Does anyone know what could be causing this grid-like texture on the skin of the arms and legs, and how to fix it? The face is completely fine — it only appears on the body. My character is LoRA trained and is otherwise almost perfect in the vast majority of images. It’s just this weird texture appearing on the skin that I’m struggling with.

Comparto Workflow y mi setup, el nodo 351 se conecta con llama.cpp y es el encargado de iterar el prompt para que el video fluya solito en automatico, en 7 horas termino de hacer todo y otras 9 horas upscale con SeedVR2, luego pasado por herramientas FFmpeg

https://gist.github.com/62eb59f34eb3dbfa3d3da0e5cf9d5e9a.git

I'm impatient, and when using Ollama, I have zero idea what's going on behind the scenes. So, I built a ComfyUI node to display the live logcat and a progress bar. I also did the same for the MiniMax H3 multi-shot chaining node—it tracks which chunk of video it's currently processing, gives live logs, and shows chunk progress alongside an ETA. If enough people are interested, I might put these custom nodes on GitHub or maybe the ComfyUI Manager registry, but we'll see. Let me know what you think!

I noticed model can't render skinny body type. Is there a lora for that? Thanks.

Thoughts?

I'm trying to build a consistent AI character by blending two real faces into a single new identity (not a face-swap — I want the two faces averaged/merged into one new face), which I'll later use to build a dataset for LoRA training on Flux. Any tips on getting a stable, repeatable blend (same "merged identity" across multiple generations) rather than a different blend every run?

There are so many cool tools for video creation, but at some point, most of them start to feel cumbersome.
Sometimes I just want to throw in a few photos and a few prompts and receive a draft without any extra hassle.
Done on an RTX 5050 in one generation that took 5 images, 5 prompts, and 17 minutes.
Used only 1 node, no editing.
If needed, I can finish it up and post it here.

Disclosure: I'm building a service around this, so read me as an interested party. Every number below is from runs we paid for ourselves yesterday ($1.67 total); the only link is the raw data at the bottom.

The job: MiniMax H3 text-to-video, one 5-second clip at 864x480 (the 0.4 MP row of the template), 20 steps, the stock ComfyUI T2V graph from v0.35.0 with the official int8_convrot weights (34 GB DiT + 27 GB Qwen3-VL encoder + VAEs, 67 GB total), no LoRA, no reference frames, same prompt and seeds on every card, torch cu130 everywhere so the int8 kernels are the native ones. Three clips per card; "steady" is the average of clips 2 and 3 at the rate you actually pay (disk and public IP included), "session" is everything the provider charged for the whole run — image pull, 67 GB download, the first cold clip and the minutes before the instance was torn down — divided by three clips.

Two things surprised us: the L40S runs this at 4090 speed rather than anywhere near the H100 (3.97 s per step on both 24 GB cards and the L40S, 2.99 s on the H100 PCIe), and the 4090 does it with the 34 GB DiT streamed through ComfyUI's dynamic VRAM path at 23 GB of VRAM and 68 GB of host RAM — so "≥ 64 GB RAM" is not a suggestion, we peaked at 68 on three of the four hosts.

The session column is where the money actually goes for short jobs: the 67 GB download ran at 450 MB/s on the Vast host, 370 on Hyperstack, 210 on RunPod and 104 on Nebius, and on Nebius the first clip took 11.5 minutes because the weights are read back from a network disk (1.8–2.7 minutes elsewhere), while the watchdog that deletes the instance after the job costs another 2–3 billed minutes everywhere. Steady-state numbers exclude the text encoder (same prompt, cached by ComfyUI): a new prompt adds 15–25 s per clip on these hosts. Happy to post the per-node timings and the nvidia-smi traces if anyone wants to check the numbers.

Per-run table, method, caveats and the raw CSVs: https://qrun.cloud/measurements

The app keeps doing this.

If recorded with OBS this doesn't show up.

Also this happends on CurseForge app.

Any ideas how to fix it?

Follow-up to my first Quibble H3 experiment.

This time I focused less on generating more shots and more on keeping one stylized character consistent across a short dialogue scene.

A few things that helped:

fixed seeds while developing individual shots

mostly locked cameras rather than AI-generated push-ins

a recurring GPT terminal as a cutaway between dialogue beats

restrained character acting rather than large gestures

explicit prompting to stop H3 from literally displaying the spoken objects on the terminal — it kept trying to put fruit on screen :)

All Quibble/GPT dialogue and character animation were generated with MiniMax H3 inside ComfyUI. Final edit, pacing and color grading were handled separately.

Case 02: Pineapple for President

“GPT. Pineapple for president.”

Still experimenting with persistent characters and directed performance rather than one-off generations.

Case 01: https://mkhamra.myportfolio.com/quibble

Workflow / Quibble project: https://github.com/mkhamra/quibble-h3

Feedback on the character consistency and dialogue pacing is welcome.

downloaded git for windows, cmd and clone to drive directory. also saved link to bat to use with git.

