Inpainting Guide

Inpainting allows you to selectively edit or regenerate specific regions of an image while preserving the rest. This is essential for fixing details, removing objects, or seamlessly adding new elements.

How Inpainting Works

Define area: Create a mask marking the region to regenerate
Encode: Convert image and mask to latent space
Denoise: Only regenerate the masked area
Decode: Blend seamlessly with unmasked regions

ComfyUI supports two inpainting approaches:

Standard models with VAEEncodeForInpaint
Dedicated inpaint models with InpaintModelConditioning

Standard Model Inpainting

Works with any Stable Diffusion model.

Workflow JSON: Standard Inpainting

{
  "load_image": {
    "class_type": "LoadImage",
    "inputs": {
      "image": "photo.png"
    }
  },
  "checkpoint": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": {
      "ckpt_name": "v1-5-pruned-emaonly.safetensors"
    }
  },
  "vae_encode_inpaint": {
    "class_type": "VAEEncodeForInpaint",
    "inputs": {
      "pixels": ["load_image", 0],
      "vae": ["checkpoint", 2],
      "mask": ["load_image", 1],
      "grow_mask_by": 6
    }
  },
  "positive": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["checkpoint", 1],
      "text": "natural background, seamless blend, photorealistic"
    }
  },
  "negative": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["checkpoint", 1],
      "text": "artifacts, seams, inconsistent, blurry"
    }
  },
  "sampler": {
    "class_type": "KSampler",
    "inputs": {
      "model": ["checkpoint", 0],
      "positive": ["positive", 0],
      "negative": ["negative", 0],
      "latent_image": ["vae_encode_inpaint", 0],
      "seed": 42,
      "steps": 30,
      "cfg": 7.5,
      "sampler_name": "dpm++ 2m karras",
      "scheduler": "karras",
      "denoise": 1.0
    }
  },
  "vae_decode": {
    "class_type": "VAEDecode",
    "inputs": {
      "samples": ["sampler", 0],
      "vae": ["checkpoint", 2]
    }
  },
  "save": {
    "class_type": "SaveImage",
    "inputs": {
      "images": ["vae_decode", 0],
      "filename_prefix": "inpaint"
    }
  }
}

Inpaint Model Workflow

Dedicated inpaint models (like SD 1.5 inpaint) produce better results for complex edits.

Workflow JSON: Inpaint Model

{
  "load_image": {
    "class_type": "LoadImage",
    "inputs": {
      "image": "photo.png"
    }
  },
  "checkpoint": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": {
      "ckpt_name": "sd-v1-5-inpainting.ckpt"
    }
  },
  "positive": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["checkpoint", 1],
      "text": "beautiful garden, colorful flowers, natural daylight"
    }
  },
  "negative": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["checkpoint", 1],
      "text": "blurry, distorted, artifacts, low quality"
    }
  },
  "inpaint_conditioning": {
    "class_type": "InpaintModelConditioning",
    "inputs": {
      "positive": ["positive", 0],
      "negative": ["negative", 0],
      "vae": ["checkpoint", 2],
      "pixels": ["load_image", 0],
      "mask": ["load_image", 1],
      "noise_mask": true
    }
  },
  "sampler": {
    "class_type": "KSampler",
    "inputs": {
      "model": ["checkpoint", 0],
      "positive": ["inpaint_conditioning", 0],
      "negative": ["inpaint_conditioning", 1],
      "latent_image": ["inpaint_conditioning", 2],
      "seed": 123,
      "steps": 40,
      "cfg": 8.0,
      "sampler_name": "dpm++ 2m karras",
      "scheduler": "karras",
      "denoise": 1.0
    }
  },
  "vae_decode": {
    "class_type": "VAEDecode",
    "inputs": {
      "samples": ["sampler", 0],
      "vae": ["checkpoint", 2]
    }
  },
  "save": {
    "class_type": "SaveImage",
    "inputs": {
      "images": ["vae_decode", 0],
      "filename_prefix": "inpaint_model"
    }
  }
}

Common Use Cases

Object Removal

Goal: Remove unwanted elements

Mask the object to remove
Prompt what should be there instead
Use high grow_mask_by (8-16) for better blending

Settings:

Prompt: "[describe background], natural, seamless"
Steps: 35-50
CFG: 7-8
grow_mask_by: 12

Adding Elements

Goal: Insert new objects or details

Mask where new element should appear
Detailed prompt describing the addition
Include lighting/style context

Example:

Prompt: "red sports car parked on street, realistic lighting matching scene, 
sharp focus, detailed"
CFG: 8-10 (higher for specific objects)

Face/Detail Fixing

Goal: Improve specific details

Mask problem area tightly
Describe desired result
Low grow_mask_by (2-4)

Settings:

Prompt: "beautiful detailed face, natural expression, sharp eyes"
Steps: 40-50
CFG: 7
grow_mask_by: 3

Background Extension (Outpainting)

Goal: Expand image borders

Use ImagePadForOutpaint node to extend canvas
Mask the new area
Prompt continuation of the scene

{
  "pad": {
    "class_type": "ImagePadForOutpaint",
    "inputs": {
      "image": ["load_image", 0],
      "left": 256,
      "top": 0,
      "right": 256,
      "bottom": 0,
      "feathering": 40
    }
  }
}

The node outputs both padded image and automatic mask.

