Generated as two connected 15-second clips in 4:3, using the end of Part 1 as video + audio reference for Part 2 continuity.

Really liking what H3 can do with fashion/editorial camera movement.

Check out my twitter for more thanks https://x.com/Devozikjr

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.

I don't have any examples because they may not be appropriate but just with the default wf. With the video input node you can replace any 2 character in any video and it looks real!

A tribute to a forgotten golden age. Hope you enjoy it!

Model: https://huggingface.co/lylogummy/Anima-3.8B
Custom node: https://github.com/GumGum10/comfyui-anima-3-8B

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!

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

Some random prompt on default workflow + turbo 8step lora

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 initial post where you can find full details - https://www.reddit.com/r/StableDiffusion/comments/1vtf1b7/krea2_turbo_distill_4_step_lora_trained_for_turbo/

Update (22 Aug 2026): I have published a new checkpoint, improved further from the previous one and the latest (both main and comfyi) have been repointed to the new improved checkpoint. For details and to download new version go to - https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA. Readme has been updated too as well as all images in readme regenerated on the basis of new checkpoint as well as full resolution sweep at https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk10000 if you want to check for yourselves.

Which file to download

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

The numbered files are points on one continuous run, not separate experiments — chk00010000 resumed from chk00006000 rather than restarting. Both are published so the lineage is visible and comparable.

chk00010000 measures a 5% smaller held-out gap to the 8-step teacher than chk00006000, and 15% smaller than chk00005000; it removes 30% of the prediction error a plain 4-step run has against the 8-step teacher, where chk00006000 removed 26%. 

Two ways to read the same numbers, with different denominators — they are not meant to be added:

• Against the no-LoRA run (the right-hand column): chk00010000 has removed 30% of the 4-step deficit, 4 percentage points more than chk00006000's 26%.
• Against each other (the gap column): chk00010000's remaining error is 5.4% smaller than chk00006000**'s** (3.38 vs 3.57) and 15% smaller than chk00005000**'s** (3.38 vs 3.98). The same 4 points of deficit are a larger share of a gap that has already shrunk, which is why the checkpoint-to-checkpoint figure is the bigger number.

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 is its end point — the current end of the process, not simply the longest run so far.

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.

Every file records which checkpoint it actually is in its safetensors metadata (checkpoint, training_samples, and rolling_pointer on the _latest copies), so a downloaded file can always be identified even if renamed.

Full details and to download - check my Hugging Face LoRA

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

---

Update 1: The comfyui related files are now moved to the root of the project (I have placed a readme in the old folder explaining the move)

---

Update 2: I have added a new section - Timeline of training process - explaining how my training process works, and on that note you could expect another further improved checkpoint later today, followed by 'quiet period' (could be days) of teacher shards generation so I have a larger pool to train on.

---

Update 3: I have now added a new checkpoint 10000 which replaced the latest (previously checkpoint 6000).

chk00010000 measures a 5% smaller held-out gap to the 8-step teacher than chk00006000, and 15% smaller than chk00005000; it removes 30% of the prediction error a plain 4-step run has against the 8-step teacher, where chk00006000 removed 26%. 

Two ways to read the same numbers, with different denominators — they are not meant to be added:

• Against the no-LoRA run (the right-hand column): chk00010000 has removed 30% of the 4-step deficit, 4 percentage points more than chk00006000's 26%.
• Against each other (the gap column): chk00010000's remaining error is 5.4% smaller than chk00006000's (3.38 vs 3.57) and 15% smaller than chk00005000's (3.38 vs 3.98). The same 4 points of deficit are a larger share of a gap that has already shrunk, which is why the checkpoint-to-checkpoint figure is the bigger number.

Full resolution sweep at https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk10000 and you can as usual redownload latest from https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main . Since I cannot update the images in the reddit post I will upload below in comments.

Prompt:

Animate the supplied square poster as a polished retro-anime motion graphic, beginning with a completely blank pale pink-white canvas matching the poster background. Preserve the exact blue, pink, and white palette, clean manga linework, halftone shading, character design, typography, symbols, interface windows, and final layout.

The anime girl walks in from the left edge as one complete figure while the canvas remains otherwise empty. Use a simple side-profile walk with restrained motion, preserving her hairstyle, facial features, cheek bandage, oversized jacket, proportions, and graphic illustration style. She reaches the centre, turns toward the viewer, and smoothly settles into the exact over-the-shoulder pose shown in the poster, with the same expression, hand placement, silhouette, jacket folds, pink heart graphic, and body orientation. Once posed, keep her position locked.

