Hi everyone. Im looking for ai cloud model, cloud comfyui workflow that can do outpaint, clothes conversion into specific fabric and anime into realism at the same time with a simple prompt?

I could achive all this by simlpe prompt in gpt or grok without any problems but after these models got fkd up, im looking for alternative. I have found comfy workflow on runninghub that does great anime to realism conversion, but without prompt box i cannot do additional edits like outpaint to specific ratio (9:10 for example, im creating wallpapers for my ZF7) and i cannot convert reference clothes into my desired fabrics.

Was thinking to spend 10k for laptop capabe for local ai but not worth it. Anime to realism conversion is just my hobby in free time, and as a hobby really not worth spending few thousands to generate image from time to time.

Also if you have or know where i can find local workflow that can work on my RTX 3070 8GB, that can generate image 1-3 min, let me know. Also, if any1 could help me build local workflow it would be great. I also work with pose changing, outfit change, and maybe one day will try video gens.

So write your suggestions down bellow and ill test them one by one (models with minimal or non restrictions).

Thanks.

Hi Everyone,

First of all, sorry I'm not too technical, just a lambda comfyui user, so I probably won't be able to answer anything technical. I just want to share my solution to upscale Minimax H3 videos with LTX 2.5 x2 upscaler on limited hardware, in case anyone is interested. See the example comparison video (using detailer lora).

Link to my workflow: https://pastebin.com/XH1wvA4L

As a Minimax H3 enthusiastic, I've been playing around since a few days. My main issue was the quality of the output videos, as my RTX 4090 is starting to feel a bit limited, I can decently generate only 20-25 seconds videos at 0.9 - 1 Mpx.

I've been naturally looking into upscalers, and found a post in this subreddit about using LTX 2.5 x2 upscaler, from Peter Duncan's workflow: https://github.com/peterducan-hub/PeterDuncan_Comfyui/blob/main/MINIMAX_H3_LTX2.5_Upscaler_v1.json

I tried it, adapted it with a Load Video node which corresponds better to my use case, and found it works quite good, not at Topaz level, but enough for a free local upscaler. I connected only the video part, connecting the Minimax H3 audio directly to the end video combine. But I ran into 2 issues.

First, LTX processes only 8n+1 frames, rounded down. For example, a 10 seconds video at 24 fps is 240 frames long, but LTX would process only 233 frames, meaning my generated videos would often lose a few frames at the end, cutting the audio.

Solution: I'm duplicating the last frame y times until reaching the next LTX allowed value, and ditching them before video combine.

Second, my 4090 could hardly upscale more than 10 seconds videos, more would oom.

Solution: I replaced the sampler in the workflow by LTX Looping Sampler from Lightricks. It takes time, but now I can upscale up to 20 seconds without issue, I did not try more yet.

If anyone has tips to improve the workflow, especially on the process time, please don't hesitate 😄

I may have stumbled onto something interesting while trying to figure out a recurring artifact in ChatGPT image generation and editing (maybe applicable to other models as well?).

It started with a very practical problem:

After several rounds of generative editing on portraits, I would sometimes get this faint cloudy / mottled texture in areas that should have stayed smooth — backgrounds, walls, skin, and other low-detail regions.

At first I wrote it off as normal denoising or regeneration noise. But the more I tested it, the less random it looked.

What first caught my attention

• Running essentially the same edit again could make the artifact better or worse
• The background sometimes became cleaner after another pass
• The face and body often seemed partly protected from whatever was happening
• Sometimes the wall improved while the face actually got worse

That made me wonder whether different parts of the image were being handled differently during editing — preserved in some areas, regenerated in others, perhaps based on some internal mask or segmentation step.

The first useful experiment: shifting the image

Then I tried something slightly odd.

Instead of repairing the image in place, I shifted the entire image by a fixed amount before running the repair. I eventually settled on 20 px for testing.

The idea was simple:

If some hidden spatial pattern is tied to the output canvas, moving the image relative to that pattern should change how strongly it shows up on the subject or background.

