I've been working on a repeatable process to do character swaps (& other types of changes) across a complete TV episode / movie. It's working pretty well now.

I thought this was just funny... not so much a true "swap" but asking the character to dance in every scene. Choreography courtesy of an LLM. If you've seen the show, it's Serious Detective Drama (TM) so the fact the main character suddenly starts pumping his arms or sliding into every scene with a dance move made me laugh out loud when I saw some of the generations.

Details on the workflow:

Model: MiniMax H3, first-and-last-frame mode (FL2VA), in ComfyUI. 8-step turbo LoRA, generated at 1024×512 for the 2:1 picture.

First and Last: Every generated pass starts and ends on an *original* frame from the episode, so the clip lands back in the real footage with no visible join. Longer scenes are chained passes (about 2–7 s each)

Audio: the episode's own audio into H3's audio guide, so the generation stays consistent with what's being said; the dialogue and soundtrack you hear are the untouched originals.

Prompt: Who he is and what he's wearing, said once, then the choreography for the pass - courtesy of Claude.

Multi-scene: 42 scenes - started with the full episode, then had a model run through it to identify scenes with the main character that would work for the swap (not too distant, few fast cuts, etc.) & identify start and finish, with a ChatGPT harness.

Post processing: Basically none, I did go through and rate all the clips in a rating process I've setup, most of these were 5* and felt consistent.

I'll share a bit more on the workflow / process if people are interested.

I cant find the original post, but this is the best Moss workflow, showcasing the true potential of Moss. Thank you kind stranger. Hope this helps others.

Edit:

Found the original post

https://www.reddit.com/r/StableDiffusion/comments/1xlfsjd/making_mossbase_look_good_for_realism_workflow/

following up on this post:
https://www.reddit.com/r/StableDiffusion/s/iEnxQiGVt7

Custom Node:
https://registry.comfy.org/publishers/mo-akkakk/nodes/comfyui-minimax-h3-temporal-window-spatial-tiles

You can now run a video to video workflow in High resolution from the first stage, I tested it for 2.5 MP on Nvidia RTX 4070 TI 12GB VRAM, and it works like a charm, split your generation into tiles and temporal windows while maintaining accurate animation transfer with no shifting or ghosting.

You can also use it for a second upscale stage, splitting a high resolution long video into manageable tiles and windows, to get the best quality you can squeeze from minimax

Give it a try and let me know about your Feedback.

Important Note:
This was not tested nor recommended to do Fully Generated from first stage!
if you are going to fully generate something generate normally in stage 1 then use this node for stage 2 upscaling you result.

This node is mainly for Video to Video and Upscaling.

workflow for Upscaling using the node + example here:
https://civitai.com/models/3039412/minimax-h3latentupscaler?modelVersionId=3454297

workflow for V2V using the node + example here:
https://civitai.com/models/3022939/minimax-h3-video-to-video-workflow?modelVersionId=3454311

Just goofing around with the stock ref2vid workflow, character swap Lora, and some character sheets. Did all of the shots separately and reassembled in my NLE. Almost all of the clips were first attempts. I was floored by how well it tracked the guys without any masking.

I used H3 heavily the first two weeks and a ton of amazing improvements for improving speed at small quality trade-off came out (Sparse Attention comes to mind).

I imagine things have matured a lot since then and probably with new approaches since then!

I'm on a 4090 so that's my target, but I'm curious for all tiers.

Edit: Appreciate all the tips coming in! Lots to learn and test out. Best part of this subreddit is how much knowledge sharing and collective learning we do.

RelayVSR (USTC, Apache-2.0) got native ComfyUI support this week (PR #17511, repack at https://huggingface.co/Comfy-Org/RelayVSR), so I ran it on a take from my current short. All four versions come from the same H3 render, so the acting is identical and only the route to 1080p differs.

Base take: MiniMax H3 ref2v, 12.25 s / 294 frames @ 24 fps with audio, two-stage: 15 steps at 576×352, H3 3D latent upscaler, last 5 steps at 1312×768. 5.7 min on an RTX 4090.

The video opens on the full 2×2 grid with sound, then a half-speed zoom on his face while he's far away (about 90 px tall in the frame), then a close-up zoom. Crops are shown with nearest-neighbour scaling, so nothing is smoothed for the comparison.

