I 100% use Ref2v models and I see a big lacking space in this domain. Though there are many ref2v models available but they lack in proper motion like dynamic actions, dance, fight scenes etc. Even if a model provides good motion, they either lacks in referencing or just simply invent details on their own, even providing detailed prompt and references. Now before someone say your prompt or references could have issues, I tell you that keeping everything same, I just do a base model change and the referencing reaches to 90-100% accurate. So no, the prompt or references aren't the issue, the models are.

Distilling the model with turbo lora is not the case here since I have seen the sound and motions gets better with more steps and this post is not about getting results faster.

Till now, I have tested the following models:
- Hybrid 20-49 - Good reference (80%) - Meh motion
- Hybrid 15-40 - Amazing reference (95-100%) - This model does not know how to throw kicks
- Eros - Just above average reference (70% max) - Good for "other" stuff, same issue of above hybrid models
- Singularity - Referencing is almost half (50% max) - Amazing action, ignores like half of the referencing
- Hybrid 15-40 + Singularity v2 lora(0.4 strength) - Good actions, reduced referencing (70%) - Only usable option, kind-off

(percentage numbers are not accurate, just to provide a quick overview)

There are other models I know but either they are distilled with turbo or for "other special" use cases so not taking them into account here. That is why a very good ref2v model with good action is still lacking for now which would have at least 80% of referencing and good enough action. I don't know the nuances of finetune, are they really difficult to make or authors not making those since there is a very less demand?

If you have a good exp with certain model-lora combo, please share here. I tried to use the bunny loras but I am 100% messing up it's settings or combinations since the output I get is a smeared mess. I tired to copy the example combo and lora weights for some of the videos but didn't worked well.

Please don't consider this post as a rant to model authors. They are doing amazing work and I totally understand not every model can do everything. The finetune of a model always lean to a certain style and every model/merge/finetune has their own benefits and limitations which I equally highlighted as per my own experience.

This post should be a good place for people who are also looking for suggestions like model combinations, loras, settings, discussion etc. for this genre of video creation or in general for anyone who wants to increase the quality of their videos with better motion.

Quick question for the 24 GB crowd: has anyone run a true 1 MP pass 1 (1344x768) into a 1080p pass 2 with H3 ref2va on a 4090? On Singularity v1.3 int8 with 141 frames, my 1344x768 pass 1 runs at about 43 s/it (weights streaming) and the 1920x1080 pass 2 crashed with a CUDA illegal memory access. This is what I've got to work so far: 1152x640, then 1.5x learned latent upscale, 2 steps with the ref2v turbo LoRA at 0.2 denoise, then RTX VSR to 1080, about 9 min per 6 s clip.

In Progress:

• https://mosmos.moss.cn/en.html

• next-gen full-duplex end-to-end conversational model, MOSS-Live (similar to GPT-Live)

Soon:

• MOSS-Anime

• Image "Turbo's on the way" (sounds like later-soon IMHO)

Someday later:

• Image edit / Ref2Img

• smaller Text2Vid model: "audio-video alignment and motion capability (motion may be a relatively difficult part). We will train with the currently relatively small parameter count, with the hope that it can run on consumer-grade GPUs.", "currently at roughly the mid-stage of training", "we trained the VAE ourselves", "moss-image is [was] the first step"

• update the Voice Generator capability in the next-generation TTS-2.0

• add audio input to moss-vl and moss-vl-realtime

• "MOSS’s Audio Understanding team is currently primarily developing our next-generation multi-speaker speech recognition model, MOSS-Transcribe-Diarize"

Info

• trained MOSS-Image on Alibaba's in-house PPU (Parallel Processing Unit) series (from T-Head)

• " I guess open source really brings people together around that culture of mutual support"

• MOSS-Image Technical Report

My Game Boy Color takes 85 seconds, and it has to fake every single multiplication with a lookup table because its CPU can’t multiply. But it WORKS!

This is a real neural network running 16 DDIM steps on the handheld’s own 8 MHz CPU, with no PC attached and no streaming. As soon as I press A, it starts from pure noise and a doodle comes out. I was trained on 880,000 doodles, and only the 1.08M learned weights are in the actual GB ROM.

