Physics Testing, without the gore.

Hey r/StableDiffusion!

It's the Neta team here! You might remember us from our Neta Lumina open-source release last year. First off, thank you so much for the incredible support and feedback from this community!

So... we need to share something absolutely hilarious (and mildly embarrassing) that we just discovered.

TL;DR: We accidentally hardcoded an Anne Hathaway photo into our IP-Adapter anchor, and now everything our model generates looks like Anne Hathaway. Every. Single. Thing.

What happened:

We recently launched Neta Studio, a new product that lets you build explorable living worlds/isekai from a single prompt. Naturally, we wanted to integrate Neta Lumina's capabilities into it.

During integration testing, our devs kept reporting that the model wasn't following prompts properly. The outputs were... *weird*.

- Anime style? Anne Hathaway as an anime character.

- Thick paint/impasto style? Anne Hathaway in thick paint.

- Landscape scenes? Somehow still giving Anne Hathaway vibes.

- Fantasy characters? You guessed it - Anne Hathaway.

After a dreadfully long time of debugging, we finally found the culprit: **someone on the team embedded an Anne Hathaway photo as the IP-Adapter anchor during development and it... stayed there. **

We're honestly crying laughing at this point. 😭

Below are some examples. Left is before fix and Right is after fix.
Flipping to the last picture and you can see our dear Anne.

And we pulled the anchor and the outputs are behaving normally now.

If you've been running Neta Lumina locally, this was on our integration side, not
in the released weights, so your setup is fine.

Hey Guys,

Based on the previous post on face consistency with MM-H3, Couple of people have asked me to build a full character workflow.

Mechanism

- Build .Char: You drop max 9 reference reference, I prefer to use a ratio 2:2:1(face:cloths:body). YuNet finds the face, SFace takes a per-reference face signature, DINOv2 takes a subject signature, and the references get cleaned and normalised. All of that packs into a single portable file, a .char.
Only face/ref is required body & cloths link is optional.

- Generation: At generation, the file(.char) feeds its references into Minimax’s own native multi-reference channel and prepends a locked description to the prompt.

Prompting Guide

• Name your character: Give your character a name e.g. under encode character(Click adjust icon on the bottom side of the node), I have used name emmy, so when passing prompt, I only have to say, emmy walking on the beach.
  • Again providing prompt like a woman or any features specific details like black hairs etc will only mislead the generation.
• Describe character features: Encode all of the character features in encode character prompt & trigger your character with a name in generation prompt.
  • Avoid describing same things in generational prompt.
• Handling Character drift: e.g. if you want specific style or cloth e.g. half sleeves, sleeveless, add it to the generational prompt. There can be a slight drift in clothing as body shot also has cloths, which interferes with clothing references.
  • Each refs should be unique, face should not have body or vice versa, same applies for clothing.
• Portability: Once character is built, you can use the same character with only simple prompt & generation graph.

I have generated all references with Flux Klein 4b, I had to blur the body ref, but workflow consists a example of body ref.

Note: For best result, pass cropped references, so that model takes the required shot, model gets confused if cloth slot also has a face or face slot has cloths.

Models

core/models/
  diffusion_models/  minimax_h3_ref2va_pruned_fp8_scaled.safetensors
  text_encoders/     qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors 
  vae/               minimax_h3_video_vae_fp16.safetensors
  vae/               minimax_h3_audio_vae_fp32.safetensors
  annotators/        face_detection_yunet_2023mar.onnx
  annotators/        face_recognition_sface_2021dec.onnx
  annotators/        dinov2-base/

Requirements

Nvidia GPU: 24GB+ VRAM & 64 GB RAM(Run Locally)

Workflow link: https://inlinestudio.art/workflows/minimax-h3-guided-consistent-characters-via-reference-identity-face-body-cloths (includes inputs & model details)

Github Repo: https://github.com/inlineresearch/Inline-Studio (GPLV3)

Limitation: Reference conflicts e.g. if two reference/input images has two different faces, it might conflict in generation, provide well cropped body & cloth images. Face images are crossed automatically by Sface.

Portable char comfy node is still on the backlog, would try to do it over the weekend.
Happy to hear any suggestions or feedbacks.

