EricRollei-Local_LLM_Prompt_Enhancer/docs/IMG2IMG_GUIDE.md

Image-to-Image Prompt Expander - Complete Guide

What It Does

The Image-to-Image Prompt Expander is a platform-aware node that:

1. Analyzes your image with a vision model
2. Takes your change request (what to modify)
3. Generates platform-optimized prompts for:
   • Flux (FLUX.1-dev/schnell)
   • Stable Diffusion XL
   • Wan 2.2 (adapted for images)
   • Hunyuan Image
   • Qwen Image
   • Qwen Image Edit

Each platform gets prompts formatted the way it works best!

Why Platform-Specific Matters

Different models need different prompting styles:

Platform	Prefers
Flux	Natural language, artistic, "in the style of", 75-150 tokens
SDXL	Front-loaded quality, 40-75 tokens, token-optimized
Wan 2.2	Technical terms, structured, cinematographic
Hunyuan	Simple English, clear, realistic focus, 40-80 tokens
Qwen Image	Natural, balanced, cultural elements, 50-100 tokens
Qwen Edit	VERY concise, change-focused, 20-50 tokens

This node knows these differences and formats prompts accordingly!

Quick Start

Basic Setup

[Load Image] → [Image-to-Image Prompt Expander] → [Your Image Gen Node]

Configuration

1. Load vision model (LM Studio or Ollama)
2. Set platform: flux, sd_xl, hunyuan_image, etc.
3. Describe changes: "change dress to red, add sunset lighting"
4. Aesthetic controls: style, lighting, composition
5. Generate!

Node Parameters

Image & Changes

image (Image Input)

• Connect from Load Image
• Your source image

change_request (String)

• What to change/modify
• Examples:
  • "change dress color to red"
  • "add sunset lighting"
  • "replace background with forest"
  • "make it look like an oil painting"
  • (Leave empty to just enhance description)

Platform Selection

target_platform (Dropdown)

• flux: Flux.1-dev/schnell (natural, detailed)
• sd_xl: Stable Diffusion XL (token-optimized)
• wan22: Wan 2.2 adapted (technical, structured)
• hunyuan_image: Hunyuan (clear, realistic)
• qwen_image: Qwen (balanced, natural)
• qwen_image_edit: Qwen Edit (concise changes)

Choose based on what image model you're using!

Vision Model Settings

use_vision_model (Boolean)

• True: AI describes the image
• False: Skip vision (manual description)

vision_backend / vision_model_name / vision_endpoint

• Same as Image-to-Video node
• Set vision_backend to lm_studio or ollama for HTTP servers (typical workflow)
• Set vision_backend to qwen3_vl to run a local Qwen3-VL model directly inside ComfyUI. In that case:
  • vision_model_name should be a Hugging Face repo id or local path (e.g. Qwen/Qwen3-VL-4B-Instruct, Qwen/Qwen3-VL-4B-Instruct@4bit).
  • Use the vision_endpoint field for optional overrides such as quant=4bit;attn=sdpa or device=cuda:0.
• Works with Llama 3.2 Vision, LLaVA, MiniCPM V, or Qwen3-VL depending on backend choice.

Expansion Model Settings

expansion_backend / expansion_model_name / expansion_endpoint

• Model that formats the prompt
• Can be same or different from vision
• Use Llama 3.1, Qwen, etc.

temperature (0.1 - 2.0)

• 0.5-0.6: Precise, platform-following
• 0.7: Balanced (recommended)
• 0.8+: More creative

Aesthetic Controls

art_style

• photorealistic, digital art, oil painting, watercolor
• anime, sketch, 3D render, illustration, concept art

lighting_type

• natural, studio, soft, dramatic
• golden hour, blue hour, rim, volumetric

composition

• rule of thirds, centered, symmetrical
• golden ratio, dynamic, minimalist

color_palette

• vibrant, muted, monochrome
• warm, cool, pastel, high contrast

mood

• serene, dramatic, mysterious
• cheerful, melancholic, epic, intimate

detail_level

• standard, highly detailed, intricate
• simplified, minimalist

quality_emphasis (Boolean)

