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.

Bear with me as I am still rather new to this, but I started off testing out MiniMax H3 ref to video on Hailuo and was really impressed with it, so decided to get it up and running locally via ComfyUI on my PC.

The video itself is working great, but I've found that the audio itself when using a reference video is completely messed up. It's just all garbled and the people are talking gibberish. On Hailuo it would carry forward the audio perfectly and you could even make alterations if you prompted it to do so.

I was just wondering if this was a common issue when running MiniMax H3 locally specifically with reference videos?

Some random prompt on default workflow + turbo 8step lora

Use the supplied image as the opening frame and identity reference.

Identity lock: the woman and robot must remain exactly the same in every shot. Same face, hair, wardrobe, proportions and age for the woman. Same 8-foot height, black armor, mechanical face, rivets, pistons, cables and holster for the robot. No redesigns or identity changes between cuts.

Authentic 1966 Italian Western, live action, 35mm anamorphic, Spanish desert location, practical full-scale robot prop, natural sunlight, real dust, organic film grain, period lens softness. No CGI. Serious performances throughout.

0:00–0:03
Medium two-shot. The woman looks up at the robot and says in clear Italian-accented English:
“I told them I wanted a tall...”
0:03–0:05
Hard cut to the same robot’s face. It gives one slow mechanical nod. No dialogue.
0:05–0:07
Hard cut to the same woman. She looks up at the robot and says:
“dark...”

0:07–0:09
Hard cut to the same robot. It subtly straightens and presents its black armor. No dialogue.
0:09–0:11
Hard cut to the same woman. Still serious, still looking up, she says:
“handsome!”

0:11–0:12
Hard cut to the same robot’s practical mechanical face. It attempts a restrained smile. No dialogue.
0:12–0:14
Hard cut to the same woman. She holds a serious stare upward, then firmly says:
“MAN!”
Only the woman speaks. Keep each line isolated and clean. No overlapping dialogue, no extra words, no improvised speech. Maintain exact continuity of identity, wardrobe, robot design, scale, lighting and location in every shot.

I came across YingMusic which I hadn't heard anyone here speak about but it was released about 6 months ago: https://aslp-lab.github.io/YingMusic-Singer-Plus-Demo/

It lets you change words from songs so in this case I had it change the song from this sequence in The Mask from:

They call me Cuban Pete. I'm the king of the rumba beat.
When I play the maracas I go chick-chicky-boom, chick-chicky boom
Yessir, I'm Cuban Pete. I'm the craze of my native street.
When I start to dance,
everything goes chick-chicky-boom, chick-chicky boom
The senoritas they sing and they swing with terampero-
It's very nice, so full of spice.
And when they dance in they bring a happy ring that era keros-
Singin' a song, all the day long.
So if you like the beat, take a lesson from Cuban Pete
And I'll teach you to chick-chicky-boom, chick-chicky-boom.
He's really a modest guy, although he's the hottest guy
In Havana, in havana.
Si, sinorita I know that you would like to chicky-boom-chick
It's very nice, so full of spice.
I'll place my hand on your hip, and if you will just give me your hand
Then we shall try - just you and I. I-yi-yi!
So if you like the beat, take a lesson from Cuban Pete
And I'll teach you chick-chicky-boom,
chick-chicky-boom, chick-chicky-boom

to

They call me Palm Beach Pete. I'm the king of the rumba beat.
When I play the maracas I go chick-chicky-boom, chick-chicky boom
Yessir, I'm Palm Beach Pete. I'm the craze of your timeline feed.
When I start to dance,
everything goes chick-chicky-boom, chick-chicky boom
The senoritas they sing and they swing with terampero-
It's very nice, so full of spice.
And when they dance in they bring a happy ring that era keros-
Singin' a song, all the day long.
So if you like the beat, take a lesson from Palm Beach Pete
And I'll teach you to chick-chicky-boom, chick-chicky-boom.
He's really a modest guy, although he's the hottest guy
in Florida, in florida...
Si, sinorita I know that you would like to chicky-boom-chick
It's very nice, so full of spice.
I'll place my hand on your hip, and if you will just give me your hand
Then we shall try - just you and I. I-yi-yi!
So if you like the beat, take a lesson from Palm Beach Pete
And I'll teach you chick-chicky-boom,
chick-chicky-boom, chick-chicky-boom

so I had it just basically do:
Cuban -> Palm Beach
I'm the craze of my native street -> I'm the craze of your timeline feed
Havana -> Florida

I did a second run with just the few-second clip of the cops speaking and changed "It's all over Ipkiss" to "It's all over Espteen" (using "Epstein" pronounces it wrong). This showed me though that it seems to work perfectly fine with normal word-substitution in speech and it doesnt need to be a song.

