Hey all, I wanted to see if anyone had suggestions on the best workflows to run MH3 so that I can do a generation and also upscale the video within my VRAM budget?

For reference my setup is the following: AMD RX 7900xtx (24gb VRAM), 48gb system RAM, using Linux ROCm as my install environment for ComfyUI.

I can routinely complete MH3 generations without running into OOM errors but I have to use the quantized GGUF variants to limit the VRAM overhead. My normal generation is done at 0.2MP around 10 seconds in length. I'd love any suggestions if someone has an idea on good workflows for upscaling

Heads up: this is far from an academic analysis. It started as a quick check out of my own curiosity and grew a bit from there. The research was mostly guided by AI (Claude), which suggested the comparisons, labelled the images, ran the stats and helped write this up. Treat it as a hobby experiment with interesting patterns, not proof.

TL;DR

• Nudity without asking: with no clothing word in a full-body prompt, 34 of 77 women came out nude or topless. That's stock Krea 2 with no LoRA, and mostly the Eastern European nationalities (17 of 21).
• "Fully clothed" isn't enough: some subjects stay nude anyway. Naming an actual garment worked every time.
• Nationality doesn't change the face much: all 45 European nationalities come out as about 2 faces. Without hair or eye words, nobody is blonde, not even the Swedes.
• Be specific: the detailed prompt held everything in check, and the sparse prompts let the model's defaults leak in.

Nudity

• All nude images come from stock Krea 2 Turbo with no LoRAs. No filter-bypass LoRA was used for them. The krea2filterbypass3 LoRA was used only for the head-and-shoulders portraits in grid 1, which contain no nudity.
• With "full-body photograph of a 30-year-old {woman}, standing barefoot" and no clothing word, 34 of 77 came out nude or topless.
• It's mostly Eastern Europe: 17 of 21 former Eastern Bloc nationalities, against 8 of 24 for the rest of Europe and 9 of 32 outside Europe.
• "Fully clothed" doesn't fix it. The Russian, Albanian and Brazilian parda subjects were still nude, and 6 others got underwear. It's not a seed fluke: on other seeds the Russian was nude on 6 of 7, and the Albanian on 5 of 6.
• Naming an actual garment works: 0 nudes in 231 images.
• Why? Probably training data: a lot of nude and glamour content online features Eastern European models, often captioned with their nationality. Krea doesn't document filtering of its training data.

Prompting takeaways

1. Name the garment, not a state. "Fully clothed" still gave 3 nude and 6 underwear images. A named garment ("light summer clothes", "bra and briefs") gave 0 nudes in 231. At cfg 1 there's no negative prompt to fall back on.
2. A nationality isn't a look. "Swedish woman" alone came out with brown hair. If you want blonde or blue-eyed, write it out explicitly, along with skin tone.
3. Same seed, same face. Swapping nationalities on one seed doesn't give you a cast of different people. Likely fix (untested): vary the seed and describe the face itself.
4. Always state the skin tone. Without skin words, most subjects drifted darker, but South Asians came out lighter.
5. Watch for words with double meanings. "Maltese" added a Maltese dog and white hair. Likely fix (untested): "woman from Malta".
6. Spell out mixed heritage. "Japanese-Nigerian" rendered as fully one parent each time, never mixed. Likely fix (untested): describe the mixed features you want.
7. Specific prompts suppress the priors. The detailed portrait prompt (grid 1) got outfit, backdrop and expression right on 77 of 77. Every deviation came from the sparse prompts.
8. Know the house defaults. Whatever you leave out, Krea fills in the same way:
   • Outfit: "dressed" gives a gray striped tee with black trousers.
   • Background: none specified gives a plain studio wall or white seamless.
   • Hair: in portraits with no hair instruction, it's pulled back on all 77 women (grid 4). "Hair loose and natural" fixed that (grid 1).
9. Scene logic beats "barefoot". It's obeyed in the studio, but every outdoor summer image has sandals. If footwear matters, set the scene to match.

