tippyentertainment/image-to-image-edit

Image-to-Image Edit

Transform existing images using AI diffusion models with text guidance. This skill enables powerful image editing capabilities including style transfer, content modification, inpainting, and outpainting.

Overview

Image-to-image (img2img) generation takes an existing image as input and transforms it based on a text prompt while preserving some characteristics of the original. The strength parameter controls how much the image changes - low values (0.3-0.5) preserve more of the original, while high values (0.7-1.0) allow more dramatic transformations.

Parameters

Required Parameters

| Parameter | Type | Description | |-----------|------|-------------| | image_url | string | URL or local path to the source image. Supports PNG, JPG, WEBP formats. Must be publicly accessible URL or local file path. | | prompt | string | Text description of the desired transformation. Be specific about style, content, and mood. Example: "transform into oil painting style, dramatic lighting, renaissance art" |

Optional Parameters

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | negative_prompt | string | "blurry, low quality, distorted, watermark" | What to avoid in the generation. Use to exclude unwanted elements. | | strength | float | 0.75 | Transformation intensity (0.0-1.0). Lower = preserve original, higher = more change. Recommended: 0.3-0.5 for subtle edits, 0.6-0.8 for style transfers, 0.9+ for near-complete rewrites. | | seed | integer | random | Reproducibility seed. Same seed + same parameters = same output. Use -1 for random. | | steps | integer | 25 | Inference steps (15-50). More steps = higher quality but slower. 20-30 is usually sufficient. | | cfg_scale | float | 7.5 | Classifier-free guidance scale (1-20). Higher = more prompt adherence, lower = more creative freedom. | | width | integer | source width | Output width in pixels. Leave empty to match source. | | height | integer | source height | Output height in pixels. Leave empty to match source. | | sampler | string | "dpmpp_3m_sde" | Sampling algorithm. Options: euler_a, dpmpp_3m_sde, dpmpp_2m, ddim, uni_pc, lms. DPM++ samplers recommended for quality. | | scheduler | string | "karras" | Noise scheduler. Options: karras, normal, sgm_uniform, simple. Karras generally produces best results. | | model | string | system default | Model checkpoint name. Examples: sdXL_v10, realisticVision_v20, dreamshaper_8. | | mask_url | string | null | URL to mask image for inpainting. White areas = inpaint, black areas = preserve. PNG with alpha channel supported. | | inpaint | boolean | false | Enable inpainting mode. Requires mask_url. Only modifies masked regions. | | outpaint | boolean | false | Enable outpainting mode. Extends image beyond original boundaries. | | outpaint_direction | string | "right" | Direction to extend: left, right, up, down, or combinations like "left,right,up". | | batch_size | integer | 1 | Number of variations to generate. Each gets a different seed. | | backend | string | "auto" | Which API to use: comfyui, automatic1111, stability, local. Auto-detects available backend. |

Returns

{
  "image_url": "https://storage.example.com/generated/image_12345.png",
  "seed": 123456789,
  "metadata": {
    "prompt": "oil painting style, dramatic lighting",
    "negative_prompt": "blurry, low quality",
    "strength": 0.75,
    "steps": 25,
    "cfg_scale": 7.5,
    "sampler": "dpmpp_3m_sde",
    "model": "sdXL_v10"
  },
  "processing_time": 4.2
}

Usage Examples

Basic Style Transfer

// Transform photo into oil painting
await imageToImageEdit({
  image_url: "https://example.com/photo.jpg",
  prompt: "oil painting style, impressionist, visible brush strokes, vibrant colors",
  strength: 0.7
});

Subtle Enhancement

// Enhance photo quality while preserving content
await imageToImageEdit({
  image_url: "https://example.com/portrait.jpg",
  prompt: "professional photography, studio lighting, high detail, sharp focus",
  strength: 0.35,
  steps: 30,
  cfg_scale: 8
});

Inpainting (Edit Specific Areas)

// Replace object in image
await imageToImageEdit({
  image_url: "https://example.com/room.jpg",
  mask_url: "https://example.com/room_mask.png",
  prompt: "modern furniture, minimalist sofa, coffee table",
  inpaint: true,
  strength: 0.8
});

Outpainting (Extend Image)

// Extend image to wider aspect ratio
await imageToImageEdit({
  image_url: "https://example.com/landscape.jpg",
  prompt: "continue the landscape, mountains, sky, clouds",
  outpaint: true,
  outpaint_direction: "left,right",
  width: 1920,
  height: 1080
});

Batch Variations

// Generate multiple style variations
await imageToImageEdit({
  image_url: "https://example.com/portrait.jpg",
  prompt: "cyberpunk style, neon lights, futuristic",
  strength: 0.65,
  batch_size: 4
});
// Returns 4 images with seeds: seed, seed+1, seed+2, seed+3

Reproducible Results

// Same seed = same result
await imageToImageEdit({
  image_url: "https://example.com/photo.jpg",
  prompt: "watercolor painting style",
  strength: 0.6,
  seed: 42  // Always produces same output
});