• Adds platform-specific quality tokens
• Recommended: True

Platform-Specific Examples

Example 1: Flux

Settings:

target_platform: flux
change_request: "make it look like a renaissance oil painting"
art_style: oil painting
lighting: dramatic lighting
quality_emphasis: True

Output style:

"masterpiece, best quality, highly detailed, [image description], 
rendered as a renaissance oil painting with dramatic lighting, 
rich textures and classical composition, in the style of Caravaggio"

Example 2: SDXL

Settings:

target_platform: sd_xl
change_request: "add cyberpunk neon lighting"
color_palette: vibrant
detail_level: highly detailed

Output style:

"masterpiece, best quality, highly detailed, [image description], 
with vibrant cyberpunk neon lighting, futuristic atmosphere"
(Front-loaded quality, ~60 tokens)

Example 3: Qwen Image Edit

Settings:

target_platform: qwen_image_edit
change_request: "change background to forest, keep subject"

Output style:

"replace background with forest, keep subject unchanged, 
seamless, natural, high quality"
(Very concise, edit-focused, ~15 tokens)

Example 4: Hunyuan Image

Settings:

target_platform: hunyuan_image
change_request: "add soft sunset lighting"
lighting: golden hour

Output style:

"[clear image description], with soft sunset lighting, 
golden hour atmosphere, high quality, realistic, detailed"
(Simple English, photorealistic focus)

Workflow Examples

Workflow 1: Style Transfer

Goal: Turn photo into painting

[Load Image: photo.jpg]
  ↓
[Image-to-Image Prompt Expander]
  - platform: flux
  - change: "convert to impressionist oil painting"
  - art_style: oil painting
  - mood: serene
  ↓
[Flux Image Gen]

Workflow 2: Lighting Change

Goal: Change time of day

[Load Image: daytime.jpg]
  ↓
[Image-to-Image Prompt Expander]
  - platform: sd_xl
  - change: "make it golden hour sunset"
  - lighting: golden hour
  - color_palette: warm
  ↓
[SDXL Image Gen]

Workflow 3: Background Replacement

Goal: Change background only

[Load Image: portrait.jpg]
  ↓
[Image-to-Image Prompt Expander]
  - platform: qwen_image_edit
  - change: "replace background with city skyline, keep person"
  - composition: centered
  ↓
[Qwen Edit Model]

Workflow 4: Quality Enhancement

Goal: Just enhance, no changes

[Load Image: low_quality.jpg]
  ↓
[Image-to-Image Prompt Expander]
  - platform: flux
  - change: "" (empty - just enhance)
  - detail_level: highly detailed
  - quality_emphasis: True
  ↓
[Flux Image Gen]

Platform Comparison

When to Use Each Platform

Use Flux when:

• ✅ You want artistic/creative results
• ✅ Style transfer (painting, illustration, etc.)
• ✅ You have VRAM for quality (12GB+)
• ✅ Longer prompts work better

Use SDXL when:

• ✅ Standard img2img workflows
• ✅ You're familiar with SD ecosystem
• ✅ Token efficiency matters
• ✅ Strong negatives help

Use Wan 2.2 when:

• ✅ You want cinematic/technical quality
• ✅ Precise lighting control needed
• ✅ Professional composition important

Use Hunyuan when:

• ✅ Photorealistic results desired
• ✅ Asian subjects/aesthetics
• ✅ Simpler prompts work better
• ✅ Clarity over complexity

Use Qwen Image when:

• ✅ Balanced natural results
• ✅ Diverse cultural elements
• ✅ Versatile style handling

Use Qwen Edit when:

• ✅ Specific edits only
• ✅ Preserving most of image
• ✅ Minimal changes needed
• ✅ Fast iteration

