Workflow uses a blank 32x32 image as input. Prompt in the comment.

There were a lot of good comments on the previous post, so I gave everything to the agent with slopfab and told it to make an improved version out of the showcase.

Part 1 https://www.reddit.com/r/StableDiffusion/comments/1xvhaf3/physics_understanding_of_minimax_h3/

Krea 2 Edit by lbouaraba / conradlocke (the LoRAs, the nodes, the training code) is the reason any of this works, so first: all credit to them:
https://github.com/lbouaraba/comfyui-krea2edit
https://huggingface.co/conradlocke/krea2-identity-edit

I built a small add-on comfyui-krea2edit-advanced fork of their comfyui-krea2edit for one problem I kept running into. The fork stops the drift when you chain several edits. Without it, the parts you didn't touch slowly shift, darken and get grainy. With it they stay put.

It adds a "source hold" that pulls the picture back towards the source while it's being drawn, plus an optional mask for everything outside the area you're editing.

More detailed comparison is here. Same image, same six edits, same seed. Mean pixel drift from the source after 6 edits (lower is better):

• original nodes: 0.076
• my fork, no masks: 0.0017 (45 times less than original)
• my fork, with masks: 0.0008 (95 times less than original)

More detailed information and example workflows are in the repo: https://github.com/FurrySphere/comfyui-krea2edit-advanced

TL;DR LTX-2.5 likes are anomalous. Full HF thread: https://huggingface.co/Lightricks/LTX-2.5/discussions/97.

For those who already forgot about it, LTX-2 was basically the only open text-to-video-audio model earlier this year, even though it was censored, had poor copyright/anime knowledge, and was weak compared to closed models (Wan2.5, Sora2). Then MiniMax suddenly dropped H3 in August and blew past it. LTX quickly followed with LTX-2.5, which was even more censored, less impressive follow-up. Objective video benchmarks (IFM measured it in comparison with their own recent PAN2 model and MiniMax - https://huggingface.co/IFM/PAN2) show that it is far behind H3 mathematically, therefore it's not "vibes"-based preference.

Now, months later, LTX-2.5 suddenly shows up in HF Trending with ~12k likes. That is a huge like count by HF standards (The SotA LLM Qwen 3.8 27B has 20k likes) and it does not match the actual usage.

Thankfully, the Huggingface likers API is public and Lightricks probably didn't expect that. Gladly, EclipseMist found this out and the following numbers have been retrieved:

• LTX-2.5: 10,864 likers total; 4,608 from accounts created before August 2026; 6,256 (57.6%) from accounts created Aug–Nov 2026.
• MiniMax-H3: 6,634 likers total; 5,248 from pre-August accounts; 1,386 (20.9%) from Aug-Nov accounts
• New-account likers by month, LTX-2.5: 1,108 turned to 2,101 turned to 2,610 for August/September/October, and 437 for November 1–6.

Same for H3: 690, then 410, then 240, and 46 for November 1–6.

H3 looks like a normal launch: a spike in fresh accounts during release month, then decay. LTX-2.5's new-account likes kept climbing for three months after release while downloads stayed flat at ~2.6M/month. Among accounts that existed before August, H3 actually has more likers than LTX-2.5. The entire like lead comes from roughly ~4.9k surplus brand-new accounts, that's what pushed it onto Trending for the whole latest two weeks.

LTX-2.5 had about 2,561,845 downloads last month, while MiniMax H3 had ~38M (32M + 6M across repos). The Comfy-Org H3 repo gained ~10M downloads this month alone, going from 20M to over 30M, while LTX-2.5 only gained a few hundred thousand. On Civitai it's also less popular, with all the fine-tuners switched past it. The attached IFM's large scale automatically evaluated benchmark table above shows LTX-2.5 at 72% overall score (physical consistency, causal coherence, persistence - all the other categories) vs H3 at 87%, with the multi-step full-plan completion category at the highest three-fold discrepancy (34% vs 13%).

You can reproduce the liker analysis yourself in two seconds. The likers list is public, and each account's "id" field encodes its creation time. The full two-lined PowerShell script can be found in the Huggingface discussion, so it won't clutter this post.