Backend Implementations

ComfyUI Backend

ComfyUI uses a node-based workflow system. The img2img workflow:

1. LoadImage - Loads source image from URL or file
2. VAEEncode - Encodes image to latent space
3. CLIPTextEncode - Encodes positive/negative prompts
4. KSampler - Denoises latents with guidance
5. VAEDecode - Decodes latents back to image
6. SaveImage - Outputs final result

Key parameters in ComfyUI:

• denoise = strength (0.0-1.0)
• sampler_name = sampler algorithm
• scheduler = noise schedule

Automatic1111 Backend

Automatic1111's img2img API accepts:

{
  "init_images": ["base64_encoded_image"],
  "prompt": "transformation description",
  "negative_prompt": "what to avoid",
  "denoising_strength": 0.75,
  "steps": 25,
  "cfg_scale": 7.5,
  "seed": -1,
  "sampler_name": "DPM++ 3M SDE",
  "width": 1024,
  "height": 1024
}

Stability AI API

Stability AI's image-to-image endpoint:

{
  "init_image": "base64_or_url",
  "text_prompts": [
    {"text": "transformation description", "weight": 1},
    {"text": "negative prompt", "weight": -1}
  ],
  "image_strength": 0.75,
  "steps": 25,
  "cfg_scale": 7.5,
  "seed": 0
}

Strength Guidelines

| Strength | Use Case | Example | |----------|----------|---------| | 0.2-0.4 | Subtle fixes, upscaling, detail enhancement | "sharpen details, fix lighting" | | 0.4-0.6 | Moderate changes, style enhancement | "add dramatic lighting, enhance colors" | | 0.6-0.8 | Style transfer, significant changes | "transform into oil painting, cyberpunk style" | | 0.8-0.95 | Major transformation, near-complete rewrite | "completely different scene, same composition" | | 0.95-1.0 | Almost text-to-image, minimal original | "use as rough layout guide" |

Prompt Engineering Tips

Be Specific About Style

❌ "make it artistic" ✅ "oil painting style, impressionist, visible brush strokes, warm color palette, dramatic lighting"

Describe Content Changes Clearly

❌ "change the background" ✅ "replace background with sunset beach scene, golden hour lighting, ocean waves"

Use Negative Prompts Effectively

negative_prompt: "blurry, low quality, distorted faces, extra limbs, watermark, text, signature"

Preserve Important Elements

prompt: "style as cyberpunk art, neon lights, futuristic city background, preserve the person's face and pose"

Common Use Cases

1. Photo to Art Style

{
  image_url: "photo.jpg",
  prompt: "oil painting style, renaissance art, dramatic chiaroscuro lighting, museum quality",
  strength: 0.7
}

2. Background Replacement

{
  image_url: "portrait.jpg",
  prompt: "professional studio background, gradient lighting, corporate headshot style",
  strength: 0.5,
  negative_prompt: "distracting elements, cluttered background"
}

3. Object Removal (Inpainting)

{
  image_url: "photo.jpg",
  mask_url: "mask.png",  // White over object to remove
  prompt: "clean background, seamless fill, natural continuation",
  inpaint: true,
  strength: 0.85
}

4. Color Grading

{
  image_url: "photo.jpg",
  prompt: "cinematic color grading, teal and orange, film look, Kodak Portra 400",
  strength: 0.3
}

5. Upscaling with Enhancement

{
  image_url: "low_res.jpg",
  prompt: "high resolution, sharp details, 4K quality, professional photography",
  strength: 0.4,
  width: 2048,
  height: 2048
}

6. Sketch to Image

{
  image_url: "sketch.png",
  prompt: "photorealistic rendering, detailed textures, professional photography",
  strength: 0.85
}

7. Day to Night

{
  image_url: "daytime_photo.jpg",
  prompt: "night scene, moonlight, street lamps, stars in sky, dark blue atmosphere",
  strength: 0.65
}

8. Season Change

{
  image_url: "summer_photo.jpg",
  prompt: "winter scene, snow covered ground, bare trees, overcast sky, cold atmosphere",
  strength: 0.7
}

Error Handling

Common Errors

| Error | Cause | Solution | |-------|-------|----------| | image_too_large | Image exceeds max dimensions | Resize to under 2048x2048 | | invalid_url | Image URL not accessible | Use public URL or local path | | timeout | Generation took too long | Reduce steps or use faster sampler | | out_of_memory | GPU memory exceeded | Reduce resolution or batch size | | invalid_mask | Mask format incorrect | Use PNG with white/black regions |

Retry Strategy

// Implement exponential backoff
async function robustImg2Img(params, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await imageToImageEdit(params);
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await new Promise(r => setTimeout(r, 1000 * Math.pow(2, i)));
    }
  }
}

Performance Optimization

Faster Generation

• Use euler_a sampler for speed (15-20 steps sufficient)
• Reduce steps to 15-20 for quick previews
• Lower resolution for iteration, upscale final
• Use smaller models (SD 1.5 vs SDXL)