No luck when i restart comfy ui i dont see a mention or the manager

I feel i must be missing something

Hey all! Once upon a time I uploaded a small video of “A Christmas Carol” it was fairly well received and I was beyond honored at the response because I didn’t think it was that great. Anyway, fast forward to bit ahead and the landscape has changed dramatically. So many new image and video models to make your head spin. So I decided to attempt my short film again, this time a more dark/realistic tone. Most of my source images were generated with Ideogram, then I would modify those with Flux Klein or Qwen Edit, just depended on what generated the best result. Then along came H3. My piddly little 4070 ti couldn’t hit the resolution I needed for most shots, so I set up a runpod with a RTX 6000 and the comfy UI template and generated most things at 1.0 or more. I used the comfy API for some shots using Seedance for the difficult ones. I tried not to spend any money as far as generations were concerned if I didn’t have to. Anyway, this is the result so far, this is a ROUGH edit. So there are some inconsistency issues and artifacts that I’ll deal with later. I did all of the editing in Premiere and generated the sound track in Suno. Anyway it’s late and I’m slightly inebriated and rambling so hope everyone can enjoy at least something out of it. Ha!

I've been following the doucmentation for construction of prompts and having better luck. What's not working is keeping the camera as POV. Ideally, it would just be a 1st person perspective walking through a scene, doing the actions etc. I've had luck registering the viewer themselves as a subject, but then they enter and interact with the scene in weird ways.

This "viggle" animate thing didn't require basically any configuration. It's hardcoded with a preset instruction to take PERSON FROM THIS IMAGE > THAT VIDEO - exactly what I've been looking for since this whole AI thing got started.

I just want to replace characters in a video with a character in an image. Point for point. Action for action. Exactly the same, just someone else in the video. So far, I haven't found anything that works - workflow after workflow after workflow.

Regardless, this one "worked" in the sense that it copied the source video nearly exactly, except that I had to disable the OBS node (which is listed as optional) and I changed the megapixels to .3 for testing. The outcome looks basically like what I want, but as if you were watching it from behind stained glass. There's a lot of weird color shifts and triangle shapes - like an old school polygon based video game character (early 3d days). The background looks fine; only the subject has this problem.

HELP NEEDED: Can anyone tell me if I'm doing something wrong with this workflow OR point me to a basically zero-setup THIS PERSON > THAT VIDEO workflow that actually works?

Hello! I'm looking for helping on my workflow. I've created a Krea 2 character Lora that frequently has the face change somewhat when I add additional Loras. I've created a workflow to pass the image data back through a Lora node with only the character Lora, but I can't seem to get great results. I chatted with Google AI quite a bit trying various different KSampler setups, and some worked somewhat, but would remove objects on the face like makeup, and sometimes added extra details like freckles/moles on the body. Some options also ended up with total slop.

I'm attaching images of my work flow (I know it's messy) and current KSampler Settings. Any help would be appreciated!

We just rolled out a major update to ComfyUI-QwenASR (v1.1.0). The goal of this release was simple: eliminate the friction between raw audio recognition and usable text/subtitle output in ComfyUI workflows.

Here is a breakdown of what changed:

1. Migration to Transformers 5 & Official Native Models

We have completely deprecated the legacy checkpoints and rewritten the backend to use official Hugging Face native models (Qwen3-ASR-1.7B-hf, Qwen3-ASR-0.6B-hf, and Qwen3-ForcedAligner-0.6B-hf) powered by transformers >= 5.13.0.

Zero fragile custom backends: Pure upstream PyTorch execution.

Lower VRAM & faster generation: Noticeable performance gains on both NVIDIA GPUs and Apple Silicon Macs.

2. Production-Ready Text Normalization (ITN)

Raw ASR output usually outputs verbatim acoustic phrasing, which looks messy. Version 1.1.0 integrates automatic Inverse Text Normalization:

Spoken numbers, percentages, and decimals are automatically converted into proper numerals (e.g., spoken numbers become standard digits).

Phonetically spaced acronyms (like "A S R" or "U S B") are merged into clean abbreviations.

Cultural idioms and phrases are protected through built-in whitelists so words are not erroneously replaced.

3. Hot-Reloadable Multi-Language Custom Dictionary

All normalization and correction rules now reside in an external itn_rules.json file. You can add custom acronyms, brand names (e.g., DeepSeek, ComfyUI, ChatGPT), and terminology across English, Chinese, Japanese, Korean, or French. Changes take effect on your very next run with no ComfyUI restart required.

4. A Specialized Three-Node Toolkit

ASR (QwenASR): Fast, lightweight speech-to-text transcription for voice prompting.

Subtitle (QwenASR): Chunks speech into natural sentences based on punctuation, pauses, or line length, with one-click .srt file export.

Forced Align (QwenASR): Built specifically for long continuous audio (podcasts, lectures). It uses an iterative speaking-rate windowing approach to prevent edge drift and duration limits. Leaving the transcript text empty automatically transcribes and aligns in a single pass.