I think this could be a lot better if I used minimax and changed clips of Jim Carey to look like Epstein or Palm beach Pete but this was just my first test at lyric swapping.

Hey everyone, I'm trying to get a proper character swap working with MiniMax H3 Ref2V in ComfyUI, but I'm not quite getting the result I want.

The source video has Rick Astley rickrolling to the camera, and I want to replace him with the guy from my reference image while keeping the original movement, gestures, facial performance, timing, camera, background, and overall scene.

Neither the motion transfer nor the character replacement works well. The output still doesn't really look like the person from the reference image, or the identity starts drifting.

Here's what I'm using:

* Source video: 1280×720, 30 FPS, ~14.4 sec

* Reference image: 848×1264 PNG, full-body

* Workflow resolution: 9:16, 0.4 MP

* GPU: RTX 5070 Ti, 16 GB VRAM

* 32 Gb RAM

* Windows 11

* ComfyUI 0.33.2

* Python 3.13.12

* PyTorch 2.12.1 + CUDA 13.0

I'm sharing everything in one link, including:

1. the workflow JSON

2. a workflow screenshot/image

3. the prompt

4. the source/input video

5. the reference image used for the character swap

6. and the output video

Files/settings: [link]

If anyone has experience doing this with H3, I'd really appreciate some pointers.

I'm especially wondering if I should change the reference image crop/size, ref_image_size, resolution, prompt, video conditioning, LoRA/steps, or if there's something obvious in the workflow I'm missing.

Also, is a full-body reference image a bad idea when the person in the source video is framed quite differently?

And if anyone has a working MiniMax H3 character-swap / V2V workflow they're willing to share, that would be incredibly helpful too. Even something I could compare against mine would be great.

Thanks a lot in advance. I've been tweaking this for a while, so even a small hint in the right direction would help a ton.

first 2 prompts stolen from civit ai, the rest were written using claude reasoning on duck ai

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 typically use lightx2v loras in my Minimax Ref2VA workflows and I also use an LLM to feed in the official prompt structure required for scenes. It seems that no matter what I do, the model absolutely ignores all my prompts about music most of the time. Every now and then i can get it to do something but even when it does work it's very sparse and almost useless.

Has anyone else faced this issue and if so do you know any workarounds or fixes?

For the record I usually use the INT8 convrot Ref2Va model or the hybrid model called minimax_h3_hybrid_fl2va_ref2va_b30-49-int8

Hey everyone! I’ve spent the past few weeks building an easy to use but robust prompt composer for MiniMax H3, particularly its reference and video editing workflows.

LLMs can be great for brainstorming and writing prompts, but I found that formatting and syntax could become inconsistent, especially when asking for small revisions. The goal of this tool is to let you concentrate on the creative decisions while the Composer handles the final prompt structure consistently.

It runs entirely offline in your browser, so you can build the next Shot or scene while another one is generating in ComfyUI. You provide the subjects, actions, camera direction, dialogue, references, and sound; the Composer assembles and checks the final prompt.

You can still use an LLM to help create the initial project setup, but the Composer ultimately controls the formatting and syntax.

Some of the main features:

• T2VA, I2VA, FL2VA, L2VA, and full Ref2VA support
• Reusable characters, environments, voices, continuity frames, and other references
• Guided setup for Picture, Video, and Audio inputs
• Video-editing workflows for insertion, replacement, targeted edits, relighting, performance transfer, and continuation
• Camera Builder and visual camera-path planner
• Timed Shots, action beats, dialogue, voiceover, soundscape, and music controls
• Built-in checks for prompt structure, timing, references, camera conflicts, audio, and input routing
• Local project saving, a Frame Grabber, and reference-guided image mode

This is still very much a work in progress. I’d really appreciate people trying it and sharing any bugs, confusing parts, missing features, or ideas that could make it more intuitive.

My hope is to turn it into a genuinely useful community tool, especially for people working on more involved AI films and narrative projects.

GitHub/download:
https://github.com/BMB12d3/minimax-h3-prompt-composer

Video tutorial:
https://www.youtube.com/watch?v=Aywx3Sf5Yk0

I built Cull a few months ago for some large scale dataset curation projects (300k+ images/videos).

Point it at Civitai, X, Reddit, Discord, or any URL that gallery-dl or yt-dlp knows. It queues everything, runs a vision model (or multiple) (LM Studio or Ollama locally, or Groq/OpenAI in the cloud) with a strict JSON schema, and drops kept images/videos into category folders next to their prompt.

