This is a completely different set of nodes than Comfy's own KSampler series. This set of nodes is based on Diffusers, which makes it easier to import models, apply prompts with weights, inpaint, reference only, controlnet, etc.
Jannchie's ComfyUI custom nodes.
This is a completely different set of nodes than Comfy's own KSampler series. This set of nodes is based on Diffusers, which makes it easier to import models, apply prompts with weights, inpaint, reference only, controlnet, etc.
In the custom_nodes
directory, run
git clone https://github.com/Jannchie/ComfyUI-J
cd ComfyUI-J
pip install -r requirements.txt
You only have to deal with 4 nodes. The default comfy workflow uses 7 nodes to achieve the same result.
ref_only supports two modes: attn and attn + adain, and can adjust the style fidelity parameter to control the style.
ContorlNet is also easier to use. A DiffusersControlnetLoader node is provided for loading models. This node automatically detects if the corresponding ControlNet has been downloaded locally, and pulls the model from the huggingface if it has not.
This is a composite application of diffusers pipeline custom node. Includes:
This is a demonstration of a simple workflow for properly dressing a character.
A checkpoint for stablediffusion 1.5 is all your need. But for full automation, I use the Comfyui_segformer_b2_clothes
custom node for generating masks. you can draw your own masks without it.
Unlike Web UI and Comfy, Diffusers is an image generation tool for researchers. It has a large ecosystem, a clearer code structure and a simpler interface.
ComfyUI's KSampler is nice, but some of the features are incomplete or hard to be access, it's 2042 and I still haven't found a good Reference Only implementation; Inpaint also works differently than I thought it would; I don't understand at all why ControlNet's nodes need to pass in a CLIP; and I don't want to deal with what's going on with Latent, please just return an Image instead of making me decode it with a vae. Diffusers provides a pipeline wrapper that makes generation a lot easier.
But combining research results is not an easy task, Comfy is good at combining and sharing combinations with others. While debugging custom nodes as a developer can be a pain, using Comfy makes it faster to verify and share.