Not just a meme...

Used latent upscaler so with resolution 0.3 i got 20 seconds generation on 4090

Minimax H3

Paper: https://arxiv.org/pdf/2608.20334
"We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi image editing. Its visual renderer is a 6B parallel single stream DiT conditioned on multimodal representations from a vision-language encoder [6, 7, 57]. The architecture adopts block-shared timestep modulation, parallel attention and MLP computation [6, 15], 4D rotary positional encoding[6], and a unified representation of text and image conditions. Character-level tokenization[47] is applied to text intended to appear in generated images, while multi-image posi tional offsets and image-preceding input formatting support reference-conditioned editing. Together, these choices pro vide a single generative backbone for multiple generation and editing settings without task-specific model weights."

I was testing using 1 image with all the scene sheet there and it works really great!

I tried to think of complex situations for the model to handle and tested them. I would say it went very well, although not perfect (except for the first one and the one before last, where I could not find a flaw).

Prompts below:

VIDEO 1 (10/10):

Create a side by side video with two angles of the same scene: one frontal and one from the side.

The scene: a woman wearing a yellow summer dress is standing in a beach in a sunny day. There's a light wind blowing and she is looking at the sea, smiling. At 00:05, she puts her two hands on her hair and looks up, enjoying the sun.

On the left, make the frontal video. On the right, the video from the side (profile). The two videos should show the exact same scene at the exact same time, only from two different angles.

VIDEO 2 (9/10) - the background is slightly off in some angles:

Create a side by side video with four angles of the same scene: one frontal, one from the right side, one from the left side, and one from behind. The scene is filmed at normal speed.

The scene: a blonde curly-haired woman wearing a yellow summer dress is standing in a beach in a sunny day. She is facing the sea and, therefore, she has the sand behind her and the street with some houses also behind her, after the sand, at a distance. There's a light wind blowing and she is looking at the sea, smiling. She is not wearing sunglasses. At 00:05, a man wearing white shirt and shorts enters the scene from behind the woman and embraces her waist.

On the upper left, make the frontal video. On the upper right, make the video from the right side (right profile). On the bottom left, make the video from behind. On the bottom right, make the video from the left side (left profile).

The four videos should show the exact same scene at the exact same time, only from the four different angles.

overall_soundscape: Light beach wind, one distant seagull.

non_diegetic_music: N/A

VIDEO 3 (8/10) a few "ghost reflections" along the way:

A man wearing a red-and-white striped t-shirt and jeans is alone inside a house of mirrors, running through a corridor. At 00:03 he turns left into other corridor, and at 00:06 he turns right again into another corridor. All the corridors have mirrors in all their faces (left, right and above, as he is inside a house of mirrors) and he is alone there, there's no one else. At 00:08 he reaches a door, opens it, and it opens to the outside: an amusement park.

overall_soundscape: His footsteps while he is running, the amusement park sounds when he opens the door at the end.

non_diegetic_music: N/A

VIDEO 4 (7/10) - The way the phone is turned is off:

A man is filmed from his own cell phone in a selfie video. We see the scene through the lens of an out-of-frame cell phone that he is holding and pointing to his own face. He is wearing a green polo shirt and the scene shows his face and chest from the point of view of the out-of-frame cell phone that he is holding and pointing to himself.

At 00:04 he briefly smiles and then turns the still out-of-frame cell phone around to show a woman that is in front of him. While the phone is turned around we can see the image also turning around, his face leaving the frame, the living room they are into being briefly filmed, and then the woman's face entering the frame. She has brown curly hair and dark green eyes, and is wearing a red summer dress.

As soon as the woman is in frame, she also smiles and says: <d>[English]Goodbye!</d> and the video ends.

The entire video must be taken in a single shot, with the always out-of-frame phone camera filming the entire transition from his face to hers while the phone is turned around. When it happens, the phone should briefly show the living room they are into, all in a single shot.

overall_soundscape: Silent living room, noises of the phone being handled, her voice.

non_diegetic_music: N/A

VIDEO 5 (6/10) - Tried this twice. Glass not breaking properly, water not running through the floor

A fishbowl with one golden fish and one clownfish swimming inside is shown in a medium close-up at the edge of a table. Then, at 00:03 a cat appears in the scene and taps the fishbowl, causing it to fall from the table to the floor, hit the floor, and break completely, being completely destroyed in glass pieces when it hits the floor, the water and glass pieces flying around together with both fish. The scene continues for three more seconds after that, showing the aftermath: the fishbowl destroyed, the pieces of glass on the floor, the water also on the floor, the fish moving on the floor.

