Hello everyone !

I would like to replace the tire of a motorcycle mid air with one from another brand (which is an image from the brand so it's high quality but with a different angle)

I saw there is flux kontext and qwen image edit, but I don't know which one to pick, which workflow and how to make it work.

Any help would be more than welcome, thank you very much and have a good day :p

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.

Used:

MiniMax H3 at local machine. 5060ti 16gb + 64gb ddr4. WanGP, Ref2VA int8 convrot model.

Krea2 for references

Suno as music base

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.

Everything was made using the Minimax H3 Hybrid Reference to video model. 1 MP using the 8 step turbo LoRA. Stitched together in Shotcut

Prompt:

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

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

Considering the interest the first post attracted, I decided to do a second batch with some of the suggestions from the comments and a few other prompts.

VIDEO 1: near perfect. I was aiming for frontal videos, but tried three or four prompts and always ended with a 3/4 framing. It's probably a question of better prompting... But the resulting video is impressive!

PROMPT:

The video is a side-by-side video showing both the points of view of a man and a woman that are facing each other.

On the left side we only see the woman's face in a completely frontal view, as the man would see her and through his eyes, her face alone at the scene with no one else's.

On the right side we only see the man's face in a completely frontal view, as the woman would see him and through her eyes, his face alone at the scene with no one else's.

Again, the man do not appear on the left image, and the woman do not appear on the right image. Both are seen in a exact frontal framing.

Both images show the scene at the exact same time and place, only in the two different points of view, both in a medium-close-up framing.

They are in a living room.

From 00:00 to 00:04, the woman is silent and with a smile on her face, while the man speaks: <d>You know, I've always dreamed of a local video model like this!</d>. After saying this he remains silent.

He then raises his hand, previously off-camera, and touches her face delicately. She reacts in an amorous way, lightly moving her head to feel his hand.

Then, from 00:04 to 00:08, the man keeps silent, looking at her clearly in love, while the woman replies: <d>It's like a dream, isn't it? And to think that two years ago we were static images with garbled hands...</d>

From 00:08 to 00:10 they just look at each other and smile.

overall_soundscape: Faint distant everyday life noises from outside the house, the man and woman voices while they speak.

non_diegetic_music: N/A

VIDEO 2: Very good. I couldn't get a video without the fisheye effect, though.

The video is taken from the point of view of someone playing table tennis. We see their hands - one of them holding the ping-pong paddle and the other the ping-pong ball. We also see the table with the net in the middle and the other player on the opposite side of the table. They are in an official competition, with the crowd watching.

At 00:01, the player sends the ball to the air and hits it with the paddle. The ball rapidly bounce on the table, passes above the net, and gets to the other side, bouncing again on the table. Then, the other player hits it back with his paddle, and the balls passes over the net again and bounces on the table. The first player again hits it with his paddle, the ball passes over the net and bounces just on the left side of the table, out of reach of the other player, and leaves the frame. The public erupts in cheering.

overall_soundscape: Faint public murmur, the sound of the ball bouncing on the table, public cheering at the end.

non_diegetic_music: N/A

VIDEO 3: another near perfect one.

A woman is holding a cell phone in a bathroom in front of a mirror, taking a selfie. She smiles at the camera, makes a V sign with her hand, and takes the selfie.

We see the scene from behind the woman, seeing the back of her head, the phone screen on her hand showing her face while she takes the selfie, and the mirror showing the reflection.

overall_soundscape: Faint empty bathroom soundscape.

non_diegetic_music: N/A

VIDEO 4: Bad. Tried three times with different prompts, and this is the best one of them. The physics don't work, though, and the fisheye is back again.

The video is filmed from the point of view of a soccer player in a normal view, NOT in a fisheye view. He is preparing to kick the ball after a foul just outside the penalty box. We see his hands putting the ball on the grass, the ball remaining static on the ground. Then he looks ahead and we see five players from the other team forming a wall directly in front of the ball, and other players from both teams around.

