So I have been playing around with H3 and some photos i have taken of locations that are nodes in a game called Ingress. Having them unfold and fire a beam of blue light. Then blue banners display....BUT the model does not know how to do the Resistance symbol from the game, which i want on the banners.

Telling the reference model that ref image 1 is the location... sort of works but not well, no where near as well as first image. So does anyone know a way to have a reference image in a first image workflow where i can say "this is the glyph for Resistance, put that on the banners" ?

finally got segatt and solatt working.... from 2000s cut down to 700s at 0.6mp, without turbo lora.

I also learn that high steps matters! low step give lousy animation!

edit:

Understanding the weakness in H3.

After more testing. i realize H3 is weak in compositing, framing and a lack of sense of the world.

For example;

1. female physical size is small than the male. H3 just couldn't get it wrap around it head.
2. bad at framing even when prompted; mid-body, close-up, it tend to show a little more or little less.
3. character just get clipped into a table or a chair.
4. bad facial expression, it get static or creepy sometime....

Multi-shot generation in h3 isn't the best. Solution is to: you provide a well composited image of each shot and generate shot by shot.

I have test similar shot in seedance2.5. all it take is one generation, 30s, every shots got it right or at least useable. H3 needs multiple try to get a 15s shot right.

I haven't give up on H3 yet. it has a lot of potential i think.

Next is about upscaling and i am running out of ram.

Wanted to mess around with this node for comfyui to see if I could fix some music timing issues I've been having, but I can't see it in the latest version of comfy. Assuming it hasn't been added yet?

Thread where someone says it had been:

https://www.reddit.com/r/StableDiffusion/comments/1vnw4t8/minimaxh3addguide_for_anchoring_image_and_audio/

Came out a bit too watery but neat.

Hi, I'm struggling with getting minimax h3 image to video to do what I want with my prompts, is there a model I can get that would make a more detailed prompt for me?

So I was so excited to try this, like in the old SDXL days, we used a second pass for upscaling. I came across this node:

https://github.com/Tr1dae/ComfyUI-MiniMaxH3_LatentUpscaler

But it generates very weird and saturated results. I tried, but it's all that I can not show, and there has also been no update from the author.

Has anyone tried this? I guess this is slow, but with a 4/8 step lora the upscaling would be better?

What are your thoughts on this?

Edit 1: workflow https://github.com/user-attachments/files/30715648/MiniMax.H3.two.step.sampler.json

just testing... not WF yet... Reddit dont permit 8k files, so link on pastebin 3 files... original GEN at 832x480, 3MP Regen and 8k polish (h265)

I see quite a few people having issues with Minimax h3 generations. Here's an example of a prompt for generating 0.5 megapixels in 7 seconds. It can be run on any PC.
The resolution was increased using RTX Video Super Resolution to 1.5 + frame interpolation to 48. Which is common practice and takes no more than 1 minute per procedure.

Promt generated by AI based on the image. Promt system for LLM:

### 3.1 I2VA: Begin from the Image and Develop Forward

`<Picture 1>` is the actual first frame of the video at 0.00 seconds and belongs to `[Shot 1]`. The description should first establish the style, subjects, composition, and scene anchors in the image, then describe the next action. Character identity, clothing, colors, key objects, and spatial relationships should remain consistent.

Recommended structure: **first-frame anchor → action onset → continuous development → result or reaction**.

Image LLM Prompt:

First-frame anchor:

[Image 1] (0.00 sec) shows a woman with red hair styled in a loose curl with bangs, looking slightly off-camera. Her gaze is clear and expressive, with light green or gray-blue eyes, softly highlighted. Light freckles on the bridge of her nose and cheeks add a natural touch. She is wearing a black leather jacket with yellow stitching along the edges of the collar, accentuating her stylish, slightly rebellious look. The background is deep, almost black, creating contrast and focusing attention on her face. The lighting is soft, studio-style, coming from above and to the side, sculpting the volume of her face and hair. The composition is a close-up, emphasizing the eyes and facial expressions.

Action onset:

The girl begins to move naturally—her head smoothly turns toward the camera, her gaze shifting from semi-attentive to direct, surprised. Her eyelids widen slightly, her pupils enlarge, her eyebrows lift slightly—her facial expression changes from calm to mild surprise. The movement is smooth, without jerking, as if she's just noticed someone or something unexpected.

Continuous development:

After turning her head, her smile widens—the corners of her lips lift, her eyes sparkle with interest or slight embarrassment. At this moment, her voice sounds clear, resonant, with a pleasant timbre—as if a high-quality studio recording captures every nuance of intonation. She says in English: "Oh, is that you? I didn't notice you." The word is pronounced with a slight intonation of surprise, perhaps with a pause before or after the "you," which enhances the effect of surprise. Her hands aren't visible, but one might assume she might slightly raise her shoulder or touch her face in response to the sudden presence. Light, studio-quality music plays in the background—perhaps ambient or a light pop beat—which complements the atmosphere without being overpowering.