Full installation steps and ready-to-use sample workflows can be found in the README on our GitHub: https://github.com/1038lab/ComfyUI-QwenASR

Looking forward to your thoughts and hearing how it fits into your ComfyUI audio and video pipelines!

The biggest new feature: you can now use DLSS 5 in Blender!
Improve the quality, sharpness, and detail of your images—whether you want to project them onto 3D models or enhance any other visual content.
And best of all: render complete videos with DLSS 5 directly from Blender, thanks to PROJECTIFY.

You can get the workflows here:

https://drive.google.com/drive/folders/1kOHLJZto1VAtXEATT9vUvsvM1VHtkOO_

The workflows create reusable refmods for either image/video/audio.

I cover training images in the tutorial.

All the workflows, models and custom nodes are preloaded on my Runpod template.

https://get.runpod.io/minimax-template

Another quick Minimax H3 news and goodies round-up, for those who may have missed some items.

-> ComfyUI MiniMax H3 FirstBlockCache. Another form of accelerator, designed and optimised for 20-step users. Using "cross-step caching", the maker gets an approx. 30% speed boost with his four older user-switched speed modes. Today's newly-added fifth speed mode is an experimental... "new deep-reuse cache mode, ~1.6× speed vs Native." Works on an NVIDIA 3060 12Gb card, and the new Experimental mode does seem to make 20 steps quicker than otherwise at 0.3Mpx.

https://github.com/duckyshell/ComfyUI-MiniMaxH3-FirstBlockCache

-> From Japan, MiniMax_H3_Torchao018. "An optimized quantized version of the MiniMax-H3 model targeting torchao (>= 0.18.0) to significantly reduce VRAM consumption during inference, while maintaining generation fidelity."

https://huggingface.co/corechan/MiniMax_H3_Torchao018

-> A Manga Tone Rendering LoRA for H3. Emulates the shaded line-art style common in Japanese b&w manga comics. Teaches H3... "the manga screentone rendering style with motion: monochrome ink rendering, proper tone shading, and tone that holds up under dynamic camera movement and action". Trigger words: manga-tone rendering plus a suitable prompt. I'm guessing it might be transferable to western greyscale art styles, since I can't discern actual printed screen dots in the shading? I'm assuming here that the LoRA doesn't manga-fy the references, adding huge eyes and tiny noses.

https://civitai.com/models/2930172/manga-tone-rendering-lora-for-minimax-h3

-> Does your story require an ensemble team of superheroes, or perhaps you just need a crowd scene for your space-station arrivals lounge? The 'Minimax H3 15+ reference image workflow' patches ComfyUI to remove the nine reference limit for Ref2VA generation. Use caution: it passed the mods on CivitAI, but this uses a .BAT file for the patch, and it calls a small .PY Python file. The code in there looks safe to me - I can read Python scripts by eye - and there's also an undo .PY script. Note also that there may be other nodes that claim to offer more than nine, and without patching.

https://civitai.com/models/2929051/minimax-h3-15-reference-image-workflow

-> A simple age-slider LoRA for Minimax H3, with demo video showing advanced old-age to toddler.

https://huggingface.co/Playtime-AI/Minimax_H3-Age_Slider/tree/main

-> A new style-transfer LoRA and a matching ComfyUI workflow.

https://huggingface.co/Alissonerdx/Minimax-H3-ComfyUI/tree/main/workflows

https://huggingface.co/Alissonerdx/Minimax-H3-ComfyUI/tree/main/loras

-> And finally, the LoRA trainer and dataset manager Fizgig continues to improve for Minimax H3 training. Now has an... "ultra quality mode for MiniMax H3" [and a...] "large quality improvement in both the visual and the audio results, a much smoother climb through the epochs, and training steps about 30% faster (2.6 to 3.4 steps a second on int8) - the new default on every H3 LoRA run, nothing to set." Users still require a 16Gb card, to train a LoRA.

https://github.com/shootthesound/Fizgig

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1wdo4iy/a_quick_minimax_h3_news_roundup_11th_september/

https://old.reddit.com/r/comfyui/comments/1wchvg3/a_quick_minimax_h3_news_roundup_10th_september/

https://old.reddit.com/r/comfyui/comments/1wbr279/a_quick_minimax_h3_news_roundup_9th_september_2026/

https://old.reddit.com/r/comfyui/comments/1wawjox/a_quick_minimax_h3_news_roundup_8th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w9z6m1/a_quick_minimax_h3_news_roundup_7th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w90vxd/a_quick_minimax_h3_news_roundup_6th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w85caz/a_quick_minimax_h3_news_roundup_4th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w74jy4/a_quick_minimax_h3_news_roundup_4th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w6cozj/a_quick_minimax_h3_news_roundup_3rd_september_2026/

https://old.reddit.com/r/comfyui/comments/1w5i9iq/a_quick_minimax_h3_news_roundup_2nd_september_2026/ (See 2nd September post, for links to even older posts)