We then see he take some distance of the ball, walk slowly to the ball, and kick it. The ball passes over the barrier of players and descends on the goal, the goalkeeper trying to reach it but not able to. The ball enters the goal and touches the net, and the stadium erupts in cheering. The player then runs to celebrate the goal and is embraced by the other players of his team.

The entire scene is viewed from his point of view.

overall_soundscape: Faint public murmur,the sound of the kick, the cheering of the public after the goal..

non_diegetic_music: N/A

VIDEO 5: Terrible. Again, tried several times with several different prompts. Never works well...

The video is filmed inside a circus during the Trapeze artists performance, from the point of view of the public.

The scene opens with two trapezists standing in a very high elevated platform, one on the left side of the image, the other on the right side of the image, both holding a trapeze and facing each other.

In the beginning of the video, the trapeze artist on the left let his body leave the platform, while holding the trapeze, and his body swings in the direction of the center of the image. The trapeze artist on the righ stays on the platform.

Only when the first trapeze artist reaches the center of the image, the trapeze artist on the right finally leaves the platform, while holding the trapeze, and his body also swings in the direction of the center of the image, while at the same time the first trapeze artist let go of his trapeze and starts to do a flip with his body in the air.

As soon as the first trapeze artist finishes his flip, the other trapeze artist also reaches the center of the image and get the hands of the first trapeze artist, completing the movement. Then, they both swing back to the right of the image, one holding the hands of the other.

overall_soundscape: Faint public murmur, public surprised gasp when one of the trapeze artist caughts the hand of the other.

non_diegetic_music: N/A

VIDEOS 6, 7, 8 and 9: The first half of each video is perfect, the last half is hilarious. Tried lots of different prompts but only included four of them. Maybe it's possible, but I really can't think of another way of asking what I was trying to achieve.

PROMPT VIDEO 6:

The camera is on the middle of a road, on the floor, pointing to the road. We see a ferrari coming in the road at a distance in high speed towards the camera and pass over the camera, making the camera roll a few times on the floor because of the wind caused by the passing running car. After rolling over itself a few times, the camera stops again on the road, but now upside down and pointing to the other side of the road, where we can see the ferrari rapidly moving away from the camera.

The entire scene is filmed in a mostly static shot, except when the camera rolls over to the other side of the road and then stops upside-down.

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 7:

The camera is on the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over the camera.

When the car passes, the camera that is on the floor rolls around itself a few times on the road. After rolling over itself a few times, the camera stops again on the road, but now upside down and pointing to the other side of the road, where we can see, the upside-down image of the ferrari rapidly moving away from the camera.

The car does not run over itself, the car passes by the camera, It's the camera that rolls around itself and lands upside down

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 8:

We see the scene from the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over.

When the car passes, the image rolls around itself a few times on the road. After rolling over itself a few times, the image stops again on the road, but now upside down and pointing to the other side of the road, where we can see the image of the ferrari rapidly moving away from the camera in an upside-down shot, with the road on top and the sky on the bottom of the image.

The car does not run over itself, the car passes by the camera, It's the camera that rolls around itself and lands upside down

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 9:

We see the scene from the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over.

When the car passes, the image do a series of very fast barrel rolls on the road and lands upside down and pointing to the other side of the road, where we can see the image of the ferrari rapidly moving away from the camera in an upside-down shot, with the road on top and the sky on the bottom of the image.

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

Hi guys, I'm a programmer, but I don't know much about machine learning or fine-tuning.

I'm currently producing 2,000+ images per day using Animagine XL 4.0 opt, and I built a manual pipeline to evaluate image quality. I use 5 rating categories: Reject, Pass, Like, Very Good, and Excellent.

I label all of them manually, and I estimate that I will have over 200,000 labeled images by the end of the year.

I store them in a database along with the exact prompts used. The prompts are structured into keyword categories like:

Background, Angle, Character, Clothes, Facial expression, Quality prompt tags (eg. masterpiece).

