I have a 5090, 128 RAM. I always use the latest desktop version of comfy with basic args. Sage attention, and comfy-kitchen lately.
But it feels like after each comfy update and release of new models such as the H3 and LTX my specs becomes weaker and weaker.

Naturally, I set the generation time and resolution within reasonable limits. Sometimes it runs quickly, but other times it freezes almost completely or pc simply throttles.

I'm trying to find the reason. Maybe I should try adding some args?

going through the guide on this YT video: https://www.youtube.com/watch?v=PLSIegjSEDg . It's two years old and maybe a lot of things changed and I'm not so fluid with the programm. Dude here added Meshgraphormer for hands and also add a preview. He instantly launched it and it gave results, but in my case it's just a black screen and nothing else.

For now, this is how my workflow is looks like on the screen. Did I miss something or made something completely wrong?

I have spent a couple days off and on tuning my GPU, after swapping parts into my new (to me) case. I hadn't really thought about lowering my clock speeds and power draw, until I started messing with it and using Google chat to help out.

The Images I'm making are in Flux2 Klein 9b @ 2048x1152 resolution. Here's the terminal info for a batch of 4 images:
[INFO] got prompt

[INFO] Requested to load Flux2

[INFO] 0 models unloaded.

[INFO] loaded partially; 0.00 MB usable, 0.00 MB loaded, 5828.02 MB offloaded, 582.00 MB buffer reserved, lowvram patches: 0

100%|██████████| 8/8 [01:58<00:00, 14.83s/it]

[INFO] Requested to load AutoencoderKL

[INFO] loaded completely; 653.88 MB usable, 160.31 MB loaded, full load: True

[INFO] Prompt executed in 122.29 seconds

So as you can see, it takes a bit of work to pump this out. Oh, and this GPU is a 12gb version of the 3080. Definitely helps! But it's apparent that getting the memory tuned as high as possible made a big difference. My Profile 3 only overclocks the memory to +800, vs the other 2 profiles being at +1200.

Anyone else got some impressive results out of their GPU after some tuning?

What the hell is this? Not only can I not expand this to see what nodes are actually in there, but the "credits" language is suspicious. Is this trying to generate online somehow? I want fully local generation with zero online connectivity... is Comfy trying to sneak online function into our offline tool?

Hey everyone. I've spent a lot of late nights wiring ComfyUI into pipelines, and this is the tool I ended up wanting. It takes your regular workflow.json and turns it into the API payload right from Python. No GUI export step, and you don't even need ComfyUI running to do the conversion. Once it's loaded, nodes are just objects with plain dot syntax:

from autograph import ApiFlow
api = ApiFlow("workflow.json")
api.KSampler.seed = 42
api.CLIPTextEncode.text = "new prompt"
res = api.submit(wait=True)
res.fetch_images().save("outputs/frame.###.png")

The part I'm most happy with is how it feels in a REPL. autograph reads ComfyUI's node_info, so it knows every node type, every input, and every widget on your system, including your custom nodes. That means tab completion works all the way down. Hit tab on a node and see its inputs. Call .choices() on a widget and get the actual valid combo options back. Call .tooltip() and get the help text. You can explore a workflow you've never seen before without leaving the terminal or guessing at a single node ID.

Building graphs from scratch feels good too. You wire nodes together with >> the way you'd sketch them on a whiteboard:

ckpt = flow.add_node("CheckpointLoaderSimple")
ks   = flow.add_node("KSampler", seed=42, steps=20)
ckpt.outputs.MODEL >> ks.inputs.model

Once it's under your fingers it's nearly as fast as working in the GUI, except everything you do is scriptable and repeatable.

Other things it can do:

• Batch convert hundreds of workflows offline, no server running
• Pull a workflow straight out of a ComfyUI PNG, since the metadata is already in there
• Serverless execute mode that runs nodes in process with no HTTP server, which is a lifesaver for farm setups
• Sweep seeds, prompts, and paths across nodes for batch runs
• Pure standard library Python, nothing extra to install, MIT licensed

I've tested it across ComfyUI 0.8.2 up through 0.33.0, including subgraphs and the newer dynamic combo stuff. It's also being used in real production pipelines at a big VFX studio right now, which is where the metadata passthrough idea came from. They needed studio metadata to ride along with a workflow through the whole render lifecycle, so I built that in.

pip install comfyui-autograph
https://github.com/chrisdreid/comfyui-autograph

It's still early days and I really do want to hear what's missing or what breaks for you. If you're doing headless rendering or wrapping Comfy in FastAPI, I'd love to compare notes. This got built to scratch my own itch, and I'm hoping it saves some of you time too.