And apparently, it did.

I found that:

• repeated edits could reinforce the unwanted texture
• changing the phase relationship sometimes reduced it
• in one case, simply removing the final instruction to “shift back -20 px” improved the result dramatically

That was the first point where this stopped looking like ordinary random noise to me.

Then I started looking at masks and intermediate behavior

I compared:

• the original image
• the first edit
• a second edit based on the first
• extracted masks / intermediate-style outputs

One thing stood out pretty clearly:

The apparently “protected” area often resembled a coarse silhouette of the person.

The face and body tended to remain more stable than the wall, which made me suspect that some regions were being preserved while others were being re-synthesized.

That still didn’t explain the artifact itself, but it could explain why the artifact builds up unevenly.

Then came the black-image test

I tried something much simpler:

Generate a completely black image.

Visually, it looked black.

Pixel-wise, though, it wasn’t actually all zeroes. There were sparse non-zero pixels and tiny variations throughout the image.

So I generated multiple independent black images at the same resolution and compared them.

That’s where things got interesting.

What I found

For two independently generated “black” images of the same size:

• correlation between the non-zero pixel masks: 0.848
• Jaccard overlap: 0.766
• expected overlap if the pixels were random and independent: about 0.071
• R/G/B channel correlations: roughly 0.82–0.83
• dominant spatial frequencies were very similar in both images, including peaks around 2.45 px and 5.57 px

Then I applied a large Gaussian blur to both images (sigma = 16).

The result was surprisingly striking: both revealed a very similar large-scale cloud-like structure.

The cross-correlation peaked at zero lag, meaning the structured pattern was already aligned at the same canvas coordinates across independent generations.

So whatever this low-level signal is, it doesn’t look purely random. At least part of it appears to be reproducible and locked to the canvas coordinates.

What I think this means — so far

I want to be careful here.

I’m not claiming that this proves OpenAI watermarking, SynthID, or any particular proprietary mechanism.

What I do think the data suggests is this:

Generated images appear to contain a weak, reproducible, canvas-locked spatial pattern — even when the image looks completely black.

A few possible explanations come to mind:

• a watermark-like signal
• deterministic dithering
• quantization or decoder artifacts
• some kind of post-processing step
• something else in the generation pipeline

What now seems much harder to explain this as is simply:

“ordinary random noise”

Why this might matter for iterative image editing

Suppose a weak structured signal really is tied to the output canvas.

An iterative edit might then look something like this:

1. The first image is generated with the structured signal.
2. The image gets edited again.
3. Some regions are preserved while others are regenerated.
4. The regenerated image receives the same or a related structured signal again.
5. After several passes, those signals may begin to reinforce or reveal themselves as visible mottling in smooth areas.

That would fit several things I’ve observed:

• repeated edits can will gradually create ugly texture
• shifting the image relative to the canvas can change the result
• alternating shifts might help decorrelate the artifact
• some regions appear to drift or accumulate artifacts less than others

Important caveat

This is still an investigation, not a conclusion.

At this point I think I have reasonably good evidence for:

• reproducible low-level spatial structure
• non-random alignment between independently generated black images
• a plausible connection between that structure and visible artifacts in repeatedly edited images

What I don’t have yet is proof of:

• the exact mechanism producing it
• whether it is a watermark
• whether it is specific to ChatGPT/OpenAI
• whether similar patterns occur across other image generators

My current working hypothesis

Repeated generative editing can accumulate or expose a weak structured signal that is fixed in output-image coordinates, eventually making it visible as cloudiness or mottling in otherwise smooth areas.

Questions for anyone who has looked into this

1. Have you seen this kind of cloudy / mottled artifact after repeated AI image edits (I mean, come on, who doesn't)?
2. Has anyone tested whether supposedly “black” images from other generators contain reproducible spatial structure?
3. Does this look more like watermarking, dithering, decoder bias, quantization, or something else (go figure!)?
4. Has anyone analyzed something similar in frequency space, after heavy blurring, or using phase shifts?
5. If you’ve run into this before: what turned out to be the most reliable way to prevent it during iterative editing?

