MV-Adapter | High-Resolution Multi-view Generator

1. What is the ComfyUI MV-Adapter Workflow?

The Multi-View Adapter (MV-Adapter) workflow is a specialized tool that enhances your existing AI image generators with multi-view capabilities. It acts as a plug-and-play addition that enables models like Stable Diffusion XL (SDXL) to understand and generate images from multiple angles while maintaining consistency in style, lighting, and details. Using the MV-Adapter ensures that multi-view image generation is seamless and efficient.

2. Benefits of ComfyUI MV-Adapter:

• Generate high-quality images up to 768px resolution
• Create consistent multi-view outputs from single images or text
• Preserve artistic style across all generated angles
• Works with popular models (SDXL, DreamShaper, Animagine XL)
• Supports ControlNet for precise control
• Compatible with LoRA models for enhanced styling
• Optional SD2.1 support for faster results

3. How to Use the ComfyUI MV-Adapter Workflow

3.1 Generation Methods with MV-Adapter

Combined Text and Image Generation (Recommended)

• Inputs: Both reference image and text description
• Best for: Balanced results with specific style requirements
• Characteristics:
  • Combines semantic guidance with reference constraints
  • Better control over final output
  • Maintains reference style while following text instructions
• Example MV-Adapter workflow:
  1. Prepare inputs:
     • Add your reference image in Load Image node
     • Write descriptive text (e.g., "a space cat in the style of the reference image") in Text Encode node
     
  2. Run workflow (Queue Prompt) with default settings
  3. For further refinement (optional):
     • In MVAdapter Generator node: Adjust shift_scale for wider/narrower angle range
     • In KSampler node: Modify cfg (7–8) to balance between text and image influence
     

Alternative Methods in MV-Adapter:

Text-Only Generation

• Inputs: Text prompt only via Text Encode node
• Best for: Creative freedom and generating novel subjects
• Characteristics:
  • Maximum flexibility in subject creation
  • Output quality depends on prompt engineering
  • May have less style consistency across views
  • Requires detailed prompts for good results

Image-Only Generation

• Inputs: Single reference image via Load Image node
• Best for: Style preservation and texture consistency
• Characteristics:
  • Strong preservation of reference image style
  • High texture and visual consistency
  • Limited control over semantic details
  • May produce abstract results in multi-view scenarios

3.2 Parameter Reference for MV-Adapter

• MVAdapter Generator node:
  • num_views: 6 (default) - controls number of generated angles
  • shift_mode: interpolated - controls view transition method
  • shift_scale: 8 (default) - controls angle range between views

• KSampler node:
  • cfg: 7.0-8.0 recommended - balances input influences
  • steps: 40-50 for more detail (default is optimized for MV-Adapter)
  • seed: Keep same value for consistent results

• LoRA settings (Optional):
  • 3D LoRA: Apply first for structural consistency
  • Style LoRA: Add after 3D effect, start at 0.5 strength

3.3. Advanced Optimization with MV-Adapter

For users seeking performance improvements:

• VAE Decode node options:
  • enable_vae_slicing: Reduces VRAM usage
  • upcast_fp32: Affects processing speed

More Information

For additional details on the MV-Adapter workflow and updates, please visit MVAdapter.