Major Release - Complete Advanced Node Redesign ✨ New Features: - Advanced node: 4 operation modes (expand/refine/modify_style/add_details) - Advanced node: Clear detail levels (concise/moderate/detailed/exhaustive) - Advanced node: Optional image input with Qwen3-VL integration - Video nodes: Emphasis syntax preservation (keyword:1.5) - Video nodes: Alternation syntax support {option1|option2} 🔧 Changes: - Replaced confusing tier system with intuitive detail levels - Added operation modes for modifying existing prompts - Removed manual mode selection (auto-detected from image presence) - Added Qwen3-VL backend for image analysis - Backward compatible tier name mapping in expansion_engine 📚 Documentation: - README.md completely rewritten - docs/ADVANCED_NODE_REDESIGN.md - Complete redesign guide - docs/BUGFIX_ADVANCED_NODE.md - Syntax preservation details - Organized old docs into docs/archive/ 🐛 Fixes: - Emphasis syntax now properly preserved through LLM processing - Alternation syntax for random element selection - Image-to-video mode now functional with Qwen3-VL ⚡ Breaking Changes: None (fully backward compatible) See RELEASE_NOTES_v1.8.0.md for complete details.
472 lines
11 KiB
Markdown
472 lines
11 KiB
Markdown
# Image-to-Image Prompt Expander - Complete Guide
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## What It Does
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The **Image-to-Image Prompt Expander** is a platform-aware node that:
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1. **Analyzes your image** with a vision model
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2. **Takes your change request** (what to modify)
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3. **Generates platform-optimized prompts** for:
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- Flux (FLUX.1-dev/schnell)
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- Stable Diffusion XL
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- Wan 2.2 (adapted for images)
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- Hunyuan Image
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- Qwen Image
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- Qwen Image Edit
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**Each platform gets prompts formatted the way it works best!**
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## Why Platform-Specific Matters
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Different models need different prompting styles:
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| Platform | Prefers |
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|----------|---------|
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| **Flux** | Natural language, artistic, "in the style of", 75-150 tokens |
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| **SDXL** | Front-loaded quality, 40-75 tokens, token-optimized |
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| **Wan 2.2** | Technical terms, structured, cinematographic |
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| **Hunyuan** | Simple English, clear, realistic focus, 40-80 tokens |
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| **Qwen Image** | Natural, balanced, cultural elements, 50-100 tokens |
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| **Qwen Edit** | VERY concise, change-focused, 20-50 tokens |
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**This node knows these differences and formats prompts accordingly!**
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## Quick Start
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### Basic Setup
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```
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[Load Image] → [Image-to-Image Prompt Expander] → [Your Image Gen Node]
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```
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### Configuration
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1. **Load vision model** (LM Studio or Ollama)
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2. **Set platform**: flux, sd_xl, hunyuan_image, etc.
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3. **Describe changes**: "change dress to red, add sunset lighting"
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4. **Aesthetic controls**: style, lighting, composition
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5. **Generate!**
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## Node Parameters
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### Image & Changes
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**image** (Image Input)
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- Connect from Load Image
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- Your source image
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**change_request** (String)
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- What to change/modify
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- Examples:
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- "change dress color to red"
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- "add sunset lighting"
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- "replace background with forest"
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- "make it look like an oil painting"
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- (Leave empty to just enhance description)
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### Platform Selection
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**target_platform** (Dropdown)
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- `flux`: Flux.1-dev/schnell (natural, detailed)
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- `sd_xl`: Stable Diffusion XL (token-optimized)
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- `wan22`: Wan 2.2 adapted (technical, structured)
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- `hunyuan_image`: Hunyuan (clear, realistic)
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- `qwen_image`: Qwen (balanced, natural)
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- `qwen_image_edit`: Qwen Edit (concise changes)
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**Choose based on what image model you're using!**
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### Vision Model Settings
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**use_vision_model** (Boolean)
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- `True`: AI describes the image
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- `False`: Skip vision (manual description)
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**vision_backend** / **vision_model_name** / **vision_endpoint**
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- Same as Image-to-Video node
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- Set `vision_backend` to `lm_studio` or `ollama` for HTTP servers (typical workflow)
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- Set `vision_backend` to `qwen3_vl` to run a local Qwen3-VL model directly inside ComfyUI. In that case:
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- `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`).
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- Use the `vision_endpoint` field for optional overrides such as `quant=4bit;attn=sdpa` or `device=cuda:0`.
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- Works with Llama 3.2 Vision, LLaVA, MiniCPM V, or Qwen3-VL depending on backend choice.
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### Expansion Model Settings
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**expansion_backend** / **expansion_model_name** / **expansion_endpoint**
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- Model that formats the prompt
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- Can be same or different from vision
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- Use Llama 3.1, Qwen, etc.
