ComfyUI#
The most powerful and modular stable diffusion GUI and backend.
Online#
Shortcuts#
| Keybind | Explanation |
|---|---|
| Ctrl + Enter | Queue up current graph for generation |
| Ctrl + Shift + Enter | Queue up current graph as first for generation |
| Ctrl + Z/Ctrl + Y | Undo/Redo |
| Ctrl + S | Save workflow |
| Ctrl + O | Load workflow |
| Ctrl + A | Select all nodes |
| Alt + C | Collapse/uncollapse selected nodes |
| Ctrl + M | Mute/unmute selected nodes |
| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
| Delete/Backspace | Delete selected nodes |
| Ctrl + Backspace | Delete the current graph |
| Space | Move the canvas around when held and moving the cursor |
| Ctrl/Shift + Click | Add clicked node to selection |
| Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
| Shift + Drag | Move multiple selected nodes at the same time |
| Ctrl + D | Load default graph |
Alt + + |
Canvas Zoom in |
Alt + - |
Canvas Zoom out |
| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out |
| Q | Toggle visibility of the queue |
| H | Toggle visibility of history |
| R | Refresh graph |
| Double-Click LMB | Open node quick search palette |
Installing#
Windows#
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the releases page.
Jupyter Notebook#
To run it on services like paperspace, kaggle or colab you can use my Jupyter Notebook
模型复用#
extra_model_paths.yaml.example重命名为extra_model_paths.yaml编辑path/to/stable-diffusion-webui/
使用ComfyUI的第一步#
预览生成的图像而不立即保存图像：
右键点击
Save Image节点，然后选择Remove。在画布的空白部分双击，输入
preview，然后点击PreviewImage选项。找到
VAE Decode节点的IMAGE输出，并将其连接到您刚添加的PreviewImage节点的images输入上。在菜单中点击
Queue Prompt，或在键盘上按Cmd+Enter或Ctrl+Enter，来生成第一张图像
K采样器#
输入#
Model：用于去噪的模型Positive：正向条件Negative：负向条件latent_image：将被去噪的潜在图像seed：用于创建噪声的随机种子control_after_generate：在每个提示后更改上述种子号的能力。节点可以randomize、increment、decrement或保持种子号fixedsteps：去噪过程中使用的步骤数。采样器允许进行的步骤越多，结果就越准确cfg：分类器自由引导（cfg）比例决定了采样器在实现提示内容方面的积极性。更高的比例强制图像更好地代表提示，但设置过高的比例会负面影响图像质量sampler_name：使用哪个采样器scheduler：使用哪种计划denoise：通过噪声擦除多少潜在图像的信息
输出#
LATENT：去噪后的潜在图像
Examples#
ComfyUI Basic Tutorial VN: All the art is made with ComfyUI. (early and not finished)
Runtime Environment#
Screenshots#