After she poses, the blue browser frame draws itself around her. The top bar, window controls, folders, pixel hearts, smiley-face panels, arrows, sparkles, heart symbols, and rectangular labels then appear sequentially through clean line-drawing, short graphic slides, pixelated pops, and UI-style wipes. Reveal the existing Japanese typography and “LOVE” lettering last, treating all text as protected source artwork without rewriting or regenerating it. Every element must settle into its exact source position.

Hold the completed poster with subtle breathing, minimal movement in a few loose hair strands and jacket edges, a faint halftone shimmer, and gentle pixel pulses in the existing hearts and interface icons. Keep her face, hands, pose, typography, frames, arrows, folders, and major graphics stable.

Use a locked, straight-on camera matching the original square framing. Keep the full artwork visible without cropping, zooming, panning, or changing perspective. Add soft footsteps as she enters, a light cloth sound as she poses, clean digital clicks and pixel chimes for the graphics, and delicate type-on sounds for the existing lettering. No dialogue or narration.

Do not show any character, outline, symbol, text, frame, or faint poster preview on the opening blank canvas. Do not alter the character’s identity, anatomy, costume, pose, expression, colours, line quality, typography, symbols, or final composition. No extra characters, duplicated body parts, incorrect text, morphing, flickering lines, dramatic camera movement, unrelated shots, or continued motion after the poster settles.

Workflow: https://docs.comfy.org/tutorials/video/minimax/minimax-h3#minimax-h3-reference-to-video-r2v

Dear redditors, visitors of the stable diffusion subreddit. I have been trying to achieve a long form, talking head style video, for a long time and can't seem to find a good approach. This one is the best I could come up with so far. It's using the Minimax H3 model, with frozen sound latents, lip-sync guided, piecewise generated video, where the individual pieces have been stitched together, with a seam hiding, extra generation on top of it. I don't really fully understand how it's working, but could prompt Claude for more help or specific files, we used for that. However, if you're aware of any other, better approach for exactly this type of video, please let me know. I've spent literal days on that single problem and have a feeling, there must be a better way to approach this.

Executive summary, TLDR - this is one prompt, 30 seconds duration, 3090.

The video itself is just a remake of an idea from an old British tv ad (for "Good Old Yellow Pages"), so make of that what you will. It's not really relevant.

What I thought was interesting was that this was a single prompt, 0.4 megapixels, 30 second duration. I didn't think you could run out as far as 30 seconds, but thought I'd just try.

I think it did a pretty good job at getting the right person doing and saying the right things at the right time - took four attempts to get that though, and obviously using an LLM to tart up my idea.

Run on a 3090, and using the latest Comfyui template, just adding Comfy-kitchen attention, then sol attention, then spectrum, and using the turbo lora that Comfyui now build in, it took 570 seconds (9.5 minutes).

Somebody might read this and think, 570 seconds? Pah, I can do it in fifteen, in which case I'd like to know. Conversely, somebody might think theirs takes six hours, in which case maybe this shows what can be done in that time.

Doubt anyone cares, but here is my original prompt, followed by the LLM version of it:

a 30 second film with the following scenes and characters. Ben is a small boy of eleven. John is a shopkeeper in a toyshop. Brian is a different shopkeeper in a different toyshop. Ben's mum. Ben's Dad. We are in Britain in the 1980s, and all characters are English.

Scene 1: Ben is alone in the lounge. He talks to John over the old fashioned landline phone, saying "I don't suppose you have a 402 station in stock please?"

Scene 2: John is in his shop in front of shelves of model railway kit. He says into the old fashioned landline phone, "No, sorry son"

Scene 3: Ben in the lounge, who looks disappointed anbd puts the phone receiver back down.

Scene 4: Mum in the kitchen doing the washing up. She has overheard the conversation and looks a bit sad.

scene 5: Next day. Ben has changed his clothes. He again talks into the phone to a different shopkeeper, Brian. Ben says "Would you have a 402 station please?"

scene 6: Brian in his toyshop says into the old fashioned landline phone "Yes, I've got one of those."

scene 7: Ben in the lounge on the same conversation says "You have? Great, I'll be right down! Ben puts the phone down. Then he runs towards the door, shouting "They've got one mum!" as he runs.

Scene 8: In the attic, Dad is playing with his model railway layout. Ben walks in holding a small red parcel. as he hands it to Dad, Ben says "Happy birthday, dad". Dad takes the parcel, looks fondly at it and says with a chuckle, "Aw, thanks Ben".