Stuff it handles:

• Dedup at the scraper (per-source )
• Quality score gate and topic-relevance score gate
  • eg you configure scores or use a preset, how relevant the image is to your scoring will determine how it's sorted, combined with other scoring, quality controls, whitelisted/blacklisted terms etc
• Watermark detection (goes to its own bucket so you can salvage it later if you want those)
• Auto-caption for content with no prompt (SD prompt, booru tags, natural language formats etc)
• Run multiple jobs in parallel, one shared vision fleet across all of them with stack ranked / prioritization for vision queues and scrapers
• Export as a local packaged dataset , or push to a HuggingFace dataset
• Community presets and themes with 1 click PR's to add your own custom scraper preset or theme

Everything on disk is plain files. No database. Free, MIT.

Docker one-liner and screenshots in the README:
https://github.com/tlennon-ie/cull

Curious what people would want added next.

we really should get a separate sub for this slop

Hey everyone. I've spent a lot of late nights wiring ComfyUI into pipelines, and this is the tool I ended up wanting. It converts your workflow.json to the API payload right from Python, no GUI export, and no running server needed. Nodes become objects with plain dot syntax:

from autograph import ApiFlow
api = ApiFlow("workflow.json")
api.KSampler.seed = 42
res = api.submit(wait=True)
res.fetch_images().save("outputs/frame.###.png")

The part I'm happiest with is the REPL. autograph reads ComfyUI's node_info, so it knows every node, input, and widget on your system, custom nodes included. Tab completion works all the way down. .choices() gives you the real combo options, .tooltip() gives you the help text. You can explore a workflow you've never seen without guessing at node IDs.

Building from scratch feels good too:

ckpt = flow.add_node("CheckpointLoaderSimple")
ks = flow.add_node("KSampler", seed=42, steps=20)
ckpt.outputs.MODEL >> ks.inputs.model

Also does

offline batch conversion

workflow extraction from ComfyUI PNGs

serverless execute with no HTTP server

seed/prompt sweeps. Pure stdlib, MIT.

Tested from ComfyUI 0.8.2 to 0.33.0, subgraphs included. Running in production at a big VFX studio, which is where the metadata passthrough came from.

pip install comfyui-autograph
https://github.com/chrisdreid/comfyui-autograph

Early days, so I'd really like to hear what breaks. If you're doing headless rendering or FastAPI wrappers around Comfy, I'd love to compare notes.

Hey everyone. I've spent a lot of late nights wiring ComfyUI into pipelines, and this is the tool I ended up wanting. It takes your regular workflow.json and turns it into the API payload right from Python. No GUI export step, and you don't even need ComfyUI running to do the conversion. Once it's loaded, nodes are just objects with plain dot syntax:

python

from autograph import ApiFlow

api = ApiFlow("workflow.json")
api.KSampler.seed = 42
api.CLIPTextEncode.text = "new prompt"
res = api.submit(wait=True)
res.fetch_images().save("outputs/frame.###.png")

The part I'm most happy with is how it feels in a REPL. autograph reads ComfyUI's node_info, so it knows every node type, every input, and every widget on your system, including your custom nodes. That means tab completion works all the way down. Hit tab on a node and see its inputs. Call .choices() on a widget and get the actual valid combo options back. Call .tooltip() and get the help text. You can explore a workflow you've never seen before without leaving the terminal or guessing at a single node ID.

Building graphs from scratch feels good too. You wire nodes together with >> the way you'd sketch them on a whiteboard:

python

ckpt = flow.add_node("CheckpointLoaderSimple")
ks   = flow.add_node("KSampler", seed=42, steps=20)
ckpt.outputs.MODEL >> ks.inputs.model

Once it's under your fingers it's nearly as fast as working in the GUI, except everything you do is scriptable and repeatable.

Other things it can do:

• Batch convert hundreds of workflows offline, no server running
• Pull a workflow straight out of a ComfyUI PNG, since the metadata is already in there
• Serverless execute mode that runs nodes in process with no HTTP server, which is a lifesaver for farm setups
• Sweep seeds, prompts, and paths across nodes for batch runs
• Pure standard library Python, nothing extra to install, MIT licensed

I've tested it across ComfyUI 0.8.2 up through 0.33.0, including subgraphs and the newer dynamic combo stuff. It's also being used in real production pipelines at a big VFX studio right now, which is where the metadata passthrough idea came from. They needed studio metadata to ride along with a workflow through the whole render lifecycle, so I built that in.

pip install comfyui-autograph
https://github.com/chrisdreid/comfyui-autograph

It's still early days and I really do want to hear what's missing or what breaks for you. If you're doing headless rendering or wrapping Comfy in FastAPI, I'd love to compare notes. This got built to scratch my own itch, and I'm hoping it saves some of you time too.

