Comfy-Org · mintlify · Mar 21, 2026
diff --git a/docs.json b/docs.json
@@ -120,6 +120,7 @@
                     "pages": [
                       "tutorials/basic/text-to-image",
                       "tutorials/basic/image-to-image",
+                      "tutorials/basic/style-transfer",
                       "tutorials/basic/inpaint",
                       "tutorials/basic/outpaint",
                       "tutorials/basic/upscale",
@@ -870,6 +871,7 @@
                     "pages": [
                       "zh-CN/tutorials/basic/text-to-image",
                       "zh-CN/tutorials/basic/image-to-image",
+                      "zh-CN/tutorials/basic/style-transfer",
                       "zh-CN/tutorials/basic/inpaint",
                       "zh-CN/tutorials/basic/outpaint",
                       "zh-CN/tutorials/basic/upscale",

diff --git a/tutorials/basic/style-transfer.mdx b/tutorials/basic/style-transfer.mdx
@@ -0,0 +1,144 @@
+---
+title: "ComfyUI Style Transfer Guide"
+sidebarTitle: "Style Transfer"
+description: "Learn how to apply artistic styles from reference images to your generations using style transfer workflows in ComfyUI"
+---
+
+Style transfer lets you apply the visual style of one image to the content of another. Instead of manually describing a style in your prompt, you provide a reference image and ComfyUI extracts and applies that style to your generation.
+
+Common use cases include:
+- Applying an artist's style to your own compositions
+- Converting photos into paintings, sketches, or illustrations
+- Maintaining a consistent visual style across multiple images
+- Combining the composition of one image with the aesthetics of another
+
+This guide covers three approaches to style transfer in ComfyUI, from basic to advanced:
+
+1. **Image-to-image style transfer** — the simplest method using prompts and denoise
+2. **IP-Adapter style transfer** — using a reference image to guide style without changing composition
+3. **ControlNet + IP-Adapter** — combining structural control with style guidance
+
+## Method 1: image-to-image style transfer
+
+The simplest way to do style transfer is through the [image-to-image](/tutorials/basic/image-to-image) workflow with style-focused prompts.
+
+### How it works
+
+This method encodes your reference image into latent space, then denoises it with a style-descriptive prompt. The `denoise` value in the KSampler controls how much the output deviates from the original.
+
+### When to use
+
+- Quick style experiments
+- When you want to change both style and content
+- When you don't need precise control over which elements change
+
+### Key parameters
+
+| Parameter | Recommended range | Effect |
+|-----------|------------------|--------|
+| `denoise` | 0.4–0.7 | Lower values keep more of the original image; higher values allow more stylistic freedom |
+| Prompt | Style-descriptive | Describe the target style (e.g., "oil painting style, impressionist, thick brushstrokes") |
+
+<Tip>
+Start with a `denoise` value of 0.55 and adjust from there. Values below 0.3 may not change the style enough, while values above 0.8 may lose the original composition entirely.
+</Tip>
+
+For more details on this approach, see the [image-to-image tutorial](/tutorials/basic/image-to-image).
+
+## Method 2: IP-Adapter style transfer
+
+IP-Adapter (Image Prompt Adapter) is the most popular method for style transfer in ComfyUI. It allows you to use a reference image as a visual prompt, guiding the generation style without relying solely on text descriptions.
+
+### Model installation
+
+You need two models for IP-Adapter style transfer:
+
+1. **CLIP Vision model** — Download [CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) and place it in your `ComfyUI/models/clip_vision` folder
+
+2. **IP-Adapter model** — Download [ip-adapter_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors) and place it in your `ComfyUI/models/ipadapter` folder
+
+3. **Checkpoint model** — Download [v1-5-pruned-emaonly-fp16.safetensors](https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors) and place it in your `ComfyUI/models/checkpoints` folder
+
+<Note>
+For SDXL-based workflows, use the corresponding SDXL IP-Adapter models instead. Check the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) repository for the full list of available models.