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**temperature** (0.1 - 2.0)
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- `0.5-0.6`: Precise, platform-following
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- `0.7`: Balanced (recommended)
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- `0.8+`: More creative
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### Aesthetic Controls
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**art_style**
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- photorealistic, digital art, oil painting, watercolor
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- anime, sketch, 3D render, illustration, concept art
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**lighting_type**
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- natural, studio, soft, dramatic
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- golden hour, blue hour, rim, volumetric
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**composition**
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- rule of thirds, centered, symmetrical
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- golden ratio, dynamic, minimalist
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**color_palette**
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- vibrant, muted, monochrome
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- warm, cool, pastel, high contrast
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**mood**
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- serene, dramatic, mysterious
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- cheerful, melancholic, epic, intimate
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**detail_level**
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- standard, highly detailed, intricate
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- simplified, minimalist
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**quality_emphasis** (Boolean)
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- Adds platform-specific quality tokens
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- Recommended: True
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## Platform-Specific Examples
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### Example 1: Flux
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**Settings:**
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```
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target_platform: flux
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change_request: "make it look like a renaissance oil painting"
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art_style: oil painting
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lighting: dramatic lighting
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quality_emphasis: True
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```
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**Output style:**
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```
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"masterpiece, best quality, highly detailed, [image description],
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rendered as a renaissance oil painting with dramatic lighting,
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rich textures and classical composition, in the style of Caravaggio"
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```
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### Example 2: SDXL
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**Settings:**
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```
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target_platform: sd_xl
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change_request: "add cyberpunk neon lighting"
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color_palette: vibrant
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detail_level: highly detailed
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```
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**Output style:**
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```
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"masterpiece, best quality, highly detailed, [image description],
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with vibrant cyberpunk neon lighting, futuristic atmosphere"
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(Front-loaded quality, ~60 tokens)
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```
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### Example 3: Qwen Image Edit
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**Settings:**
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```
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target_platform: qwen_image_edit
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change_request: "change background to forest, keep subject"
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```
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**Output style:**
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```
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"replace background with forest, keep subject unchanged,
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seamless, natural, high quality"
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(Very concise, edit-focused, ~15 tokens)
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```
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### Example 4: Hunyuan Image
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**Settings:**
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```
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target_platform: hunyuan_image
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change_request: "add soft sunset lighting"
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lighting: golden hour
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```
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**Output style:**
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```
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"[clear image description], with soft sunset lighting,
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golden hour atmosphere, high quality, realistic, detailed"
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(Simple English, photorealistic focus)
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```
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## Workflow Examples
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### Workflow 1: Style Transfer
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**Goal:** Turn photo into painting
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```
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[Load Image: photo.jpg]
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↓
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[Image-to-Image Prompt Expander]
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- platform: flux
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- change: "convert to impressionist oil painting"
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- art_style: oil painting
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- mood: serene
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↓
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[Flux Image Gen]
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```
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### Workflow 2: Lighting Change
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**Goal:** Change time of day
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```
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[Load Image: daytime.jpg]
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↓
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[Image-to-Image Prompt Expander]
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- platform: sd_xl
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- change: "make it golden hour sunset"
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- lighting: golden hour
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- color_palette: warm
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↓
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[SDXL Image Gen]
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```
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### Workflow 3: Background Replacement
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**Goal:** Change background only
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```
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[Load Image: portrait.jpg]
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↓
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[Image-to-Image Prompt Expander]
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- platform: qwen_image_edit
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- change: "replace background with city skyline, keep person"
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- composition: centered
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↓
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[Qwen Edit Model]
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```
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### Workflow 4: Quality Enhancement
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**Goal:** Just enhance, no changes
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```
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[Load Image: low_quality.jpg]
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↓
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[Image-to-Image Prompt Expander]
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- platform: flux
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- change: "" (empty - just enhance)
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- detail_level: highly detailed
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- quality_emphasis: True
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↓
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[Flux Image Gen]
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```
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## Platform Comparison
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### When to Use Each Platform
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**Use Flux when:**
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- ✅ You want artistic/creative results
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- ✅ Style transfer (painting, illustration, etc.)
