MASK

Added Zml_w multi Mask method, I didn't add his Manual method for custom mask for now.

As he wrote on the comment section "However, I need to remind you that the effect of the horizontal mask is not good, and the model cannot understand this composition. Among the automatically generated masks, I recommend the vertical one the most, followed by the diagonal one."

So be aware of that.

I will add explanation on the article related on how to use the "switch" for Mask and for his second option for conditioning.

SAMPLER

Added few nodes to have a better control for clownshark sampler and explanation on how to use the Sampler for chain or standard sampling.

Removed some notes for loras, after few test Loras will not work if you put them in the prompt area, you can add them in the loras stacker but the result may be very random.

Removed the save image after sampler generation and added preview for each sampling method you use.

Added a group bypasser switch for the save with Metadata

Known bug

Sometimes for some reason I can't really explain, if you don't put enough or put too much condition in the prompt area this error will pop.

"The size of tensor a (924) must match the size of tensor b (308) at non-singleton dimension 3"

Apparently this is due to attention for mask area and conditioning related to it (if I understood correctly)

I'm trying to find a way of getting rid of this error but this seems very hard as I'm not an expert and just steal some part of other good workflow and putting them together.

Be aware that even sometime the error can happen on the face detailer if you add wildcard, condition or a lora to it, or even if you let the condition I'v put by default ({face|face,detailed face}).

You can try to remove everything to see if it work.

Regional prompt workflow for XL Models

This is a combination of some workflow I'v used in the past or recently.

Most of the part from the regional prompt was originally on this workflow

Huge thx to @zml_w for all the testing and the help

Upscale Method for V2 by @LevPat
/model/1994653/custom-upscale-flow

USER GUIDE

https://civitai.com/articles/18257

FOR V2 Dense Diffusion and Attention Couple

I'v made two separte workflow with different Regional prompt method, both of them are in the acrhive if you want to try.

Dense Diffusion

So the error "The size of tensor a (924) must match the size of tensor b (308) at non-singleton dimension 3"

Is still here with Dense Diffusion BUT now I know 100% why it happens, so I will detail everythings how to avoid it.

First you can put almost everything in every positive prompt, a lot of detail or action for left char and almost nothing for 2nd character will not trigger this error.
The only way this error will pop is Positive VS Negative Prompt.

So why it happens then, almost exclusivly with Embedings, because of Tokens count and the way Dense Diffusion work, the size of Tokens need to be almost the same, and when you put emebeddings, like Lazyhand, Lazyneg, etc will use a lot of Tokens (more exactly tokens Chunks)

So how to avoid it, you can either not use emebeddings at all, I'v added note on the workflow with good (I think) quality prompt.

You can still use Embeddings but you wiil need to balance them between positive and Negative. So for example if you put Lazyhand + Lazyneg in Negative, you will need to use at least one or more embeddings in the positive quality to get the same size of Tokens chunks.

So because of that I'v added a custom nodes made by me (and GPT) to Normalize the negative quality, you can either turn it off or on, I'v put pics for comparaison in the archive.

If you'r not happy or don't like this method I'v made another Workflow with a different nodes called Attention couple.

Attention Couple

With Attention Couple this error will never pop, then why not using it and getting rid of Dense Diffusion ?
Attention couple work great and can have pretty good result, but in my testing I saw two major flaw with it.
First the Lora did not get applied the same way with it, Dense Diffusion seems to have a better blend for the model and loras.
Second the interaction seems more difficult too, I did not use it very extensively but the few image I'v made seems to have less prompt adherence.

Here is every nodes you will need for both

For Dense Diffusion

A small custom node is in the archive for the Negative Normalization, just need to put it in custom nodes folder. No requirements needed.

For Attention Couple

Everythings above +
https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb
https://github.com/laksjdjf/cgem156-ComfyUI
If you have trouble with FAILED IMPORT for cgem156 you can try this fix
https://github.com/laksjdjf/cgem156-ComfyUI/issues/17#issuecomment-2918745574
Worked for me with ComfyUI desktop App

Older Version

I'v added a chain sampler because I love this method, the render time and the result it gave.

This is quiet an advanced workflow so you will need a lot of custom nodes, some may not work with ComfyUI manager, I will link them here if you want to install manually.

Make sure you activate your venv environment for the requirements.txt for custom nodes.

There is a simplified version in the archive if you want to try without all the nodes for HiresFix, Face detailer, upscale and color match.

I'm not responsible if you break your ComfyUI install, some of the nodes may use different Pytorch version or requirements you already use.

https://github.com/ltdrdata/ComfyUI-Impact-Pack

https://github.com/pythongosssss/ComfyUI-Custom-Scripts

https://github.com/rgthree/rgthree-comfy

https://github.com/yolain/ComfyUI-Easy-Use

https://github.com/shadowcz007/comfyui-mixlab-nodes

https://github.com/jags111/efficiency-nodes-comfyui

https://github.com/ssitu/ComfyUI_UltimateSDUpscale

https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes

https://github.com/cubiq/ComfyUI_essentials