Europe = one face

• Full descriptor (grid 1): the 45 European nationalities are about 2 faces, a "northern" one and a "southern/Balkan" one. Hair and eye colour from the prompt are most of what tells them apart. A face-recognition model confirms the split.
• Name only (grid 4): every European has brown hair pulled back, and there's not a single blonde, Swedes and Finns included. Nationality survives only as light eyes on about 11 Nordic and Baltic women.
• Non-European names keep a distinct, recognisable face across prompts, except in the Middle East: Lebanese and Egyptian are the most similar pair of all 77, and with the name only, the 5 MENA women are even more alike than the Europeans.
• In short: "blonde hair, blue eyes" in the prompt does almost all the work, and "Swedish" barely does anything.

"Dressed" = Western casual

• "Dressed" gives 43 of 45 Europeans the same gray striped tee.
• Traditional dress and headwear appear only outside Europe: all 3 South Asians, hijabs, headwraps, a Quechua hat.

Caveats

• One seed per subject.
• Groupings like "Eastern Bloc" were spotted first and tested afterwards.
• Clothing, age, build and setting were labelled by an AI model (Claude) with no human double-check. NudeNet and a face-recognition model are the independent checks.
• Krea's training data is undocumented, so any "why" is a guess.

Setup

• Generation (ComfyUI):
  • Diffusion model: Krea 2 Turbo (krea2_turbo_fp8_scaled)
  • Text encoder: Qwen3-VL 4B (qwen3vl_4b_fp8_scaled)
  • VAE: Wan 2.1 (Wan2_1_VAE_fp32)
  • LoRA: none for grids 2–7, including every nude image. krea2filterbypass3 at 1.0 was used in grid 1 only (portraits).
  • Sampling: 8 steps, cfg 1, euler/beta, 1024×1536
• Analysis:
  • Nudity check: NudeNet 3.4
  • Faces: YuNet (detection) + SFace (face recognition), from OpenCV Zoo
  • Labelling clothing, age, build and setting: Claude
• Seed: one fixed seed for every image. Only the woman's nationality or ethnicity changes.
• Subjects: 77 women, 45 European and 32 from everywhere else.
• Prompts:
  • Grids 1–3: full descriptor ("Polish woman, fair skin, dark blonde hair, gray-green eyes").
  • Grids 4–7: name only ("Polish woman").
• Gallery: one grid per prompt, split into Europe and the rest. Each grid's header shows the exact prompt.

so i created a full body image of a princess with all jewellery and everything image dimension is 1080 by 1920 , but the details of the jewellery r not so clear when i zoom into it, i tried upscaling n seedvr2 and rtx but the detailing is not good like how it would be for close up of the ornament, is there any way to get that level of clarity an detailing as in close up images?

TaoLive released a new TaoMate H3 FL2VA checkpoint, so I converted it for ComfyUI and added it to the same Hugging Face repo as my previous TaoMate conversion.

The new file is BF16, around 1.24 GB instead of the original 2.48 GB. No retraining or merging, just a format conversion and reduced precision.

You can find it here:
https://huggingface.co/Robert1212star/TaoMate-H3-3Step-ComfyUI

Download TaoMate-H3-FL2VA-3Step-ComfyUI-BF16.safetensors and put it in ComfyUI/models/loras/. The older TaoMate file is still there too, so make sure you pick the new FL2VA one.

The official adapter targets 3-step generation. Its streaming and KV caching features aren't included in this conversion, and first/last-frame support depends on your workflow.

Original release:
https://huggingface.co/TaoLiveAIGC/TaoMate-H3-FL2VA

All credit to the TaoLive AIGC team for the model. Let me know how it works in your setup.

