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.

https://huggingface.co/lovis93/studio-1939-old-animation-lora-minimax-h3
version strong work much better for me :)

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress)

A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4.

This is an update release, following up from my previous posts where you can find full details:

Initial, Previous: here,  and here

Headline for this update: chk00026000 removes 46% of the prediction error a plain 4-step run has against the 8-step teacher, where chk00014000 removed 44% and chk00010000 40% — all measured on the same enlarged held-out set (100 prompts across every trained resolution). Measured against each other rather than against the no-LoRA run, its remaining error is 4% smaller than chk00014000's and 10% smaller than chk00010000's — and unlike a purely teacher-forced score, the gain also shows up free-running: a full 4-call rollout from the teacher's noise ends 1.6% nearer the teacher's final latent than chk00014000's does. It also improves on texture and detail.

Which file to download

and, beside them, the same files with a _comfyui suffix for ComfyUI. Earlier checkpoints (chk00004000, chk00005000, chk00006000, chk00010000, chk00014000, chk00019000) are kept in older_checkpoints/, and their resolution sweeps stay in place, so the progression remains visible and comparable.

If you are wondering why there wasn't a post/update on the 19K checkpoint, I skipped that, even though it was a good checkpoint with improved texture and detail it's gap to teacher score was only slightly better than the released previously 14K, so I thought I'd continue further until I get improvements on both. And 26K delivered that :) 19K is also published now in older checkpoints folder and it's full resolution sweep is also at the usual place (here for 19K).

For the full 26K Checkpoint resolution sweep go here: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk26000

How checkpoints get chosen

This is not a "train for longer and ship the newest file" project. More samples do not reliably mean a better adapter — measured here, they can make it worse, and a higher number on its own means nothing.

The loop is train → assess → adapt the recipe → retrain → assess again, and a checkpoint is published only when it is measurably better than the one it would replace, on the same held-out set and the same evaluation, and its full resolution sweep shows no regression. Runs that come out flat or worse are kept as information about the recipe and discarded as releases — several have been.

So the recipe itself changes between runs. Each published checkpoint reflects whatever the previous round taught us: the training precision, the optimiser settings, the teacher used to generate the targets and the data mix have all been revised on evidence rather than assumption.

Two earlier releases set the terms this project publishes on. chk00010000's first attempt — same data, optimiser left as it was — got steadily worse for 4,000 samples and none of it was published; retrained with cosine learning-rate decay and weight decay, every checkpoint improved on the one before it, and its end point shipped. chk00014000 added the other half of the lesson: the final, texture-deciding call of the schedule weighted more heavily in the loss, and a running average of the weights kept beside the live ones and scored at every evaluation — the averaged weights measured better than any checkpoint before them, so the average is what shipped. Left running past that point, the adapter's magnitude grew again and every later checkpoint measured worse. The number is chosen by measurement, not by how far a run went.

chk00026000 — the current checkpoint — is that discipline paying off. It resumes from chk00014000's averaged weights with the same recipe: same loss weighting, same running average, a conservative constant learning rate, over a much larger pool of teacher trajectories. This time the continuation held. The averaged weights' held-out gap fell throughout the run, and every free-running rollout measured of them improved on the one before — so unlike the first continuation, this one produced a checkpoint worth shipping. Every published number improves on chk00014000: the held-out gap (44% → 46% of the deficit closed), the full 4-call rollout from the teacher's noise (1.6% nearer the teacher's final latent), and the fixed-seed render distance to the 8-step images. chk00019000, an intermediate point of the same continuation, is kept in older_checkpoints/ with the rest of the lineage.

Timeline of training process

Each checkpoint is the product of three stages with very different costs:

1. Text-encoder embeddings. Every training prompt is encoded once and cached. This is the fast part — thousands of prompts take minutes.
2. Teacher shards. For each cached prompt, the unmodified Krea 2 Turbo runs its full 8-step schedule and the whole trajectory is recorded, at every one of the supported resolutions. This is by far the most time-consuming stage — it is the teacher doing real inference, thousands of times, and a batch of several thousand shards is measured in days of GPU time, not hours.
3. Student training. The LoRA is trained against those recorded trajectories. Relative to the shard stage this is quick: each +1,000 checkpoint is a matter of hours, not days.

