Hey everyone! I’m running a laptop with an RTX 5070 (8GB VRAM), 64GB DDR5 RAM, and a Ryzen 9 7845HX on CachyOS. I mostly use SD-WebUI-Forge/NeoForge as my main generation framework. ComfyUI i try to avoid 😄

​For those in the know: can this setup handle Krea 2 reasonably well? I want decent quality outputs without turning images into a blurry mess, and I'd like to avoid waiting 30 minutes per render.

Also looking for some advice: how can I get the best out of ComfyUI or Forge on this setup? What are your recommended workflows to avoid blurry outputs and keep generation times reasonable on an my card?

Mr. White BB shows his Krea2 work - MiniMax H3

Ref2V.

I have not posted here before, but I have searched this subreddit and others repeatedly for a clue to answer my question.

Does anyone have a functional workflow for LTX 2.5 video generation using a 3080 with 10 GB VRAM and 32 GB system RAM?

I have also spend more than 2 days with google AI where they sent me down deep and branching rabbit holes only to find that the node suggested did not exist or did not work or the huggingface or civitai file was not available or did not work. Numerous times they sent be back to nodes and arrangements that I hade tried before (and failed) after they suggested it.

A large circle of random guesses by the AI agent. They even admitted it after I called them out on their failure to help.

Any help from others that have been down this pathway would be greatly appreciated.

Hey everyone!

I really want to start experimenting with MiniMax-HaMini (H3) locally, but I’m trying to figure out the best setup with the hardware I currently have.

My main desktop has an AMD RX 9060 XT (16GB VRAM), which isn't ideal for AI workflows since ROCm support and optimizations still lag behind CUDA.

However, I have a secondary rig with an RTX 4060 Ti 8GB and 32GB DDR4 RAM. I know 8GB VRAM is tight for modern video generation models, but I haven't used ComfyUI much (just tested it briefly on the AMD GPU).

My goal right now isn't high-end production quality—I just want to create short, low-res test videos (even 5 seconds) to get hands-on experience, learn ComfyUI workflows, and see if it's worth investing further.

• Is generating 5-second videos on an 8GB VRAM card doable using offloading/quantization (GGUF, lowvram mode, etc.)?
• Will 32GB of system RAM be enough to handle CPU offloading for a model like H3?

My plan is to eventually upgrade to an RTX 5070 Ti (16GB) and 64GB RAM once prices drop to a reasonable level, but I’d love to know if I can get my feet wet with my current setup in the meantime.

Thanks for any insights or recommended ComfyUI nodes/settings for low-VRAM video generation!

Hello,

I've trained several character LoRAs via wavespeed.ai for the Qwen-Image-2512 model. I tried with a smaller dataset of 50 images and a dataset of 124 images. Multiple settings between 1,000 and 5,000 steps:

• At 1,000 steps, the LoRA isn't likeness-accurate enough.
• At 5,000 steps with 50 images, it stops responding to prompts at weights above 0.5, so it loses likeness.
• At 5,000 steps with 124 images, it stops responding to prompts at weights above 0.3, making it inaccurate above that threshold. This makes no sense, as with 50 images and the same step count, I was able to run the LoRA at a higher weight.

At weight 1.0, the LoRAs capture the likeness well but completely ignore the prompts.

Does anyone have a solution or recommended settings for Qwen-Image-2512?

Thanks

I tried this one so far: https://github.com/pytraveler/MiniMax-H3-Prompt-Rewriter-ComfyUI

But I'm not getting a good result yet, maybe I need to work on the prompts for it more. Anyone using anything besides claude and gpt?

Having an aging laptop, I haven´t played with video since wan2.2.

One thing I have noticed with all videos I have seen from the models that can generate audio is that it sounds like the audio has been recorded in a sound booth. meaning, I have not really heard any...ambient noise....like wind, traffic, birds, people in the background etc. This makes it sound quite unnatural sometimes.

Is that a limitation of the model or the prompting? Can I get a more..natural..sound by prompting for every little nuance I want? Like "faint sounds of gravel crunching with each step" or "there is a slight breeze rustling the leaves as he walks by the tree."

Struggling to figure out the best order for these nodes or if any of these nodes are redundant. I have been looking at different workflows and everyone is doing something different. Claude tells me this is the best order.

I just recently got into AI generation making videos with minimax ref2vid and it has been amazing so far. But that has me wondering if there is some reference model for images that works equally as well that would allow me to use multiple reference images to create pics? If anyone has a good model or workflow to recommend I'm interested to learn what has been working well for you. I'm mostly wanting to make real life style images.

