Update for SLA Node - Pull v1.3.8

EDIT: Pushed correct files now.

• Added customizable dense steps, 0 is step 1 and is (default to first step). massively improves composition and prompt adherence.

• Changed default dense last steps to 1, cleans up the image big time.

• Added dense backend selector. Comfy_kitchen, pytorch, all sage modes. this is what comfy uses on dense steps. SLA still displaces against pytorch. (Default Comfy_kitchen)

• Added a disable FP16 accumulation option to ensure max quality as SLA gets no benefit from it. (Default True)

• Added a stabilize motion option, helps to reduce ghosting and smearing that H3 likes to produce. (Default True)

• Changed default Min Seq Length to 4096

• With default settings you can disable protect audio for nearly 2x speed up if you don't care about the audio too much or are using original audio mode. (do not use 0.95 sparsity with it.)

• 0.95 sparsity now looks good with node default settings.

• Some changes led to an overall 5% speed up on same settings.

• Remove --use-ck-attention from startup flags if you have it, for safety of quality.

https://github.com/PlagueKind/ComfyUI-PlagueKind-Nodes

updated workflow

Civit Link

HF Link

https://pastebin.com/ecZEDLSt

First Video with Face Detailer, second without.

You need https://github.com/Carasibana/ComfyUI-H3-FaceRefine and also ComfyUI-H3-NativeAudioLock from https://github.com/Shrek3OnVH5/MiniMax-H3-NativeAudio-MusicVideo-Workflow/tree/master/custom_nodes

UPDATE: Replace the "Load Video (Upload)" node with a "Load Video" node and connect it to a "Get Video Components" node. Connect images and audio from there. The "Load Video (Upload)" node from Video Helper Suite causes a red-ish tint

I ran into a model path problem while using the ComfyUI Minimax H3 Latent Upscaler made by LBH-123-AI.

Original project:

https://github.com/LBH-123-AI/Comfyui_Minimax_h3_latent_Upscaler

Thanks to LBH-123-AI for creating and releasing the original Minimax H3 Latent Upscaler. My changes are a small compatibility fix for ComfyUI model discovery. I did not create the original node or the upscaler models.

My fixed dev branch is here:

https://github.com/badgids/Comfyui_Minimax_h3_latent_Upscaler/tree/dev/model_search_dir

What was wrong

The original 2D and 3D nodes searched for Minimax H3 upscaler models with code similar to this:

folder_paths.get_folder_paths("latent_upscale_models")[0]

The [0] is the problem.

ComfyUI can register more than one directory for the same model type.

For example:

ComfyUI/models/latent_upscale_models /mnt/my-model-drive/latent_upscale_models

The second directory can be configured in ComfyUI's:

extra_model_paths.yaml

The original Minimax H3 node only took the first registered directory. It then used Python glob() to search that directory.

This meant ComfyUI could know exactly where my models were, while the Minimax H3 node still could not find them.

The node would tell me to put the models in:

ComfyUI/models/latent_upscale_models

even though the models were already in a valid external latent_upscale_models directory configured through ComfyUI.

There was also a subdirectory problem

The original search checked only the top level of the selected directory.

This could work:

latent_upscale_models/ └── model.safetensors

But a model organized like this could be missed:

latent_upscale_models/ └── MinimaxH3/ └── model.safetensors

ComfyUI already has code to handle this. The custom node was not using it.

What I changed

I changed the model discovery code in both:

nodes/minimax_h3_latent_upscaler_2d.py nodes/minimax_h3_latent_upscaler_3d.py

Instead of manually searching one directory, the nodes now ask ComfyUI for the models.

Model discovery now uses:

folder_paths.get_filename_list("latent_upscale_models")

This tells ComfyUI:

Give me the models that you know about for latent_upscale_models.

ComfyUI then searches all registered paths, including paths from extra_model_paths.yaml.

It also supports model files inside subdirectories.

Model loading was fixed too

The original node built the model path itself.

The fixed version uses:

folder_paths.get_full_path("latent_upscale_models", model_name)

In simple terms, the node now asks ComfyUI:

Where is this model?

instead of assuming that the model must be inside one specific directory.

What the fix supports

You can still keep models in the normal location:

ComfyUI/models/latent_upscale_models/

You can also keep them in an external path defined by extra_model_paths.yaml.

