Hi folks,

I've just published a huge update to the Inpaint Crop and Stitch nodes.

"✂️ Inpaint Crop" crops the image around the masked area, taking care of pre-resizing the image if desired, extending it for outpainting, filling mask holes, growing or blurring the mask, cutting around a larger context area, and resizing the cropped area to a target resolution.

The cropped image can be used in any standard workflow for sampling.

Then, the "✂️ Inpaint Stitch" node stitches the inpainted image back into the original image without altering unmasked areas.

The main advantages of inpainting only in a masked area with these nodes are:

• It is much faster than sampling the whole image.
• It enables setting the right amount of context from the image for the prompt to be more accurately represented in the generated picture.Using this approach, you can navigate the tradeoffs between detail and speed, context and speed, and accuracy on representation of the prompt and context.
• It enables upscaling before sampling in order to generate more detail, then stitching back in the original picture.
• It enables downscaling before sampling if the area is too large, in order to avoid artifacts such as double heads or double bodies.
• It enables forcing a specific resolution (e.g. 1024x1024 for SDXL models).
• It does not modify the unmasked part of the image, not even passing it through VAE encode and decode.
• It takes care of blending automatically.

What's New?

This update does not break old workflows - but introduces new improved version of the nodes that you'd have to switch to: '✂️ Inpaint Crop (Improved)' and '✂️ Inpaint Stitch (Improved)'.

The improvements are:

• Stitching is now way more precise. In the previous version, stitching an image back into place could shift it by one pixel. That will not happen anymore.
• Images are now cropped before being resized. In the past, they were resized before being cropped. This triggered crashes when the input image was large and the masked area was small.
• Images are now not extended more than necessary. In the past, they were extended x3, which was memory inefficient.
• The cropped area will stay inside of the image if possible. In the past, the cropped area was centered around the mask and would go out of the image even if not needed.
• Fill mask holes will now keep the mask as float values. In the past, it turned the mask into binary (yes/no only).
• Added a hipass filter for mask that ignores values below a threshold. In the past, sometimes mask with a 0.01 value (basically black / no mask) would be considered mask, which was very confusing to users.
• In the (now rare) case that extending out of the image is needed, instead of mirroring the original image, the edges are extended. Mirroring caused confusion among users in the past.
• Integrated preresize and extend for outpainting in the crop node. In the past, they were external and could interact weirdly with features, e.g. expanding for outpainting on the four directions and having "fill_mask_holes" would cause the mask to be fully set across the whole image.
• Now works when passing one mask for several images or one image for several masks.
• Streamlined many options, e.g. merged the blur and blend features in a single parameter, removed the ranged size option, removed context_expand_pixels as factor is more intuitive, etc.

The Inpaint Crop and Stitch nodes can be downloaded using ComfyUI-Manager, just look for "Inpaint-CropAndStitch" and install the latest version. The GitHub repository is here.

Video Tutorial

There's a full video tutorial in YouTube: https://www.youtube.com/watch?v=mI0UWm7BNtQ . It is for the previous version of the nodes but still useful to see how to plug the node and use the context mask.

Examples

'Crop' outputs the cropped image and mask. You can do whatever you want with them (except resizing). Then, 'Stitch' merges the resulting image back in place.

(drag and droppable png workflow)

Another example, this one with Flux, this time using a context mask to specify the area of relevant context.

(drag and droppable png workflow)

Want to say thanks? Just share these nodes, use them in your workflow, and please star the github repository.

Enjoy!

You can find the guide here.

I want to share my creative workflow about Krita.

I don't use regions, i prefer to guide my generations with brushes and colors, then i prompt about it to help the checkpoint understand what is seeing on the canvas.

I often create a layer filter with some noise, this adds tons of details, playing with opacity and graininess.

The first pass is done with NoobAI, just because it has way more creative angle views and it's more dynamic than many other checkpoints, even tho it's way less sharp.