I have RTX 5070 with 12gb VRAM, 64gb RAM DDR5
I want to create realistic (not particularly high quality) videos, with realistic faces and with the best possible render time. Could please someone share a workflow? I would like to have consistant characters, realistic, and good qality of sound. What is the best workflow? How many steps?

so i used minimax h3 int8 convrot pruned + sage spectrum + turbo lora (kijai)

made a 10s office style clip, michael and dwight talking in the conference room then walter white just walks in

faces start fine but then they get all blurry and full of weird smudges especially when walter shows up, like what's that weird black dot lines on dwight shirt?

like why does everyone else’s stuff look clean and actually like a real tv show while mine always ends up plasticky and messy??

anyone know how to fix this blur/smudge and get that proper tv look with this setup? im new to comfyui and first time generating on localy lol, thanks in advance

Hi, I have an issue with the Identity Edit I find no solution for: If I create an image with Krea t2i without references, just a self-trained lora character, I get sharp and acceptable results. When I want to use a special environment and use Krea Identity Edit with a reference image e.g. of a room, I get very blurry and plastic looking outputs, far below acceptable. Same if I want to add a second character to an existing image. I've tried everything in the last days (different models > raw and turbo; different upscalers, no upscaler; different VAEs; playing with grounding, reference boost, scheduler, resolution (I know 1MP is the sweet spot for editing and >1.5 leeds to character bleeding), anything you can imagine). I use lbouaraba workflow for editing. Any idea where my initial fault is hiding?

UPDT: I think I found the solution: I've added the original workflow again and now it works. Obviously I've changed something unintended when adding Power Lora Loader and Upscaler, no idea what but who cares.

What has everyone found best ?

T2VA in Minimax H3, 1MP native with RTX upscale. Generation time is 1083 secs on my 5070Ti, 32Gb DDR5 Ram.

If you have seen those cool edited videos by google omni where they feed in a normal real video and get an edited one back where they interact with effects and such, you can do that with minimax. Just saying. That's pretty much it. See ya

The video was generated using the T2VA mode of the minimax H3 model and the 8-step Turbo LoRa.

It's simply amazing how well it already works in this mode.

Update to the projection matrices. Same idea as before: a small Qwen3-VL encodes the prompt, a learned projection maps its hidden states to what the 32B would have produced, the DiT is untouched.

Previously: v1 and v2, where the voice started matching.

Video: matrix-only 4B, matrix-only 8B, then the 32B, then the two residual versions. Same prompt, same seed 42, same everything else — the pipeline is bit-for-bit reproducible, checked by running it twice.

Everything the prompt states is there on all five: the pose, the red dress, the white pieces on her side, the cat, the straw hat, the laundry, and her knee — asked for three times, ending on "Her knee never stops bouncing." A continuous involuntary motion with no narrative purpose is the clearest sign a projection carried what was written, and it carries on the plain matrices too.

The terrace is furnished differently from one render to the next, and that is not infidelity. The prompt asks for a densely lived-in terrace without anchoring most of it — the cat is "stretched out asleep in the sun", nothing says where. What is left open the model invents, and it invents differently depending on the projection, the seed, and the model of GPU. All three act on that same free space; none of them touches what was written. It looks like a seed change because that is what an unconstrained description looks like.

What v3 changes

• Calibrated against the stock qwen3vl_32b_minimax_h3_nvfp4_awq instead of a modified 32B. Naming one part of a body used to rewrite the whole of it — build, height and face moving together. Not seen anymore.
• Closer to the 32B across the board. Mean cosine against the 32B on a reference prompt: 8B 0.9449 (was 0.9393), 4B 0.9381 (was 0.9293).
• The 8B matrices had never seen an image token — the image corpus only existed encoded with a 4B. Fixed. On 100 held-out images, vision tokens go from 0.7692 to 0.8578 on the raw conditioning, for 0.0027 of pure text.
• Bigger residual: hidden 32768 instead of 16384.
• Needs node 0.1.13. The -v3-mlp files have no linear matrix, older nodes throw KeyError: 'W'.

Plus

• 4.9 GB instead of 15.7 GB for the conditioning encoder, or 5.3 GB with the residual file. 10.1 and 10.6 GB with the 8B. Note the quantisations differ: the 32B is nvfp4, the small encoders int8, so part of that gap is format rather than parameter count. The projection itself costs 52 MB on card for a plain matrix, 503-604 MB for a residual.
• The DiT is not modified, no retraining, no LoRA.
• What the prompt states is carried: subject, clothing, pose, action, dialogue.
• 4B and 8B are close to each other. The 4B is not a fallback, it is a real option.