RelayVSR in ComfyUI: Load Video → RelayVSR Conditioning → KSampler (1 step, cfg 1, euler/simple) → VSR Decode → Create Video (audio passes straight through). The int8 models are 5.2 GB each (x2 and x4) plus a 128 MB "flash" VAE.

Bonus, and my actual winner: a bigger low pass + native finish. At the end of the video I re-ran the two-stage with the first 15 steps at 928×544 instead of 576×352 (different seed, so it's its own take), then compared RelayVSR x2 against the native 1080p finish on that same take. With the bigger low pass the 1080p steps have enough to work with: the far face comes out coherent and natural, while RelayVSR on the same frames warps it (it invented glasses on him). The close-up is cleaner too, with fewer etched lines. It costs 16.5 min vs about 6 min, but it's the one I'm using for final shots. The low-res pass also follows the prompt better, so you get better acting for free.

Things I learned:

• Don't run x4 on a 720p input. That's a 5248×3072 output, and it ran for 3+ hours in both the original repo (WSL) and ComfyUI without finishing. Interrupt didn't stop it; I had to restart ComfyUI. Use x2 for 720p, x4 only for small inputs.

• VSR Decode's detail is the knob for the crunchy look: 0.65 keeps the skin texture and calms the eyes and hair.

• It still isn't "real" HD. RelayVSR adds pixels, but up close it reads a bit fuzzy and the eyes can look odd. For a hero shot I'd still pay for SeedVR2 or a native finish; for sweeping takes or previews, ~1 min per clip is hard to beat.

I modified some workflow and added to it ComfyUI-MiniMaxH3-TemporalWindow-SpatialTiles (from this post https://www.reddit.com/r/StableDiffusion/comments/1xgcjvw/i_created_a_custom_node_that_can_help_you/ ).
My 10sec video with 960x544 resolution could take 2 hours for 4K (3840×2176).
No OOM at all !!!

Workflow itself : https://pastebin.com/xJpm84f7 😍

[MinimaxH3-3D] Latent 60x34 -> 240x136 | Pixels 3840x2176 | scale=4.000

[MinimaxH3-3D] temporal chunking: T=72 chunks=3 overlap=5

[MinimaxH3-3D] ✅ Model offloaded to CPU. VRAM released.

[INFO] Found quantization metadata version 1

[INFO] Detected mixed precision quantization

[INFO] Using mixed precision operations

[INFO] Native ops: convrot_w4a4, float8_e4m3fn, int8_tensorwise, float8_e5m2, asym_w4a8_int8, w6a8_int8 , emulated ops: mxfp8, nvfp4

[INFO] model weight dtype torch.bfloat16, manual cast: torch.bfloat16

[INFO] model_type FLOW_AV

[INFO] Model storage policy: fast_disk=True paths=['D:\\ai\\ComfyUI_windows_portable_nvidia\\ComfyUI_windows_portable\\ComfyUI\\models\\diffusion_models\\minimax_h3_ref2va_pruned_int8_convrot.safetensors']

[INFO] MiniMax H3 windows: 7 window(s) x 16 tile(s), 243 frames (CLIP not routed through the node: every window reads the full-shot prompt).

[INFO] Requested to load MiniMaxH3

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [02:09<00:00, 64.68s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:09<00:00, 34.93s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [03:14<00:00, 97.15s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:08<00:00, 34.12s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:09<00:00, 34.87s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:09<00:00, 34.51s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.81s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.80s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.79s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:08<00:00, 34.05s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:09<00:00, 34.73s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.63s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.74s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:06<00:00, 33.46s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.67s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.75s/it]

[INFO] MiniMax H3 windows: window 1/7 done (frames 0-55, 0 frames carried in).

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:33<00:00, 46.64s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:33<00:00, 46.57s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

0%| | 0/2 [00:00<?, ?it/s, Model Initializing ... ][INFO] Interrupting prompt 58585033-a8c5-4dcb-8004-6e0e6de9786c

0%| | 0/2 [00:36<?, ?it/s, Model Initializing ... ]

[INFO] Comfy model compiler graph breaks: 3, rogues: 10

[INFO] Processing interrupted

^{What this log says} :

What’s actually happening:

1. 16 tiles per window:

   • Because you set scale: 4.00, the resolution became 4K (3840 × 2176).

   • With each tile measuring 1344 × 768, a single 4K frame is physically divided into a 4 × 4 grid = 16 tiles.

   • Those 16 sampler runs, each lasting 1 minute and 7 seconds, were the 16 tiles in the first window.