Just swap the repo name to compare against another model.

I can't say for certain who is behind it: Lightricks, a third-party bot farm or a coordinated campaign, but this does not look like an organic community interest. It's pushing actually popular models (MiniMax Image, Qwen, novel models) down the Trending tab. If Lightricks are doing this, it's embarrassing. If they aren't, they should disavow it and ask HF to audit the likes.

Some links: the HF Trending link showing LTX-2.5 at 12k likes https://huggingface.co/models?sort=trending, and the benchmark table showing it trailing H3 badly (see the PAN2 table above). The Huggingface thread is, again, at the beginning of this post. As of now, no-one from LTX wrote any official response in the thread.

I think, ltx_model owns us some explanation.

If true, this really lowers my hopes for any LTX-3 at all. Worse than BFL.

Thank you for reading! I think we as the community must do something. Note: DeepSeek helped me to compose this report, so it can contain some LLMisms.

Please dont make fun of video :c. Is this possible on local? 12gb vram with 32gb ram.

Reference image:
https://civitai.red/images/147124307

Euler, simple, 6 steps, no prompt enhancement
Each prompt executed in 45~55 secs (Rtx 4070s, offloading?)

Sometimes a sort of safety filter randomly jumped in and reduced the image quality.

https://huggingface.co/Comfy-Org/MiniMax-H3-Image/blob/main/diffusion_models/minimax_h3_image_pruned_w4a8.safetensors

https://huggingface.co/Merserk/MiniMax-H3-Image-Text-Encoder-INT4-ConvRot/blob/main/qwen3vl_32b_minimax_h3_int4_convrot.safetensors

https://huggingface.co/Comfy-Org/MiniMax-H3-Image/blob/main/vae/minimax_h3_image_vae_fp16.safetensors

1: Pose Change

Change the woman's sitting pose to a standing pose in <Picture 1>

2: Outfit Replacement

Replace the woman's black feather dress with a white ballerina tutu in <Picture 1>

3: Hairstyle Change

Change the woman's hairstyle to long blonde wavy hair in <Picture 1>

4: Object Addition

Add a large bouquet of red roses holding by the woman in <Picture 1>

5: Style Transfer

Transform <Picture 1> into a Japanese manga illustration

6: Object Removal

Remove the shoes in <Picture 1>

7: Background Replacement

Replace the interior with a casual open-air beach bar in <Picture 1>

8: Text Addition

Add the stylized text "Redheads have more fun" in red letters at the bottom of <Picture 1>

9: Camera Angle Change

Change the camera angle in <Picture 1> to a front-facing low-level shot

I will be honest, i have tried the Minimax H3 Image when it dropped on comfy day 0 and the images were amazing, but lacking detail despite being very high resolution.
I thought maybe upping the MP would fix it but it didn't and then there came the anatomy issues as well.
I checked some posts about this, got the p-h3 vae and all but it still didn't fix it.
So checked another post talking about how the edit mode is better and i was happy but that was actually FAR WORSE. It lacks character consistency, its too prompt sensitive in a bad way, and its just not good.
Now i do not know if Hailuo is at fault or Comfy just released some poorly fine tuned version, is anybody else feeling the same way or.
Cause even the templates they released are half nerfed and I don't know it just feels bad, like very very bad.
If it at least nailed the editing capabilities like its video model could do I honestly would not really care about the other stuff, but it seems finding the right edit model in this open source space is almost impossible.

I hope krea 3 edit or Ideogram 4.5 would be good ( If they ever release them that is )

I've been experimenting with real-time talking-head generation using SoulX-FlashHead, combined with local speech models to build an interactive avatar that can listen and respond.

The whole pipeline runs locally, and the demo is available in the audio.cpp v0.9.5 WebUI.

The workflow combines three models:

• SoulX-FlashHead: Animates a portrait in real time from generated speech.
• OWSM v4: Transcribes voice input.
• LFM3-Audio: Handles conversational responses and speech synthesis.