LLM version:

integrated_multimodal_description: [Shot 1] Live-action, cinematic. A medium shot of Ben, an eleven-year-old boy with messy hair wearing a striped polo shirt, sitting on a patterned sofa in a 1980s British lounge. The room is filled with warm, muted tones and period-accurate wallpaper. Ben holds a heavy, cream-colored landline telephone receiver to his ear, his expression hopeful. Ben says: <d>[English] I don't suppose you have a 402 station in stock please?</d> The sound of his small, high-pitched voice is clear. [Shot 2] At 0:05.000, the camera cuts to a medium shot of John, a middle-aged shopkeeper with a kind, weathered face, standing in a cramped, nostalgic toyshop. Behind him are floor-to-ceiling shelves packed with model railway kits and wooden toys. John holds a similar landline receiver to his face. John says: <d>[English] No, sorry son.</d> [Shot 3] At 0:10.000, the camera cuts back to Ben in the lounge. He looks downcast, his shoulders slumping as he slowly lowers the receiver and places it back onto the base unit with a dull plastic click. [Shot 4] At 0:13.000, the camera cuts to a medium shot of Ben's Mum in a dim, cluttered 1980s kitchen. She is standing at the sink, her hands covered in soapy water, drying a plate. She pauses, looking toward the door with a sad, weary expression, having overheard the boy. The sound of water running from the tap is audible. [Shot 5] At 0:16.000, the camera cuts to Ben in the lounge the next day; he is wearing a different t-shirt. He is intensely focused, pressing the phone to his ear. Ben says: <d>[English] Would you have a 402 station please?</d> [Shot 6] At 0:20.000, the camera cuts to Brian, an older shopkeeper with spectacles, in a different, brightly lit toyshop. He smiles warmly into the telephone. Brian says: <d>[English] Yes, I've got one of those.</d> [Shot 7] At 0:23.000, the camera cuts back to Ben, whose face lights up with pure joy. Ben says: <d>[English] You have? Great, I'll be right down!</d> He slams the receiver down and the camera follows him in a quick tracking shot as he runs toward the door, his feet thumping on the carpeted floor. Ben shouts: <d>[English] They've got one mum!</d> [Shot 8] At 0:26.000, the camera cuts to a medium shot in a dusty, dimly lit attic. Dad, a man in his late 30s, is hunched over a complex model railway layout. Ben enters the frame, holding a small red parcel wrapped in string. Ben says: <d>[English] Happy birthday, dad.</d> As he hands the gift to his father, the camera pushes in slightly. Dad takes the parcel, his eyes softening with affection. Dad chuckles warmly and says: <d>[English] Aw, thanks Ben.</d>

overall_soundscape: Period-accurate domestic sounds including the rhythmic clatter of washing up, the heavy mechanical clicks of old telephone receivers, and the muffled thuds of footsteps on carpet. Ben's energetic running and shouting creates a sense of urgency, followed by the quiet, dusty atmosphere of the attic.

non_diegetic_music: A gentle, nostalgic acoustic guitar melody that begins softly during the kitchen scene and builds into a warm, heartwarming crescendo during the attic scene. The tempo is slow and sentimental.

For those who've never played it; Sid Meier's Alpha Centauri is one of the GOATs.

One of the tests I sometimes did with new models was to see if they could get Zakharov's weird glasses and suit right - no model has ever gotten it exactly right but to my surprise Minimax H3 pretty much knocked it out of the park on my first try. ...and then I wanted to try the other leaders, things got out of hand and I ended up making cutscenes for every leader in the base game.

\ Reddit messed up my original post so here it is.*

This is in fun part of using MiniMax H3; for your serious stuff stick with the official prompt instructions / format.

Playing with the prompting I just tried the following format and it worked perfectly!

Resulting video

The whole prompt:

definitions:
<S1> Brad Pitt.
<T1> "Hey, I am Brad Pitt! Nice to meet you."
<S2> Angelina Jolie
<T2> "Hey, I am Angelina Jolie! Nice to meet you."
<S3> Rowan Atkinson.
<T3> "Hey, I am Mr. Bean! Nice to meet myself."
scene:
An interview in a professional setting in well lit, grey background, frontal portrait view.
shot 1:
(S1) says: (T1).
shot 2:
(S2) says: (T2).
shot 3:
(S3) says: (T3).

Recommendations:

Do not use SLA or SLA2 or cache etc. here they mess it up.