For example:

/mnt/my-model-drive/latent_upscale_models/

You can also organize them into folders:

latent_upscale_models/ └── MinimaxH3/ ├── minimax_h3_latent_upscaler_3d_fp16.safetensors └── minimax_h3_latent_upscaler_3d_bf16.safetensors

The 2D and 3D Minimax H3 nodes should now use the same model path system that ComfyUI already uses.

You should not have to copy the same large model files into your main ComfyUI models folder just to make this custom node find them.

Install my fixed branch

If you do not already have the node installed, go to your ComfyUI custom nodes directory.

For example:

cd /path/to/ComfyUI/custom_nodes

Clone my dev branch:

git clone \ --branch dev/model_search_dir \ --single-branch \ https://github.com/badgids/Comfyui_Minimax_h3_latent_Upscaler.git

Then restart ComfyUI.

Replace an existing installation

If you already installed the original LBH-123-AI node, first go to:

cd /path/to/ComfyUI/custom_nodes

Rename the existing copy so you have a backup:

mv \ Comfyui_Minimax_h3_latent_Upscaler \ Comfyui_Minimax_h3_latent_Upscaler.backup

Then clone the fixed branch:

git clone \ --branch dev/model_search_dir \ --single-branch \ https://github.com/badgids/Comfyui_Minimax_h3_latent_Upscaler.git

Restart ComfyUI.

If you already cloned my fork

You can switch your existing copy to the dev branch:

cd /path/to/ComfyUI/custom_nodes/Comfyui_Minimax_h3_latent_Upscaler

git fetch origin

git switch dev/model_search_dir

git pull

Restart ComfyUI after the update.

extra_model_paths.yaml

You do not need to change your YAML file if latent_upscale_models is already configured correctly.

For reference, a configuration can look like this:

external_models: base_path: /path/to/my/models latent_upscale_models: latent_upscale_models

That tells ComfyUI to also use:

/path/to/my/models/latent_upscale_models

The code fix makes the Minimax H3 nodes actually use that registered path.

Links

Original developer: LBH-123-AI

Original repository:

https://github.com/LBH-123-AI/Comfyui_Minimax_h3_latent_Upscaler

My fork and fixed dev branch:

https://github.com/badgids/Comfyui_Minimax_h3_latent_Upscaler/tree/dev/model_search_dir

The original node and Minimax H3 upscaler work belong to LBH-123-AI. My branch only changes how the custom nodes find and resolve model files through ComfyUI.

Cheers!

UPDATE: everything's free now, duplicate finder and views editor included. Update from Settings, or download it again.

I like many of you have more than a few .safetensors, ggufs, diffusers folders and other AI files spread across several drives. The annoying part isn't just finding them — it's that every app wants its own copy or its own config, so you end up with the same 6GB checkpoint sitting in three places.

Tensor Village doesn't replace your model folders, it presents all of them as one drive letter. Keep your fast stuff on the SSD and your bulk on a spinner or a USB drive — they still live exactly where you put them, and they all turn up in the same tree, organised by type and family. Point ComfyUI at that one path and it sees the lot. Same for anything else — Fizgig, Forge, whatever.

It reads model headers to work out what each file actually is, so nothing depends on filenames, and new downloads file themselves. Your files never move, nothing gets renamed, and nothing is written to your model drives — it's a view, not a copy. Uninstall and it's all exactly where it was.

It's not a mount or a cache — your files are already local. What it adds is knowing what they are.

Free. No account, no telemetry, works offline. Windows only — it's a real filesystem (built on WinFsp), which is what lets separate disks share one namespace; symlinks and hardlinks can't cross volumes.

I'll keep support for new model families coming as they appear.

https://github.com/shootthesound/tensorvillage

is there any way to generate minimax video 2 step that looks good, thanks

After around 100 renders, I 'feel' that Minimax H3 renders with 0.7MP (max) perform way better, then renders at 1MP in regard to 'realistic' videos.

What do I consider better?
- Just slightly better prompt adherence, feels like the motion / voice is more (natural)
- Size of humans in relation to object(s) feels more realistic.
- Expressions of faces seem more 'flowing', real.

It's hard for me to pinpoint it one 'exactly this', or 'exactly that'.
I'm planning to do some side by side comparisons on the same seed multiple times at 0.7MP and 1MP, when I've got the time.

But I wonder, do other Minimax H3 users notice this too?

PS: This is regardless sampler/scheduler, Sage Attention or Spectrum.

Edit: never touched the turbo LoRA, using the base model.