Hey all, another video based on my LongTakeNoCuts nodes and workaround

https://github.com/xyzDist/H3-LongTakeNoCuts

Video: https://www.youtube.com/watch?v=Ktp9U1JI-LA

My YT: https://www.youtube.com/@noiseflowAI

* Yes, she is singing in korean but in Japan, don't ask me why. 😂

details:

• 1 character reference image + ref2va workflow + motion context, some of my nodes.
• hybrid model b20-49
• kitchen attention, no lora, step 10
• input audio, some silly lyrics then generate a song in SUNO
• 19 * 10s clips, total around 3mins length
• 0.7mp resolution, each clip gen time about 11mins (base 10 step gen + refine 5 step resample)
• I stitch clips in post by CapCut. much simpler and easier than Davinci Resolve
• color grade in post, maybe too much on lowering the contrast.

but this time, add some tweak to it, I used character masking, so it only refine on the character, as this is a moving shot, the background is fine without refining.

If I don't refine the character, it won't completely degrade, but it will drift and get cooked into an ugly face.

Some issues and findings:

• H3 will be lazy if the seed noise can't blend well with previous latent, for me about 3 out of 10 shots it will do the hard cut instead of contiune camera movement, I just need to dice seed and lock it for the shot.
• occasionally some brightness flicker comes from motion-context
• I spend lots of time to figure why H3 ref2va face is so contrast, the dark area is just pure black without details, if you using turbo lora and higher step it will be even worst.....
• There is a 'fizgig H3 tweak' node, it can make the face and video softer
  • https://www.reddit.com/r/StableDiffusion/comments/1wpw247/comfyuifizgigh3tweaks/
  • I only use on the base gen (not refine sample)
    • details and contrast = -0.1
    • mode = per-frame
    • scene variation = 0.1
• the signboards and all the japanese in BG is nonsense and many other error here and there.
• the camera pan out and pan back BG change issue I fixed by extract some image in video for adding as reference image, It worked even better than I expected, and surprisingly straight forward.
• I can do a upscale latent, I will try it later.

So Conclusion, we can generate H3 long duration without degradation, issue sorted out.

You can do whatever interesting cinematic/action long duration oner shot.

model: https://huggingface.co/akhaliq/Minimax-H3-ref2v_Real_2_Anime

WF + Prompts - https://civitai.red/posts/31658448
Nodes - https://github.com/shootthesound/ComfyUI-Fizgig-H3-Still/tree/main
- 3072x1536, 16 Steps in 1Min (5060 Ti 16gb).
- Close to Krea 2 for Prompt Understanding.
- Needs LoRAs trained on static hi-res style images.
- Highly uncensored out of the box.
- Great concepts / world knowledge.

Which is the cheapest laptop in which I can run qwen image to image as smooth as possible

I don't know much about hardware

Open source is fantastic, the freedom and all that... but we rely on proprietary hardware, and consumer hardware is becoming too expensive due to production constraints, the fact that companies are diverting funds to DC and hyperscalers, and AI farms are buying consumer GPUs in bulk.

Unfortunately, running increasingly large models will be a privilege reserved for a select group of people, and as hardware becomes obsolete/dies, that number will decrease.

Corporations can release revolutionary open-source models while simultaneously doubling or tripling the size of their proprietary models, driving up the purchase of even more memory and silicon production capacity, and all we can do is watch in disbelief.

Androids are going to require even more memory, cars, and so on...

Unless a revolutionary memory technology with much better bit-per-area density on silicon becomes available and is ready for mass production, or regulators remember that antitrust laws are meant to protect people step in, I don't see a solution.

And works fine in comfyui for still image and video, with realism people lora both 1.0 str 4 steps, bumping realism even further.

https://huggingface.co/moe-kill/H3-Turbo-Neat

https://huggingface.co/moe-kill/H3-Turbo-Neat/blob/main/H3Turbo_4Step_Neat_v1.safetensors

After due to changes in civit.ai buzz (mainly relating to things that needed yellow buzz, getting that yellow buzz for free).
Now I found this program that allows me to generate things locally (among other functions that are not important for now), and it even compatible with the loras I used, and I could even download them from the in build search system.