After this i do a second pass with a denoise of about 25% with another checkpoint and tons of loras, as you can see, i have used T-Illunai this time, with many wonderful loras.

I hope it was helpful and i hope you can unlock some creative idea with my workflow :)

Bladeborne Rider - By HailoKnight

"Forged in battle, bound by steel — she rides where legends are born."

Ride into battle with my latest Illustrious LoRA!

These models never cease to amaze me how far we can push creativity!

And the best part of it is to see what you guys can make with it! :O

Example prompt used:
"Flatline, Flat vector illustration,,masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, dynamic close up angle, close up, Beautiful Evil ghost woman, long white hair, see through, glowing blue eyes, wearing a dress,, dynamic close up pose, blue electricity sparks, riding a blue glowing skeleton horse in to battle, sitting on the back of a see through skeleton horse, wielding a glowing sword, holofoil glitter, faint, glowing, otherworldly glow, graveyard in background"

Hope you can enjoy!

You can find the lora here:
https://www.shakker.ai/modelinfo/dbc7e311c4644d8abcbded2e74543233?from=personal_page&versionUuid=a227c9c83ddb40a890c76fb0abaf4c17

Hey everyone! This is my second post here — I’ve been experimenting a lot lately and just started editing my AI-generated images.

In the image I’m sharing, the right side is the raw output from Stable Diffusion. While it looks impressive at first, I feel like it has too much detail — to the point that it starts looking unnatural or even a bit absurd. That’s something I often notice with AI images: the extreme level of detail can feel artificial or inhuman.

On the left side, I edited the image using Forge and a bit of Krita. I mainly focused on removing weird artifacts, softening some overly sharp areas, and dialing back that “hyper-detailed” look to make it feel more natural and human.

I’d love to know:
– Do you also edit your AI images after generation?
– Or do you usually keep the raw outputs as they are?
– Any tips or tools you recommend?

Thanks for checking it out! I’m still learning, so any feedback is more than welcome 😊

My CivitAI: espadaz Creator Profile | Civitai

Check it out

https://preview.redd.it/7577qecrp7te1.png?width=2560&format=png&auto=webp&s=092d3421d904385f482d67b04442cd1fe7dda9f6

Hi, I've been using a1111 SD 1.5 for over a year, but recently I get this error. Can i get some help? I also get prompted to log-in to github now which didn't happen until recently...

Images created with Flux Dev. Animated with Wan 2.1-Fun 1.3b with keyframes at the beginning, middle and end.

Prompt: The cosmic entity slowly emerges from the darkness. Its form, a nightmarish blend of organic and arcane, shifts subtly. Tentacles writhe behind its head, their crimson tips glowing faintly. Its eyes blinks slowly, the pink iris reflecting the starlight. Golden, jagged horns gleam as they catch the cosmic star light in outer space.

See Static? You’re Seeing The Last Remnants of The Big Bang

One percent of your old TV's static comes from CMBR (Cosmic Microwave Background Radiation). CMBR is the electromagnetic radiation left over from the Big Bang. We humans, 13.8 billion years later, are still seeing the leftover energy from that event

Hi everyone,
It’s been about 4 months since TRELLIS was released, and it has been super useful for my work—especially for generating 3D models in Gaussian Splatting format from .ply files.

Recently, I’ve been digging deeper into how Trellis works to see if there are ways to improve the output quality. Specifically, I’m exploring ways to evaluate and enhance rendered images from 360-degree angles, aiming for sharper and more consistent results. (Previously, I mainly focused on improving image quality by using better image generation models like Flux-Pro 1.1 or optimizing evaluation metrics.)

I also came across Hunyan3D V2, which looks promising—but unfortunately, it doesn’t support exporting to Gaussian Splatting format.

Has anyone here tried improving Trellis, or has any idea how to enhance the 3D generation pipeline? Maybe we can brainstorm together for the benefit of the community.

Example trellis + flux pro 1.1:

Prompt: 3D butterfly with colourful wings

Image from Flux pro 1.1

Output trellis