Higher Quality

• Use dpmpp_3m_sde sampler with karras scheduler
• Increase steps to 30-50 for final output
• Use SDXL or fine-tuned models
• Higher cfg_scale (8-12) for prompt adherence

Memory Efficiency

• Process one image at a time
• Use tiled VAE for large images
• Enable memory-efficient attention
• Clear cache between generations

Integration with TaskingBot

This skill integrates with TaskingBot's image generation pipeline:

// Use with generate_image for comparison
const original = await generate_image({prompt: "landscape"});
const transformed = await imageToImageEdit({
  image_url: original.image_url,
  prompt: "oil painting style",
  strength: 0.7
});

Step-by-Step Instructions

Basic Image Editing Workflow

1. Prepare Source Image

   • Ensure image is accessible via public URL or local path
   • Recommended size: 512-1024px (will be resized automatically)
   • Supported formats: PNG, JPG, WEBP

2. Craft Your Prompt

   • Describe the desired transformation clearly
   • Include style, mood, lighting, and content changes
   • Use negative prompts to exclude unwanted elements

3. Set Strength Appropriately

   • 0.3-0.5: Subtle changes, enhancement
   • 0.5-0.7: Moderate style transfer
   • 0.7-0.9: Major transformation

4. Generate and Iterate

   • Start with default parameters
   • Adjust strength if too much/little change
   • Use same seed for reproducibility
   • Generate multiple variations with batch_size

5. Post-Process

   • Download result from returned URL
   • Use enhance_image for upscaling if needed
   • Use edit_image for additional refinements

Inpainting Workflow

1. Create Mask Image

   • Use image editing software (Photoshop, GIMP)
   • White (#FFFFFF) = areas to change
   • Black (#000000) = areas to preserve
   • Save as PNG with same dimensions as source

2. Configure Inpainting

   {
     image_url: "source.png",
     mask_url: "mask.png",
     prompt: "what should appear in masked areas",
     inpaint: true,
     strength: 0.8
   }
   

3. Best Practices

   • Mask slightly beyond the area you want to change
   • Use higher strength (0.8-0.95) for complete replacement
   • Describe the full scene context in prompt

Outpainting Workflow

1. Set Target Dimensions

   • Original: 1024x1024
   • Target: 1920x1080 (extends left/right)

2. Configure Outpainting

   {
     image_url: "1024x1024_image.jpg",
     prompt: "continue the scene naturally",
     outpaint: true,
     outpaint_direction: "left,right",
     width: 1920,
     height: 1080
   }
   

3. Direction Options

   • "left" - extend left side
   • "right" - extend right side
   • "up" - extend top
   • "down" - extend bottom
   • "left,right,up" - multiple directions

API Reference

Endpoint

POST /api/image-to-image

Request Headers

Content-Type: application/json
Authorization: Bearer <api_key>

Request Body

{
  "image_url": "https://example.com/image.jpg",
  "prompt": "transformation description",
  "negative_prompt": "what to avoid",
  "strength": 0.75,
  "seed": -1,
  "steps": 25,
  "cfg_scale": 7.5,
  "width": 1024,
  "height": 1024,
  "sampler": "dpmpp_3m_sde",
  "scheduler": "karras",
  "model": "sdXL_v10",
  "mask_url": null,
  "inpaint": false,
  "outpaint": false,
  "batch_size": 1
}

Response

{
  "success": true,
  "image_url": "https://storage.example.com/output.png",
  "seed": 123456789,
  "metadata": {
    "prompt": "transformation description",
    "strength": 0.75,
    "steps": 25,
    "processing_time_ms": 4200
  }
}

Best Practices Summary

1. Start Conservative: Begin with strength 0.5-0.6, adjust based on results
2. Use Negative Prompts: Always exclude "blurry, low quality, distorted"
3. Match Dimensions: Keep aspect ratio similar for best results
4. Iterate with Seeds: Note successful seeds for reproducibility
5. Batch for Variations: Use batch_size to explore options efficiently
6. Quality vs Speed: More steps = better quality, but slower
7. Prompt Specificity: Detailed prompts yield better results
8. Test Different Samplers: DPM++ samplers generally best for img2img

Troubleshooting

Image Not Changing Enough

• Increase strength (try 0.8-0.9)
• Make prompt more specific
• Increase cfg_scale (try 10-12)

Image Changing Too Much

• Decrease strength (try 0.3-0.5)
• Add preservation hints to prompt
• Use lower cfg_scale (try 5-7)

Artifacts or Distortions

• Increase steps (try 30-40)
• Try different sampler (dpmpp_3m_sde)
• Add "artifacts, distortion" to negative prompt

Slow Generation

• Reduce steps to 15-20
• Use euler_a sampler
• Lower resolution
• Reduce batch_size

See Also

• generate_image - Generate images from text prompts
• edit_image - Simple image editing (crop, filter, etc.)
• enhance_image - Upscale and improve image quality
• generate_3d_from_image - Convert image to 3D model

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Version: 1.0.0 Author: TaskingBot Created: 2026-03-20 Tags: image, generation, editing, img2img, inpainting, outpainting, style-transfer, diffusion