Tips for Best Results

1. Match Platform to Task

Creative/Artistic → Flux

change: "make it look like a Studio Ghibli scene"
platform: flux

Realistic/Photo → Hunyuan or Qwen

change: "enhance details, improve lighting"
platform: hunyuan_image

Specific Edits → Qwen Edit

change: "change shirt color to blue"
platform: qwen_image_edit

2. Be Specific with Changes

❌ Bad: "make it better" ✅ Good: "add dramatic rim lighting, increase contrast"

❌ Bad: "change the vibe" ✅ Good: "make it moody and mysterious with dark blue tones"

3. Use Aesthetic Controls

They adapt to each platform automatically:

Flux: Adds "in the style of..." SDXL: Front-loads in prompt Qwen Edit: Skips if irrelevant Hunyuan: Simplifies terminology

4. Temperature by Platform

Platform	Recommended Temp
Flux	0.7 (creative OK)
SDXL	0.6 (precise)
Qwen Edit	0.5 (very precise)
Others	0.6-0.7 (balanced)

5. Quality Emphasis

Turn ON for:

• Final renders
• Flux, SDXL
• When quality matters

Turn OFF for:

• Qwen Edit (conciseness > quality tokens)
• Quick iterations
• When prompt too long

Outputs

1. positive_prompt - Platform-optimized prompt
2. negative_prompt - Platform-specific negatives
3. image_description - Vision model's analysis
4. status - Processing info

Troubleshooting

"Platform not working as expected"

Check:

• Using correct model for platform?
• Flux needs FLUX.1-dev/schnell
• SDXL needs Stable Diffusion XL
• etc.

Fix:

• Verify model matches platform
• Check platform documentation

"Prompt too long/short"

Flux/SDXL: Node automatically optimizes Qwen Edit: Should be very short (working as designed) Others: Adjust via temperature and detail_level

"Changes not applied correctly"

For specific edits:

• Use qwen_image_edit platform
• Be very explicit: "change X to Y"
• Lower temperature (0.5)

For creative changes:

• Use flux platform
• Be descriptive
• Use aesthetic controls

"Vision model fails"

Same as Image-to-Video node:

• Check model loaded
• Verify it's vision-capable
• Test endpoint

Comparison: Img2Vid vs Img2Img Nodes

Feature	Image-to-Video	Image-to-Image
Purpose	Animate images	Transform images
Focus	Motion, camera	Style, appearance
Platforms	Video models	Image models
Controls	Camera movement	Art style, lighting
Output	Video prompts	Image prompts

Use Img2Vid for: Hunyuan Video, CogVideoX, etc. Use Img2Img for: Flux, SDXL, Qwen, etc.

Advanced: Two Models, One Node

Optimal setup:

Vision Model (Port 1234):
  - Llama-3.2-11B-Vision
  - Analyzes image
  
Expansion Model (Port 5000 or Ollama):
  - Llama-3.1-8B-Instruct
  - Formats prompt

In node:
  vision_endpoint: http://localhost:1234/v1
  expansion_endpoint: http://localhost:5000/v1

Why?

• Vision models are large (11B)
• Text models faster for formatting
• Better resource usage

Summary

Image-to-Image node gives you:

• ✅ Platform-aware prompt generation
• ✅ Vision model image analysis
• ✅ 6 platforms optimized
• ✅ Advanced aesthetic controls
• ✅ Automatic format adaptation

Supported platforms:

1. Flux - Creative, artistic, detailed
2. SDXL - Token-optimized, standard
3. Wan 2.2 - Technical, cinematic
4. Hunyuan - Realistic, clear
5. Qwen Image - Balanced, versatile
6. Qwen Edit - Concise, edit-focused

Each gets prompts formatted the way IT works best!

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Ready to transform images with AI!

Load vision model → Select platform → Describe changes → Generate!