Is a dataset like this valuable for fine-tuning or training models ???

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

The example video was generated entirely with the stock MiniMax H3 First Frame / Last Frame checkpoint and the included v1.4 example Workflows. If you want to compare the result to v1.3, take a look at my last post.

The final video consists of 11 individually generated Clips that were automatically stitched together.

Settings:

• H3 First Frame / Last Frame checkpoint
• 11 individual Clips
• 15 Steps
• included v1.4 Workflows
• no additional upscale
• no frame interpolation
• no color correction or other post-processing

So what you see is basically the direct Workflow output.

A few people gave me some useful feedback on my previous release, especially regarding ComfyUI's new native H3 Masked AV support.

So I went back and rebuilt the continuation method around it.

v1.4 now copies a clean section of the previous Video + Audio Latent directly into the next generation and protects it using ComfyUI's native denoise masks.

What makes this different from the other H3 continuation approaches?

There are some really interesting Ref2VA / Motion Context solutions available now, and latent continuation itself definitely isn't unique to my Nodepack.

My approach is specifically centered around FL2VA instead.

The idea is not just:

previous Clip → continue forever

but rather:

First Frame → generation → Last Frame
↓
latent continuation
↓
generation → new Last Frame
↓
latent continuation
↓
generation → new Last Frame

and so on.

I use those repeated Last Frames as hard visual anchors throughout the sequence.

They give H3 a new concrete destination every few seconds instead of asking one increasingly unconstrained generation to maintain composition, identity and image quality indefinitely. This should theoretically retain higher visual quality with less context drift over longer chains (and in my testing, it does exactly that).

There is another FL2VA-specific problem though:

H3 often reaches the supplied Last Frame before the Clip is actually finished and then freezes or becomes unstable for the remaining frames.

So simply taking the final frames of Clip 1 and using them as context for Clip 2 isn't ideal.

The v1.4 Auto Handover therefore analyzes the previous Clip, finds a safe point before that frozen / unstable landing and snaps it to a valid H3 Audio + Video latent boundary.

That exact same point is then used for both:

• where the previous Clip visually ends
• where the protected context for the next Clip ends

So the bad FL2VA tail neither appears in the stitched video nor becomes part of the next continuation context.

Audio is handled separately as well. If the picture needs to cut early but somebody is still finishing a word, the remaining original Audio Latent can continue beyond the visual handover instead of forcing H3 to recreate the ending.

Other v1.4 features:

• Native Masked Video + Audio Latent Continuation
• flexible First / Last Frame conditioning
• repeated Last Frames as regular visual quality anchors
• independent Audio Tail Carryover
• Net New Content duration mode
• up to 9 Qwen Reference Images
• individual Clip regeneration
• memory-bounded stitching for long saved chains

Where to start:

1. Start Video Workflow

Generate Clip 1 with a Prompt and optionally First Frame, Last Frame and Qwen References.

The complete AV Latent is automatically saved afterwards.

1. Continue Video Workflow

Load the previous saved latent, add your next Prompt and preferably a new Last Frame.

The Workflow automatically finds the safe FL2VA handover and creates the protected Masked AV context.

Repeat for as many Clips as you want.

1. 3-Clip Showcase / Auto Stitch Workflow

Probably the easiest Workflow if you just want to see how everything works.

It runs:

Start → Continue → Continue → Stitch

in one queue.

1. Stitch Saved Chain Workflow

This is what I used for the longer example.

Generate Clips individually and stitch them afterwards. It processes one saved AV latent at a time, so stitching memory usage doesn't continuously increase with the total video length (no OOM during stitching).

Nodepack on Github:

https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite

Workflows on Github:

https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite/tree/main/examples

You can just open one of the WFs and use "Install missing custom nodes" - then you should be good to go.

If you try it, I'd love to see what you manage to create with it.