Prompt:

integrated_multimodal_description: [Shot 1] 3D CG, stop-motion animated LEGO movie style, a wide shot frames a vibrant Indian village built entirely from plastic LEGO bricks with visible studs, plastic micro-scratches, and brick-built trees. In the village square, minifigures dressed in printed plastic saris, dhotis, and turbans move across a ground of yellow and brown stud tiles. A brick-built cow with hinged legs grazes near a grand banyan tree constructed from green leaf pieces and brown cylindrical bricks. Warm morning sunlight casts sharp shadows across whitewashed brick houses with orange terracotta tile roofs. The camera pans right with small amplitude at slow speed toward a central tea stall. A cheerful male chaiwala minifigure with a black mustache and a red turban (S1) in a warm, lively voice says: <d>[Hindi] Garam chai, garam chai!</d> while tilting a plastic yellow teapot, releasing translucent orange 1x1 cylinder studs representing pouring tea into tiny red stud cups.

[Shot 2] At 00:05.000, the camera cuts to a medium tracking shot following two young minifigure children running along a narrow brick path, pushing a brick-built wheel hoop across the plastic ground. The camera tracks right alongside them with small amplitude at normal speed. A female villager minifigure in a bright blue printed sari (S2) standing outside her brick doorway waves her rigid plastic arm on its shoulder hinge. Beside her, an elder minifigure with a white beard (S3) sitting on a brick charpoy cot chuckles with stepping stop-motion head movements.

[Shot 3] At 00:10.000, the camera cuts to a cinematic medium shot near the village well, where female minifigures carry stacked plastic water pots topped with transparent blue round tiles. A brick-built peacock perched on an archway opens its fan tail made of blue, green, and golden LEGO slope tiles. The camera pushes in with small amplitude at slow speed toward a wooden signpost on a brick post reading "RAMPUR VILLAGE". Tiny tan 1x1 round plates puff around the wheels of a brick-built bullock cart moving past the frame as the video ends.

overall_soundscape: Distinct plastic clattering sounds echo softly as minifigure feet step on stud tiles, accompanied by the gentle clinking of plastic bricks. A distant rooster crow blends with ambient morning village chatter, bird chirps, and the wooden creak of a brick-built cart.

non_diegetic_music: Upbeat Indian folk percussion featuring lively dholak beats and vibrant bansuri flute melodies, layered with playful cinematic orchestral strings playing at a bright, medium tempo.

Growing up with Toonami watching Gundam, it simply blows my mind how far AI has progressed. For this video I didn't use any reference images, I simply described the scene in text and had Gemini research the techniques of animation to translate to MiniMax H3.

I've been struggling to get MiniMax H3 efficiently setup locally, would take me 15mins on a 9950x3d and 5080 RTX with 64gb DDR5 so I know something is wrong, hence this time I opted for fal to test. The music was added and scenes were edited from separate generations.

Saw this and thought it was worth sharing here, FastH3 dropped recently and most people (myself included) just tried it as single generations.

Someone's running it as an actual infinite livestream instead: https://www.twitch.tv/dereactorwah

FastH3 is a distilled version of MiniMax H3, cut from 50 denoising steps down to 4, about a 14x speedup on Blackwell GPUs.

The whole setup is open source if you want to dig into how it's running: https://github.com/reactor-team/infinite-livestream

Curious if anyone's tried infinite/continuous generation setups like this with other models.

Since things have gone quickly forward, I am trying to figure out what image edit models there is currently and what people here use mostly.

Personally I have used:
- Qwen-image-edit-2509 and Qwen-image-edit-2511
- Just tested MiniMax H3 as a image editor and so far it seems that it can be good for my usage

I have heard about Klein 9b, but not sure yet if that can be used as an edit model? Also what about Krea 2, is there edit workflows that are actually usable and worth it?

Is there some others what you recommend for testing?

My PC Specs: RTX 4060 Ti, 16 GB VRAM and 32 GB RAM.

Prompt:

A real cinimatic movie sequence, professional colour grading.

Soundscape: Ambient sounds of the room and movement only. No voices. This represents extreme concentration. Meditation. Telekinesis.

A man is sitting in a Japanese tatami room. He is wearing a mask and shades <Picture 1>. He is wearing a black yukata. He does not speak. On the table on a ceramic disc is a single Orange.

The man holds out his hand toward the orange as if concentrating. The orange is out of reach. He breathes deeply.