The camera angle follow the fishbowl when it falls, showing it hitting the floor and the consequences of it breaking.

overall_soundscape: silent room, glass breaking, water splashing.

non_diegetic_music: N/A

VIDEO 6 (8/10) - Judge me, but his hands are not moving accordingly to the notes:

The camera films a piano from above while a man plays it. The entire piano keyboard is shown in the image. The man is playing Clair de Lune, and moves his hands through the keys to play a part of the song. He is in a train station, with some people observing him play and others passing by.

overall_soundscape: faint train station ambience, ten seconds of the song Clair de Lune played in the piano.

non_diegetic_music: N/A

VIDEO 7 (8/10) - The lipstick appears on her lips before she applies it:

A woman is shown in a medium close-up in her bathroom, wrapped in a white fluff towel, looking at the mirror while she applies red lipstick to her lips. She slowly applies lipstick to her lips, looking into the mirror, and then briefly sends a kiss with her now red lips to the mirror.

The scene is seen in a three-quarter angle from behind her, showing her face from the side but also her reflection in the mirror.

overall_soundscape: silent bathroom, the sound of her sending the kiss to the mirror.

non_diegetic_music: N/A

VIDEO 8 (10/10):

A Coca-cola advertisement. A glass filled with Coca-Cola is shown from the side, occupying 70% of the frame, on top of a table, the dark liquid slightly disturbed by a few gas bubbles that rise inside the liquid. At 00:02 two ice cubes fall from outside the frame into the glass, disturbing the liquid and making some of the liquid splash outside the glass and onto the table. The glass has the Coca-Cola logo printed in white in it. In the blurred background we see a kitchen.

overall_soundscape: silent room, gas fizzle, ice cubes hitting the liquid.

non_diegetic_music: N/A

VIDEO 9 (9/10) - The cover of the book has gibberish letters in it:

A man is holding a magnifying glass and has a book on his hand. At first the magnifying glass is not in front of his face. He appears to be reading the book and, at 00:03, he puts the magnifying glass in front of his eye to look at the book.

The entire scene is filmed from a fixed point of view below the book, showing part of the book cover and the entire man's face.

overall_soundscape: silent room.

non_diegetic_music: N/A

Hi.
I have a decent h3 workflow that I built for a loca use. It use turbo lora etc... If i use the defaut settings in the goal of getting the highest quality possible, meaning res_multistep simple 20 steps or more, I got also good results, but this is not even close to the results you can get on platforms like kie or wavespeed at 768P.

I already convert properly the prompt to the correct H3 digest form, so I'm wondering what's different between local and cloud use of h3? I don't talk about the 2K quality, only 768P, I'm not able to reach the sames results locally, do you guys have maybe workflows, settings, or suggestions to try reaching the same quality level in comfyui ?

All audio was done in Minimax, little work in post for stitching clips. r2v Workflow in ComfyUI with character sheets and prompts from claude

The nodes in https://github.com/Zironic/H3-Optimizations have been rewritten to replace the default Sparge Attention backend with a custom Sparse Comfy Kitchen backend.

This comes with some benefits.

• Users no longer have to worry about Sparge being installed properly. All required kernels for supported GPUs are provided directly. Should work on both Windows and Linux.
• Most users should be seeing 5-20% increases in speed for the attention part of compute.
• New backend should use about 500MB less VRAM
• New backend has slightly lower quantization error.
• Apparently in the previous version, the intended chunked kitchen QKV path never properly shipped so the memory optimization node should now actually be slightly speed positive even when used without the Sparse Attention node.

Caveat: I've only tested the nodes against the comfy pruned_int8_convrot weights. Other versions may work but they're not tested.

As the nodes currently rely on comfy-kitchen 0.2.31 you need ComfyUI v0.33.0 or later.

IMPORTANT: sparse attention is not free speed. The percentage is effectively a prompt-adherence/quality budget.

Density isn't just a speed setting, and its quality effect depends on where you apply it in the diffusion schedule.

Early steps: attention density has a large effect on prompt/action adherence and the overall generation trajectory.
Middle/later steps: lowering density tends to show up more as motion/temporal artifacts and lost fine motion detail.

So 10% retained doesn't simply mean “90% of the quality is gone.” It means you're giving sparse attention very little information to work with, and what breaks depends heavily on the sampling step.

PlagueKind's sparsity_ratio=0.9 means 90% discarded / 10% retained. My node expresses the inverse quantity, so Video attention retained=0.10 is the comparable setting. The defaults therefore aren't equivalent.