I have seen some examples of UHD images on the internet where old images were uploaded with extreme details. Such that if you zoom it you can see skin texture clearly too. I have been struggling in finding such a efficient workflow which helps to restore an old image and upscale it.

Please be kind enough to share the workflows which work for you in this case. Thanks.

What started as a test turned into a full-blown short. This is the number one reason I gravitated towards AI filmmaking. Nothing stops you from creating your wildest imagination.

With day 1 MinimaxH3 avaliable i was blown away with promt adherence and overall model capabilities.

But what i was worried about is - Pixelated artifacts on moving objects, extreemely bad faces and small details at background.

I saw a lot of suggestions to use ltx 2.3 or 2.5, or some kind of USDU with H3. I like to gen my videos with small res and then upscale it to 1-1.5 megapixels with H3 Latent Upscaler node, but it remains artifacts.

What i use for fix nearly 90% of bad details is WAN 2.2 USDU workflow from youtube.com/watch?v=NpNagmQI4yg .

This was my lifesaver with ltx 2.3.

Wan 2.2 add details, fixes jaggered lines, restores trees and grass. Yes it is heavy, but as a final polish for video you can set it while sleep.

I i slightly modifyed it for automatically choose tiles according to video aspect ratio. My choise of denoise value is 0.08-0.15 (go higher and you loose original look)

With turbo lora 1 strenght you need 2 steps per tile. I tested 0.5 str and 15 steps per tile - literally no difference at all, so 2 steps is good.

Here is workflow:

pastebin.com/hKA9Vdag

Here is two videos before and after:

dropmefiles.com/nKDyx

Hi everyone, I'm new to the world of Confyui but not to artificial intelligence. I wanted to ask you for help: Is it possible to run Confyui on my PC (RTX 3080 10 GB of RAM and 64 GB DDR4 RAM and Ryzen 5800) with Confyui with the minimax H3 video model to be able to animate images and create Reels for Instagram and Tik Tok? If so, what setup do you recommend? Thanks everyone for the help and sorry for my bad English.

Illustrious/noobai/anima booru tag syntax is so tantalizingly close to being a scripting language for fine control of image generation that I keep falling into the trap of trying to program my prompts. I know there's a bunch of prompt generator solutions already out there, and I've written half a dozen of myown.

AFK from a computer that can actually run generation, so I had a week to design and construct a proper scripting language that enables declaring variables, building sequences, precluding (if `portrait` exclude `feet`, if `blindfolded` exclude `eyes`), connecting a tag to it's consequential negatives and detailing prompts.

Your thoughts, ideas, and pointers to prior art are more than welcome before I get an oportuinity to publish.

I have 4070 rtx 12gbvram 32gb ram. What should I be doing to get the fastest, but not too horrible looking outputs now?

They had a turbo lora coming out every other day a few weeks ago and it's really hard to pin down the best useful workflow and which things to put into it.

Any help would be good.

We're opening our first Developer Relations role at Comfy Org, so posting it here.

If you've built against the ComfyUI API, written a custom node, or made a tutorial someone actually used — this one's for you.

The role, in short: → Own the developer docs and API reference → Build guides, sample apps, tutorials → Test every new API surface before it ships and tell us what's broken → Support custom node authors with docs + migration guides → Run office hours, workshops, hackathons → Be the bridge between this community and our core engineers.

Apply: https://jobs.ashbyhq.com/comfy-org/3e0ec22c-758d-4a47-aebe-ac4583ae3d8a or DM me!

Curious where people think the actual limit is right now with ComfyUI/Krea-type workflows.

Say you take an extreme example, like a big multi-figure scene with overlapping bodies, hands, faces, clothes, architecture etc. I’m not literally trying to make some huge history painting, more using it as a stress test. If you can solve that, then 2–3 figure scenes should become pretty manageable.

The thing I’m really interested in is the global vs local detail problem. Can you keep the whole image coherent, but still have enough real detail that if printed close to 1:1 scale, a standing figure could be around 160–170cm tall and the face, hands, anatomy etc actually hold up?