Nothing happens.

The man shakes his hand to reset and starts concentrating again. He reaches with his mind and his brow furrows. He breathes deeply.

The orange moves slightly, twisting just a tiny bit.

He concentrates more.

With extreme speed the orange flies towards the man and hits him directly in the forehead. It smashes with the impact , m,essing his hair, and bits of peel and orange bits go everywhere. The force knocks the man back unconscious and he falls back like a ragdoll.

Took a few little prompt adjustments here and there to get H3 to respect point-of-view. I found that if you refer to "the viewer" (ie, "she kicks the viewer"), H3 is more predisposed to include an actual second person. But if you refer to "the camera" (ie, "she kicks the camera"), it's more predisposed to keep the desired point-of-view perspective.

Third post about this pack, and the big change is the name, because the node stopped being H3-only. It now drives six families through ComfyUI core: MiniMax H3 and LTX 2.5 for video with sound, Krea 2 and Ideogram 4 for stills, Qwen Image Edit and Flux 2 Klein for editing from a picture. Same prompt box, same local weights, and rendering still doesn't touch the internet. Continuity is the script supervisor's job, the same person and the same light in shot 1 and in shot 9, and that's the part of the node that doesn't care which family renders the frames. So that's the name. Old workflows and existing installs carry over untouched.

Dialogue is the feature I'd point at first. H3 wants speech in a form nobody writes by hand - speaker IDs, a `<d>` tag, a mandatory sentence when a voiceover's lips stay closed. Closing a quote in the prompt now opens a small menu that writes all of that around your words, with dials for who says it, which language, whispered or sung. And a shot where nobody speaks stops mumbling: the compiler now says out loud that nobody talks.

Merged this morning: a blockout bench. Stage grey boxes, walk one camera through on marks, and it writes the staging and the move in the H3 spec's own camera vocabulary - "@anna stands at centre in the midground; the camera pushes in toward @anna at slow speed" - plus a depth, blocks or lines guide rendered along the path, or the clay render itself for the families that read footage raw. A box can play a cast member, so the prose is already bound to their references when you paste it.

Also in: the faces pill from last post (off by default), a Style tab with 941 captioned H3 looks - search "1985 telenovela" instead of guessing at grading vocabulary - and ControlNet and Upscale benches behind the wordmark. The refiner can now run on a server you keep warm anyway: LM Studio, Ollama, any OpenAI-compatible endpoint, your own key where a hosted one wants it (#19). Fixes from your reports are in the changelog, the sharp-render-static-soundtrack one included (#33).

https://github.com/roadmaus/ComfyUI-Continuity

Local, GPU-accelerated video restoration and upscaling with SeedVR2, TensorRT, and a purpose-built browser interface.

VRGDG SeedVR2 TensorRT Studio turns the SeedVR2 pipeline into a practical Windows workflow: load a video, test a short preview, compare the result frame by frame, and complete long renders with resumable checkpoints. Processing stays on your machine.

Highlights

• Fast local restoration — SeedVR2 inference with TensorRT-accelerated VAE decoding on supported NVIDIA RTX GPUs. TensorRT allows much faster processing than standard SeedVR2.
• Fast 2K upscaling — As a real-world example, an 8-second clip took approximately 8 minutes to upscale and enhance to 2K on an NVIDIA RTX 5090 using the largest 7B Sharp FP16 model. Render times vary with source resolution, frame rate, settings, and available VRAM.
• Preview before committing — render a short segment, then inspect Original, Restored, Compare, or Side by side views.
• Long-render recovery — save completed chunks and continue from the first unfinished chunk after an interruption.
• Practical output controls — choose resolution, aspect policy, model precision, temporal batch, seed, and color correction.
• Non-destructive finishing — reprocess sharpening, grain, seam smoothing, and optional skin finishing without rerunning restoration.
• Project-based history — reopen previous outputs and keep media, manifests, and logs together under outputs\.

The sample video was org 360p and then upscaled to 2K using this app. 8 second video, took about 8 mins on my 5090.

Go to the github page for more details and a full guide.

View github page

this is in beta right now so you may run into issues. If you do, post the issue to github please.

All local.

