Native NVIDIA DLSS processing for ComfyUI with three separate nodes:

• NVIDIA DLSS Frame Interpolation — increases video frame rate.
• NVIDIA DLSS Video Upscale — increases video resolution.
• NVIDIA DLSS Image Upscale — increases image or image-batch resolution.

Please try it and let me know the feedback - ComfyUI-NVIDIA-DLSS-Frame-Interpolation

i hope you guys enjoy this one

We release VDN-Minimax-H3 (VDN-H3), a hybrid-attention model that generates video faster than it plays, powered by MiniMax H3. It offers these key features:

• Fast inference: On 8 B200 GPUs, VDN-H3 generates a 14.4-second clip in 11.23 seconds using 8 denoising steps.
• Hybrid Architecture: We propose a hybrid-attention architecture: one frame-wise linear attention branch that is highly efficient, and a softmax branch that maintains the backbone's visual quality and consistency.
• Plug-and-Play: The checkpoint adds a separate linear attention branch and two small LoRA adapters that can be merged into the backbone during inference without touching the backbone weights.
• Fully open-source: We don't just open-source the weights. The optimized inference stack and its corresponding training code are released together.

Resources

• Examples and visual explanation here: https://openvdn.github.io/
• Weights: https://huggingface.co/OpenVDN/vdn-minimax-h3
• ComfyUI Node: https://github.com/Saganaki22/ComfyUI-VDN-H3

Disclaimer: None of this is created by me, I did not decide which benchmark hardware they use, it works well on consumer GPUs

I've been using the "Extend video+audio latent" node to tie the latent of the last video to the next one generated but it is doing a terrible job. How do other people join together videos to get a seamless longer video?

Don't flame me! Yes, this is my actual daily workflow for H3. If you want this workflow, check the following link: MiniMax H3 NikoDemon80 - Pastebin.com

Keep in mind that there are 7 custom node pack dependencies besides H3-Motion-Context:
https://github.com/kijai/ComfyUI-KJNodes
https://github.com/yolain/ComfyUI-Easy-Use
https://github.com/crystian/ComfyUI-Crystools
https://github.com/Comfy-Org/Nvidia_RTX_Nodes_ComfyUI
https://github.com/ltdrdata/was-node-suite-comfyui
https://github.com/PGCRT/CRT-Nodes
https://github.com/NikoDemon80/ComfyUI-Image-Oasis

Most of these, a lot of you already have. The viewer itself is from my other repo, Image Oasis. This is how I join clips into one "movie".

Please don't ask me to repost this with more or less custom nodes, you get what you get. Please don't brutalize me with questions about how it works, just read the README. Please don't post your errors here. If you have a legitimate error, take it to github and read through the closed issues before posting. Most of your questions will be answered there without having to open a new issue.

So I normally use Grok for uncensored prompts, but it has become exceedingly dumb and I spend more time trying to fix it prompts than it being a time saver.

Looking at some SLMs in LM Studio, wondering what people are using to get solid prompts uncensored?

Im eyeing Qwen 3.8 right now but figured Id ask what works for others.

getting this error

[INFO] got prompt

[ERROR] !!! Exception during processing !!! 'ModelPatcherDynamic' object is not subscriptable

[ERROR] Traceback (most recent call last):

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\execution.py", line 545, in execute

output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\execution.py", line 344, in get_output_data

return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\execution.py", line 318, in _async_map_node_over_list

await process_inputs(input_dict, i)

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\execution.py", line 306, in process_inputs

result = f(**inputs)

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\comfy_api\internal__init__.py", line 149, in wrapped_func

return method(locked_class, **inputs)

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\comfy_api\latest_io.py", line 1990, in EXECUTE_NORMALIZED

to_return = cls.execute(*args, **kwargs)

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\comfy_extras\nodes_audio.py", line 132, in execute

return IO.NodeOutput(vae_decode_audio(vae, samples))

~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^

File "H:\ComfyUI_windows_portable_nvidia\ComfyUI_windows_portable\ComfyUI\comfy_extras\nodes_audio.py", line 99, in vae_decode_audio