+</Note>
+
+### Workflow overview
+
+The IP-Adapter style transfer workflow uses these key nodes:
+
+1. **Load Checkpoint** — loads your base model
+2. **Load Image** — loads your style reference image
+3. **CLIP Vision Encode** — encodes the reference image into a visual embedding
+4. **IPAdapter Apply** — applies the style embedding to guide generation
+5. **KSampler** — generates the final image
+
+### Key parameters
+
+| Parameter | Recommended range | Effect |
+|-----------|------------------|--------|
+| `weight` | 0.5–1.0 | Controls how strongly the reference style influences the output |
+| `noise` | 0.0–0.5 | Adds variation; higher values create more diverse results |
+
+<Tip>
+For pure style transfer (keeping your own composition), use a weight around 0.6–0.8. Higher weights may start transferring content elements from the reference image as well.
+</Tip>
+
+### Style vs. composition control
+
+IP-Adapter transfers both style and content by default. To focus primarily on style:
+
+- Use a lower `weight` value (0.5–0.7)
+- Write a detailed prompt describing your desired composition
+- The prompt guides the composition while IP-Adapter guides the style
+
+## Method 3: ControlNet + IP-Adapter
+
+For maximum control, combine ControlNet (for structure) with IP-Adapter (for style). This lets you precisely define the composition while applying a reference style.
+
+### How it works
+
+- **ControlNet** extracts structural information (edges, depth, pose) from your input image and enforces that structure during generation
+- **IP-Adapter** provides style guidance from a separate reference image
+- Together, they let you say: "generate an image with *this* structure in *that* style"
+
+### Additional models needed
+
+In addition to the IP-Adapter models above, you need a ControlNet model. For example:
+
+- **Canny ControlNet** — Download [control_v11p_sd15_canny_fp16.safetensors](https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors) and place it in your `ComfyUI/models/controlnet` folder
+
+### Workflow overview
+
+This workflow extends the IP-Adapter workflow with ControlNet:
+
+1. **Load Image (content)** — the image whose structure you want to preserve
+2. **Canny edge detection** — extracts edges from the content image
+3. **ControlNet Apply** — enforces the structural guidance
+4. **Load Image (style)** — the reference image whose style you want to apply
+5. **CLIP Vision Encode + IPAdapter Apply** — applies style from the reference
+6. **KSampler** — generates the final image combining both controls
+
+### Recommended settings
+
+| Parameter | Recommended value | Notes |
+|-----------|------------------|-------|
+| ControlNet `strength` | 0.7–1.0 | Higher values enforce structure more strictly |
+| IP-Adapter `weight` | 0.6–0.8 | Balance between original and reference style |
+| KSampler `denoise` | 1.0 | Full denoise since ControlNet provides structure |
+
+## Tips for better results
+
+- **Choose clear style references** — images with distinct, consistent styles work best. Avoid reference images with mixed or subtle styles.
+- **Match model to task** — SD 1.5 models work well for general style transfer. SDXL models produce higher quality results but require SDXL-specific IP-Adapter models.
+- **Iterate on weights** — small changes in IP-Adapter weight (0.05 increments) can significantly affect results. Take time to find the sweet spot.
+- **Combine with LoRA** — for consistent style across many images, consider training a [LoRA](/tutorials/basic/lora) on your target style and combining it with IP-Adapter for even stronger style adherence.
+- **Use negative prompts** — describe what you don't want (e.g., "blurry, low quality, distorted") to improve output quality.