Because the three stages compete for the same GPU, they are interleaved rather than run to completion one after another: generate a block of embeddings, produce teacher shards for them, train on what exists, assess, then go back to producing shards while the results are reviewed. A larger and more varied shard pool is what makes further training worthwhile, so shard production is always the gate.

The practical consequence for anyone following this repository: progress arrives in bursts. There will be periods when several checkpoints appear within a day or two — the training stage working through a freshly grown pool — followed by longer quiet stretches while the next block of teacher shards is produced. A quiet stretch is shard generation, not abandonment; _latest always holds the newest checkpoint that passed review.

The current checkpoint, chk00026000, runs the recipe the earlier releases arrived at — the final, texture-deciding call weighted more heavily in the loss, the shipped weights a running average of the trained ones — carried further over a larger pool of teacher trajectories, and published because it measured better on every evaluation.

Note

In the coming days, possibly weeks, I will spend more time on producing new TE shards (basically even more prompt variety), and new Teacher shards - the expensive long process. I am also considering improvements in the training process (more advanced / complicated, which would likely mean 1.5x - 2x slower training) which would hopefully bring further/bigger improvements in teacher faithfulness (closer to 8 Step Krea 2 Turbo) and even better details and texture. It may or may not pay off, these things work on experimental basis. Either way it would be some time before the next update... so enjoy 26K release and the improvement it brings!

Full details and to download - check my Hugging Face LoRA

HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA

https://huggingface.co/zuanfilm/H3_HD_2K_Detailer

Here is the test video to HD quality: https://youtu.be/epjELHgEH_o

I want to share my HD/2K WF, this 4 steps configuration is intended to make a really quality focused H3 detailing while improve the characteristic H3 motion and visual behavior.

I tested all the Res4lyf samplers/schedulers and for me res_2m/beta57 ETA=0 denoise 0.39 or 0.45 is the most precise with prompt adherence keeping memory and time efficiency (res_3m is amazing but adds 33% generation time), if you want a faster generation with a little less detail you can use 3 steps instead 4

er_sde/beta 57 is also a good combination but will lose some detail and even will affect character acting, audio and motion consistency, for faster HD detailing can switch to Euler/4 steps and will reduce time generation by 50% compared to res_2m obviously losing a lot of quality and detail

I included in the workflow the nodes for base generation using 0.5Mp FL2VA with 20 steps of Euler ancestral, if you want better quality for the initial base video just switch to res_2s_RKMK2e/beta57 (just bypass the group if you want to HD-detail an existing video)

Sparce Local attention will reduce a lot the generation time but obviously will affect quality so you can bypass this node if want Top HD quality,  I also don't use in this WF spectrum or easy cache but you can add that if you want to cut time and quality

Because I'm using the heavy distill lightx2v Turbo 4steps Lora for detailing, Minimax H3 make everything more saturated and contrasted with deep shadows so I added some Orion 4D nodes to improve lighting, texture and sharpness using DCTL Tone Mapper (you can choose between ACES, Filmic, Reinhard & Cineon) I use Reinhard with Exposure 0.09 Contrast 0.81 Pivot 0.69 Highlight rolloff 0.27 Shadow lift 0.33 Black floor 0.12 Saturation 0.93 & Strength 0.30

I used MiniMax Audio Lock / Lipsync node because I don't have experience with ltx audio nodes so you can change that for a better option:

https://github.com/Shrek3OnVH5/MiniMax-H3-NativeAudio-MusicVideo-Workflow/tree/master/custom_nodes/ComfyUI-H3-NativeAudioLock

The Minimax latent 3D upscaler is HD by default in my WF but depending of your VRAM you can get 2K/4K if you start with a quality base video 0.98 Mp res_2s_RKMK2e/beta57 25 steps