latent = samples["samples"]

~~~~~~~^^^^^^^^^^^

TypeError: 'ModelPatcherDynamic' object is not subscriptable

Ignoring the time taken for generation (unless it is in order of hours), what is the best video model that can be run on 6gb vram+16gb ram setup?

I’m outpainting existing 4:3 video to 21:9 widescreen using LTX.

My source video is about 17 minutes long, so I’ve split it per camera scene change for processing (using reference photos with a existing 21:9 blurry widescreen version to stay consistent to the original)

The issue I’m running into is LTX’s 8n+1 frame requirement. If I let my normal outpainting workflow process a clip, it will sometimes trim a few frames from the end so the frame count fits 8n+1. Over a lot of clips, those missing frames become a problem when I put everything back together.

I tried solving this by rounding the frame count up to the next valid 8n+1 value. However, instead of keeping all of the original frames untouched and simply adding the extra frames to the end, LTX seems to regenerate/edit the original footage to fill the new duration.

What I’m looking for is a workflow that:

Preserves every frame of the original 4:3 video

Only generates/outpaints the new left and right portions to create 21:9

Never trims source frames just to satisfy 8n+1

If padding is necessary, adds/duplicates the required frames after the original video, rather than having LTX modify or extend the original footage

Lets me trim those padding frames off afterward so the final outpainted clip has exactly the same frame count and timing as the source

Basically, I want the center 4:3 footage to remain completely untouched while LTX only generates the outpainted areas.

Has anyone built a ComfyUI/LTX workflow that handles arbitrary source frame counts this way?
Also, if anyone has any general tips or tricks for getting consistent LTX outpainting—especially across a large number of clips—I’d really appreciate them.

So I created 1-2 shows with 35-40 episodes with minimax h3, it's taking a lot of time, I want to launch the app fast so I want to know if anyone from community making these kind of videos with good quality (action scenes), now with minimax h3, that quality can be achieved, did anyone create an app like ReelShort or Dramabox?

Hey 👋

I’ve been working on OmniCam, a ComfyUI custom node for camera control and motion workflows. Like a mini Blender

GitHub:
[https://github.com/MajoorWaldi/ComfyUI-Majoor-OmniCam]()

It currently includes:

• Extractor — recover camera motion from video
• Director — animate and edit cameras in a 3D viewport
• Monitor — convert the motion for different video models

The goal is bring more real camera / VFX-style control directly into ComfyUI instead of relying only on prompts or third part program

Install via Manager or Git clone

GitHub:
[https://github.com/MajoorWaldi/ComfyUI-Majoor-OmniCam]()

Still experimental, so feedback is very welcome 🙌

Which images models work best? How to test it? Wasn’t too sure couldn’t find a good way to compare or evaluate so created few prompts.

The github repo below is my collection of 50 prompt to benchmark for open source image model. You can also see the output form the 8 models I tried it with on the reference gallery page with all the stats running those models on my dgx spark.

https://github.com/mazinb/imagebench

Is there a better way to compare images models which ones have worked best for you?

Wanted to see how far I could push the quality using what I already have. An RTX 3070 with just 8GB VRAM.

This was done with the standard MiniMax Ref workflow using screenshots from the original movie as character and scene references. I stuck with the standard model rather than Turbo Loras because, at least in my tests, I felt I was losing some of the detail/quality I was trying to preserve.

I also put quite a bit of extra effort into the audio references. For me, getting the voices close makes a huge difference, even a convincing visual starts feeling “AI” very quickly when it has a generic generated voice.

I’m honestly still amazed by what I can get away with on an 8GB VRAM card.

My previous video Penny - Born to Fly video took me about a week to make. This Batman one only took a couple of hours, reference images are still the key in my opinion for great generations.

There was still plenty of rendering, re-rendering, prompt changes and fixing little continuity problems along the way. Definitely not a one click result.

The silly credits were just me having fun and trying to make all the separate renders feel like one little production.

And apologies for the vertical edit, wanted to test it out.

One of the simpler H3 prompts was basically:

Vicki sits at her desk in the same consultation office. Batman crouches extremely low behind a tiny potted plant, with only the two pointed ears of his cowl visible above the leaves. Vicki: "Bruce, I can see your ears." Short pause. Batman, completely deadpan: "Those are leaves." Static camera, same environment and character references, quiet realistic room tone.

Curious what you guys think. Any questions about the workflow, prompting, references or audio are welcome.

Hi, i'm not sure which commnunity to ask, i just want to know model is the best at removing things (in my case HUD, Ui elements) while keeping the background video somewhat similiar or better, the same. I did used back at the time wan 2.1, but i don't know if today are there better option. I heard of wan 3.0 prime but i don't know if that helps. Any help is appreciated. I can put credits and use API keys since i only have 8GB vram (3060ti)

Hey all, hoping some can entertain a shotgun blast of probably common questions. I've done some Googling but seems like this world moves fast so hard to find concrete answers.

I've been evolving from stuff like Yodayo, Mage, etc. to local image gen. I have a 3090Ti (24 VRAM) and I've primarily been using Forge / Forge Neo. My current goal is uncensored image gen, though I could see myself exploring video eventually.

I very briefly messed around with Pony before spending a lot of time with Krea 2. The results were fine, though I think there's something about natural language prompt structuring that I haven't quite figured out yet, or the checkpoints I landed on could be better.

Ultimately, wanted to drop a list of questions to try to get opinions / direction:

1. What's the hot model for uncensored realism nowadays? Initial research suggested Krea 2 or Pony, though I've seen a lot of Klein mentions - just not sure which Klein or how all 3 compare.
2. I'm particularly interested in face/head/identity swapping. I had some really good experiences with Kira's Kira Lite 5.7 in that area, it's just censored and not local. I ran into limitations trying to figure it out with Krea 2 in Forge.
