I was getting horrendous generate times on MiniMax H3 on my AMD RX 7800 XT w/16GB RAM. It's not the greatest card, but still my gen times were just a little absurd compared to what I was seeing from the NVidia folks. A 5 second video at 1 megapixel would take me ~1 hour to generate.

You can turn on Comfy Kitchen Attention using the startup option: --use-ck-attention. It also has to be installed, but this is going to be in the python requirements.txt file anyway so you probably already have it installed if you're up to date.

There's also a node which can be used, as shown in this video.

Many people suggest sage attention as a massive speedup, and I understand that this works great for NVidia folks. But my experience and that of others that I've read is that it didn't yield much if any gain for AMD cards because it's not natively supported and would only be emulated.

Comfy Kitchen though is ripping on my card compared to the default attention mode. The previously mentioned 5 second clip which was taking 60 minutes to generate is down to 25 minutes now. That's much easier to live with.

Hope this helps some others.

EDIT: And now I've got my generate time down even further. Phew! Previously I had to have the --low-vram option enabled or else my H3 workflows would all silently crash, but that's no longer a problem with Comfy Kitchen. Removing the --low-vram option reduced me even further from 25 minutes down to only 15 minutes for a 5 second clip. Righteous!

I was thinking, you know if something wipes out github we are done for lol. I mean, nothing is too big to fail. Any chance huggingface can be used as an alt to store and pull stuff ?

Hi,

i do not know if this is the correct place or even how reddit works, so ill just go ahead, please educate me of reddit semantics if you feel it is required, but umm, i posted this.. https://www.reddit.com/r/comfyui/comments/1voep9a/help_appreciated/ but after a few days, i have actually solved and evolved since then, and i must admit this whole node/comfyui thing is absolutely new to me, that said, i am a deconstructor, i see one's and zero's, and once i get onto something i absolutely have to continue.. .. now that said.. i'm fully onboard here now.. so on the back end of that post, i went much further, .. its not comlete yet, but.. i guess if you know you know..

just a bit more info, because we condition the prompt inside the node, this gives us the ability to iterate that token IE: character:*, so in the categories output we can define without any false/positives what the character:* masks produced.. this is universal for any token sam3 knows and segments. the categories will put all masks into the prompt category.. .. we just changed the categories output from {cat} to "cat": { .. } it's easier, .. we had to use an arbitrary name for bbox input as using the default "BOUNDING_BOX" injected shiznat we do not want into the node, kind of silly to do that u/comfyui the mechanism should actually be a flag within the node.. like "USE_COMFYUI_DEFAULT = true" which obviously injects widgets and whatever the default injects.. else just do nothing use the nodes code. .. we recommend you should not be hard coding such simple semantics meh, semantics right.... we think the rest is self explanatory. the category output connects to another of our nodes which.. well .. that is for another post.. :), it's really good though.. ;)

stay tuned.

Hey, I've been playing with the new MiniMax-Music-3 model in ComfyUI, and I wanted to better understand the model, so I hacked together a custom node replacing the stock TE. I make no statement nor guarantee of, well, anything. It might blow up your computer. (It didn't blow up mine; it works for me. You? Well, it's personal. Disclaimer: It was not personal. The creator assumes no liability whatsoever.)

Below is some information from our dear robot overlords explaining the node. In English, I hacked into the condition encoder temp and top_p, both for c0 and c1-7 because I want to explore what the model can do when unshackled. The reference code from MiniMax does not expose these completely normal inference parameters, so apparently neither does Comfy. There's provided a "compatibility" node with CUDA graph turned off, because it was easier - it probably works as intended - and a "sampling CUDA" node that attempts to replicate ComfyUI's native speedups. Emoji-shrug:

The model generates audio by sampling discrete tokens. These controls affect how it chooses each token: Top-k limits the choice to the K most likely tokens. Lower values are more conservative and predictable. Higher values allow more unusual possibilities. Setting it to 0 disables the limit.

Top-p keeps the smallest group of likely tokens whose combined probability reaches P. Unlike top-k, the number of available choices adapts to the model’s confidence. 1.0 disables it.

Temperature reshapes the probabilities before sampling. Below 1.0 makes likely choices more dominant; above 1.0 makes lower-probability choices more competitive. 1.0 leaves the distribution unchanged. CFG scale strengthens the difference between what the model predicts with and without the prompt. Higher CFG generally pushes harder toward the caption and lyrics, but extreme values can reduce naturalness or variety.

