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

(drag and droppable png workflow)

(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!

I have never charged a dime for any LORA I have ever made, nor would I ever, because every AI model is trained on copyrighted images. This is supposed to be an open source/sharing community. I 100% fully encourage people to leak and pirate any diffusion model they want and to never pay a dime. When things are set to "generation only" on CivitAI like Illustrious 2.0, and you have people like the makers of illustrious holding back releases or offering "paid" downloads, they are trying to destroy what is so valuable about enthusiast/hobbyist AI. That it is all part of the open source community.

"But it costs money to train"

Yeah, no shit. I've rented H100 and H200s. I know it's very expensive. But the point is you do it for the love of the game, or you probably shouldn't do it at all. If you're after money, go join Open AI or Meta. You don't deserve a dime for operating on top of a community that was literally designed to be open.

The point: AI is built upon pirated work. Whether you want to admit it or not, we're all pirates. Pirates who charge pirates should have their boat sunk via cannon fire. It's obscene and outrageous how people try to grift open-source-adjacent communities.

You created a model that was built on another person's model that was built on another person's model that was built using copyrighted material. You're never getting a dime from me. Release your model or STFU and wait for someone else to replace you. NEVER GIVE MONEY TO GRIFTERS.

As soon as someone makes a very popular model, they try to "cash out" and use hype/anticipation to delay releasing a model to start milking and squeezing people to buy "generations" on their website or to buy the "paid" or "pro" version of their model.

IF PEOPLE WANTED TO ENTRUST THEIR PRIVACY TO ONLINE GENERATORS THEY WOULDN'T BE INVESTING IN HARDWARE IN THE FIRST PLACE. NEVER FORGET WHAT AI DUNGEON DID. THE HEART OF THIS COMMUNITY HAS ALWAYS BEEN IN LOCAL GENERATION. GRIFTERS WHO TRY TO WOO YOU INTO SACRIFICING YOUR PRIVACY DESERVE NONE OF YOUR MONEY.

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 :)

It can make a 10 Step 1024X1024 Flux image in 3.6 seconds (on a RTX 3090) with a First Bock Cache of 0.150.

Then upscale to 2024X2024 in 13.5 seconds.

My Custom SVDQuant finetune is here:https://civitai.com/models/686814/jib-mix-flux

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

I will make this post so I can quickly link it for newcomers who use AMD and want to try Stable Diffusion

So hey there, welcome!

Here’s the deal. AMD is a pain in the ass, not only on Linux but especially on Windows.

History and Preface

You might have heard of CUDA cores. basically, they’re simple but many processors inside your Nvidia GPU.

CUDA is also a compute platform, where developers can use the GPU not just for rendering graphics, but also for doing general-purpose calculations (like AI stuff).

Now, CUDA is closed-source and exclusive to Nvidia.

In general, there are 3 major compute platforms:

• CUDA → Nvidia
• OpenCL → Any vendor that follows Khronos specification
• ROCm / HIP / ZLUDA → AMD

Honestly, the best product Nvidia has ever made is their GPU. Their second best? CUDA.

As for AMD, things are a bit messy. They have 2 or 3 different compute platforms.

• ROCm and HIP → made by AMD
• ZLUDA → originally third-party, got support from AMD, but later AMD dropped it to focus back on ROCm/HIP.

ROCm is AMD’s equivalent to CUDA.

HIP is like a transpiler, converting Nvidia CUDA code into AMD ROCm-compatible code.

Now that you know the basics, here’s the real problem...

ROCm is mainly developed and supported for Linux.
ZLUDA is the one trying to cover the Windows side of things.

So what’s the catch?

PyTorch.

PyTorch supports multiple hardware accelerator backends like CUDA and ROCm. Internally, PyTorch will talk to these backends (well, kinda , let’s not talk about Dynamo and Inductor here).

It has logic like:

if device == CUDA:
    # do CUDA stuff

Same thing happens in A1111 or ComfyUI, where there’s an option like:

--skip-cuda-check

This basically asks your OS:
"Hey, is there any usable GPU (CUDA)?"
If not, fallback to CPU.

So, if you’re using AMD on Linux → you need ROCm installed and PyTorch built with ROCm support.