Mask Creation Tips

Manual Masking

Use image editor (Photoshop, GIMP, etc.)
White = regenerate, Black = keep
Soft edges with blur for better blending
Save with alpha channel

Mask from Alpha Channel

Use LoadImageMask:

{
  "mask": {
    "class_type": "LoadImageMask",
    "inputs": {
      "image": "mask.png",
      "channel": "alpha"
    }
  }
}

Channels: alpha, red, green, blue

Feathering

grow_mask_by creates smooth transitions:

Small objects: 4-8
Medium areas: 8-16
Large regions: 16-32
Full image blend: 40+

Advanced Techniques

Multi-Region Inpainting

Create mask with multiple separate areas
Prompt describes all areas together
Higher steps (40-60) for complexity

First pass: Rough inpaint, denoise 1.0
Save result
Second pass: Load result, smaller mask, denoise 0.6-0.8
Refine specific details

Compositional Inpainting

Use ConditioningSetMask for regional prompts:

{
  "masked_conditioning": {
    "class_type": "ConditioningSetMask",
    "inputs": {
      "conditioning": ["positive", 0],
      "mask": ["load_mask", 0],
      "strength": 1.0,
      "set_cond_area": "mask bounds"
    }
  }
}

This applies prompt only to masked region, giving precise control.

Parameter Guide

grow_mask_by

0: Hard edges, visible seams
2-4: Tight blending, detail work
6-8: Standard blending (default)
12-16: Soft transitions, large areas
32+: Very gradual blend, outpainting

Steps

20-30: Quick edits, simple fills
30-40: Standard quality
40-60: Complex inpainting, multiple objects
60+: Challenging blends, high detail

CFG Scale

6-7: Natural blending, less artifacts
7-8: Balanced (recommended)
8-10: Strong prompt adherence, adding specific objects
10+: Risk of oversaturation at mask edges

Troubleshooting

Visible seams

Increase grow_mask_by (try 12-20)
Lower CFG scale (try 6-7)
Increase steps (35-50)
Use softer mask edges

Generated area doesn’t match

Describe surrounding context in prompt
Include lighting/style references
Try dedicated inpaint model
Increase CFG for stronger adherence

Blurry results

Increase steps (40+)
Use better sampler (dpm++ sde karras)
Check VAE quality
Try dedicated inpaint model

Changes bleed outside mask

Reduce grow_mask_by
Use hard-edged mask
Enable noise_mask (inpaint models)
Lower denoise slightly (0.95)

Best Practices

Match the style: Prompt should describe existing image style
Contextual prompts: Reference surrounding elements
Start simple: Test with small masks first
Iterate: Run multiple generations, pick best
Feather appropriately: Match grow_mask_by to edit size
Consider dedicated models: Better for complex edits
Higher steps: Inpainting benefits from extra refinement

Next Steps

Combine with ControlNet for precise inpainting
Explore regional prompting with ConditioningSetMask
Try different inpaint-specific models
Experiment with outpainting for image extension

Tutorials

Integration

Troubleshooting

Inpainting Guide

How Inpainting Works

Standard Model Inpainting

Workflow JSON: Standard Inpainting

Inpaint Model Workflow

Workflow JSON: Inpaint Model

Common Use Cases

Object Removal

Adding Elements

Face/Detail Fixing

Background Extension (Outpainting)

Mask Creation Tips

Manual Masking

Mask from Alpha Channel

Feathering

Advanced Techniques

Multi-Region Inpainting

Iterative Refinement

Compositional Inpainting

Parameter Guide

grow_mask_by

Steps

CFG Scale

Troubleshooting

Visible seams

Generated area doesn’t match

Blurry results

Changes bleed outside mask

Best Practices

Next Steps

​How Inpainting Works

​Standard Model Inpainting

​Workflow JSON: Standard Inpainting

​Inpaint Model Workflow

​Workflow JSON: Inpaint Model

​Common Use Cases

​Object Removal

​Adding Elements

​Face/Detail Fixing

​Background Extension (Outpainting)

​Mask Creation Tips

​Manual Masking

​Mask from Alpha Channel

​Feathering

​Advanced Techniques

​Multi-Region Inpainting

​Iterative Refinement

​Compositional Inpainting

​Parameter Guide

​grow_mask_by

​Steps

​CFG Scale

​Troubleshooting

​Visible seams

​Generated area doesn’t match

​Blurry results

​Changes bleed outside mask

​Best Practices

​Next Steps

How Inpainting Works

Standard Model Inpainting

Workflow JSON: Standard Inpainting

Inpaint Model Workflow

Workflow JSON: Inpaint Model

Common Use Cases

Object Removal

Adding Elements

Face/Detail Fixing

Background Extension (Outpainting)

Mask Creation Tips

Manual Masking

Mask from Alpha Channel

Feathering

Advanced Techniques

Multi-Region Inpainting

Iterative Refinement

Compositional Inpainting

Parameter Guide

grow_mask_by

Steps

CFG Scale

Troubleshooting

Visible seams

Generated area doesn’t match

Blurry results

Changes bleed outside mask

Best Practices

Next Steps