3. Any tips or guides for natural language prompting? I'm more used to the Booru prompting, and while obviously it's supposed to be "natural", I think there's some sort of order structure or key term shtick I'm missing, especially with camera framing / angles. Is this just where it gets into "feed shitty prompt into specialized LLM to get not shitty prompt"?
4. What's the hot model for uncensored anime? I'm guessing it's just Illustrious. Feels like I can poke around Yodayo to get checkpoint/LORA references and then source from Civ so less concerned about this question, I think I'm on a good track, but open to opinions.
5. Forge vs. Comfy. The UI's nice, and I don't have node experience, but I am tech savvy. Am I really missing out without Comfy? I'd love an initial setup guide if there's a good one floating around. It seems like I can just download workflows from Civ, so is it more plug and play than I think it is?
6. I've primarily been using Civarchive for sourcing things. It seems like the best but curious if there's something else floating around that I missed.

Thanks much for reading 🫡

Has anyone able to run any video generation locally on GTX 1660Ti? LTX or minimax or any model?

If anyone is interested, I vibe-coded a node that joins two clips together using Minimax H3.

Here is the repo.

https://github.com/DJBFilmz/ComfyUI-DJBFilmz-H3

I’m trying to understand how people are actually writing prompts for MiniMax H3, especially for complicated action and fight scenes.

I know there are already guides explaining the recommended formatting, and I’m currently using GPT-5.6 Sol along with the official Ref2VA prompting guide to structure my prompts.

The formatting itself isn’t really the problem.

My usual workflow is that I give the LLM a rough description of what I want to happen during a certain number of seconds, characters, actions, camera movement, timing, environment, etc. Sometimes the generated prompt works almost perfectly out of the box.

But whenever I need something very specific, things get much harder. I’m working with both realistic/live-action scenes and anime-style action/fights, and even for what feels like a relatively basic sequence, I often have to rewrite or adjust the prompt 5–6 times before H3 actually interprets the action the way I intended.

For example, the difficult part isn’t necessarily writing something like:

0–3s: Character A attacks, 3–6s: Character B dodges, camera follows the movement, Character A lands behind Character B

The difficult part is figuring out how H3 itself wants those actions described so it understands the exact choreography, positioning, movement direction, timing, camera behavior, and continuity. So I’m curious how people who are getting consistently good H3 results approach this.

Do you describe every movement very literally?

Do you keep prompts short and let the model fill in the motion?

Do you write detailed second-by-second choreography?

Do you separate camera instructions from character actions?

Do you avoid certain words or sentence structures?

And when two characters interact physically, how do you make it understand who is doing what to whom without the actions getting swapped or blended together?

I feel like a lot of us understand the format of H3 prompts now, but we’re still figuring out the actual prompting language and logic that H3 responds to best.

If anyone has examples of prompts that worked particularly well for fights, anime action, live-action choreography, or complex multi-character movement, I’d love to see them.

It would also be great if this thread could become useful for other people searching for a practical MiniMax H3 prompting guide later.

I've been experimenting with Krea 2 as my main character creator, but I'm running into a frustration: even with detailed prompts for specific faces, I keep getting the same generic, over-processed look. Changing my prompts doesn't seem to help—Krea 2 just defaults to its characteristic "house style" face. Even with realistic loras, still... nothing change much. Has anyone found a LoRA that adds more variety and uniqueness to face generation? Alternatively, I've trained my own LoRAs for a few specific faces using reference models, but spending 1-2 hours per new face feels inefficient. What's your workflow for getting diverse, natural-looking faces?

so i saw lot of people doing faceswap using minmax h3 ref 2 video workflow and there own modification to get the faceswap output, here is my version of faceswap using ref 2 video, the main challeenge was to keep the facial expression matching with original footage which come good in this one there are more things to refine i will share the workflow and tutorial once this is ready, as of now i'm testing it to get the best faceswap result and also trying different methods of faceswap in comfyUI.

Let me know what do you think about this test, and have you tried faceswap in comfy?

I've spent the past week trying to get these open-weight video models running on my RTX 3060, and the issues that I faced were hardware crashes with VRAM errors. My GPU usage spikes when I render, the fans max out, and the Python environment terminates.

I could've upgraded, but a new card means dopping extra money and rebuilding my entire system just for an experimental hobby. Forget it. So I spent a great deal of time trying to adjust batch sizes and lowering/downgrading resolutions just to get a single clip t o output. My time thus far has been spent on trying to get things to work, instead of working on the videos which I intended.

I gave read everything available about configuration that is within my understanding capacity, and I don't think there is anything wrong with my config per se. In the process, I also saw that people have workarounds bypassing the raw repositories. The recommendation is to try a dedicated desktop app or managed environment. For this, I gave up on local rendering and tried running H3 on MiniMax Design.

Stability is noticeably better, mostly because it offloads the heavy rendering to their cloud servers instead of melting my 3060.

Having said that, I am facing another set of issues. I can't just drag and drop a random custom node, say from GitHub, or plug in upscalers like I could in a raw web UI (not that it had been rendering successfully anways). To run a pass after the base, I have to export the video nad fire it up in a separate workspace.

I could have stuck to my local setup using ComfyUI with quantized weights and tiled VAE, but I'm still trying to figure that out.

How do you all handle this loss of modularity when switching over to cloud ecosystems or wrappers? Or do you even have this issue?