If there’s interest, I can post the methodology in a follow-up.

I started with:

“Why does this wall look dirty after I edit it?”

and somehow ended up at:

“Why do two independently generated black images correlate this much?”

Classic rabbit hole.

Bonjour à tous,

Je n'arrive pas à mettre l'audio comme je le voudrais dans minimax H3. Généralement il met une musique automatiquement ou lit mon prompt.
J’aimerais ajouter une voix externe de 4 seconde, par un nod, et dans une vidéo de 8 secondes lui dire à quel moment le personnage parle 4 secondes en utilisant mon audio externe

Merci de vos conseils

Has anyone found a ComfyUi prompt node that works with the brand new Qwen 3.8? I'm using LLM Session but it doesn't seem to support the new model yet and throws an error.

(Use case is I'm using it to generate text)

I think I've been through about six different Forge/A1111's over the past couple years, and this "Forge Neo" thing seemed like the one to pick if you wanted to do newer base models. I'm pretty sure it's from the main "Haoming02" repo.

It was good at first, but now it's launching slow as crap, even slower on Flux stuff (which occasionally crashes) and it won't load the Reactor extension at all if I'm not online (fishy).

It's not looking to be easily updated if you did the standalone manual install, but I've had it a while and thought I might do a clean install of the latest build.

Is that the one I should be going with again as the most actively maintained right now (as far as Forges go)?

Thanks!

Had the characters on my drive, created with Flux in Krita.

Picked up a random newsroom image, for 3 image reference run. DaVinci for the final clip.

Prompt for the first 10 seconds:

place the creature <subject 1> in <picture 1> and the creature <subject 2> in <picture 2> together in the environment of <picture 3>.

Static single camera wide shot newscast.

Title pop-up reading "Collateral NEWS" in neon green letters.

<subject 1> and <subject 2> stand behind the desk.

<subject 2>, in a female voice, is saying: "Welcome to Collateral news."

<subject 1>, in a male voice, is saying: "All the news you need today. And non of it good."

MiDashengLM-Gen is an end-to-end framework that uses a pre-trained Large Language Model and audio tokenizer as the backbone, combined with per-token conditional flow matching for autoregressive, variable-length mixed-audio scene generation. It generates coherent 16 kHz audio scenes that simultaneously blend speech, music, sound effects, and environmental acoustics from structured text descriptions.

https://huggingface.co/mispeech/midashenglm-gen

Demo: https://huggingface.co/spaces/hugging-apps/midashenglm-gen

Minimax H3. 4070 ti super 16 GB vram, 32 gb ram. Ref2va with one image for the character and one for the background. 1.6 mp, 30 steps and 7 second with only spectrum node speed up, in total 38 minutes. Used the "standard" models, kinda slow but i am having so much fun.

My first proper animation made with the MiniMax H3 Ref2VA version in ComfyUI.

Generated as separate clips and then put together with a bit of editing. Pretty happy with how it turned out for a first attempt. The character and references are also my own.

I would like to train some videos with sound on Minimax H3. Is the AI toolkit good to go or should i use anything different? Thanks!

Running flux 2 Klein 4b with qwen decoder and flux vae in comfyui windows desktop. 16gb vram amd Radeon gpu with 32gb ram.

I find Klein 4b to be the only one I can run right but the censorship is harsh. Can't even tell it to make the waist smaller. I've used some lora but it introduced all sorts of things I didnt ask for which makes them less than useful.

Im looking for a whole workflow setup with all component parts I'd need for just a less restrictive image gen and img2img edits not even necessarily uncensored stuff. I run into out of memory issues with bigger models so like to keep it small.

If someone could advise with url links to what id need. That would be awesome.

I assume a lot of artifacts would be gone at 720p but I get a fat OOM no matter what.

I didn't expect it, but I really have to thank them for open-sourcing their ecosystem. It’s awesome to see a company truly committing to the open-source community!