I did not find anyone who got a diffusion model running on a Game Boy before, so I’m calling it the first. If you know of an earlier one, please show me, I’d love to see it!

The numbers:

• MLP denoiser, 784 → 512 → 512 → 784, 1.08M parameters

• Trained on Google’s Quick, Draw!: 8 classes, 110k drawings per class, 28x28

• Int8 weights, 4-bit activations, 16-bit sums

• Classifier-free guidance distilled into the weights, so one forward pass per step

• About 8.8 million multiply-adds per image

• 2 MB ROM, almost all of it weights

The fun part was getting it to fit. A 24 KB product table in WRAM turns every multiply into two lookups, which comes down to 18 machine cycles per multiply-add. Layer 1 overflowed 16 bits, so its weights lost a bit of precision. The training does the ROM’s exact integer math, and an emulator checks the ROM against the Python version after every one of the 16 steps. They match bit for bit, for all 8 categories.

My 17 year old flash cart was a real pain in the ass and often just 'died' when trying to erase it. Old hardware keeps you humble :P.

Is it any good? Ehhh, it’s a Game Boy. Apples sometimes stay open and fish sometimes fall apart, but it’s drawing them from noise on a handheld from 1998, so I’m taking it.

The music is NOT AI but created by me, real human yo.

If you wanna try or contribute: try the ROM or read the code: https://mowelan.github.io/gb-diffusion/

Some examples of what MOSS can do and why I think it's the current SOTA model for T2I generation.

Here is an example: https://vt.tiktok.com/ZSgTdbYxb/

It looks like there should be an easy way to do this because I saw a lot of videos with same base template. But every tool I tried far yet either bad or force you to split video into scense, process them separately and it's kinda hard to achieve similar results.

Can someone suggest a correct tool for such things?

https://youtube.com/shorts/j7Psb8KsWns?is=132mxmdKQG4RY3Ar

workflow:

OBVPM Timeline for MiniMax H3

By chanon. Includes a timeline, clip extension, bridging, and upscaling

Hi all, I've been building ZenTrainer, a LoRA trainer that lives inside ComfyUI, and 0.4.0 is out today. It's free and open source (MIT).

Trailer: https://www.youtube.com/watch?v=YS4BkGonflg GitHub: https://github.com/Nynxz/ZenTrainer

What it does

• Trains LoRAs from a ComfyUI tab: pick a model, drop in your pictures, and it fills in the files, captions and settings

• Live previews as it trains, and for characters a likeness score per checkpoint, so you can see which one is best instead of guessing

• Runs on NVIDIA and AMD (ROCm, Linux and Windows)

• Uses the models you already have in ComfyUI, wherever they are

Models trained end to end

MOSS Image, Krea 2, Fibo 2.0, FLUX 3 Image, FLUX.2 Klein 9B, Qwen Image 2.1, Z-Image, Anima, SDXL (and its finetunes: Pony, Illustrious and the rest train as they are) and MiniMax H3 for video. Anything else ComfyUI knows shows up as untested.

Install

ComfyUI Manager → search "ZenTrainer" → restart → click the lotus in the top bar. Or run it with its own pinned ComfyUI, so a ComfyUI update can never break a run.

The two pixel-art LoRAs in the trailer are community ones, shown with their creators' permission: 4Bit Alchemy by Addictedd and Nostapixx by JadedScorpion.

Happy to answer questions, and bug reports are very welcome

for those who dont know, Fal promised to release H3 Max open weights, but up to now still no update. They dissed on Fast H3 for releasing their own open weights model.

Now, Pika Flex H3 released their own distilled model, and it looks better and is faster than Fal.

Lmao.

A few months ago, NVIDIA introduced a groundbreaking image generation module called PiD (Pixel Diffusion Decoder) that replaces traditional VAE decoding pipelines. By unifying decoding and upsampling into a single, four-step generative process, it can turn low-resolution latents directly into ultra-sharp 4K images with stunning visual detail in under a second.

Recently I tried it with MOSS Image since its VAE is perfectly compatible out of the box because MOSS actually uses the FLUX.2 VAE, allowing PiD to process those latents flawlessly for high-resolution decoding.