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- ✅ You have VRAM for quality (12GB+)
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- ✅ Longer prompts work better
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**Use SDXL when:**
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- ✅ Standard img2img workflows
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- ✅ You're familiar with SD ecosystem
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- ✅ Token efficiency matters
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- ✅ Strong negatives help
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**Use Wan 2.2 when:**
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- ✅ You want cinematic/technical quality
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- ✅ Precise lighting control needed
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- ✅ Professional composition important
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**Use Hunyuan when:**
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- ✅ Photorealistic results desired
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- ✅ Asian subjects/aesthetics
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- ✅ Simpler prompts work better
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- ✅ Clarity over complexity
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**Use Qwen Image when:**
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- ✅ Balanced natural results
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- ✅ Diverse cultural elements
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- ✅ Versatile style handling
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**Use Qwen Edit when:**
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- ✅ Specific edits only
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- ✅ Preserving most of image
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- ✅ Minimal changes needed
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- ✅ Fast iteration
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## Tips for Best Results
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### 1. Match Platform to Task
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**Creative/Artistic → Flux**
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```
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change: "make it look like a Studio Ghibli scene"
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platform: flux
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```
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**Realistic/Photo → Hunyuan or Qwen**
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```
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change: "enhance details, improve lighting"
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platform: hunyuan_image
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```
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**Specific Edits → Qwen Edit**
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```
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change: "change shirt color to blue"
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platform: qwen_image_edit
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```
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### 2. Be Specific with Changes
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❌ Bad: "make it better"
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✅ Good: "add dramatic rim lighting, increase contrast"
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❌ Bad: "change the vibe"
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✅ Good: "make it moody and mysterious with dark blue tones"
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### 3. Use Aesthetic Controls
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They adapt to each platform automatically:
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**Flux:** Adds "in the style of..."
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**SDXL:** Front-loads in prompt
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**Qwen Edit:** Skips if irrelevant
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**Hunyuan:** Simplifies terminology
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### 4. Temperature by Platform
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| Platform | Recommended Temp |
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|----------|------------------|
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| Flux | 0.7 (creative OK) |
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| SDXL | 0.6 (precise) |
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| Qwen Edit | 0.5 (very precise) |
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| Others | 0.6-0.7 (balanced) |
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### 5. Quality Emphasis
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**Turn ON for:**
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- Final renders
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- Flux, SDXL
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- When quality matters
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**Turn OFF for:**
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- Qwen Edit (conciseness > quality tokens)
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- Quick iterations
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- When prompt too long
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## Outputs
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1. **positive_prompt** - Platform-optimized prompt
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2. **negative_prompt** - Platform-specific negatives
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3. **image_description** - Vision model's analysis
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4. **status** - Processing info
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## Troubleshooting
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### "Platform not working as expected"
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**Check:**
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- Using correct model for platform?
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- Flux needs FLUX.1-dev/schnell
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- SDXL needs Stable Diffusion XL
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- etc.
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**Fix:**
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- Verify model matches platform
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- Check platform documentation
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### "Prompt too long/short"
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**Flux/SDXL:** Node automatically optimizes
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**Qwen Edit:** Should be very short (working as designed)
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**Others:** Adjust via temperature and detail_level
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### "Changes not applied correctly"
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**For specific edits:**
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- Use qwen_image_edit platform
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- Be very explicit: "change X to Y"
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- Lower temperature (0.5)
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**For creative changes:**
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- Use flux platform
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- Be descriptive
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- Use aesthetic controls
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### "Vision model fails"
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Same as Image-to-Video node:
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- Check model loaded
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- Verify it's vision-capable
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- Test endpoint
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## Comparison: Img2Vid vs Img2Img Nodes
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| Feature | Image-to-Video | Image-to-Image |
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|---------|----------------|----------------|
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| Purpose | Animate images | Transform images |
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| Focus | Motion, camera | Style, appearance |
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| Platforms | Video models | Image models |
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| Controls | Camera movement | Art style, lighting |
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| Output | Video prompts | Image prompts |
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**Use Img2Vid for:** Hunyuan Video, CogVideoX, etc.
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**Use Img2Img for:** Flux, SDXL, Qwen, etc.
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## Advanced: Two Models, One Node
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**Optimal setup:**
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```
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Vision Model (Port 1234):
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- Llama-3.2-11B-Vision
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- Analyzes image
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Expansion Model (Port 5000 or Ollama):
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- Llama-3.1-8B-Instruct
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- Formats prompt
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In node:
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vision_endpoint: http://localhost:1234/v1
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expansion_endpoint: http://localhost:5000/v1
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```
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**Why?**
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- Vision models are large (11B)
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- Text models faster for formatting
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- Better resource usage
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## Summary
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**Image-to-Image node gives you:**
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- ✅ Platform-aware prompt generation
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- ✅ Vision model image analysis
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- ✅ 6 platforms optimized
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- ✅ Advanced aesthetic controls
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- ✅ Automatic format adaptation
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**Supported platforms:**
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1. Flux - Creative, artistic, detailed
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2. SDXL - Token-optimized, standard
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3. Wan 2.2 - Technical, cinematic
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4. Hunyuan - Realistic, clear
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5. Qwen Image - Balanced, versatile
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6. Qwen Edit - Concise, edit-focused
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**Each gets prompts formatted the way IT works best!**
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---
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**Ready to transform images with AI!**
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Load vision model → Select platform → Describe changes → Generate!
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