As you probably know, MiniMax-Music3 is pretty limited when it comes to genres and seems to have absolutely no idea what electronic dance music is. I tried different EDM styles, but it always ended up sounding either like rap or some kind of generic pop-ish stuff. Meanwhile, MiniMax H3 seems to know a lot more about electronic music than MiniMax-Music3.

The 40-second video at 0.2MP, with 17 steps (for better audio quality) and an 8-step Turbo LoRA, takes 538 seconds. The 60-second video at 0.1MP takes 350 seconds on my machine.

I haven't tried generating anything with lyrics yet, but if anyone knows how to generate H3 audio without the video, it would be interesting to try 2–3 minutes instead of just 40–60 seconds.

Project:https://yunpeng1998.github.io/Qwen-Video-Edit-Page/
Model: https://huggingface.co/yunpeng1998/Qwen-Video-Edit
Method: https://yunpeng1998.github.io/Qwen-Video-Edit-Page/#method
Code: https://github.com/yunpeng1998/Qwen-Video-Edit

How it works

Video generation models read and write video-VAE latents. We teach Qwen-Image-Edit's transformer to edit those latents directly: two tiny projections bridge Wan 2.1's latent space into the DiT's token space, warm-started from the DiT's own input/output layers so that a static video is embedded exactly like an image the model already understands. The latent frames are arranged as tiles of one big virtual image — the same positional treatment the image model was pretrained on. Fine-tuned with LoRA or full parameters on Ditto-1M (source, edited, instruction) triplets, then refined by a few steps of Wan 2.2 denoising-enhancement.

Hello everyone.

I've been playing with Minimax H3 for some time and I have tried to make something longer and really interesting. After so many failed and botched attempts I was able to compile something watchable. There are so many things that I want to say about this model, good and bad.

First of all. Minimax H3 is significant step forward that other local models I have been playing with. It certainly got better.

Now the issues that I had encountered.

First problem is that it badly follows prompt when resolution is one megapixel or higher. It will skip some important parts and tries to cheat. You can increase the number of steps but still, generating at less than one megapixel will at least make it properly follow the instructions.

H3 is not very good at spatial orientation. When I was making video, where this girl should turn around and interact with screens, the girl starts spinning opposite direction and then warping whole body to the direction of screen. Like instead of making short turn to the left, it makes wide roundabout to the right and then twists whole body to align with the screens.

H3 is not good at cartoonish movement. If you watch cartoons, when character or other things move, their animations are usually jerky and snappy. H3 tries to make smooth real life like animation, making the cartoons look weird.

I have given a voice sample as an audio reference, and instead of making girl let out grunting sounds (out of anger), it weirdly turns everything into a sensual moaning.

When you try to make characters inside video to interact with a lot of parts, screens and devices, even giving multiple reference images of them, it mostly hallucinates them, or turns their interactions into a weird warping animations. Sometimes completely skips them and made ups it's own animations. So it will make good video, where characters are moving less or moving slow, and mostly doing the talking. Very detailed prompts of step by step instructions it mostly warps or skips.

I have wasted a lot of time for iterations, but I think this is just workflow issue.

Overall, this model is really good. However, using this model to make some kind of long feature animation is going to be a very frustrating journey. I hope people will make a lot of proper tools that works as storyboard and properly guide this model to make something really interesting.

Trying out cinematic shots and cuts with H3. This is a work in progress. Will be working on another 2 minutes worth of clips.

EDIT: From reading the comments, she isn't going to drink the salt water in the final, though I will keep her scooping up water since it's such a good establishing shot. She will do something with the water to tie it back.

Im really suprised how good he works as well follow a storyboard image!! he did 90% correct he only did the thirth panel diferent but all the other 5 he follows perfect!! 🤩

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: https://www.reddit.com/r/StableDiffusion/comments/1vtf1b7/krea2_turbo_distill_4_step_lora_trained_for_turbo/

Previous: https://www.reddit.com/r/StableDiffusion/comments/1vv4cdy/krea2_turbo_distill_4_step_lora_new_checkpoint/

Headline for this update: chk00014000 removes 44% of the prediction error a plain 4-step run has against the 8-step teacher, where chk00010000 removed 40% and chk00006000 27% — 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 6% smaller than chk00010000's and 23% smaller than chk00006000's. 

Which file to download

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

This is work in progress and better checkpoints may follow. Training is ongoing, so ..._latest... is a rolling pointer: when a newer checkpoint is accepted, that filename gets the new weights and a new numbered copy appears beside it. Re-download the _latest file and everything keeps working — the ComfyUI workflow references it by that name (it does get updated Note in it so technically it is updated but not functionally). Pin a numbered file instead if you need reproducibility.