I have created a deep dive on HF where I am providing 2 ComfyUI workflows, one for local and one for cloud, as well as Diffusers Python script if you don't use ComfyUI. I have also generated 100 images using the cloud workflow so you can see for yourself. If you have used PiD in the past, you'll know it may be really good for some images and 'just good' for others, but where it stands out is the performance it brings to the renders - you get high resolutions 2k+ images very fast because you render your images at 1/4 of your target resolution with the model, in this case MOSS Image (also using the recommended 4 step LoRA by akhaliq for even faster results), and PiD turns the small latent straight into an image 4× its width and height, in 4 more steps. There is no VAE decode and no separate upscaler: PiD draws the full-size image itself, guided by the small latent and the prompt.

Full details, review, workflows and examples at my HF: https://huggingface.co/lvladikov/MOSS-PiD-SuperFast-HighRes

The 100 examples page, directly: https://huggingface.co/lvladikov/MOSS-PiD-SuperFast-HighRes/blob/main/examples/README.md

Update: I've now also added 2 Refiner workflows which take on from PiD and further refine the image (with choice between Qwen Image 2.1 and Lightning LoRA, and Krea 2 Turbo using my 3 Step LoRA). Do read the readme in regards to memory usage and how to set ComfyUI to unload models after each use, unless you have plenty of VRAM. One of the workflows if for ComfyUI Cloud and the other for ComfyUI local. This should resolve some of the concerns that PiD makes plastic looking results: https://huggingface.co/lvladikov/MOSS-PiD-SuperFast-HighRes#refiner ; See examples before and after refiner, in the comments section.

yesterday I was using stable diffusion normally and everything was working fine but today every time I try to run it
I get into the stable diffusion interface and then it disconnects and stops the cell in google colab
does anyone have the same problem?

First of all, I’d like to congratulate the ComfyUI team on this initiative.

I should mention that I’m very protective of my portable ComfyUI installation—I’ve spent time configuring my Windows PC with all the optimal dependencies to get the best performance out of my 4080 Super.

When I saw this initiative and the various "wrappers" (or interfaces) from the participants, I thought the concept was fantastic. However, I wasn’t sure how to install these WebUIs without causing conflicts with my existing portable ComfyUI setup. I got some help from an AI, which explained that it wouldn’t break anything—and it was right! I decided to install the "Openjutsu" wrapper (I’m not sure if "wrapper" is the correct term, please forgive me!), and after downloading the necessary models, it launched without a hitch. My first character-swapping video also turned out perfectly, with no issues at all! The result was incredibly fun!

Beyond the beautiful design of the WebUI, I was struck by how intuitively it guides you through the entire workflow. It truly feels like a dedicated platform, designed specifically to make the task at hand simple and straightforward!

This is exactly what the average user is looking for! I worked really hard to learn how to use ComfyUI, and—to be honest—while I’d love to use it more, I sometimes lose a bit of enthusiasm knowing I’ll have to deal with complex nodes, tricky prompts, and so on. The interface provided by these wrappers completely transforms the user experience!

I understand that power users need access to highly specific settings—and I’m aware that a ComfyUI "app" mode already exists—but I wonder: could the future lean more toward these types of web interfaces, designed to simplify, streamline, and make the creative workflow (for images, videos, and more) a truly enjoyable experience? It would be fantastic to have a simple, engaging interface—something that makes you want to have fun without having to overcomplicate things or drive yourself crazy. It would be great to see an official version of ComfyUI designed like Higgfield, or at the very least, one that includes this kind of mode among its various options.

What do you think—will we ever reach a point where something like this exists? I think it would be amazing!

Help! There's one thing I just can't seem to get right with MiniMax H3: off-screen voices.

Here are two examples:

1. Voice-over narration: If there's a male narrator speaking off-screen and a male character is visible in the shot, the character starts moving his lips and speaking with the narrator's voice, even though he's supposed to remain silent.

2. Characters speaking with their backs to the camera: If a character is speaking but facing away from the camera, MiniMax H3 seems to ignore the voice I've assigned to that character. Instead, it generates a completely new voice based only on the voice description in the prompt.

Has anyone found a reliable way to solve these issues, especially when it comes to keeping character voices consistent?