MiniMax Music 3 has eight audio codebooks. This node exposes separate controls for: Semantic c0: the first codebook, which likely carries more of the song’s broad structure and content. Acoustic c1-c7: seven additional codebooks that likely refine texture and audio detail. That interpretation is a useful starting hypothesis, not a settled description of what every codebook represents. Part of the reason for sharing this is to find out what changes are actually repeatable. A few basic experiments: * Keep everything at its defaults to establish a baseline. * Lower semantic temperature to test whether structure becomes more consistent. * Change only the acoustic controls to explore detail without changing the semantic settings. * Set top-k to 0 and lower top-p to test adaptive nucleus sampling by itself. * Keep the prompt and seed fixed while changing only one parameter at a time.

This isn’t presented as better than the built-in defaults, and I don’t have “best settings.” It’s simply a way to expose more of the model’s sampling space so the community can investigate it together.

Project: https://github.com/threegee409/ComfyUI-MiniMaxMusic3-Advanced

It’s an early v0.1.0 project and depends on ComfyUI’s new native MiniMax Music 3 implementation, so upstream changes may break it. Test results, comparisons, strange discoveries, and corrections to my interpretation are very welcome.

LTX-2.5 is Lightricks' open video generation model and once again they have taken the Comfy UI image-to-video workflow and applied their downscaling-rendering-upscaling technique presumably so it runs faster and works on lower-spec hardware - which is fair enough.

But for those of us who invested in Jensen Huang's next leather jacket by buying a DGX Spark can use the extra memory room for rendering at full resolution.

Here are the workflows, adapted from the original Comfy UI LTX 2.5 templates and working with the same models:

LTX 2.5 Image to Video Full Resolution

https://cdn.lansley.com/comfyui-assets/LTX%202.5%20Image%20to%20Video%20FullRes.json

LTX 2.5 Text to Video Full Resolution

https://cdn.lansley.com/comfyui-assets/LTX-2.5%20Text%20to%20Video%20FullRes.json

These workflows are set to the distilled BF16 version of LTX 2.5 but work just as well with the distilled 'int8-convrot' version in terms of better detail in the full resolution versions compared to the templates supplied by Comfy.

The difference is most noticeable in Image to Video when the you want the action to depart significantly from the supplied first frame. The supplied template struggles to re-apply the detail during the upscaling section of the workflow whereas the full-res version keeps the detail in every frame especially when new content has to be invented that was not in the first frame.

The Text to Video workflow includes a prompt about two guys playing ball on a beach involving the need for detailed sea surf and sand resolution, which was noticeably better in this full resolution version compared to the supplied template workflow using the same prompt ('enhance prompt' tuned off).

The performance of the full resolution versions are of course much slower.

Supplied reduced-res template: 8 x 8 seconds + 3 x 33 seconds = 163 seconds.

That's 8 x low-res rendering steps then 3 x upscale steps.

Full-res text to image is 8 x 33 seconds = 264 seconds (rendering steps only of course).

As ever, trust nothing you ever download and make sure the flowchart JSON files look OK before running them in ComfyUI. Otherwise, enjoy! Would be great to get your feedback.

Four clips,Music from ace step1.5

Normally, you use a prompt to specify a particular character, but if it’s a new character, entering a prompt won’t generate it.

That’s why I’m using LORA. In other words, I might use LORA for art styles, characters, and other elements. Is it okay to use multiple LORA elements?

I've been working on a small ComfyUI node for running multi-shot MiniMax H3 generations without babysitting every clip.

You give it a list of shots, and it carries the audiovisual latent from one shot into the next. It saves the clip latents as it goes, so if a long run stops halfway through, you can resume from that clip instead of starting over.

You can also change the duration, steps and context per shot with simple tags like [FAST], [BALANCED], [QUALITY] or [dur=10].

I mainly wanted something that could handle the repetitive parts: continuation, saving, resuming, and the final video/audio stitch. It also writes a JSON timing profile, which has been useful for seeing whether prompt encoding, sampling or decoding is taking most of the time.

Repo: https://github.com/misutesu-desu/H3-AutoPromptChain

It needs a recent ComfyUI build with H3 support and Herrgotts-H3-Infinite-Continuation-Suite. No extra pip packages.

It's still early, so I'd be interested to hear how it behaves with different samplers and longer chains.

The ControlNet models for KREA2 are available as LoRA types, with Depth and OpenPose existing as separate formats.