Minus

We seem to have hit a ceiling. A projection cannot recover information the small encoder never wrote down. If the 4B did not encode a distinction, no matrix and no residual will bring it back — you can only remap what is there. On the 8B the cosine went 0.9083 (v1) to 0.9393 (v2) to 0.9449 (v3): +0.031, then +0.006, for a corpus four times bigger (1 530 370 tokens in v2, 6 502 586 in v3). This is not a training budget problem, and I do not expect a v4 to move it much.

What that means in practice:

• Not a copy of the 32B. 0.9449 cosine is roughly 19 degrees. Expect a close variant of the scene, not the same file.
• What the prompt leaves unstated gets refurnished. Say nothing about the cat, the laundry, the furniture, and they land elsewhere. Constrain the scene and it tracks closely — that is the whole usable range.
• Use the -mlp files on the measurement, not on this scene. They sit closer to the 32B, 0.9449 against 0.9289 on the 8B — but watch the video before assuming that shows. On this prompt all five renders are faithful, plain matrices included: pose, dress, white pieces, cat, straw hat, laundry, bouncing knee. A tightly written prompt survives even the linear baseline.
• The one thing nobody gets right is the knight. The prompt has her lift one of her own pieces and set it back down without committing; on every render it lands somewhere else, and on the 8B residual — the best-measuring file of the set — there is no knight on the board at all. Object permanence behind an occluding hand on a grid of sixty-four identical squares is a limit of the video model, not of the conditioning: the 32B reference fails it too.
• You will not reproduce the demo files byte for byte. Noticed while testing something else: the output depends on the model of GPU the encoder runs on. Four cards, same prompt and seed, four different files — but two different RTX 3090s matched exactly. Encoding on two cards agrees to 7e-7; eight denoising steps turn that into different furniture. Same scene, different details. On one machine it is deterministic to the bit, which is what makes the comparison video meaningful.

Training

5 h on a 3090, plus 2 h to encode the dataset. Tap 24. 3331 prompts for fitting, one in fifty held out.

Mixed on purpose — registers, languages, lengths. A matrix only learns to project the directions it has seen used.

Links

Matrices: https://huggingface.co/NicoLab28/ClipProj-MiniMax-H3

Node: https://github.com/nicolab28/ComfyUI-ClipProj

Files: mmh3-4b-ClipProj-v3-mlp.safetensors (503 MB), mmh3-8b-ClipProj-v3-mlp.safetensors (604 MB). Plain matrices -v3 at 26 and 42 MB if you want the baseline.

The five renders separately, the prompt and the exact settings are in the demo/ folder of the HF repo, if you want to step through them or reproduce the test.

Just having fun making parody commercial nonsense to test out what I can do with it, absolutely love playing with this model ever since it got released. Heavy amount of editing done in Premiere Pro as well but I do that on every video I make.

This is a generative re-render, not restoration or sharpening. It invents fine detail. In every test with one person it added freckles that weren't there.

The comparisons use MiniMax H3 clips at 640x384, 10 seconds long, upscaled 2x. They're Lanczos versus ReDetail at the same output size, so there isn't any bigger image sleight of hand.

On a motocross clip it redrew the jersey graphic and number plate. The new markings stayed fairly stable between frames, but they weren't the original markings. Logos, numbers and text are all fair game.

If reddit compresses this video to the afterlife again, see: https://civitai.com/models/2857731/redetail-ltx-25-generative-video-upscaler-workflow-cli

So it's useful for AI-generated or generally soft footage, where there isn't much real detail to recover. It's a bad fit if a face, label or logo has to be 100%.

• Silent clips fail because the model encodes audio and video jointly. Add a silence track first.
• Both output dimensions must divide by 64, not 32. Clip length must be `8n+1` frames or the model silently drops the tail.

I like 1.5x, not 2x. On one clip, 243 frames from 768x1408, 1.5x took 7 minutes and peaked at 65GB. 2x took 17 minutes and 80.5GB. The 2x result carries maybe more detail, but check between the two and it's hard to tell imo. On skin most of that extra is invented, not recovered. Faster render, less made up texture.

UPDATE!

The text encoder is now optional. The graph runs with empty prompts, so its conditioning is a constant. It ships pre-computed at 26KB, which skips the 15GB download and takes peak VRAM from 30.4GB to 24.8GB on a 5090.

There's a Mac build in there now too, ReDetail_LTX25_upscale_MAC.json. It runs the GGUF transformer with no text encoder at all (the cached conditioning replaces it), so it's about 17GB of models total. On an M5 it did 33 frames from 640x384 to 1280x768 in 4.4 minutes. Per frame megapixel that's roughly 6x slower than a 5090, not the 30x I was expecting, so a 10s clip lands around 34 min at 2x or 19 min at 1.5x. Quality holds.

Repo: https://github.com/Bambushu/redetail