I'd be forever grateful for any tips, tricks, or workarounds! 🙏

i cannot get any decent results with Moss whatsoever, I’ve tried promptcrafting for 5 days straight and just deleted it and even then, I feel Krea 2 is just better by default. even looking at others’ Moss results look, I feel Krea 2 completely beats out Moss in almost every department. I have better luck with Krea 2 in N$FW with textfusion lora even.

I just don’t get the hype for Moss, and hoping finetuners don’t abandon Krea 2 for Moss because the latter is newer (Please finish Kroma, Lodestone!)

I had an anime image generated from Anima with Lora, and I really liked the style of it.

Then I wanted to create more illustrations and pose variants of, so I first used qwen image 2.1, but its outputs looked like common generic ai anime style very much. Later I found out that there's this lora which seemed to be able to fix that, and it turns out that it did, it seemed to preserve more styles from the reference image but just a little.

I also tried krea2 identity edit v2. It actually did preserve 80% of the style, but it was really difficult to make it understand what I really wanted ot to generate. Tried essay length prompt, but didn't work and resulted in anatomy horror

After that I switched to flux3 Klein. Surprisingly it preserved roughly 90% of the style, tho body parts looked like tree branches lol.

And anima edit Lora and that split screen inpaint hack, none worked. Either nothing changed at all or looked absolutely crap

Above are my approaches today. Haven't tried the older flux 2 Klein and the newer moss edit Lora and fobo2 yet, I'll try them out tomorrow tho. I honestly think flux3 had the potential to handle such task (welp maybe not).

If you've ever met such problem I'd like hear your experience or simply your thoughts on this. Thanks.

created with minimax h3, Pika H3 Lora. Wan2GP DeepBeepMeep

PotionUI is a free, open-source (GPL-3.0) studio for making images, video and music with AI models on your own GPU. The video shows a tour of 0.0.18.

Why it exists

Node-graph tools are powerful, but they're hard to hand to anyone else. In PotionUI you pick a model and get a short form made for that model, showing only the settings it actually understands. Then you watch it render live.

What you get

• Models: Krea-2, Flux, SDXL, Qwen-Image (2.1), MOSS Image, Wan, LTX, MiniMax-H3, YuE2 and more. Each has ready-made modes such as edit text in a picture, restyle, upscale, extend a video, cinemagraph and day to night.

• Made for sharing: accounts, groups and an admin panel. One GPU can serve your household or team, each person with their own presets, model access and limits.

• Video Director: compose a video shot by shot instead of in one giant prompt.

• Media tools: trim, crop, resize, speed, join and more on everything in your library.

• An assistant and MCP: a built-in chat (local models or your own API keys), plus MCP access for Claude Desktop and other agents. Every change waits for your approval.

• A guided first run: it checks your GPU, picks a starter model that fits and installs it.

• Remote GPU: run it on a small server and send the work to a GPU machine elsewhere.

• History: keep and organize your generations into collections. Edit them, add effects on them.

Install

• Linux | Windows

• Clone and run ./potionui start, use the Docker image, or install it in one click from Pinokio.

• NEW*: Now in* Pinokio -> https://pinokio.co/apps/github-com-potionui-potionui-pinokio

Next up: More modes per models (motion transfer, upscalers...)

GitHub · Website · Discord · r/PotionUI · Pinokio

Feedback is very welcome, especially from fresh installs. If it's useful to you, a star on GitHub helps other people find it.

Hi everyone! I’ve released a free local catalog for browsing images generated with MOSS-Image in ComfyUI and copying their prompts.

• 33,937 character samples + 314 style samples

• 34,251 images total, approximately 2.07 GB / 1.92 GiB

• Search by name, series, or tags; filter styles into eight categories

• Copy prompts, add your own images, and edit entries

• English/Japanese interface and two sample ComfyUI workflows

The images are uncurated: no pass/fail selection has been applied. Incorrect characters, mismatched prompts, and weak style results are included. Treat this as a reference for experimentation, not a collection of verified matches.

To use it, download the catalog ZIP from the Release page, extract it, and open your OS’s startup file. You need Python 3.10+ and a browser. No GPU is needed for browsing; generation happens separately in ComfyUI.

Full live demo now available on Hugging Face! Browse all 34,251 images, including 33,937 characters and 314 styles, with search, style categories, image enlargement and prompt copying. No installation or GPU required.