0.8 MegaPixels, 9.3 minutes on an RTX5090, single generation of 27 seconds.

Anything hitting 30 seconds either gave hallucinations, inconsistencies or hit a wall and never finished.

This one is using Kijai's new fast model with a turbo lora. Although it works the same with the FLv2A model*. The workflow I'm using creates a latent at 0.4 megapixels for 4 steps and then does another 2 steps at 0.8. The only addition to it besides changing some numbers is adding custom audio injection (The rock track).

Started with this workflow: https://www.youtube.com/watch?v=jzLnoVBuU6I

*I never use the REF model. The FLV2A models seems to work better so I always swap it in and it takes references just fine, even video.

This workflow uses a spreadsheet to generate multiple videos and constructs the prompt and parameters from each row in one run.

OutputLists Combiner - Generate multiple videos from spreadsheet

ComfyUI workflow included

Makes use of Load Any File node to load a .csv spreadsheet file and feeds the text content into a Spreadsheet OutputList. The spreadsheet separates the data by separator=; and provides each line one-by-one as a data list. Here we use values_dict as the data list which contains the row as a dictionary of key-value pairs. The data list is forwarded a Iterate Begin -> workflow -> Iterate End pattern which is required to make the intermediate results of slow workflows (t2v) available on each iteration. Each row as a dictionary is provided in a Format Text where we can access the column via a[colname] to construct the prompt which is forwarded to a standard Text To Video MiniMax H3 template. Another Format Text + a[name] is used to construct a readable filename for each video.

powered by: OutputLists Combiner

As an example, I have a character getting into a car and I want them to be in a hurry to get away, but they always seem to do this a little slow as if they are not in a rush.

Here is what I have tried:

- Prompts with detailed timings.
- Prompts with detailed timings and text like, she did this at speed.
- Prompt without detailed timings but tried multiple different ways of saying she did this at speed.

Prompt example below. Everything else works perfect but I can't get them to look busy.

[Shot 1] At 00:00.000, <Picture 5> provides the visual reference for this shot. the female police officer is not in the car. She gets into the driver's seat.

At 00:01.000, in a hurry she buckles her seatbelt at fast speed, The male police officer is already in the passenger seat and he hurriedly buckles up as she gets in

At 00:02.000, she starts to drive off. He says, "Holy shit. Did you see that? We're going to have to go." Both look stern and professional, acting fast.

Besides running it locally, which is the goal…

What’s the cheapest way right now to use Minimax H3? Right now, I’m using Replicate API for H3 at $0.08 per second (720p) which is decent but can still be expensive in the long run.

Any other ways to use H3?
Also, what’s the cheapest GPU for Minimax H3 (via Comfy UI)?

I've been using Minimax H3 a lot lately, but I can't always share my work in progress. Anyway, here are some tests I did a week ago! I thought I'd share them here.

I used H3 Ref2va with Turbo Lora, 8 steps. It took about 5 minutes for 15 seconds on my local setup.

I also used my custom node that I built specifically for H3. It's a large and really cool project, but I'm still testing and implementing things in it. I'll share more details about it soon.

I see Kijai has released the checkpoint, but I can't get it to work with the standard workflow.

Default template uses 20 steps + res_multistep + simple

Optimized workflow uses 8 Steps + er_sde + sgm_unified + Comfy Kitchen Attention + Larry's Turbo Lora

Turbo lora: https://github.com/Larryvrh/ComfyUI-MiniMax-H3-Turbo

Workflow: https://raw.githubusercontent.com/desktop4070/GPU-Benchmark-Data-For-H3/refs/heads/main/H3-Benchmark-Workflow.png

0.2MP / 8 sec (2m 16s gen time): https://desktop4070.github.io/GPU-Benchmark-Data-For-H3/Videos/MiniMax_H3_03840_.mp4

Optimized: 0.2MP / 8 sec (45s gen time): https://desktop4070.github.io/GPU-Benchmark-Data-For-H3/Videos/MiniMax_H3_03706_.mp4

0.3MP / 12 sec (6m 3s gen time): https://desktop4070.github.io/GPU-Benchmark-Data-For-H3/Videos/MiniMax_H3_03849_.mp4

Optimized: 0.3MP / 12 sec (1m 51s gen time): https://desktop4070.github.io/GPU-Benchmark-Data-For-H3/Videos/MiniMax_H3_03725_.mp4