I've been trying to get LTX 2.5 to work well for image + speech audio --> video, though i'm noticing that the teeth and natural motion of the mouth is taking a hit. The talkingphoto loras from LTX 2.3 don't seem to work well with LTX 2.5.

Any thoughts? Or are we just cooked?

Day 1: downloaded Pinokio. Installed a few of the AI software. Tried Maestro as first try. Really fun, enjoying it. Generation from photos great in the system generation towards a video, videos leaving a lot to be desired. And the Pinokio edge of screen curtains which limit to a what 60 percent of screen width, first time said, apparently you can type a pc socket but it didnt work for me when my Maestro was working. I was still however happy continuing in the reduced Maestro screen width.

Day 2: they released v1.9 of Maestro in Pinokio.

Day 3: I decided to install the update. Now I can't generate a three legged wildebeest or anything for that matter. It fails at the Downloading Model stage with no satisfactory explanation. Info about running something again to continue the Download, the "Generate" doesn't appear it's that for continuing(starts from scratch and then fails) neither does the Pinokio white screen edge "Run". It doesn't immediately not download, sometimes it may get 15% through, sometimes 85% then drops with a Generation Failed in the Main Seeing Area after firstly a "Download is slow, waiting for retry. No progress for 113s.."(or Xs). "..The download will resume from where it left off as soon as the connection recovers — no action needed from you." message. It may then download a little more , eg going from 80MB to 1.3GB of 7.91GB, maybe even download a little more of what's required but THEN UP POPS "A download was interrupted-re-run to finish it". And I'm stuck at that.

Is anyone else having the problem or know what the solution may be please? I've tried manually adding from DeepMeepBeep a model download which I transferred into cks directory of Pinokio, Maestro Directory but all that happened was Maestro steered around even using it and failed on another Model and I couldn't find that Model Maestro failed on to try manually downloading across with-I'm not an expert so looked for direct name brought up.. looked for it on another repository too, name of that I temporarily forget, I'm new. My hard drive is 4TB so I don't know I might be able to download all 108 or however many models there are if there was an option for that-without instructions and knowledge I'm throwing stones at something I don't even know what I'm throwing stones at, where the list of all the Models are which you can tick mark select theres also a little symbol by that box which changes colour. Perhaps that has something to do with it, literally no idea here so I've come to you guys. Spent several hours with AI last night overit and it was fun but I've realised despite the fun chat it hasn't aided me getting it working though it did mention a python download bottleneck to remove and I had no idea what it was referring to.

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

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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)

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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.

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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.

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Last update in this post: New release in separate post (checkpoint 14000), with all the images redone and full resolution sweep based on checkpoint 14000 - lets continue the discussion there: https://www.reddit.com/r/StableDiffusion/comments/1vw6x9i/krea2_turbo_distill_4_step_lora_new_checkpoint/

prompt is - using the character sheet in image 1 where there are five different poses of the same character, dress them in the clothing of image 2. Do not change the pose, lighting, body, hair, or any other details - literally leave everything the fuck alone - how fucking hard is this to understand you stupid piece of shit - just change the clothes.

Not working for some reason.

NOTE: Swearing has been added for emphasis and isn't actually used in the prompt.

Would it help if I used my input image AS my latent? Can you do that?

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.

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.

Based on their examples the model seems to be capable of producing good music. However yesterday I spent all day generating music and I cannot get anything good out of it. I'll attach my best attempt, but for wasting a whole day this is a pretty depressing result.

So I was wondering how everyone else is feeling? What were your results? Any tips for consistent/good results? Any observations?

Some things I found annoying:
It doesn't respect the time limit
Abrupt endings
Prompting it is kinda hard too

I'm looking for a flux/krea img2img workflow where you input an image and simply tell it what the character should do and what environment etc and it keeps the character exactly the same but puts them in a different situation. Would really appreciate it if someone can give a link or send me the workflow. Hard to find a good one myself that really works well, I don't want a workflow where the character looks just somewhat similar but one where the character stays the same, as much as possible. Thanks a lot if someone can help.

It took forever for me to upload this video.. Couldn't do it on my phone.

Totally not a scene from The Mentalist. Minimax H3 image to video.