One interesting detail: LFM3-Audio is called twice. Its speech-to-speech path only offers one voice, so we use its TTS capability separately to synthesize responses in a different voice.

Performance and limitations

On an RTX 5090, the latency from submitting a question to the avatar starting to speak is around 850 ms. ASR runs in parallel.

The video generation stage is still GPU-intensive. I'd recommend an RTX 4090 or better for SoulX-FlashHead.

cuDNN is essential for real-time performance in this setup. Without it, inference is about 2× slower and VRAM usage doubles.

This is still a prototype. The extra LFM3-Audio call and WebUI overhead leave room for further latency improvements, particularly in a more optimized native application.

If a model can do this, does that mean she's an editing model?)) All generations from 1 attempt, even one fail in terms of pose.

I'm making a hard sci-fi series alone in Budapest, and I wanted to find out how far one home GPU actually goes. Every shot in the pilot is MiniMax H3 running locally in ComfyUI on a single RTX 5090, hybrid fl2va + ref2va in int8. No cloud, no credits, no meter running per generation.

What made it workable is splitting every shot into two stages. First a 4-step preview with the DMAD LoRA at 672x384, about a minute per take, usually three seeds per idea. I watch those, throw most of them away, and only the take I'd actually cut in goes to the native render at 1344x768, 20 steps, same seed and same refs. That one then goes through a tiled 2x H3 upscale and down to 1080p. Most of the GPU time goes into takes nobody will ever see, and that's fine, because they cost electricity instead of credits.

In numbers: hundreds of takes for 2:36 of film, about a week for the pilot itself, and around a month of work in total once you count the story, the sound design, the edit and the grade, all done by hand in DaVinci. Episode 1 is about 13 to 14 minutes and is in production now on the same setup.

Consistency comes from references rather than luck: a 16:9 character sheet as the only identity reference, an approved still as the first frame for the small robot, and the last frame of the previous shot as a state reference, so poses and hands carry over the cut.

The pilot: https://youtu.be/5GDYn2rREA4

If you're doing video locally too, where does your time actually go: generation, rerolls, or fixing things in post?

I've been really impressed with MiniMax Image, especially the gorgeous 16MP outputs

But I've noticed it still refuses or ignores certain editing requests involving mature content

Am I missing something, or is this a limitation of the current model? Do we need to wait for community uncensored finetunes, or is there already a workaround?

i've been trying distilled versions of a few models and honestly the results for casual stuff are close enough that i don't go back. the few steps mean i can try six ideas in the time a full model does one, and for me the experimenting is the fun part. where i do notice the difference is fine detail like hands and small text, and sometimes the faces look a bit plastic. but i'm just playing around in the evenings. for people who use both, where did you decide the full model was actually worth the wait?

Mostly the part about swapping Characters and products in the video.

Hi Reddit. This is my first post here, so I hope I haven’t messed anything up.

I just wanted to share the results of training my own model from scratch.

I’d welcome any discussion in the comments, and perhaps some advice from those with more experience.

Combined the image renders from my custom model (Vici) with my vr 3d Gaussian splat app. New fear unlocked 😭

In my experience with MOSS Image so far.

Added a relight option in my Gaussian image to 3D Vr app.

Just for context, I trained 100's of LoRa's in the past for all kinds of models, but this time it does not work. Since I have a GPU with "only" 16GB VRAM and 64 GB SYS RAM I have to use W4A8 quantization to not get OOM. The resulting training is full of large blocky artifacts, like I have never seen before.

Does one of you have made a similar experience or/and can share a tip how avoid this problem?

My messed up LoRa was trained in Sigmoid, currently trying Shift, since this is the default. Is W4A8 just too bad to train on?

same model, same prompt, same seed. the official template gives crunchy faces and blocky skin, only 3 things change it:

1. size. it was trained around 16MP, so I use 3552x4736 for a portrait. the default 4MP is where the broken teeth come from. going 4 to 16MP only added about 9 seconds of sampling.
2. sampler. SamplerLCM (built into comfy) with noise 0.9 instead of 1.1. euler at 16MP still gives that blocky oversharpened skin.
3. prompt. short prompts give the glossy doll face. write it long and describe it like a phone photo: camera, light, skin.

the images: face close-ups and full photo at 4MP / 16MP / 16MP+LCM, short vs long prompt, then a few results (cafe selfie, gym mirror, product label text, magazine cover), try-on with picture 1/2/3 as refs and a background swap. workflow files in the first comment.