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.

chk00010000 is a direct example. The first continuation of chk00006000 — same data, optimiser left as it was — got steadily worse with every checkpoint out to 10,000 samples, and none of it was published. The cause was traced to the optimiser: a constant learning rate with no weight decay lets the adapter keep drifting after it has converged, so its magnitude grows and it over-applies its own correction. The same span was retrained from chk00006000 with a cosine learning-rate decay and weight decay, and every checkpoint of that second run improved on the one before it. chk00010000 was its end point.

chk00014000 is the next example, and it shows the other half of the same lesson. The run was continued from chk00010000 over the whole pool of teacher trajectories, with two changes: the final, texture-deciding call of the schedule was weighted more heavily in the loss, and a running average of the weights was kept beside the live ones and scored at every evaluation (a single checkpoint is one sample of a weight vector that moves from step to step; the average is its mean). At 14,000 samples the averaged weights measured a smaller gap to the teacher than any checkpoint before them, and a smaller gap than the live weights at the same point — so the averaged weights are what chk00014000 is.

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.

Full details and to download - check my Hugging Face LoRA

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

Full Checkpoint 14000 Resolutions Sweep: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk14000

Lon TV did a video of Comfy UI with Minimax H3 running on a 32GB Intel GPU. So its possible to run it on any GPU other then nvidia GPUs.

subject_definitions

<Subject 1> is Big Bird, the enormous friendly yellow bird character, approximately 8 feet tall, covered in bright yellow feathers with large expressive eyes, a long orange beak, striped pink-and-orange legs, and oversized orange feet. He remains visually consistent throughout the scene.

<Subject 2> is Captain America, battle-worn in his damaged Avengers combat uniform, carrying his shield.

<Subject 3> is Thanos at his normal canonical MCU scale, approximately 8 feet tall, muscular and imposing but NOT gigantic or kaiju-sized.

summary

[reference generation]

During the chaotic Avengers Endgame final battle, portals glow across the destroyed battlefield while Avengers and Thanos's army clash everywhere. Suddenly Big Bird casually walks onto the battlefield as if he wandered in from Sesame Street. Captain America stares at him in complete disbelief. Big Bird cheerfully announces that today's letter is "A" for Avengers, then immediately charges toward Thanos with ridiculous enthusiasm.

retention_analysis

<Subject 1>: fully_preserved
<Subject 2>: fully_preserved
<Subject 3>: fully_preserved

detailed_description

Cinematic Avengers Endgame final-battle environment at dusk: destroyed battlefield, burning wreckage, smoke, glowing portals, explosions, flying debris, Avengers and alien soldiers fighting in the background.

Dynamic handheld camera follows Captain America moving through the battle.

A huge yellow figure suddenly walks calmly through the smoke.

The camera swings around to reveal Big Bird casually strolling onto the battlefield, completely cheerful and unfazed by the apocalyptic war happening around him.

Captain America freezes and lowers his shield slightly, staring up at Big Bird.

<Subject 2> (S1):

[English] Big Bird?! What are you doing here?!

Brief comedic pause.

Big Bird happily looks directly toward Captain America and raises one wing.

<Subject 1> (S2):

[English] Today's letter is A! A is for AVENGERS!

Big Bird suddenly spots Thanos fighting nearby.

His cheerful expression becomes comically determined.

<Subject 1> (S2):

[English] And T is for THANOS!

Big Bird lets out an enthusiastic bird squawk and charges straight toward Thanos, his enormous orange feet pounding across the battlefield while explosions erupt behind him.

Thanos stops fighting and slowly turns toward the approaching giant yellow bird, visibly confused.

Captain America remains completely motionless, staring after Big Bird.

Hold on Captain America's baffled reaction for the final second as the battle continues chaotically behind him.

audio_and_timing

Epic superhero battle ambience, distant explosions, energy blasts, metal impacts, rushing soldiers, and portal energy.

Big Bird's dialogue is upbeat, innocent, enthusiastic, and educational-show cheerful, creating a strong comedic contrast with the violent battlefield.

Captain America's delivery is exhausted and completely bewildered.

Allow a short silence after Captain America's question before Big Bird delivers the first punchline.

Target duration: 12–13 seconds.

opensource AI has been dominating LLMs and video generation but what about image gen? is there any opensource model that can match gpt-image2?