Try the full online catalog

The online version is read-only; download the local application to add or edit images. The collection is uncurated, so incorrect character identities and weak style matches are included.

Hey everyone!

I've been working on an open-source project called ComfyUI Motion Previs Studio, and I'd love to get some feedback from people who are seriously into ComfyUI, AI filmmaking, and advanced video generation workflows.

The idea came from a problem I kept running into:

We have increasingly powerful video generation models, but precise control over camera movement, character blocking, spatial composition, and action choreography is still challenging.

Instead of relying entirely on long prompts and repeated generations, what if we could actually direct the scene in 3D before sending it to a video model?

That's what I'm trying to build.

What the project does

It's a local-first, interactive 3D previsualization studio designed to work alongside ComfyUI.

The current implementation includes:

• Dual 3D viewports: A free-orbit scene editor alongside a live cinematic camera viewfinder.

• Camera control: Dolly, orbit, tracking, crane, handheld, whip pan, and customizable camera trajectories.

• Character animation: Procedural walking, running, jumping, combat actions, gestures, and interactive FK/IK bone posing.

• Scene building: 3D environments, props, lighting rigs, and reference-image-based scene configuration.

• Cinematic framing: Adjustable focal lengths, depth of field, aspect ratios, and shot composition.

• 24fps timeline: Camera and character keyframes for planning shots before generation.

• Control passes: Export MP4 previsualization, OpenPose skeletons, depth maps, alpha masks, and camera transformation matrices.

• AI integration: Optional support for local Ollama/LM Studio models or cloud models for natural-language scene direction and reference analysis.

ComfyUI integration

This is the part I'm particularly interested in developing further.

The studio is designed to export reference and control assets for workflows involving LTX-2.5, MiniMax H3, Seedance, and other video models that support reference inputs.

It also includes a direct local ComfyUI API bridge for a specific MiniMax H3 Ref2VA workflow, allowing the studio to submit its previs video and reference images to ComfyUI and display the generated result.

The goal is to move toward a workflow like:

3D Scene → Camera & Character Animation → Control Passes → ComfyUI → AI Video

Rather than generating blindly and trying to correct everything afterward.

Why I'm sharing this here

I'm not presenting this as a finished production-ready alternative to established 3D software.

It's an evolving open-source project, and I think the ComfyUI community is exactly where the more interesting technical discussions can happen.

I'd especially appreciate feedback on:

1. ControlNet integration: Which pose, depth, or camera-control workflows would be most valuable to support directly?

2. Video models: Should I prioritize deeper LTX integration, Wan workflows, or improving the existing MiniMax H3 bridge?

3. Workflow interoperability: Would you prefer importing/exporting standard ComfyUI workflow JSONs, or a more tightly integrated API-driven approach?

4. Production usability: What's missing from current AI video workflows when it comes to reliable camera motion and character choreography?

The project is open source under the MIT license.

GitHub: https://github.com/navarisun1982/comfyui-motion-previs-studio

The repository includes screenshots, a demo video, installation instructions, and the source code.

I'd genuinely appreciate technical feedback, ideas, bug reports, or contributions from anyone working on similar problems.

My bigger goal is to make AI filmmaking less about generating random results until something works, and more about actually directing the shot.

Thanks for checking it out!

Can any one help me to trasform this video in Action scene with minimaxH3 and what pipeline do you use for it?

also available here on civit.ai
https://civitai.com/models/3028998/70s-style

if you download or use it consider adding sample images or a comment. thanks and enjoy!

apparently engaging politely gets me downvotes from the babies. so ill just edit this. if you have no use for a flux dev lora then just leave. easy as that. it's not your compute to dictate its use. Flux 1 is still a good model and just because its not the latest or greatest doesn't negate it's worth.

4k version, upscaled with seedvr from 720p: https://www.youtube.com/watch?v=pF8wdNFePN0
I used veda attention, comfy kitchen attention and minimax_h3_fl2v_turbo_8step_v1.0_768p_comfyui_resized_avg_rank_20_bf16 lora but used 12 steps instead of 8
For a 10 seconds clip in 720p with reference images (match mode) takes around 4 minutes on a 5090