If you’re using AMD on Windows → you can try ZLUDA.

Here’s a good video about it:
https://www.youtube.com/watch?v=n8RhNoAenvM

You might say, "gee isn’t CUDA an NVIDIA thing? Why does ROCm check for CUDA instead of checking for ROCm directly?"

Simple answer: AMD basically went "if you can’t beat 'em, might as well join 'em." (This part i am not so sure)

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

I wanted to share Flux Image Generator, a project I've been working on to make using the Black Forest Labs API more accessible and user-friendly. I created this because I couldn't find a self-hosted API-only application that allows complete use of the API through an easy-to-use interface.

GitHub Repository: https://github.com/Tremontaine/flux-ui

What it does:

• Full Flux API support - Works with all models (Pro, Pro 1.1, Ultra, Dev)
• Multiple generation modes in an intuitive tabbed interface:
  • Standard text-to-image generation with fine-grained control
  • Inpainting with an interactive brush tool for precise editing
  • Outpainting to extend images in any direction
  • Image remixing using existing images as prompts
  • Control-based generation (Canny edge & depth maps)
• Complete finetune management - Create new finetunes, view details, and use your custom models
• Built-in gallery that stores images locally in your browser
• Runs locally on your machine, with a lightweight Node.js server to handle API calls

Why I built it:

I built this primarily because I wanted a self-hosted solution I could run on my home server. Now I can connect to my home server via Wireguard and access the Flux API from anywhere.

How to use it:

Just clone the repo, run npm install and npm start, then navigate to http://localhost:3589. Enter your BFL API key and you're ready.

Bonjour,
cela fais plusieurs fois que j'essai de créer un lora
le fichier safetensors est correctement créer sans erreur sur la console, mais quand je le sélectionne dans stable diffusion j'obtiens un truc affreux pendant la creation et j"obtien une image gris, c'est vriament frustrant quand je pense au temps que j'y passe pour le créer, ca me prend la journée.
auriez vous une réponse a mon problème merci par avance

It's quite good at making stylized sprites in perspective, but it seems to really suck at actually replicating a general in-game sprite art style that could be used for real-time gameplay. Or am I just prompting it wrong?

Hello,
i need help, i just download pinocchio in order to setup wan 2.1 on my windows, and i see that it has a warning stating (NVIDIA only), is there a way to setup wan 2.1 with RADEON cards ?
Thank you,

Ive experimenting lately with ChatGPT enhancing my prompts and ive always seen in ChatGPT templates for enchancing to limit the enhanced prompt to 150 words but the prompts kinda seem rather short to me if you compare them to other stuff you find on civitai for example so how long as in prompt length exactly can i "enhance" my prompt with chaptgpt without overstressing my model?

After a lot of work, I managed to get Stable Diffusion to work on my PC (Ryzen 5 3600 + RX 6650 XT 8GB). I'm well aware that the use of SD on AMD platforms isn't yet complete, but I wanted recommendations for improving performance in image generation. Because a generation is taking 1 hour on average.

And I think SD is using the processor, not the GPU.

This was the last video I used as a tutorial for the installation: https://www.youtube.com/watch?v=8xR0vms0e0U

This is me arguments:

COMMANDLINE_ARGS=--opt-sub-quad-attention --lowvram --disable-nan-check --skip-torch-cuda-test --no-half

Edit 2 - Yes, Windows 11

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 there,

I'm a 3D modeler who cannot draw to save my life. I downloaded SwarmUI with some models from CivitAI with the plan to take my 3D models, pose them in blender, and then have the AI model handle turning them into a anime style drawing essentially.

I've been messing around with it and it works so far using my 3D render as a init image but I have a few questions is I do not actually fully understand the parameters.

If I'm using an anime diffusion model for example, and I wanted my 3D character to come out looking fully drawn but with the exact same pose and hairstyle is in the 3d render, what would be the best way to achieve that? If I have the strength on the init image too low, it copies the 3D render style graphically instead of anime style, but if I put it too high then it mostly ignores the pose and the details on the 3D characters.

Is there a better way to do this? I'm a complete novice to all of this. So sorry if the question is stupid and the answer is actually really obvious

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