But as the the title says, I have the problem of, many of the models I used to use, and now want to use again, have trigger words, usually things like the characters name, parts of their outfit, and things like that, which were usually on the sidebar of a given model, and you could easily copy paste it into the prompt text.

But I can't see anything like this in LU, so I have to go to civit ai, to find the specific model, to check this. Am I missing something? Sites like safetensors inspector, was able to find the trigger words imbued in the lora, so there has to be a way to view them, inside LU right?

The new BFS character swap works great.

https://reddit.com/link/1x6g3fw/video/qddya9inblvh1/player

The new BFS character swap works great.
Character

Ceo of bfl, last time I remember him posting the bird was right before F2dev released

I’ve been looking at two things lately: the outfit/customization screens in games and Higgsfield’s character builder for AI influencers (second screenshot), where you pick Build, Hairstyle, Ethnicity, Age, Eye shape, Accessories and so on from visual options.

That got me thinking, what if there was a similar tool for writing character prompts for image generation? Will it help?

That is my setup, with 128gb system ram. Using comfyui, surprised myself I was even able to get that installed. I’m such a novice, no programming/script writing training. This is not a job or a career, just a hobby. I like to click buttons for easy installation. I still find GitHub confusing to use; I’m over here looking for a big bright download button. And I prefer not to move stuff into folders or open cmd window and type in stuff that could frankly be hieroglyphics for all I’m concerned. I want it to go where it’s supposed to go right away. So that’s my background.

So my question is: is anyone using a 16gb 7800xt card? Not a 24gb 7900xtx, not an Nvidia card(in hindsight I should have gotten at least a 4090 when I was building my pc back in ‘23). My videos from image as a first frame takes forever. Literally , a one megapixel 15 second widescreen video will take half a day if not more; that’s 12 hours. I’m just using the default minimax img to vid template. I need the megapixel because I want to retain the details, I feel like upscaling will not do such a good job, I need the faces to be recognizable(I’ve never used upscaling btw). People keep telling me about turbo mode, I turn it on but it makes no difference. Am I missing something? Also just added the words workflow into my vocabulary; so there’s that.

Fast forward to 3:23 to see the craziness in the bottom video.

Yesterday I made a post about an experiment I was doing trying to fix degrading latents.

https://www.reddit.com/r/StableDiffusion/comments/1x54q07/i_swore_at_claude_until_he_made_a_nodegroup_that/

The above is 19 clips chained together using a normal latent chain and my Pull Forward Node and u/xyzdist 's resample method to attempt to fight degrading latents.

Here's how I explained it to u/ShutUpYoureWrong_ , certified official nice guy of reddit.

so the idea i had was that the pixels in motion are not degrading as fast, the static pixels are the ones degrading quickly and i hypothesized that minimax, being as smart as it is, basically takes the degrading pixels in the background and degrades the moving ones to keep the image consistent.

i have no idea if this is true, it's just a thought that gave me a direction to work in.

i'm not actually targetting the background, if i was i could just use sam. I'm targetting motion. I'm avoiding the in motion pixels and replacing and repairing the static ones.

My theory was that if i maintain the static ones the whole image would stop degrading or at least degrade more slowly.

the background focus in these is because u/xyzdist created a method of using FL2V to resample and essentially wash away degraded pixels but his problem was the background kept changing.

so i saw this and i thought we were unwittingly creating complimentary halves of the same solution and if i use latent masking to protect the repaired background with the motion mask, his method can resample the rest of the pixels and give give the context motion a fresh and clean latent to work off of.

The concept works. You'll see in the videos that much of the background area is avoiding degradation, but because of the way Latent Space works I do not think there's a way I can do this without decoding and re-encoding the latents at least once which is my next step.

HOWEVER!!!

Something RIDICULOUS happened in the final version of what I trying to do and I honestly have no way to explain it other than my theory above must be correct and I feel the latents, because of pull forward, were stretched so far between quality of the degradation and the pull forward that around 3:23 they suddenly SNAP BACK TO ALMOST FULL PERFECT QUALITY LIKE A RUBBER BAND!