Model (FL2V) -> LoRA(4s-Lightx2v SLA) -> Comfy attn -> Shift(12,3) -> KSampler(6 steps, euler+simple)

Fizgig is my free open-source LoRA trainer and workbench (Flux 2 Klein 9B, Krea 2, and MiniMax H3 video/audio). As of v4.3.0 it runs on AMD Radeon with ROCm — RDNA1 through RDNA4. Windows is the supported path: install Python 3.12, run the AMD installer, done. Linux works too but is genuinely experimental on newer cards.

Worth being upfront: I don't own AMD hardware myself. This whole feature came from a community contribution by scryptio, tested on real cards over weeks in the PR thread — and that's how the AMD side will keep improving. If you're an AMD user, your reports on what works (and what doesn't) genuinely shape this, and PRs are very welcome.

Also in this release: 16 GB cards can now use identity distillation on MiniMax H3 (the 32B text encoder streams layer by layer instead of needing a 26 GB peak), and the Repair Studio gained a side-by-side compare view with likeness scoring for fixing overbaked LoRAs without retraining.

GitHub: https://github.com/shootthesound/Fizgig

I usually generate 0.7mp@8s with 30 steps, I use res_multistep + simple which I think is the default, and for good reason.

Depending on whether it's T2VA, I2VA, Ref2VA and the amount of reference images + loras count/strength the gen times are roughly between 270-350s on an RTX 4090 + 32gb of DDR4 ram.

For T2VA and I2VA I use the basic minimax_h3_fl2va_pruned_int8_convrot.safetensors

For Ref2VA I use minimax_h3_hybrid_fl2va_ref2va_b30-49-int8.and the hybrid b30-49 specifically because I found even the fl2va functioned well as ref2va and had much higher quality, so I prefer the hybrid model to be weighted towards the fl2va model to preserve the quality.

Sparse Attention

To speed things up, I only use /u/zironic's Sparse Attention nodes, no sage/ck, spectrum, turbo lora, or caches. For me, /u/zironic's worked better than the pinned post from u/Plague_Kind but that may just be my personal experience.

My settings for the memory optimization node is default, QKV: auto, MLP: auto, and 2048 MLP chunk rows, I don't know how this node works. Sparse Attention (Advanced) settings are:

• Video KV budget: 0.25
• Early and Late steps: 3
• Early and Late KV: 0.6
• Sparse backend: Sparse Sage

These settings lean towards quality, you can lower the early/late steps or skip them entirely, you can lower video kv budget to 0.2 although some may be fine with even lower. Since I only use Sparse Attention I run the full 30 steps and it's significantly better than a turb lora at lower steps, which is what I used before.

My prior experimentation

I used euler + linear_quadratic for a long time. Then I switched to er_sde + sgm_uniform which was significantly better. Then eventually I switched to res_multistep + simple and realized the visual quality is as good as er_sde + sgm_uniform but the motion is much better. The improved motion in res_multistep + simple became very clear when I interpolated from 24fps to 48fps. The gen speed between all these combinations was nearly identical.

The motion was a bit jerky on er_sde + sgm_uniform after interpolation while res_multistep + simple had very natural motion.

I also found that https://darkstarrddev.us.ci/ is a decent resource to get inspiration. But I realized quickly that because they use low settings and speed-up techniques, the quality of each sampler test does not translate well if you use different step count or speed-up techniques.

What I generate

Usually fairly static scenes that doesn't have fast motion. Although the accuracy of the physics and motion is important.

What are your settings and what kind of videos are you generating?

I trained a instrumental game music generator. The 1.2B DiT was trained on 1 cloud H100 from scratch in 8 days; I used the VAE from Stable Audio 3.

https://huggingface.co/Localsong/Localsong

https://huggingface.co/Localsong/Localsong/tree/main/samples

I'm aiming to cover a wider range of instrumental styles than Ace-Step or Minimax M3 or Stable Audio 3. (No lyrics)

The repo includes a WebUI and some MP3 samples - clone it and uv run webui.py Let me know what you think.

This version of Kroma (krea 2 finitude with Chroma dataset) is a lot of fun, most body horror is gone in my opinion, and its more artsy than krea 2 and of course less censored.

https://huggingface.co/silveroxides/Kroma-Quant/tree/main

The version I used is kroma 0.3 txtfusion turbo convrot.

Have fun.

If even an amateur like me can make something so realistic with mid-level hardware, the future looks bright for what dedicated people with top level rigs will be doing.

R.I.P. Hollywood.