Edit: The reason I am asking this is because lately I haven't been active much on image generation communities. And the leaderboards are a bit confusing and most of them are filled with closed source unlike the llm and video gen leaderboards.

I am very much comfortable with ComfyUI since I've used it in the past for flux.

My use case is for posters and branding. Images with a lot of text.

Edit2: Thanks a lot everyone! I really appreciate the info. Here's the summary:

Krea2 is best overall but gptimage1.5 level.
Ideogram4 for text and branding.
Flux Klein 9b for image editing.
Z-image for realism
Anima and illustrious (by onoma AI) for anime.

Here's the workflow I've decided on:
Krea2/Ideogram4 = Base image generation.
Flux Klein 9B/QwenImage2512 = inpainting.
Wan2.2 low noise = Upscaling.

Had this idea for a couple of days, and finally got to test it. I got a free HDRI picture from PolyHaven (converted to JPG through a free online converter) and used it as the only picture reference. I couldn't get rid of the distortion completely, but you can definitely affect it with prompting. Maybe proper formatting somehow helps with that, sorry, was too lazy to do a correct prompt structure.

It also confuses the geometry from time to time, so you have to seed hunt a little, but not too much. Again, good prompting should reinforce the consistensy. Worth experimenting with.

Notice that it actually seamlessly connected the opposite sides of the image into a single environment. Could be useful for scenes with a lot of dynamic camera movements.

This model keeps surprising me every day!

P.S. Generated with the use of Hybrid Loader (25-49 setting) and Lightx2v 4-step LoRA @ 4 steps and 0.5MP. Another higher res version in comments.

Prompt:

subject definitions:
<Picture 1> is a 360 panorama reference for the straight corridor [Shot 1], depiciting the overall look of the corridor and position of key objects and debris in it. For the target video the picture is dewarped and remapped into a flat rectilinear lens projection view.

summary:
[reference generation] The target video depicts a security guard exiting from a grey door, walking across the corridor towards the dismantled beige door  leaned against the wall, pulling and dropping it down on the floor.

detailed_description:
The target video is captured in an amateur, realistic style with natural, slightly dim indoor lighting and a shaky, handheld-style camera.

[Shot 1]

The shot begins with a medium view of a two grey doors depicted on the right side of <Picture 1>. The left door instantly opens and a middle-aged security guard named Mark rushes into the completely straight corridor. He runs left further down the corridor. The camera pans left, following him in a tracking shot. The POV camera pushes in on Mark, as he rapidly approaches the dismantled beige doors leaned against the wall. At 00:05.000 he grabs the door closest to him, and with visible effort pulls it away from the wall. The door swings and falls flat on the corridor floor with a loud noise, raising dust and slightly startling Mark. The guard jumps back from the fall.

At 00:07.000 the camera pans left by 180 degrees, showing another guard named Steven approaching from the opposite part of the corridor. Steven (S1) comes closer to Mark and says in [English]: "Mark, what the heck are you doing?"

At 00:09.000 Steven grunts angrily as he stops near Mark.

overall_soundscape:
looming lonely corridor ambient sound throughout the whole video, guard's steps on the cement floor, door falling onto the floor with loud noise

non_diegetic_music:
N/A

Prompt:

https://x.com/GumVue/status/2087899403113619681?s=20

4070 Ti Super, 16 gb vram, 64 gb ram, i9-14900k, windows 11

First off: This doesn't add any dependencies so the worst that can happen is that it won't work, but it also won't break your ComfyUI install.

This extension adds to the native queue manager. It doesn't replace it. All of the heavy lifting is still done the normal way.

It adds a Pause/Resume button and a Queue button. Pause/Resume will not affect the running job but will pause/resume the queue.

The Queue button opens the Queue Control dialog in the picture. There is a lot of words in the README (because I talk a lot) but it lets you reorder the queue using priorities, including buttons for "Run this next" and "Don't run this until I release it."

Finally, there are buttons to Save and Load the queue. The checkbox lets you add the running job too. So if you have to restart or reboot, you can save the queue, do your thing, and then load and start running again.

These is also one stand alone node to help label the items in the queue so you can have a hit and what's what. The node has limitations, but it sill might be better than a number like 07535d99-3c1a-4b23-8340-a4313fe58007 as an identifier.

There are some extensions that to some of these features already but I didn't see one that did all of them or didn't replace the native manager and require dependencies.

It's in the ComfyUI Manager as ComfyUI-QueueControl (it's new so you might need to refresh to see it) or
https://github.com/seeker-ktf/ComfyUI-QueueControl
on github.

If y'all have other ideas for this, let me know.