldm_lora.json for loading ldm-format models with LoRA for text-to-multi-view generation

workflows/t2mv_sdxl_ldm_controlnet.json for loading diffusers-format controlnets for text-scribble-to-multi-view generation

workflows/i2mv_sdxl_diffusers.json for loading diffusers-format modelsworkflows/i2mv_sdxl_ldm.json for loading ldm-format models
workflows/i2mv_sdxl_ldm_lora.json for loading ldm-format models with LoRA for image-to-multi-view generation

workflows/i2mv_sdxl_ldm_view_selector.json for loading ldm-format models and selecting specific views to generate
The key is to replace the adapter_name in Diffusers Model Makeup with mvadapter_i2mv_sdxl_beta.safetensors, and add a View Selector node to choose which views you want to generate. After a rough test, the beta model is better at generating 2 views (front&back), 3 views (front&right&back), 4 views (front&right&back&left). Note that the attribute num_views is not used and can be ignored.
Python
100.0%
This extension integrates MV-Adapter into ComfyUI, allowing users to generate multi-view consistent images from text prompts or single images directly within the ComfyUI interface.
ComfyUI/custom_nodes/ directory.pip install -r requirements.txt.We provide the example workflows in workflows directory.
Note that our code depends on diffusers, and will automatically download the model weights from huggingface to the hf cache path at the first time. The ckpt_name in the node corresponds to the model name in huggingface, such as stabilityai/stable-diffusion-xl-base-1.0.
We also provide the nodes Ldm**Loader to support loading text-to-image models in ldm format. Please see the workflow files with the suffix _ldm.json.
If your GPU resources are limited, we recommend using the following configuration:
upcast_fp32 to False.
enable_vae_slicing in the Diffusers Model Makeup node to True.
However, since SDXL is used as the base model, it still requires about 13G to 14G GPU memory.

workflows/t2mv_sdxl_diffusers.json for loading diffusers-format modelsworkflows/t2mv_sdxl_ldm.json for loading ldm-format models
workflows/t2mv_sdxl_ldm_lora.json for loading ldm-format models with LoRA for text-to-multi-view generation

workflows/t2mv_sdxl_ldm_controlnet.json for loading diffusers-format controlnets for text-scribble-to-multi-view generation

workflows/i2mv_sdxl_diffusers.json for loading diffusers-format modelsworkflows/i2mv_sdxl_ldm.json for loading ldm-format models
workflows/i2mv_sdxl_ldm_lora.json for loading ldm-format models with LoRA for image-to-multi-view generation

workflows/i2mv_sdxl_ldm_view_selector.json for loading ldm-format models and selecting specific views to generate
The key is to replace the adapter_name in Diffusers Model Makeup with mvadapter_i2mv_sdxl_beta.safetensors, and add a View Selector node to choose which views you want to generate. After a rough test, the beta model is better at generating 2 views (front&back), 3 views (front&right&back), 4 views (front&right&back&left). Note that the attribute num_views is not used and can be ignored.
Python
100.0%