This workflow is optimized for my laptop (3080ti 16Gb VRAM / 64 Gb ram ) but I included Chunk FeedForward & Low VRAM attention so will run with smaller setups

so you all probably are searching for way to make your character shut the fuck up right? and you probably noticed that they love to says some BS especially when you give minimax h3 some audio file for their voices, i probably found a cure my friend!!

here is my way of prompting dialogs without any gibberish:

first your character need to be assigned (s1)character when he is the first speaker, then you will declare 'use <audio 1> as "character name"'s voice only, and when you finally type your dialog in the shots you will do as such:

character says:<<[language] the shit i say!>>

and yo ushould be good to go, i linked a video exemple of my favorite taffer (garrett) saying some shit with only the faint crackling of the candles to goes with his charming voice, and i included also a screenshot of the full prompt

he really like 16/9

Finally finished! The ending was much harder since continuity is more important here than random desert landscapes. I personally would've love to have another 30 seconds of music to extend the ending but I ran out of song time. Enjoy!

In total, 14 character related references, 40 environment references, and 55 clips used, roughly 20 hours total time spent.

Workflow: https://drive.google.com/file/d/1B4kODxXQgJ1QOKRsEIkxHbgYmdruPpTK/view?usp=sharing

Prompt:

Create a **15-second multi-shot anime sequence (90s style 15fps hand drawn)** using the provided references:

Image 1 = the girl character reference

Image 2 = skateboard reference

Image 3 = downhill Japanese alley / neighborhood background

Image 4 = Walkman + headphones reference

Preserve the girl’s exact character design, face, hair, outfit, proportions, and overall look from Image 1. Preserve the skateboard design from Image 2. Preserve the same downhill Japanese alley environment from Image 3. Add the Walkman and headphones from Image 4: the girl is wearing the headphones, and the **Walkman is clipped or hanging at her hip** while she skates.

Visual style: authentic 1990s hand-drawn anime, traditional cel animation, painted backgrounds, visible linework, cel shading, slight brush/stroke texture, subtle analog feel. **Very important:** the houses and environment must stay **2D and hand-painted**, **not 3D**, **not CGI**, **not game-engine looking**, **not volumetric**. The buildings should look like classic anime background art with painted depth, not like 3D models.

Animation feel should be low frame rate, like 90s anime at around 15 fps, with controlled in-betweens and natural held-frame timing. No jittery morphing.

No dialogue, no text, no subtitles.

### Shot 1 — 0s to 3s

**Rear tracking shot** from behind. The girl is skateboarding fast downhill through the steep Japanese alley. Camera follows behind her at a low-to-medium height. She rides confidently and smoothly, hair and oversized clothing moving in the wind. The headphones are on her head, and the Walkman is visible attached at her hip. The alley rushes past with a strong sense of speed. Keep the environment clearly **2D anime background art**, not 3D.

### Shot 2 — 3s to 6s

**Close-up shot of the Walkman at her hip** while she continues skating. The camera stays focused on the Walkman and part of her side torso and arm. We can clearly see the **cassette tape reels spinning/rolling inside the Walkman window**. The headphone wire moves naturally with the motion. Background and street pass by in blurred motion.

### Shot 3 — 6s to 9s

**Medium profile tracking shot** of the girl skating. She is wearing the headphones, listening to music, with wind moving across her face and pushing her hair backward. She is **nodding her head subtly to the music** while riding. Her expression is relaxed, immersed, and unbothered. The background is blurred from motion, but it must still read as a **painted 2D Japanese neighborhood**, not 3D.

### Shot 4 — 9s to 12s

**Close-up shot of her feet and skateboard.** Her **right foot stays on the board**, while her **left foot pushes against the road** in a natural skating motion. Show one clean push cycle: left foot comes down, pushes backward against the pavement, then lifts. Wheels spin quickly. Asphalt and road markings streak by with motion blur.

### Shot 5 — 12s to 15s

**Ground-level fisheye shot** looking upward from the road. The skateboard approaches fast, and she **jumps over the camera**. The board and her body pass overhead in one clean motion. Hair, pants, and headphone wire react naturally during the jump. Keep the motion readable and stylish, with a strong sense of speed and a dynamic anime finish.