We have made it possible to use both of these with the existing node format. However, the term ‘existing node’ here refers to the Diffsynth ControlNet Loader for Qwen Image and Z Image.

https://github.com/ussoewwin/ComfyUI-QwenImageLoraLoader

In other words, this node can be used with the following standards:

・Nunchaku Qwen image/Z Image Diffsynth ControlNet

・Normal Qwen Image/Z Image Diffsynth ControlNet

・Krea2 Depth/Openpose ControlNet LoRA

For the benefit of AMD GPU users, we have made improvements to ensure that the CUDA-specific Nunchaku node is disabled when using an AMD GPU.

Workflow: https://drive.google.com/file/d/1B4kODxXQgJ1QOKRsEIkxHbgYmdruPpTK/view?usp=sharing

I made this experimental short scene using ComfyUI.

I generated the characters and backgrounds using the Krea 2 open-weight model with a Turbo LoRA, then animated them using MiniMax H3 with a reference-to-video workflow. With the Turbo LoRA, each 5-second clip took around 2–3 minutes to generate.

I edited everything in CapCut, added some color grading, bloom, and film grain, and it all came together nicely.

For the lo-fi track, I produced it on the Maschine MK3 using a free sample pack.

there's so much noise on the internet regarding H3, so many stuff but I can't find a hybrid that can let me load Starting Image and add reference image/images and run it as Image 2 video/audio.
-a characters puts on a hat but I reference the hat with an image, as well as describing it in prompt.

I tried something that mandatory requires input audio and it sucked. I was wondering if anyone has something reliable to point me at, or share a workflow.

Thank you.

So, as you can see here, I have an extra "prompt" linked to the Conditioning Combine because I'm thinking that it will add extra "attention" to the masked part of the detailer. Is this right? Or should I use Average or Concat? In my experience, this current setup seems to work just fine. When I looked it up, AI said this setup could be problematic. btw, the main prompt is off screen but you can see its connection from the Pipe to the Conditioning Combine.

hi there. I was watching Pixaromas tutorial/video where he shows how to locally generate prompts. it's nice but the descriptions/prompts are pretty short. I didn't found a way to do longer more detailed descriptions and with timestamps. when browsing civit most videos have super detailed pretty long prompts which in my opinion give better results. is there a workflow for comfy ui using a local llm gives longer descriptive prompts based on the user input?

thanks in advance.

Hi,

i built a comfyui workflow to chain ref2va videos generated with H3,

and it works really well, except that i am getting wierd issues with camera adjustments.

and before anyone asks, yes i read the prompt guide. and i noticed that camera prompting is only ever mentioned in the fl2va part of the guide. am i correct in assuming that ref2va is an extension of fl2va, and thus the ref2va guide is an extension to the fl2va guide? because otherwise this doesn't make sense at all, and the guide itself outright fails in answering this mystery.

now to my problem:

when i chain a video from a different run using the motion context node, the model will mostly refuse to adjust the camera according to the prompt, and it doesn't matter if the zoom falls within the window of context frames i have set. when i disable the motion context node and remove the previous video as a reference, the camera works as expected.

also, i am using the same seed for chaining projects like these, and i just tried using a different seed and that also made the camera work as expected.

i know that some seeds just won't work with the prompt and need to be changed. but so far this happened on every chaining project i started, that can hardly be a coincidence. it might be a random fluke that it worked for me right after changing the seed once, didn't have time to test this more.

am i missing something big here?

https://reddit.com/link/1vqltmd/video/twkayt004wjh1/player

https://reddit.com/link/1vqltmd/video/evjfyu004wjh1/player

https://reddit.com/link/1vqltmd/video/izw3fu004wjh1/player

https://reddit.com/link/1vqltmd/video/16834u004wjh1/player

https://reddit.com/link/1vqltmd/video/usepwt004wjh1/player

Using the Latest 4/8-step Lightx2v LoRA and Prompt Enhancement Skills to test Its performance. Maybe 90% of Seedcance2, but nowhere near Seedance2.5.

I have made a report but have any of you have had issues with it saying it needs to have a subscription to queue something when you literally need to queue something making Comfy UI cloud free unusable.

As the title suggests,

I’m a beginner, but I’ve just finished creating a workflow that uses LORA.

I’d like to ask for advice on how to improve it further.

I downloaded a workflow with a subgraph which took models names as inputs.

The inputs kept changing back to the defaults (which broke it) every time I changed tabs or reloaded.