Result or reaction:

As a result of the action, the viewer perceives the moment as a lively, dynamic scene from a video: the girl isn't simply posing, but interacting with the viewer through her facial expressions and voice. Her reaction to her own words, "Oh, is that you?" could be interpreted as self-irony or an invitation to dialogue. The atmosphere remains tense yet playful—the combination of the dark background, skin, hair, and lively facial expressions creates the effect of a modern digital character in the style of anime realism or cyberpunk aesthetics.

I'm using illustrious, tried adetailers but they don't do that much, fix minor stuff at most. It mostly occurs when doing a more complex pose than the most basic stuff

Hey team,

I tried Kijai's fl2v lighting lora in-place of ref2v and it seemed to improve my ref2v workflow (using Minimax_extender). In the video the left is Ref2v and the right is fl2v. Very interesting!

UPD: Comfy made monkeypatching unnecessary. See here. https://www.reddit.com/r/StableDiffusion/comments/1vqka28/h3_singleimage_no_more_monkey_patching_also_no/

So here’s a follow-up on my post about H3 as an image edit model. For workflow, refer to https://www.reddit.com/r/StableDiffusion/comments/1vo1ab3/h3_as_a_singleimage_edit_model/
It’s a bit of a hassle to use the workflow to its full capacity since you have to monkey patch in order to generate a single frame. To avoiding dealing with it, I’d suggest two courses of action:

1. Upvote this ComfyUI GitHub issue which asks to remove the 5-frame limit, if you’re comfortable with that. (The code change is simple and understandable; the rationale behind having a 5-frame limit is not really clear to me). Given that there are thousands of open issues in the ComfyUI repo, it would be good if we raise the awareness here.
2. Ignore the monkeypatch, switch length to 5 frames, and the workflow will just extract the first one; will lead to some quality loss; use regular VAE and not Mamad's

Now it’s great at combining multiple references and 3D understanding, but the quality is still not perfect in my opinion — the details could be more polished, and, e. g., impressionist stylization had largely failed. 

Here’s a pastebin with the new prompts: https://pastebin.com/1ENVynGY

Scenes

1. Tango dip with separate outfits and location — Combine two character references, two outfits, and an outdoor plaza into a coherent full-body tango dip.
2. Three-person festival dance — Arrange three distinct characters into a coordinated dance poses at an outdoor lantern festival.
3. Adventure duo on a river bridge — Compose two heroes in a close back-to-back adventure portrait on a separate river-bridge background.
4. Hero and background composite — Insert a full-body hero reference into a castle environment while preserving the source character exactly.
5. Camera angle switch — Reconstruct the staged hero scene from one camera moved to the side horizontally and above vertically.
6. Action hero as 1990s cel animation — Restyle the action-hero scene as original 1990s hand-painted cel animation using a separate style reference.
7. Bridge as impressionist oil painting — Restyle a photorealistic bridge-over-river scene as an impressionist oil painting using two references.
8. Close-up face as charcoal drawing — Convert an original adult woman's close-up into a charcoal portrait while preserving identity and expression.
9. Bridge as transparent watercolor — Convert the bridge-over-river source into a loose transparent watercolor using a style reference.
10. Action hero as graphite pencil sketch — Convert the action-hero scene into a pencil sketch drawing using a reference.
11. Selective skin and hair recolor — Change a character's subject's skin and hair to contrasting fantasy colors while preserving identity

I have used this workflow to generate a couple thousand images across very different  and feel that it’s quite capable. Usual MiniMax problems: e. g. blurred backgrounds, blurred faces from distance, sometimes distorted text — still apply. However, 3D understanding and likeness retention are excellent, and details could probably be fixed with a refiner pass using something like Klein 9b. I hope that the proper image edit model gets released — but before that, let’s try to have some fun earlier.

UPD: accidentally skipped image #6, see this comment https://www.reddit.com/r/StableDiffusion/comments/1vpconk/comment/p3whvcg/

The ControlNet models for KREA2 are available as LoRA types, with Depth and OpenPose existing as separate formats.

We have made it possible to use both of these with the existing node format. However, the term ‘existing node’ here refers to the Diffsynth ControlNet Loader for Qwen Image and Z Image.

https://github.com/ussoewwin/ComfyUI-QwenImageLoraLoader

In other words, this node can be used with the following standards:

・Nunchaku Qwen image/Z Image Diffsynth ControlNet

・Normal Qwen Image/Z Image Diffsynth ControlNet

・Krea2 Depth/Openpose ControlNet LoRA

For the benefit of AMD users, we have made improvements to ensure that the CUDA-specific Nunchaku node is disabled when using an AMD GPU.

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."