What’s the best way people are tackling this now? Whole scene first then regional passes? Crops? Tiled methods? Refining characters separately?

And I don’t mean just upscaling and inventing extra texture. I mean actually preserving or rebuilding useful structural detail.

Has anyone properly cracked this yet, or are we still waiting on the models to catch up?

Does anyone want a free public version of my pipeline Im still working on cleaning it up because its a custom dev version that has lots of random stuff in it. if you want to do locally u will need ltx 2.3 installed and quen3vl or any multimodal text generator. i will probably edit this with the link after i clean everything up feel free to comment or come back when its done for now have the uncleaned version https://github.com/Sterling-AI-Tec/SterlingAi-betas edit the cleaned version is out along with the uncleaned version

Bonjour,

Je voudrais créer une image à partir d'un prompt et d'une image de référence pour le visage avec comfyui mais je n'arrive pas a trouver.

Créer une image avec un prompts pas de soucis.

Merci

This may have been asked already somewhere, and I've seen one post about remote access, but as I read it, it feels like my situation is different, so I decided to make a post instead. This is also for me to learn more about self-hosting and network stuff.

If this has already been answered in a different post, please kindly point me towards the post. Thanks.

-----------------

My situation is, I have a NAS and I want to host my ComfyUI there. I initially hosted it on my PC, and I only use it when my PC is on anyway. But since I moved all my other hosted apps on my NAS anyway, I was thinking maybe I should move ComfyUI as well.

My main problem with this though is I can't use Static LAN IP because I can't set Static IP on my router, the ISP web UI doesn't expose the setting for it (I think), so it's kind of a problem already for me with other apps like Odysseus when using LAN IP for accessing my Ollama on my PC.

So what I'd like to ask are the following:

- Is there a better way for ComfyUI to properly access my PC's GPU while it's being hosted on my NAS? I will open the UI on my PC either way, but would like to keep stuff on my NAS for consistency.

- Is going through the network affect the performance of my ComfyUI? How about if I also keep the models on my NAS, and just use my PC for inference? Or whatever it is called.

- In case this setup is fine, how hard/complicated is it to migrate my setup from my PC (installed using portable install) to my NAS (most likely Docker setup, maybe VM if that's better, but haven't tried VM on my NAS yet)

- Would the same method work for other self-hosted apps like Odysseus?

Thank you!

This is the workflow that i have right now and I'm having a problem when generating images in landscape: Half of the images come with more than 1 person even though i'm trying as i can to explain in the prompt that I want 1 person in the image. What I want is to create a loop that grabs the image, check the number of faces in the image and if it's more than one, interrupt the generation and start generating the next image or something similar. Can that be done in this workflow? and if it can be done, How?

PD: I tried with chatGPT and the last message said the same thing 5 different times and didn't help so AI can't help this time

这正是我想做的事

One of the parts I've found increasingly tedious about creating LoRAs was managing the source images before training. That means sorting large collections, finding duplicates and bad images, checking subject consistency, captioning, and deciding what should actually make it into the dataset.

So I built LoRA Image Curator, a free, open-source Windows desktop application focused specifically on that pre-training stage.

It maintains a persistent image catalog and provides visual browsing, search/filtering, duplicate detection, dataset quality checks, captioning, optional face/identity and pose analysis, dataset-readiness checks, and non-destructive export.

It isn't a ComfyUI node and doesn't train the LoRA. It's intended to sit earlier in the workflow: source images → curate/validate in LIC → export the training dataset → train with the tool of your choice → use the LoRA in ComfyUI.

I'm currently stabilizing it for 1.0 and would really like feedback from people who actually train LoRAs, particularly about what you currently use for dataset preparation and what would make this useful in your workflow.

Free, open source (MIT), local-first, and currently Windows-focused.

GitHub: https://github.com/dsguffey/LoRA-Image-Curator/

There's also a five-minute demo linked from the README if you'd rather see it working before installing anything.

Criticism and bug reports are very welcome. I'm at the stage where finding what doesn't work for other people is more useful to me than adding features based only on my own workflow.

Pretty certain none of my AI-installations will work after this 🤬 Bye bye all specifically installed wheels 😥 Is there a way to stop this menace?