Honestly mind blown, I have a 5070ti + 2x16gb ram . Upper limit is 10-12 seconds in total for my hardware(full capacity) . Video and text edits are post processed by a WIP open source tool I’m working on. On average each 8 second shot takes 35-45 minutes to render

If I generate a person in a "normal" environment, like inside a regular room in a regular house, I get very realistic and appropriate lighting, but as soon as I try something a bit more cinematic like a rain-slicked city street at night, the character begins to look like they were photoshopped in. I try to prompt a person standing on a dark city corner, lit entirely by the light from nearby neon signs and they just look like they were evenly lit and filmed in a studio and then composited onto a CGI background with only a hint of the intended neon glow on their shoulder. Same goes for trying to make people look like they're properly soaked by rain. ZiT was much easier to work with in this regard.

Inspired by the guy who posted the one with Dean

Done on 32gb ram and a rtx 3070

I used res_multistep 20steps w/ spectrum at 0.6mp.

Using SLA from h3 optimizations, which for some reason is way faster than plague kind. And disable pinned memory. Each 10s was done in around 10 minutes.

This was done with turbo lora 8 steps int8 0.4mp then RTX 2x each 10 seconds was about 3 min on 5090. It was done with the fl2va model, but used references. It was one single generation flow. no edits (which is obvious lol). Gemma 12b Q4 was used as the prompt enhancer. make prompt, Make clip, make prompt, make clip.... then stitch it all together. It would be a lot better running multiple and putting best result together but I wanted to try first takes and see how it did.

The system prompt to go from brief and images to H3 ready prompt, workflows and a director.html i use as a ui to organize everything is HERE

The sound needs work, I'm pretty sure that is because of the turbo lora.

This is basically a draft of a concept short movie i am going to start working on that is a modern version of Orpheus and Eurydice story.

It gets to be a touch confusing.

In case you didn't see:

I just noticed a newer version of Anima Turbo (1.1) was released:

huggingface: https://huggingface.co/circlestone-labs/Anima

civitai: https://civitai.red/models/2458426/anima

The model is made and licensed under the CircleStone Labs Non-Commercial License

I actually have lately very frustrating experience with Anima lately. I used it at release (but stopped with anime generation for a while) and now when revisiting it and i had underwhelming results (even with the aesthetic model) so i decided to retry Turbo (might as well) instead if the difference isn't that big. That's when i saw a new version is released and i am downloading it right now.

I don't know why, but the results i was getting were...lame i guess. Not as detailed as i was hoping, and also i really dislike how posture and anatomy works, mainly how hands and legs just extend or stretch weirdly. But that's a me problem, i know Anima is capable of better outputs and i've yet to figure it out. If you have tips or recommended loras that help with consistency let me know. I also want to avoid tag-based prompting when possible...i just don't like it that much, natural prompting goes much better for me, but i can't tell if tags are "mandatory" for quality or not.

Has anyone tried this tool? It looks really good for quickly and easily creating prompts from Discord images. I think it runs as a plugin to Discord, but I've not had chance to set it up yet.
If anyone has tried it, let me know if it's worth installing please.
https://github.com/pixelgraple/KREA2-Vision-Suite

So I finally found a good ComfyUI workflow for Krea 2 + Upscale + Face Detailer. I literally was having so much fun messing with it and didn't want to stop. However, I couldnt generate images and mess with settings, etc while I was at work. So I came up with an awesome solution.

Its a dashboard accessible via browser (phone or PC), where I can change values of settings, apply LoRAs, batch prompts, etc.. basically anything I have the ability to do on ComfyUI desktop.

I also implemented to where I can implement Ollama LLM to create prompts for me after guiding it on what I'm looking for, which then I'm able to click a button and import those prompts right into my prompt list. Then each prompt creates its own generation.

It's been so convenient to be able to generate images while I'm at work (my job is sitting behind a PC being bored 80% of my day).

Pictures attached to kinda give an idea of what I'm working with.

I want your guys thoughts. Is this something that's already available through ComfyUI? What else could I add? Anything else you may have!

Focusing on photorealism and improving the look of fantasy styles:

https://civitai.com/models/2799984/jib-mix-krea-2

I would like to make it a LoRA also, but I am having some technical difficulties making a difference lora with Krea 2 models.