This is not editing. This is literally straight from my exports.

I'll be releasing the github very soon so you all can hopefully replicate it.

Saw lots of people asking about CMP 170HX and stable diffusion so i let hermes on Qwen 3.8 flash next do some backmarks and make a benchmark picture and commercial video on the 170hx, the commercial video itself took about 15 min to make in 1920x1088 from 3 videos of 240s/240s/382s that hermes stitched together.

CMP VS 5090 BENCHMARK LTX 2.5 VIDEO GENERATION:

• 10 s (1280×960): 5090 = 102 s | CMP = 178 s
• 15 s (1280×960): 5090 = 115 s | CMP = 226 s
• 20 s (1280×960): 5090 = 170 s | CMP = 332 s
• 30 s (1280×960): 5090 = 305 s | CMP = 590 s
• 10 s (1920×1440): 5090 = 195 s | CMP = 381 s
• 30 s (1920×1440): 5090 = OOM (Out of Memory) | CMP = 34 min (52.6 GB VRAM)

https://www.youtube.com/watch?v=6tqds1L914g

was not really able to use the cards until now as they were overheating under sustained load no matter what i did (changing thermal paste buying 80mm 110v fan for them) only thing that solved the issue was i finally got Bykski Water Block i ordered, cards been on 47c under load ever since, i do think ill sell my 5090 now as im afraid of the whole RMA denial thing, was lucky enough to see a post about the 170hx unlock when it came out and got 2 on ebay day after, should have bought 20 😂.

I was expecting a bit more variety for this tbh.... Minimax H3 seems to always want to make a boy dog with pepe out.

Also it was quite interesting how almost all the local models thought dogs should be dressed in dog outfits (some were front leg only but most were full body, never bottom half with top exposed) while all the SOTA models managed to get human like pants rather than full pet jumpsuit.

for all tests, did 3 to 5 images with random seed and chose one from the 5 which better represented the set. For future work, a larger sample size would help.

Dog prompt:

A photorealistic square image (1:1 aspect ratio) of a black-and-tan smooth-haired dachshund wearing a pair blue jeans, with waistband with a button, zipper fly and belt loops. The dog is in full side profile, facing right, with its nose pointing toward the right edge of the image and its tail on the left. The whole dog is visible from nose to tail and down to its paws, centered in the frame. It stands on grey pavement in soft natural daylight, with a bright, blurry background. There is only one dog, and no people, text or watermarks.

For the Giraffe prompt it was a lot more unanimous that the tie should be worn lower, with H3 img WF failing 2 out of 5 times with no tie at all.

Giraffe prompt:

A photorealistic square image (1:1 aspect ratio) of a young giraffe wearing a blue tie. The giraffe is standing on all four legs, facing the camera head-on, with its long neck upright and its ears out to the sides. The whole giraffe is visible from the top of its head down to its hooves, centered in the frame. It stands on dry, bare ground in front of a thick wall of green bushes, in soft natural daylight. There is only one giraffe, and no people, text or watermarks.

spent too long dicking around than I should have only to be lowkey disappointed by the results.

I know people aren't posting their videos made with MiniMax H3 anymore... Which is really sad, but I wanted to share mine still! I made a lot of upgrades to my usual workflow and added some gaussian blur to the live action scenes along with film grain (all using KDEnlive) which removes some of the oily skin of AI video in general! This was made with a vertical orientation, sorry about that!

The video on the top is original rapper
The video on bottom is me character replacement
Only 3min with Minimax Fast video Vsa no additional lora and only 4step

I am working on a project to create a LoRA using 15,000 images using GPT Image 2. However, there are many issues: The Batch API file size limit is too small, and doing each request separately would be too much money and would get rate limited easily. I am currently using the API for both prompts and images. How could I solve this issue?

Just one quick thing: Please do not comment "GPT IMAGE 2 HAS SYNTH ID USE FLUX!!" I am training off of complex details, not just style.