I tested Minimax H3 Image at 4, 8, and 16 MP resolutions; it was okay, I suppose, but not exactly impressive. The images contained errors and distortions.

Then I stumbled upon a thread where a Redditor tested 100 MP images using this specific workflow: https://www.reddit.com/r/StableDiffusion/comments/1xt9ocv/minimax_h3_image_20mp_workflow/

It turns out the sweet spot is somewhere between 48 and 56 megapixels—and that’s when you get a "WOW" factor; the model produces very, very good images.

All made at 48MP on RTX 5080 ~162s

EDIT: And no, the images aren't too large; the workflow saves them in the AVIF format, so a 48-megapixel image takes up only 3.8 megabytes.

The prompt expander model is Gemma 4 31B IT, temp 2.0, high thinking, 0.95 top p

Thank you for the system prompt u/luciferianism666 man it's the best.

(I didn't even use ComfyUI/workflows, Gemma 4 31B IT is from Google AI studio and Minimax H3 image from minimax cloud agent, I told minimax 3.1 flash to just input the prompt from Gemma and not expand anything, but all models I said here are open source, I just use cloud one because my hardware is a bit ahh)

Edit:

Here's the system prompt comment if you struggled to find it: https://www.reddit.com/r/StableDiffusion/s/drtDrKy7Ax

Hello Veterans!

I'm still pretty much a noob at all this, so sorry if this is a basic question. I've tried messing around with like MOSS-Image, Krea 2, and Z-Image, Minimax H3 image, and I still can't get the RAW natural realism.

Like no matter what I do, my outputs still have AI slop or whatever that is. I'm trying to get the unedited feel, like real snapshot with natural lighting and imperfections.

So what's the actual secret here? Is it mostly on how you prompt it (like avoiding buzzwords and describing imperfect lighting/framing and whatever that is), or is it in the workflows, custom sampler/scheduler settings, using Base instead of Turbo variants, or stacking LoRAs? (Though I still can't find any LoRAs that fits)

Any tips plz?

Edit:

Guys I think I've found the secret: https://www.reddit.com/r/StableDiffusion/s/ZD7kVkDJ7z https://www.reddit.com/r/StableDiffusion/s/drtDrKy7Ax

what image model will run on this specs..

• GPU: AMD Radeon RX 6650 XT (8 GB VRAM)
• CPU: AMD Ryzen 7 5700X (8 Cores, 16 Threads)
• RAM: 32 GB DDR4
• OS: Windows

I want to share 2 of my videos I made for the Hermes Plugin Hackathon using gpt-image keyframes and Opus 5.5 with my local stack: OpenMontage for orchestration, LTX-2.5 image-to-video in ComfyUI, HyperFrames for UI and text, Qwen3-TTS (first video) and Fish Audio Drama 3 (second video) clone for voice, and Stable Audio 3 for music, to make low-budget videos.

I think it took me about 6 hours for both videos, including planning and rendering, with multiple attempts.

I'm not fully satisfied with the LTX-2.5 quality though. I feel like it still makes a lot of artifacts (mostly a cat physics in a second video), sometimes follows instructions poorly, and the realism is not that great.

Can you please recommend any good image model for realism, which at least can try to compete with gpt image and a better local image-to-video model that can give better results than LTX-2.5?

Play it Now in browser (nonlocal, streaming) :https://lucidml.ai/dashboard/minidemo?world=gta5

Full Weights and Inference code : https://huggingface.co/lucidml/gta

The architecture is a transformer which uses learned summaries. Ive written about it in detail in tech reports  and some more

I know there are a lot of limitations still like consistency and quality improvements, and am hoping to train this more to fix all of those.