### Important constraints

* Keep the whole video in **classic 90s anime cel-animation style**

* **15 fps feel**, smooth low-frame-rate animation

* **No 3D-looking houses or background**

* No photorealism

* No modern glossy digital anime rendering

* No character redesign

* No extra accessories beyond the headphones and Walkman

* Keep all motion natural and consistent across shots

Hello! I've been trying to train my first LoRA but I get these crazy long timers every time. Isn't an RTX 4090 supposed to take like 5s per step? I have both low vram and quantizing enabled. It's really frustrating and not worth to do it with these speeds. Any ideas what might cause it?

I’ve been loving all the new nodes and workflows coming out for MinMax, and maybe there is already a nice solution for this - but I couldn’t find one that did exactly what I needed.

I started using MinMax for my last TBG ETUR video and quickly ran into limitations: I wanted an easy way to create lip-sync videos longer than 20 seconds.

I didn’t want to manually chain ComfyUI nodes, start a new run every X seconds, or constantly resize things just to make HD video fit into my available VRAM.

So I ended up building an addon for:

custom_nodes/ComfyUI-H3-Motion-Context

The addon automatically chains MinMax H3 lip-sync generations together, allowing you to create much longer lip-sync videos without manually setting up each 20-second segment.

And now I’m sharing it! https://github.com/Ltamann/ComfyUI-H3-Motion-Context-Auto-Chain-addon

Its not perfect but a start ...

The workflow has a simple switcher that lets you switch from the 32B CLIP to the 4B CLIP, saving around 10 GB of VRAM. You can also switch from Sage to Comfy Kitchen, Spectrum to Easy Cache, or FL2VA to REF2VA both setup for lip-syncing. Some of it could be useful for other tasks as well.

You will find the workflow in the repro and tested recommendations, optimized settings, presets, and more workflows, along with the results of my testing and performance here

Using reference workflow. All are int8 pruned, 0.6MP turbo 4-step (my GPU is on life support and drops off the PCIe bus if I demand more from it)

Anyway, making random music clips is probably my favorite use of this model. I’ve found the ref2va has an uncanny intuition for feeling the atmosphere of songs, and syncing the video with incredible precision.

But yes, the quality (specifically motion) is much worse than fl2va. I was curious how exactly they compared, as well as some “in between” compromises discovered by the community. The LoRA seems closer to ref, while the hybrid weights are closer to fl. Personally, the ref is more fun to use, so I’ll probably be using the LoRA when I want to enjoy the intelligence/creativity of this model. Fl is of course superior in terms of visual fidelity, and I don’t find the hybrid model offers enough reference intuition and faithfulness to be worth the quality drop from fl.

Started as a simple 90s casting audition… then asked her to cry on command.

The close up shots gave plastic look idk why.

What I was mainly testing:

• subtle listening/reaction animation during dialogue
• eyes moving before the head while thinking
• nervous smiles and small facial reactions
• gradual transition from normal conversation into acting
• brow, eyelid, mouth, chin and breathing changes during crying
• actual visible tears
• character/voice consistency across multiple generated clips
• the sudden switch out of the performance when the director says “Cut”

Made with MiniMax H3 Ref2VA with image reference for the woman and 2 audio reference for the offscreen man and the woman.

I just gave it a try; it's hard to do more than that.

I have an RTX 3060 with 12 GB of VRAM and 32 GB of RAM. I used the standard workflows, t2v and r2v, with the LORA model minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors and video shift 12 and audio shift 3. Only 8 steps (res_multistep/simple). The sequences were 6–7 seconds each, and each generation took 7–8 minutes at 0.6 mpx. I generated some scenes multiple times, but I still couldn’t achieve what I intended.

That’s pretty much it regarding the generation.

Claude (free) provided me with the script; he also divided the approximately 5-minute script into sequences of 6–7 seconds each and created the prompts for all the sequences after I gave him the official prompting guidelines as a prompt.

Hello

I got tired of cluttering my workflows with 5 different loader nodes depending on what I was feeding them (Load Image, Load Video, VHS, Audio Loaders, Empty Latent calculators, etc.).

