Hello,

So some time ago I've found a way to "train" embeddings without using an image dataset.

I didn't gave it much attention but I searched and asked arround and I couldn't seem to find this existing anywhere so I just want to double check is this something novel?

Without getting into too much details on how this works atm, I take a text and compress it into a small reusable identity file. The trained embedding is a standard textual inversion that works with any sd / sdxl model.

Takes about 1-2 minutes to train, 2gb vram and it's 70 ish kbs.

I made this because I wanted to increase character consistency and to diminish prompt bleeding. And it does the job.

I usually don't engage in posting, heck this is my first reddit post ever, but I'm really curious if this is something new or if I just reinvented the wheel. Also I'm curious if y'all find this useful.

If you got questions I'll gladly provide more details.

Last I tried to install it a few months ago it completely broke my comfyui, and AI chats keep saying "comfyui has built in attention mechanisms that give the same speedup" which... might or might not be true?

I'm on a 4090 running fp8 models, mostly F2K 9b.

What is your experience with SageAttention today? Is there any more foolproof way of installing it?

How in the world can I find a node by name or id, when you have a workflow with like 9485934 nodes? There used to be an option from Easy Use on the left side where there was a nodemap you could search and then click a little eye to bring you to it, but that doesn't seem to work for me anymore.

Hi everybody,

Most likely you all already know that LTX 2.3 sucks when it comes to teeth, especially lower teeth. Even some high quality generations (over 1440p) gives crooked or AI teeth, which bothers me a lot. (I tried prompting something like "clear, defined teeth" etc but it didn't work well.

I have never trained any LoRAs for LTX 2.3 before. So my question is, do you think it is possible to train a teeth LoRA for LTX 2.3. For example, if I find some high quality photos of people smiling, and crop the teeth area for 512x512px (or even 1024px), then train it all, will I get good teeth in videos?

Any other suggestions?

Thanks for your time!

Hey guys, is there a workflow/model local (hopefully) or non local that let’s you remove objects as good as Photoshop’s Content Aware Fill for VFX Cleanups and general purpose. Ideally removing objects on a mask-selected area (not generating the whole image), and not replacing objects with something else, straight up removal. Help is appreciated. This is literally the only reason I pay for the whole Adobe’s subscription, and I feel there must be another way.

So, for example, I can create some characters in 3D on white background, upload them to, say, Gemini and ask it to place those characters in a specific environment, and make them realistic, while preserve their clothes, poses, etc. With this request Gemini generates exactly what I asked for and the characters are put into the environment with correct lightning, shadows, etc.

When I use image to image flow in ComfyUI, I'm unable to get the same results.

I understand why it happens, LLMs use multimodal models where texts and images are processed together, while ComfyUI processes each media type separately. But is it possible to recreate similar experience in ComfyUI?

What is all the hype and should it bother me?

I assembled an 80gb lora library for Illustrious and don't feel like moving to a new model. Would it be stupid for me to stick to it, or is it just another hype cycle that will die in half a year? I've seen people who still use sd1.5/sdxl1.0 get dunked on so how do we determine the life cycle of a model?

I am currently using Wan 2.2 template downloaded in ComfyUI's 'Template', which its workflow is simple with only two nodes and has 'Turbo Mode', which helps tremendously with the speed compare to previous workflow I used with KSampler and all that.

However, I noticed the prompt box does not seem to recognize a wildcard or randomize texts, the one that use { } and | character.

For example:

A young {American|Japanese|blonde|Polish} female is sitting in {studio with black backdrop|sunny outdoor|well-lit cafe} wearing {red|blue|black} dress.

If I queue them, the prompt should get any one of those wildcard selection randomly that I wrote, but from the rendered videos, it only get the first one wildcard from the prompt and never the other one.

May I know what thing I should get in order to get the wildcards working for the prompt with this workflow?

Hello,
I have an AMD laptop

CPU: AMD Ryzen 9 5900HX

GPU: 6800M (12 GB VRAM)

RAM: 16 GB

Hooked via USB-C to an external monitor (not sure if this is relevant, maybe it would only work using the laptop's screen ?)

And running the latest Fedora Linux and ComfyUI

I have tried to install, reinstall comfyui in many different ways, I tried with things like pinokio, I tried to reinstall pytorch and other packages with specific rocm versions, I tried adding the user to different groups, I tried to mess with json or js files to add stuff like

HSA_OVERRIDE_GFX_VERSION: "10.3.0"

export HSA_OVERRIDE_GFX_VERSION=10.3.0

HIP_VISIBLE_DEVICES: "1"

ROCR_VISIBLE_DEVICES: "1"

But whatever I do does not seem to work. I run a lightweight test on purpose (e.g. generating an image that would take 30 seconds on 8gb VRAM), but:

to me, it seems like that instead of using the VRAM fully (altho some of it seems to be used, which is confusing), the RAM is fully used, which I think means that comfyui must use the CPU integrated graphics + RAM instead of the GPU VRAM.

Using the entire RAM eventually makes the app crash. I made a huge swap partition, and while it stopped crashing, something that should take 30 seconds to generate still does not generate after 20+ minutes (not surprising given how slow swap is). And even if it worked, it is still not an acceptable solution to not be able to use my fast VRAM.

(I am open to dual boot with windows if the OS is the issue).

Hello.

I was frustrated by the lack of tooling around image model conversion / quantization, or the extreme RAM requirements and complexity of the scant existing tooling, so I wrote my own. People have said I should post it here, so here it is:

https://github.com/qskousen/ggufy

It has a CLI and a GUI. The GUI is easy to use, you can drag and drop files in. Both CLI and GUI are single-file executables, written in Zig because I like writing in Zig. It's pretty efficient with RAM, and takes about 1.5 minutes to quantize ZiT on my machine.

It supports all the main models that I am aware of, and you can convert to/from gguf or safetensors. It supports I think all the datatypes that are generally supported, such as q3_k through q8_0, f32, bf16, f16, f8_e4m3, f8_e5m2, scaled fp8, mxfp8, and nvfp4. It doesn't do SDNQ yet, but I would like to add it if I can get some time to figure out the format.

It's cross platform, and builds for Linux, Windows, and MacOS (both ARM64 and x86). Github Actions pre-built binaries are available on the releases page.

If there are features you think are in scope and would be useful, or additional models or formats that it doesn't support yet, please open an issue or let me know here. Thanks.

Cross-posted to r/StableDiffusion.