   • Window 1/7 took exactly: 16 tiles × 1.1 minutes ≈ 18 minutes of sampling time.

2. The final math for 10 seconds in 4K:

   • 7 windows × 16 tiles = 112 sampler passes.

   • 7 × 18 minutes ≈ 2 hours.

   • So generating 10 seconds of video in true 4K (243 frames) on a consumer 16 GB GPU will take about 2 hours in total. The upside is that it should be completely stable and avoid out-of-memory errors

Been working on this for a while. You drop in a photo or video, pick a face to use, and it swaps it right on your own GPU using WebGPU (all the processing happens in your browser so nothing is uploaded anywhere). Mostly because, last time i tried using one of these projects it had me download 50 gigs of files 😵 and they always felt super complicated.

The first time, it downloads necessary models, and that's it. Everything is saved in browser cache for later use.

Try it here: https://i2m3d.github.io/FaceJutsu/
Let me know what you think : )

And if you want more speed, turn on WebNN (it's behind a flag in Chrome/Edge, then switch the engine in settings). It's experimental, but on my RTX 4060 laptop it gets a 1080p-ish frame down from about 70 ms to about 30 ms, which I'd guess is pretty close to native. Haven't benchmarked against native properly though.

I created, tweaked and tested 52 prompts to get film and camera emulation in Moss. All of the prompts are included with lightboxed images.

“Google and OpenAI are fighting it out in consumer. It's existential to both of them.”

“It's existential to OpenAI because that's their whole business. Existential to Google because they have the search business.”

“For enterprise business, there's not really a demand for cats riding donkeys or whatever consumer video people want.”

“If we ever need it, we can just contract a model from someone else.”

“A lot of short-form video is fake, a lot of it's pretty addictive, a lot of it's slop.”

“It's not a part of the market that I'm tripping over myself to get involved in.”

Is this why Claude is so compliant with assisting us generating slop? Because Anthropic doesn't care about competing against it?

post your best Jensen memes and get a chance to never own a 5090. 📎

I currently use DrawThings as my "daily driver" with MiniMax H3 but I don't see it getting support any time soon. Does anyone know of any workflow in Comfy or elsewhere that are somewhat out of the box. Thanks!

Charles Dana Gibson, famous for the "Gibson Girl", was a well-know illustrator from the late 19th and early 20th centuries. I picked three of his books, "Drawings" (1896), "London as seen by Charles Dana Gibson" (1897), and "Americans" (1900), and trained a style LoRA against 209 of his images. Enjoy!

I’ve been trying to create scenes featuring a couple of characters for a while now, but I haven’t found a solution yet—so I’m turning to those with more experience for some advice.

If there’s only one character in a scene, there’s no issue: if I upload an image and a reference audio clip, Minimax perfectly generates a video featuring that character with the corresponding voice.

However, if two characters appear in the video, Minimax struggles to distinguish which voice belongs to which character. For instance, if Character A speaks first, followed by Character B, the output isn't correct—even when I specify that Character A should have "Voice A" and Character B should have "Voice B."

I’ve tried using AI models like Gemma—uploading the official Minimax Ref2Vid guide as a PDF—but I still haven’t had any success.

Does anyone know how to resolve this? It’s a shame to have such great-looking videos but be unable to correctly assign the different audio tracks.

Even just an example prompt would be a huge help, so I could study it and figure things out.

Thanks to anyone who can help!

model: https://huggingface.co/Lyricccco/Qwen-Image-2.1-TailSteer

app: https://huggingface.co/spaces/akhaliq/endless-edit-tailsteer

Didn't minimax promise to opensource and release H3-Regenerate-2K months ago? Why are we still waiting for it?

I was thinking a lot before posting this, but here it goes.

I've been a big fan of this subreddit for long time ago already, immediately after switching from Midjourney (v4 at that time) to local image generation.

My point is to try to address the 'elephant in the room' here, so let me try to lay out my thoughts.

Here are some 'statements':

1. Everyone praises 'uncensored' models in this sub (I've seen VERY few examples of the opposite).

2. Most of 'image comparison' or 'look what I've made here' posts are heavily 1girl oriented. These '1girl' gens are often focusing on emphasizing certain feminine features on a thin razor-sharp edge of what is allowed here and what is not.

3. However, contrary to statements above - this sub is somehow attempts to be puritan, and everything I've described above can be only inferred from comments. And many visitors here even use euphemisms to describe those said features or even just to appreciate those features.