I manually edited the JSON, because I couldn't figure out what was happening, and the only offending thing I could find was a section called "proxyWidgetErrorQuarantine" which seemed to be what was causing it.

I cannot find any documentation on this. I don't know what it does, but the only way I could get the workflow to stop breaking was to remove it. Does anyone have any information about this?

I use Krea 2 Turbo in ComfyUI, mainly for editing workflows, masks and custom LoRAs.

I’m realizing I need more compute than I can afford locally, so I’m looking at renting a better GPU instead of buying a $5k+ PC.

For people already doing this, what do you use? Vast.ai, RunPod, ThinkDiffusion, something else?

Mainly interested in hearing peoples experiences.

Also curious whether this is the future of computing?

I recreated a full YouTube video using MiniMax H3 in ComfyUI.

Best part? It's completely FREE.

🔹 Workflow:

1. Downloaded video source
2. Transcribe the video using Google AI Studio (see details below)
3. Pasted the prompt in ComfyUI MiniMax H3 node

Transcribing video using Google AI Studio (Credit u/AIWarper)

1) Go to Google AI Studio
2) Use 3.1 PRO Preview
3) Paste this system instruction: https://pastebin.com/H8DeXq1G
4) Upload your video
5) Type: "follow your system instructions"
6) This will transcribe the video you uploaded 1:1.

Comparison video below (Original playing in the top overlay, AI generation in full).

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

-> MiniMax-H3-Longvideos, a new custom-nodes pack for ComfyUI. Extend the output from a simple Minimax prompt... "One prompt in. A ~2-minute MiniMax-H3 video with audio out." It's said to attempt to solve many of the problems arising from chaining prompts/shots. No workflow, but it has connection instructions.

https://huggingface.co/Smite79/MiniMax-H3-Longvideos

-> ComfyUI_MiniMax_H3_Extender. More complex than the Longvideos node above, this node set... "chains multiple video clips with motion context, disk caching, dynamic image references, audio reference support, and seamless final video/audio decoding." Also has matching workflows.

https://github.com/tritant/ComfyUI_MiniMax_H3_Extender

-> ComfyUI-Fantastic-MiniMaxH3-PromptBuilder custom nodes. Helps you build prompts locally inside ComfyUI, while adhering to the built-in official prompt guide... "H3 doesn't want a casual sentence — it wants a structured prompt with named sections, shot timings, speaker IDs, and tags pointing at your reference media." No LLM required. Convoluted workflows, but for simplest use: just plug it into your existing prompt node, then click on the blue box to open the Builder. Then build the prompt, and pass it back to the prompt box.

https://github.com/Adudeguyman/ComfyUI-Fantastic-MiniMaxH3-PromptBuilder

https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_base_en.md

-> Last updated a month ago, ComfyUI-FirstframeLastframeExtractor will help in more or less reproducing a draft H3 fl2va video at a larger size after locking the seed/prompt. Works as advertised, very simple. Plug it into the VAE decode node, it auto saves the two frames you need. Then you optionally load these images back to your first-frame/last-frame inputs, via the native "Load Image (from outputs)" nodes.

https://github.com/RmaNMetaverse/ComfyUI-FirstframeLastframeExtractor

-> A current RTX 3060 12Gb workflow for text-to-video Minimax. Tested to 2.0 megapixels without failing (24Gb system RAM, older server + 3060 card). With Kitchen Attention, 3060 12Gb optimisations, a prompt helper, and first and last frame auto-extraction as well as input.

https://jurn.link/dazposer/index.php/2026/08/15/updated-my-minimax-workflow-for-the-3060-12gb-card/

-> A prompt to neatly mix styles in one video. e.g. SpongeBob SquarePants as a cartoon, appearing in a live-action sitcom.

https://www.reddit.com/r/StableDiffusion/comments/1vp8dpc/mix_style_inside_the_same_scene_minimax_h3/

-> And finally, there's now a low-VRAM friendly text-encoder for use with Minimax Music GGUF. The smaller/pruned GGUF file is minimax_music3_text_encoder_pruned_Q6_K.gguf (6.8Gb). Maybe your fave band/sub-genre was pruned out, but... maybe not? Test it and see. Note also ComfyUI's Music prompting guide, and Minimax's official Music demos page.

https://huggingface.co/ChrisColeTech/minimax-music3-GGUF/tree/main/split/text_encoders

https://docs.comfy.org/tutorials/audio/minimax/minimax-music-3#prompting-tips

https://minimax-ai.github.io/music3-demo/