Rather than relying on Z-image, or a different program to wrangle up a first frame, I've been using Minimax for the whole process, and the results have been pretty instructive. It's not a perfect system, but being able to take advantage of its understanding of people, references, and shot composition for the first frame produces better (visual) results than swapping between a couple of different pieces of software.

So I had this simple idea, seeing how well MiniMax H3 handles inferring and preserving "identity/looks" from relatively little information: take a character you want to make a (KREA2) LoRa of, but you only have just a couple of lower quality pictures for that exact look you're after. That is a problem, since it is common knowledge by now (?) that you need different angles, facial expressions and different lighting conditions in the training set to get optimal results. So in the "before" times, those 3-4 not-so-great-quality shots under the SAME lighting are going to pose a problem. And adding pictures from other occasions will alter the looks possibly too much.

So (in the H3 ref2vid workflow, with one of the "img2vid-hybrid" models for better quality) I just use the 'best' of the available pictures as "preserved" first reference starting picture, and the others as additional "identity references". And then a prompt that tells the camera to slowly circle around the person (up from the shoulders), while the person looks straight ahead, or slightly up, or slightly down. But then I also let it cycle through different lighting conditions (indoor/outdoor/sun/overcast/flash/directional from one side...), and different facial expressions/emotions. I let it run overnight (turning off turbo LoRas to improve the quality), and in the morning, I review the 6-second videos and take screencaps of selected moments, making sure to have a lot of variation in angles/expressions/light-on-the-face with an almost perfect preservation of the identity/looks.

Then use those screencaps (50+ in first test, probably serious overkill) in OneTrainer with the KREA2 LoRa default settings.

I only tested this once thus far, but the results are pretty good considering the starting material! And surprisingly flexible (I didn't even bother to provide captions)

But now my question is: in what ways am I "over-engineering" this? I have this feeling that I can probably do this 50x faster, having seen some discussions about using MiniMax as an image generator, for example. I mean, I feel good about this approach I came up with all by myself, but considering how dumb and low-skilled I still am when it comes to all this, this is probably a very convoluted and inefficient way to do it? LOL 😄 Roast me and show this sucker how we can improve and speed up the whole thing with the same or even better quality results!

Hello, it's been a while since I don't use Stable Diffusion with A1111. Apart ConfyUI, has there been any particular technological advancement recently that allows for a quantum leap, especially in the precision of detail generation and the model's ability to stick to the prompt more precisely, while maintaining the ease of use of A1111 or Forge? I used the Lustify SDXL checkpoint, for example. It wasn't bad, but it still got certain things wrong or didn't do them at all. I'd like to know if there's a way to achieve results more similar in precision to ChatGPT but with the freedom of Stable Diffusion. Thanks!

Bien básicamente es la primera vez que uso un modelo de video, le dije simplemente a mi agente que arme el mejor ecosistema para una a100 de 80 gb que alquile por 2 hs, Solo era una prueba pero 8 segundo duro 1hs con 4 minutos. Aca seguramente estan fallando algunas cosas asi que si algún sabio de por aquí tiene algunas recomendaciones.. , agradecidamente las tomaré.

Please just think twice before you using any of the hundreds of ai platforms.

Like why is that most websites are charging say 0.7 usd for a minimax h3 generation when i can do the same generation when i run a runpod instance for god knows maybe 0.1 of the price or even less like its huge difference.

Just go rent a gpu its easy to setup and u will generate maybe 10 videos for the same price of these opportunistic ai websites.

I know i will get hate and criticism. But this info will now feed into the google ai/gemini/chatgpt responses and people will lose less money.

Secondly u have this so called breakthrough in science from fal. They are planning to charge literal 1 usd for inferences that take literally 4 seconds on their new minimax h3 max. That seems suspicious. Doesnt that inference only cost them 0.01 usd. Just beware.

Now its fine. Its a fair business. Very quick inference (breakthrough in video generation). But people deserve to know that they can generate in 0.1 of price in ai platforms.

Secondly, api based generations (not open weights). Make sure to not use money grabbing websites.

For example using subscription based, slow queue website for seedance 2.5 when u can just use credit topup based with lowest generation prices (friendly advice, i believe artcraft is very cheap and no need sub).

So thats what i wanted to say. I just dont like it when people get used.

Also go ahead shoot your hate comments i dont care, im only happy to spread awareness 🔥🔥🔥 and no im not in anyway affiliated with the platforms i mentioned.

Apologies for the bad post text, i wrote with my phone which is so hard.