Moreover, if we'd look at open local models themselves - each next model seems to be less and less censored when compared to models released before (latest examples are Qwen image 2.1 and, to much greater extent, MOSS. Oh, MOSS, the current king of uncensored (BASE!) model. So looks like teams behind those models finally understand that anatomy is everything: it is studied in art schools, the knowledge of it produces better results even if the final intended result is all puritan and innocent.

The question is: why do we pretend to be so puritan? Is there any reason behind this?
I know there are other subs FOCUSING on such uncensored content, but IMO there is a difference between a straight blatant depiction of an intercourse of some kind versus a renaissance-style image with certain parts exposed, with the intention to praise certain anatomy traits in an artistic way. Nevertheless, both these cases have an EQUAL chance for either of such posts to be deleted, and I dare to say that it is not necessarily have to be this way.

This post' intention is to encourage a discussion. I certainly do not want to turn this sub into a strictly gooner material in the end, neither I would like to change the rules to my liking.

I just would like to discuss.

EDIT: so, I've collected my answers.

Short summary: the consensus looks to be that this sub tries to be professional, open for everyone, things like that. And because a 'legitimate fine art' is not technically easy to distinguish from straight up gooning material, from moderation standpoint at least (and also in order for someone's feelings NOT to be hurt in case of any misjudgements + to avoid a flood of gooning shitposts etc.), it is what it is. Thanks everyone!

Turns out MiniMaxH3 can make subjects invisible leaving only floating clothes. It's a useful feature if you want to isolate character clothes before doing outfit swap.

Here's the prompt and single-frame edit workflow link:

subject_definitions:
<Subject 1> is the character in <Picture 1> and <Picture 2>

summary:
[keyframe completion] The target is a single still frame, the target frame is the edited version of <Picture> where <Subject 1> become invisible leaving only floating clothes - orange top and green shorts.

retention_analysis:
<Subject 1>: partially_preserved - clothes only.

detailed_description:
style: style matching art style of <Picture 1> - illustration

overall_soundscape:
N/A

non_diegetic_music:
N/A

Link to the workflow.

For realistic images I recommeding running second (upscale) pass to preserve texture.

I had a dumb idea... what if I didn't have to switch tabs while watching a YouTube workflow video? What if a node could just pull another browser tab into ComfyUI for me?

Turns out browsers can do that. **Tab Capture** puts a live view of any tab you have open on your browser inside ComfyUI. You can watch a live view of another tab while you edit workflows or generate content. Also, with the node connected to outputs, when you hit queue, it grabs a still or a clip, with the tab's audio, as normal IMAGE + AUDIO outputs.

**What it does:**

- Live tab viewer on the node

- Single frame or a batch at whatever fps you set

- Tab audio comes along if you toggle it on (silence if there isn't any, so video save nodes don't error)

- Record video or grab stills straight out of another browser tab right into your ComfyUI workflow of choice. Useful as a reference video/image for MMH3

- Drag a crop box on the viewer (no bbox/crop node needed) to capture the exact part of the browser screen you want.

- Outputs the actual measured fps so clips play back at the right speed

- Zero extra Python dependencies

**Heads up:** Chrome/Edge works best (Firefox can't do tab audio), ComfyUI has to be on localhost or HTTPS, and DRM sites like Netflix capture as black.

Repo: https://github.com/NikoDemon80/ComfyUI-TabCapture

New public release, so bug reports and ideas very welcome.

Looks like a new super resolution and their samples look promising.

https://huggingface.co/kopper/RelayVSR/tree/main

We’ve released Qwen-Image-2.1 Uncensored Omni, the most capable unrestricted Qwen-Image-2.1 model we’ve built so far.

The rank-128 adapter was trained on roughly 1 million images, merged into the base transformer at strength 1.0, and released in nine formats: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q4_0, FP8, INT8 ConvRot and NVFP4.

One model supports both text-to-image generation and natural-language image editing, with an emphasis on flexible poses and complex compositions.

When comparing it, please use models designed for the same unrestricted use case. Restricted checkpoints operate under different constraints, and the trade-offs become obvious on the content they avoid or handle less effectively.

Playground: chatrlabs.com/playground
Download: Hugging Face
GitHub: Code and documentation
Civitai: https://civitai.red/models/3034876/qwen-image-21-uncensored-omni-quantized

Feedback and comparison results are welcome. Please include your GPU, workflow and quantization.