+
+## Try it yourself
+
+1. Start with method 1 (image-to-image) to understand how denoise affects style transfer
+2. Move to method 2 (IP-Adapter) for more precise style control using a reference image
+3. Combine ControlNet with IP-Adapter (method 3) when you need both structural accuracy and style transfer
+
+For more background on style transfer techniques in ComfyUI, see the [complete style transfer handbook](https://blog.comfy.org/p/the-complete-style-transfer-handbook) on the Comfy blog.
diff --git a/zh-CN/tutorials/basic/style-transfer.mdx b/zh-CN/tutorials/basic/style-transfer.mdx
@@ -0,0 +1,144 @@
+---
+title: "ComfyUI 风格迁移指南"
+sidebarTitle: "风格迁移"
+description: "学习如何在 ComfyUI 中使用风格迁移工作流，将参考图片的艺术风格应用到你的生成图像中"
+---
+
+风格迁移（Style Transfer）可以将一张图片的视觉风格应用到另一张图片的内容上。你不需要在提示词中手动描述风格，只需提供一张参考图片，ComfyUI 就能提取并应用该风格到你的生成结果中。
+
+常见使用场景包括：
+- 将某位艺术家的风格应用到你自己的构图中
+- 将照片转换为油画、素描或插画风格
+- 在多张图片之间保持一致的视觉风格
+- 将一张图片的构图与另一张图片的美学风格结合
+
+本指南涵盖 ComfyUI 中三种风格迁移方法，从基础到进阶：
+
+1. **图生图风格迁移** — 使用提示词和去噪参数的最简单方法
+2. **IP-Adapter 风格迁移** — 使用参考图片引导风格，不改变构图
+3. **ControlNet + IP-Adapter** — 结合结构控制与风格引导
+
+## 方法一：图生图风格迁移
+
+最简单的风格迁移方式是通过[图生图](/zh-CN/tutorials/basic/image-to-image)工作流配合风格描述提示词。
+
+### 工作原理
+
+该方法将参考图片编码到潜空间，然后使用风格描述提示词进行去噪。KSampler 中的 `denoise` 值控制输出与原图的偏离程度。
+
+### 适用场景
+
+- 快速风格实验
+- 需要同时改变风格和内容时
+- 不需要精确控制哪些元素发生变化时
+
+### 关键参数
+
+| 参数 | 推荐范围 | 效果 |
+|------|---------|------|
+| `denoise` | 0.4–0.7 | 较低值保留更多原始图像；较高值允许更多风格自由度 |
+| 提示词 | 风格描述 | 描述目标风格（如 "oil painting style, impressionist, thick brushstrokes"） |
+
+<Tip>
+建议从 `denoise` 值 0.55 开始调整。低于 0.3 的值可能无法充分改变风格，而高于 0.8 的值可能会完全丢失原始构图。
+</Tip>
+
+更多详情请参阅[图生图教程](/zh-CN/tutorials/basic/image-to-image)。
+
+## 方法二：IP-Adapter 风格迁移
+
+IP-Adapter（Image Prompt Adapter）是 ComfyUI 中最流行的风格迁移方法。它允许你将参考图片作为视觉提示，引导生成风格，而不仅仅依赖文字描述。
+
+### 模型安装
+
+IP-Adapter 风格迁移需要两个模型：
+
+1. **CLIP Vision 模型** — 下载 [CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) 并放入 `ComfyUI/models/clip_vision` 文件夹
+
+2. **IP-Adapter 模型** — 下载 [ip-adapter_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors) 并放入 `ComfyUI/models/ipadapter` 文件夹
+
+3. **基础模型** — 下载 [v1-5-pruned-emaonly-fp16.safetensors](https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors) 并放入 `ComfyUI/models/checkpoints` 文件夹
+
+<Note>