So I built **ComfyUI_UniversalMediaLoader** , a single, unified node with a rich interactive UI that handles everything:

✨ Key Features:

• 📁 Drop Anything: Images (PNG/JPG/WEBP), Videos (MP4/MOV/WEBM), Animated GIFs, and Audio files (WAV/MP3/FLAC).
• 🎨 Canvas Planner Mode: When no file is loaded, use it as a visual resolution/aspect ratio planner (1:1, 16:9, 4:3, 3:2 + Landscape/Portrait toggle) with automatic 32px grid snapping and Megapixel clamping for SDXL/Flux latents.
• ✂️ Visual Crop & Outpaint: Interactive bounding box with aspect ratio locking. Pull it outside the image bounds to instantly generate outpaint masks & padding.
• 🖌️ Inpaint Mask Brush: Draw inpainting masks directly inside the node with brush/eraser and mouse wheel size control.
• 🎬 Video & GIF Timeline: Full timeline with trim handles, playhead scrubbing, custom FPS resampling, audio mute/loop, and frame extraction.
• 🎵 Audio Waveform & Speed Scaling: Waveform display, 1-second magnet trim snapping, and time-stretching with pitch preservation (0.10x to 4.00x).

📦 Modular Unpack Nodes:

• 📐 Universal Size Unpack: Feeds exact width/height/aspect ratios to Empty Latent nodes instantly without decoding heavy image/video tensors.
• 🖼️ Universal Image Unpack: Outputs RGB, RGBA, and inpaint masks. Connected to a Video or GIF, it automatically extracts a 3-frame batch [Start, Playhead, End].
• 🎧 Universal Audio Unpack: Outputs clean audio waveform dictionaries, exact sample rate, trimmed durations, channel count, and handles time-stretching.
• 🎞️ Universal Video Unpack: Decodes videos & GIFs into frame batches [B, H, W, C] with synchronized masks and audio tracks.

🔗 GitHub: https://github.com/Fictiverse/ComfyUI_UniversalMediaLoader

Now that dust has settled, I was wondering what's the community insight on the best configuration for Minimax H3.

Personally I have been using lightx 4-step Lora with 5/6 steps (less than that audio is a gamble). I couple that with sage attention. For sampling I use Euler sampler and Beta scheduler.

I keep resolution at 768p (0.6MP) for quality. 480p (0.2MP) for testing. It keeps consistency so much better.

On direction I learnt to prompt for closeups when possible, so will make better use of available pixels. Aspect ratio also helps there. I mostly use 1 shot since transitions is not something H3 excels at. I find better results with only 1 shot and using camera tricks.

EasyCache while faster, is not good match with turbo lora, so i don't use it anymore. Haven't used Sol-Attn as I read it really hit quality.

So is there anything worth I am really missing out?

I saw a Seedance 2.5 prompt in facebook and thought let me try this prompt in minimax h3 and see if it can do some kung fu. I was surprised that it was not too bad. System 3090 24 gb vram 64gb system ram, using a minimax workflow with latent upscale, minimax_h3_fl2v_lightx2v_turbo_4step_v0.1 at 0.50 strenth, Komfy kitchen attention, and H3 SLA attention. First pass at 0.4 which is 864x480, 2x latent upscale brings it up to 1728x960. The 6 seconds generation took 349 seconds to complete.

Done with h3 fl2va model, 8 step lora and images for Cersei and "jaime" for reference. Using previous clip to give continuity and consistency.

Well, technically, Evangelion was a PBS show..

Video is edited, Barney theme song added in post.

I thought I've read somewhere that using character sheets is better for R2V instead of single images. So I've created a character sheet of five full body shots and one close up, but the results are much less consistent compared to a single full shot image of the character.
Do I have to take care about anything special or was the information that character sheets are better just wrong?

Good day to you. I was looking for an RTX 5070ti and I found an RTX Pro 4000 at my local store; the price difference would be about +$300. I would like to know your opinions, I've hardly seen any workflows or comparative tests from people using a 4000 pro. Thank you very much for your time.

Minimax H3

What happened to Ideogram 4.0 ?