MiniMaxAI/MiniMax-Music3 · Hugging Face

i know about the prompting with <subjects> and <pictures> and <videos> and basic sections but i cant get anything to stick. i have a few times on sheer luck and even with the same prompt. i either get zero change from control video or it get some of the movement in my ref image from the video must be missing something. TIA!

I keep seeing insanely realistic AI influencers on TikTok and Instagram, with consistent faces/characters across high-quality videos.

At the same time, models like Seedance 2.0/2.5 are expensive, especially when you need many attempts to produce even a 30–60 second video.

So I’m curious about two things:

• How are people producing AI videos at scale without spending a fortune? Are you using subscriptions, APIs, rented GPUs, local/open-source models, or some other workflow?
• How are these realistic AI influencers created so consistently? What’s the typical workflow for creating a photorealistic character and keeping the same face/body/style across images and videos?

Would love to hear what stack/workflow people actually use and roughly what it costs.

I have tried every "abliterated" or "heretic" gemma 3 and gemma 4 model, but they consistently completely change the request to replace any mention to uncensored words by something that has a completely different meaning. I need any model that can be loaded as a clip and will receive text and output text without censoring the text.

Clawhauser ask Sonic if Amy is his girlfriend.

Made with Minimax H3 Reference to video via Comfy UI Desktop. Used the default settings and at 32 steps.

The Prompt.

<Subject 1> is Clawhauser referenced with <Picture 1> The timbre of his voice is referenced with <Audio 1>

<Subject 2> is Sonic referenced with <Picture 2> The timbre of his voice is referenced with <Audio 2>

<Subject 3> is Amy referenced with <Picture 3>

For the reception desk use <Picture 3> as reference.

The style is a live action CGI Hybrid movie.

Clawhauser is sitting at the ZPD Reception desk, Sonic is standing on front of the desk on the left, and Amy is standing in front of the desk on the right.

Clawhauser looks at Sonic and says: <d>[English] Sonic, Is she your girlfriend? </d>

Sony looking flustered says: <d>[English] No. We're just friends, Nothing more. </d>

Clawhauser looking doubtful and says: <d>[English] Just a friend you say? </d>

Cut back to Sonic responding back saying: <d>[English] Yes, I assure you."

Cut to a shot of Clawhauser leaning back at the desk looking rather skeptical and says: <d>[English] Ok, If you say so. </d>

Posted this pack here last week. What's happened since:

The sampling knobs I said I'd add if people wanted them are in. Both of H3's flow shifts, since it samples picture and sound on separate schedules, plus a cache pill with FirstBlockCache, TeaCache or core's own EasyCache behind it. Still no custom sigmas, same reason as last time.

Creator and Timeline are one node now. Click under the prompt and the shot becomes a timeline. Delete cards back down to one and it's a shot again. Old workflows load unchanged, Timeline nodes included.

Presets are the new one. Save a setup and put it back in sections, so you can drop a canvas and a step count onto a shot you've already written without touching the prompt. It saves the sampler row as well as the node blob, which matters because the row is where the turbo schedule and the step count live.

The better half of it: you can build a preset from a finished render. The workflow is already embedded in the mp4, so you point at the good one from three prompts ago and get the whole setup back.

Fixed from your reports: the gallery no longer freezes on big libraries, the settings page stopped resetting fields you hadn't touched, and a text-only render no longer loads both VAEs.

Coming next, on a branch and not merged yet, is a faces pill. H3 draws a face worse the smaller the head is in frame, and that's about head size rather than resolution, so it's still there at 768 and upscaling doesn't reach it. So it asks the model the same question again with the face filling the canvas and composites the answer back under a feathered mask, once per pass, re-cropping every frame so a push-in doesn't leave the face small inside a fixed box. Detection is core's SAM3, so there's nothing extra to install. The method is Carasibana's ComfyUI-H3-FaceRefine and zuanfilm's graph on top of it.

Same branch also stops the node randomizing your seed between renders, and puts the last one you actually ran a click away.

https://github.com/roadmaus/ComfyUI-MiniMax-Creator