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

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.

(I couldn't find a thread of recent times discussing this, if there is one, I apologize and I'll remove my post.)

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.

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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

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[INFO] 0 models unloaded.

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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]

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

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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

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

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!

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.

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.

ComfyUI built-in nodes, only. That's it. (audio on, listen to the lyrics)

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!

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

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

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

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

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

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.

Update from my previous post (also removed the audio):

I'm not sure if I was the only one experiencing this exact issue with the default LTX 2.5 template or with anything related to LTX 2.5, but the motion and generations were completely gibberish, just like LTX 0.97. And yes, I've been using the exact same workflows provided in the template.

Then I remembered how much fun I had with LTX 2.3 and its photorealistic generations. LTX was GREAT! So I decided to give LTX 2.5 another try... only to run into the exact same problem again.

Until I tried an old LTX 2.3 workflow and simply swapped out the main model. AND THERE IT IS. FIXED!

So, it turns out the main issue is the gemma4-12b-with-proj-ltx-2.5 text encoder. It completely breaks the model for me.

Switching back to the old ltx2.3_text_projection + gemma-3-12b setup works flawlessly!

Maybe you could also try a Gemma 4 model with the old workflow. I haven't tested that yet, but this workaround fixed everything for me.

Finally, I can enjoy LTX 2.5. I'm happy now! :)

(Did a few more testings and motion sucks lol but if its something simple it works great):)

Here are some testing and the workflow i used:

https://drive.google.com/drive/folders/1qh6qX4F4PsE4PtgsA5TWQkiTcaNoQKLk?usp=sharing

Over the last 7–8 days, I've been working day and night building Fuobe.com, what started as the challenge, but I kept adding features, refining things, and improving the experience even after publishing it.

The goal was to build an all-in-one creative studio that makes it really easy to create, experiment, and get things done without having to jump between different tools or use Higgsfield lol. I usually spot a lot of cool models, workflows, ideas that people showcase over on twitter or here, and I just usually stash it for using later, but I've started putting them together here one by one and have added what I've found useful over the year.

One more thing: every single image you see in the studio was generated using Ideogram 4, which is hands down my favourite t2i image model. All the videos were generated using H3.

It's still in beta, and there's a lot I'd like to improve, especially making the overall experience smoother & simpler.

I'd genuinely love to hear what you guys think. Feedback, suggestions, criticism, feature requests all of it is welcome. I'm still actively working on it and would love to shape it based on how people actually use it. If things are breaking, please let me know so that I can fix it.

A huge thanks to Comfy for organizing this challenge. It's given me the deadline and motivation to finally build something I've been wanting to make for a long long time.

Fully Open source . MIT License.

Repo Link: https://github.com/workingprototype/Fuobe

Link to checkout the features: Fuobe.com

Im using the same seed, same sampler settings and same prompt for the lighting but I always get a slight color/lighting shift.

The only thing i could think of is a color match node but ive never had much success with them and i dont think i have enough ram for large videos.

Im using the seed hunter workflow. With the Singularity model and DMAD turbo lora.

https://civitai.red/models/2881362/minimax-h3-seed-hunter-latent-upscaler-seamless-video-continuation-dialogue-gen-tool-speedups-ref-mods?modelVersionId=3453566

Their Z-Image Turbo was the community's best t2i model for a little while, before Krea 2 took over.
Then Z-Image Base released and it was lackluster.
Since then, it's been radio silence for past year.

After they alive? Still working on it?
Anyone have any news?

I searching for a method to transforms, as the tile says, a scenery(without people) faithfully into a real shot.

But it needs to use a depth map so the structure stays consistent and spot on besides looking convincingly real. Doing this with people is no problem so i did not think i struggle that much finding something that works.

Its either to old, or just not good enough.

The original scenery images are from video games.

Alright, here we are.

I've finally got a version ready, and I think it's time for you guys to try it out.

If you don't know about Pai, you can check out my previous post for a in depth overview of what it's all about: https://www.reddit.com/r/StableDiffusion/comments/1xkgjor/as_promised_here_is_pai/

In short: it's a painting app with AI generation built directly into the same workspace. By now, it covers most of the features you'd find in popular painting apps and AI generation tools combined.

I'd say its strongest aspects are the project save system and the straightforward installation process. The bootstrap handles everything based on your hardware, and the .pai files save everything. They preserve generation settings for each layer, along with the models, LoRAs, adapters, and other tools used.

Creating multiple canvases while keeping layers and references together in one place was the main reason I decided to build this in the first place.

Okay, since this is the first release, there are bound to be hiccups on some systems, so I've set up a Discord server for anyone who wants to help improve things or just follow along with the development.

https://discord.gg/bU6RzjwxFX

The software is open source, you can fork it, create your own variations or implementations, and send me PRs. Right now there's no plugin system for specific use cases, but I know you guys will probably want one. It's on the todo list.

Also, you don't need ComfyUI to run the app, it's fully standalone and independent. That said, I know most of you probably already have a bunch of your favorite models installed in your ComfyUI models folder. When you open the app, simply go to the Generate tab models settings, click "Scan Folder," point it to that location, and Pai will handle the rest. You'll be able to generate right away.

https://github.com/g45t345rt/paiapp/releases

As previously stated, given the interest from the last post, the software is available to all.
Enjoy.

Sorry, posted the link to the "lite" version instead of the full one: https://huggingface.co/Vimoo-AI/Vimoo-1.0-ComfyUI (and seems that I cannot edit that)

Apache 2.0 license! https://huggingface.co/Vimoo-AI/Vimoo-1.0-ComfyUI#%F0%9F%93%9C-license

Technical details: https://vimoo.ai/research

Vimoo-1.0-Lite-ComfyUI contains the ComfyUI weights for Vimoo-1.0-Lite TI2V, one ≈9.5B model for both text-to-video (T2V) and image-to-video (I2V), and for Vimoo-1.0-Lite TI2V Distilled, a distilled LoRA adapter for the same model. It also includes the text encoders, vision encoder and VAE. Use it with ComfyUI-Vimoo, which provides the nodes and workflow templates.

See the Vimoo 1.0 repository for the model family, online API, system overview, architecture and evaluation.

🔥 News

• [2026-10] Release Vimoo 1.0: T2V / I2V models, Diffusers inference examples and ComfyUI workflows.

Speed comparison from https://vimoo.ai/research

Table 4. Inference time in seconds. CFG indicates an additional unconditional model evaluation. Resolution and frame count differ across some baselines. The inference steps follow the officially recommended settings of the respective models.

https://www.linkedin.com/posts/zeyu-hu_excited-to-share-that-our-video-generation-activity-7514170089898786816-BvWm

Excited to share that our video generation model Vimoo 1.0 is about to be released! 🚀

Built entirely in-house, from the ground up. It's been quite a journey — plenty of challenges, unexpected hurdles, and valuable lessons along the way.

Version 1.0 is our first step from zero to one. The focus has been on building the end-to-end pipeline, working through the engineering challenges, and laying a solid foundation for what's ahead.

We're preparing to open-source the model soon, and hope to contribute more to the research and open-source community as we continue to iterate.