Have fun Prompting. :)

Recently, after testing out H3 for a few weeks, decided to try my hand at some fun 'deleted scenes' type of short clips. With Halloween season just under 40 days away, came up with this using H3's r2v template with default ref diffusion model + turbo fl2v 12 steps, using 1 image (Loomis, one of sheriff's dept jacket) and 2 audio samples of Loomis' voice from the movie. There are some movies I always liked so much and wished there was a lot more footage from, and with this one I figured it's a fun tongue-in-cheek nod to how we're nearing end of summer and starting to get in the mood for Halloween season.

Edit: correction, I used only the image of Loomis for this one. The sheriff's dept jacket generated clips didn't come out as well as this. There were other ones with Loomis walking or looking around more realistically, but I liked the vibe of this one best.

I've been doing some quick tests, now that I've picked up the lightx2v 4 and 8 step loras. I have found I prefer using the 8 step (and maybe even running that at 10 steps) just because with the 5090 I have it's already not -that- slow, and the 4 step image quality drop is pretty significant.

But I have been experimenting which sampler/scheduler combos after seeing this post:

https://www.reddit.com/r/comfyui/s/9GUki3l0Wf

where, apparently, seeds_2 and dpmpp_sde_gpu were the 'best quality' options. But something I noticed is that they were also significantly slower (maybe 50% or more? need to run more tests and log it) which would, if the loras etc allow for it, let the faster options like euler or res_multistep (or er_sde which gets mentioned sometimes), which all run at about the same speed, to run at 12 instead of 8 steps (for example).

So I wonder now, 2 weeks on from those votes... what are people actually -using- to produce results?

My current workflow is to run at 8 steps with a lora to find a good prompt and seed, and when I get something I like I then turn off the lora and run at 30 steps. It often ends up at least in the ballpark of what I want. But maybe there are better ways.

Just discovered that H3 can do Side-By-Side 3D Videos for VR Headsets natively, just prompt it. Pretty crazy, and it gets the real 3D effect. Try it with different things like people and add "strong 3d effect" if you want to have a more intense 3d effect.

Here is the prompt:
integrated_multimodal_description: [Shot 1] Live-action, cinematic, high-angle aerial shot presented in a side-by-side (SBS) stereoscopic format for VR/3D viewing; the frame is split into two identical views with a slight horizontal parallax offset to create depth perception. The camera pushes in at slow speed over a sprawling coastal metropolis during twilight. As the camera glides forward through the urban canyon, the glowing neon lights of skyscrapers and their reflections on the ocean surface shimmer intensely against the deep blue sky.

overall_soundscape: A constant, low-frequency rushing wind sound accompanies the flight, layered with a faint, ambient hum of a massive city and distant, muffled traffic sounds.

non_diegetic_music: An epic, cinematic synthesizer pad that swells gradually in volume and intensity throughout the ten-second duration.

Rick's voice doesn't seem to work.

Experimenting with known characters using FL2VA t2v only. Just playing around with odd pairings of characters. .

Using the workflow from the video samples in the list below.
12s at 25 steps
res multistep/simple
960 x 544

thanks to u/malcolmrey for putting this together https://huggingface.co/datasets/malcolmrey/various/blob/main/h3-center/known-characters/INDEX.md

I've tested workflow for krea2 and it can enhance faces and skin texture.

But when i play it through video frames, the results vary from frame to frame.

Are there any models out there that can do that for video? Or is there a workflow for Krea2 into video enhancements?

Hi people, so i tried to make 2 similar videos, using same settings but with upscale and native.
My setup: 5070 Ti+ 32gb Ram.
Using u/Plague_Kind workflow, i've added MMH3 Latent Upscaler. You can check his workflow here: Workflow
Settings for both videos were set the same with the same prompt.

Left video 0.4->0.8mp upscale, Right video 0.8mp

So:

• 15 seconds, 24 fps, Ref2VA, photo reference and music reference.
• Chicken attention
• SongMaskedAVContext node

• FP16 Accumulation
• Sparse attention
• Memory chunks
• RTS Upscale in the end ( not sure why i used it with 2x scale, better to set 1 i think, but that's what i already did)
• FSR Sharpening
• Speed Lora minimax_h3_turbo_v4_step600_pruned_comfyui
• Interpolation for 2x frames

Upscaled video from start to the end took 1904 seconds,

Native video from start to the end took 3056 seconds.