+对于基于 SDXL 的工作流，请使用对应的 SDXL IP-Adapter 模型。完整模型列表请查看 [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) 仓库。
+</Note>
+
+### 工作流概述
+
+IP-Adapter 风格迁移工作流使用以下关键节点：
+
+1. **Load Checkpoint** — 加载基础模型
+2. **Load Image** — 加载风格参考图片
+3. **CLIP Vision Encode** — 将参考图片编码为视觉嵌入
+4. **IPAdapter Apply** — 应用风格嵌入来引导生成
+5. **KSampler** — 生成最终图像
+
+### 关键参数
+
+| 参数 | 推荐范围 | 效果 |
+|------|---------|------|
+| `weight` | 0.5–1.0 | 控制参考风格对输出的影响强度 |
+| `noise` | 0.0–0.5 | 增加变化；较高值产生更多样的结果 |
+
+<Tip>
+对于纯风格迁移（保持自己的构图），建议使用 0.6–0.8 左右的 weight 值。更高的值可能会开始从参考图片中迁移内容元素。
+</Tip>
+
+### 风格与构图控制
+
+IP-Adapter 默认同时迁移风格和内容。如果主要关注风格：
+
+- 使用较低的 `weight` 值（0.5–0.7）
+- 编写详细的提示词描述你想要的构图
+- 提示词引导构图，而 IP-Adapter 引导风格
+
+## 方法三：ControlNet + IP-Adapter
+
+要实现最大程度的控制，可以将 ControlNet（用于结构）与 IP-Adapter（用于风格）结合使用。这样你可以精确定义构图，同时应用参考风格。
+
+### 工作原理
+
+- **ControlNet** 从输入图片中提取结构信息（边缘、深度、姿态），并在生成过程中强制执行该结构
+- **IP-Adapter** 从另一张参考图片提供风格引导
+- 两者结合，你可以说："生成一张具有 *这个* 结构、*那个* 风格的图像"
+
+### 额外需要的模型
+
+除了上面的 IP-Adapter 模型外，还需要一个 ControlNet 模型。例如：
+
+- **Canny ControlNet** — 下载 [control_v11p_sd15_canny_fp16.safetensors](https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors) 并放入 `ComfyUI/models/controlnet` 文件夹
+
+### 工作流概述
+
+此工作流在 IP-Adapter 工作流基础上增加了 ControlNet：
+
+1. **Load Image（内容图）** — 你想保留其结构的图片
+2. **Canny 边缘检测** — 从内容图中提取边缘
+3. **ControlNet Apply** — 强制执行结构引导
+4. **Load Image（风格图）** — 你想应用其风格的参考图片
+5. **CLIP Vision Encode + IPAdapter Apply** — 应用参考图的风格
+6. **KSampler** — 生成结合两种控制的最终图像
+
+### 推荐设置
+
+| 参数 | 推荐值 | 说明 |
+|------|--------|------|
+| ControlNet `strength` | 0.7–1.0 | 较高值更严格地强制执行结构 |
+| IP-Adapter `weight` | 0.6–0.8 | 在原始风格和参考风格之间取得平衡 |
+| KSampler `denoise` | 1.0 | 完全去噪，因为 ControlNet 提供了结构 |
+
+## 获得更好效果的技巧
+
+- **选择风格鲜明的参考图** — 具有明显、一致风格的图片效果最好。避免使用风格混杂或不明显的参考图。
+- **匹配模型** — SD 1.5 模型适用于一般风格迁移。SDXL 模型可产生更高质量的结果，但需要 SDXL 专用的 IP-Adapter 模型。
+- **反复调整权重** — IP-Adapter weight 的微小变化（0.05 的增量）可以显著影响结果。花时间找到最佳值。
+- **结合 LoRA 使用** — 如果需要在多张图片间保持一致风格，可以考虑在目标风格上训练一个 [LoRA](/zh-CN/tutorials/basic/lora)，并与 IP-Adapter 结合使用以获得更强的风格一致性。
+- **使用负面提示词** — 描述你不想要的内容（如 "blurry, low quality, distorted"）以提高输出质量。
+
+## 自己动手试试
+
+1. 从方法一（图生图）开始，了解去噪参数如何影响风格迁移
+2. 进阶到方法二（IP-Adapter），使用参考图片实现更精确的风格控制
+3. 当需要同时保证结构准确性和风格迁移时，结合使用 ControlNet 和 IP-Adapter（方法三）
+
+更多关于 ComfyUI 风格迁移技术的背景知识，请参阅 Comfy 博客上的[完整风格迁移手册](https://blog.comfy.org/p/the-complete-style-transfer-handbook)。