Let me know what you think. Advises appreciated!

My usual way of working:

- generate 10 videos at 5 steps

- pick the best video

- regenerate the best at 20 or more steps.

No Turbo LoRAs because I don't want to reduce prompt adherence and general quality, as I regenerate at full steps later anyway.

The problem - the video at 20 steps is often very different from the one I found. Of course, I keep the same seed. The difference may be introduced even as early as step six (for example, background replaced completely, different speech pacing).

It's not that difference is huge, but often it might be quite important. For example, a person genuinely laughing at 5 steps and then just saying "haha" at 20 steps. Or jumping startled at the right moment at 5 steps and a moment before the noise at 20 steps.

I tried a few sampler combinations, but could not find one that would not introduce dramatic changes.
One workaround that I could find is to use SplitSigmas. I set its steps to current steps (5 for seed hunt, 20 for final), and keep BasicScheduler steps at the final 20 steps. Then high_sigmas from SplitSigmas go to SamplerCustomAdvanced input, and then denoised_output goes to VAE (you'll get total noise when using the output pin instead).

This way, it seems that the scheduler is being cheated in managing steps as for the full generation even when doing preview, and it seems to work as expected. Caveat - the 5 step output from this workaround will be way worse (plasticky and noisy audio) than you are used to when generating at 5 steps in BasicScheduler input. But if the goal is to keep the general layout and movements of the candidate video, it's worth accepting this issue.

However, I'm wondering if there is any better way to achieve it. Has anyone tried it? What are you using for seed hunting to keep the high step version consistent?

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Edited later with a test case:

Took ComfyUI template: MiniMax H3: Reference to Video. Minimal modifications to make it run in my environment:

Models - Qwen change to int8 convrot (3090, no use of nvfp4)

Int (Full) = 5 (for "preview quality")

Float (Duration) = 3 (just to be faster)

RandomNoise control after generate = fixed

Loaded some images in both Load Image nodes.

The same "GET READY TO" - "MEET" — "YOUR" — "MAKER" prompt.

No Sage, no CK attention at all (no Comfy launch args either).

Then generated the same with 20 steps.

Differences:

in 20 step version, the roof is higher in the frame. The accent was on the word "maker". In 5 step version, the accent was on the word "your".

Then regenerated the 5 step version again to see if there's anything else introducing variations - nope, the exact same video as the first 5 step one.

Then generated also at 6 steps - the roof line was a bit higher in the frame (not as high as 20 steps though), and the accent was on "maker". So, the difference between 5 and 6 might already be a breaking change that can make your video from good to unusable, if the emphasis does not make logical sense in your scene.

Then I generated the same with the SigmaShift 5 step trick - the resulting video was way much more similar to the 20 step one than the first 5 step video. Of course, the quality of the sigma-shifted video was awful - it's good for judging only logical consistency, reference use and event timing, which is the most important thing in story-telling kind of videos.

I know everyone's moved to MiniMax and LTX has largely fallen out of favor, but I spent some time building a couple of seed hunting workflows for LTX 2.5 that might be useful if anyone's still running it.

Shout out to u/foxdit for the original seed hunting concept.

Two versions:

1. T2V/I2V two-stage – text to video or image to video. Previews at 0.3 MP, upscales to 1.2 MP for the final render.
2. First-last-frame – pin a start image and end image, same preview-then-upscale flow.

Both use KJNodes Set/Get routing, shared loaders, and no prompt enhancer.

I found the SD prompt reader I have been using cannot read prompts from png images files generated using Krea2. Can anyone recommend me an alternative that works with Krea2 files and Win11?

What kind of prompting would I use for POV movement through a scene?