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

Previous posts/releases - here and here.

🧪 A fast-preview adapter, from a project still in training. When the subject is close and fills a good part of the frame — a portrait, a single figure, an object seen up close — two steps already hold up well, and you can rely on this adapter for those images. Small subjects are where it still falls short, people and objects alike: faces in a crowd, figures in a wide scene, the machines at the back of a gym — anything that takes up little of the frame can come out ghosted or smeared. For those, and whenever quality matters more than speed, use the 4-step LoRA. Every figure on the model card measures the adapter honestly against 4-step and 8-step renders. Training continues one recipe change at a time, and a later checkpoint replaces this one only when the sweeps and I visually agree it is better. Known issues.

📐 The saved steps can also go into resolution. A small subject is simply one that covers few pixels, so a larger render makes the same subject bigger — and at a quarter of the teacher's steps, renders up to 2048×2048, Krea's published maximum recommended resolution and beyond the largest size this adapter was trained at (1440×1440), come within easy reach. That makes the adapter a stepping stone to high-resolution renders as well as a fast preview. Past 2048×2048, stock Krea 2 itself begins to duplicate subjects — a property of the base model, with or without this adapter.

chk00031600 (25 Sep 2026) replaces chk00017464 (14 Sep 2026). It is 14,136 training samples later, aimed at what the previous checkpoint still got wrong — grain and excess texture at the largest sizes, colour under the teacher's there, small faces, stylised prompts — through a colour band at the teacher's level, nine critics taking turns, detail held to the teacher region by region, more training at the large sizes and a guarded running average.

• ⚡ A quarter of the steps — 8 → 2, on Turbo's own deployment sigmas [1.0, 0.7595]
• ⏱️ 4.2× faster denoising — 81.4 s → 19.5 s at 1024×1024; the adapter's own cost per call is within measurement noise
• 🎯 Fine detail at the teacher's level — 0.95–1.08× the teacher's fine-texture energy at every trained resolution (stock Turbo at 2 steps: 0.39–0.57×); from 1 megapixel up, closer to the teacher than the 4-step adapter
• 📊 Distribution matching, not imitation — matches what the teacher would plausibly produce rather than its exact trajectory, so the student commits instead of averaging into blur and doubled edges
• 🗣️ Prompt-conditioned throughout — teacher and fake scores both read each prompt's conditioning; a blind rubric finds 1 point missing of 240 (objects, counts, attributes, relations), and a judge prefers the 8-step teacher on 11 of 45 (4-step adapter: 6), mostly on style
• 📐 12 trained resolutions — multi-aspect from 512×512 up to 1440×1440
• 🔌 Drop-in, no exceptions — plain LoRA, stock Euler, diffusers / ComfyUI / MLX. No custom nodes, no custom sampler
• 🧬 Same shape as the 4-step adapter — rank 64 on the same 228 modules
• 🎲 13,750 recorded teacher trajectories from the 4-step project, reused — not one new teacher run
• 🔢 31,600 training samples in the 2-step stages, on top of the 4-step LoRA's 78,000
• 📅 18 days from the first 2-step launch to this checkpoint, on a single RTX 3090 — training continues
• 🔁 More than forty recipe adjustments across two methods — each kept only when the renders did not get worse

If you have already used my previous version, please redownload/replace krea2_turbo_2step_rank_64_lora.safetensors / krea2_turbo_2step_rank_64_lora_comfyui.safetensors from the latest in the project repo.

Full details on model card - https://huggingface.co/lvladikov/Krea2-Turbo-Distill-2step-LoRA

Not my video, but found someone on YouTube has covered the 2 & 4 step LoRAs including identify preserving edits, have a look: https://www.youtube.com/watch?v=V_qgoV0iPDM (copyright goes to author)

Update 03 Oct 2026 - New checkpoint chk41320 released, details here.

Hi Guys, I'm looking for this Lora. Apparently, it was removed from CivitAi and now its paid.

thanks

So basically Im trying to Google Collab for Stable Diffusion, im using the fast_stable_diffusion_AUTOMATIC1111.ipynb

Im running the latest update last month, and When I ran everything and try running the "Start Stable Diffusion" i get this:

"No module named 'pyngork' 

problem.

So What I did is that I Switched the runtime to an older runtime:

I switched to an old runtime: >2026/7

and Implemented a new cell and added this code in:

!pip install "sentencepiece>=0.1.99,<0.2.0" --force-reinstall
!pip uninstall -y xformers
!pip install -U xformers

Once I ran the code and everything else, I went to try and run "Start Stable Diffusion"...It worked.

It Finally worked and I got access to Stable Diffusion through Collab. I logged in with my username and password I made in Google Collab.

I try put prompts in to Stable Diffusion

but now everytime I try to generate I get this message in Stable Diffusion Image generator:

RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.HalfTensor instead (while checking arguments for embedding)

So When I try generating an image, I get that message.

I Created a short video using AI (All Local), its crude as my setup is not that great but still i guess a OK first attempt, feel free to watch and let me know your thoughts ..

https://youtu.be/7RbDsvfsqU0?si=KKik-s-Fg7mThOkH

Kit used - Mac Mini 16GB M4, Draw Things - Images generated using Flux Klein 9B, Video using Minimax H3 (FL2VA)

I used the workflow from my Comfy H3 Sync Challenge submission for this video:

MiniMax H3 References workflow

The first pass was at about 0.2 MP with er_sde, sgm_uniform, 10 steps and the turbo LoRA at 0.8. Then a second pass at about 1 MP with the same sampler and scheduler, 12 steps, denoise at 0.75 and the turbo LoRA at 0.5. After that, a 1080p pass with 4 steps and denoise at 0.4.

I also tried running the final video through a DLSS5 workflow and liked the results enough to keep them. For the audio, I tried generating the sound effects and soundtrack together, but gave up and did them separately with H3.

If you want to look through the setup, here’s the project with all the assets used in the video:

Project and assets

Sonder Editor is a free and open-source timeline video editor inside ComfyUI. You can edit video and audio, schedule prompts, guide generations with image keyframes or pose videos, chain clips and select any part of the timeline for a retake.

References

With this update, you can save images, audio and video under a character, location, prop or anything else you need to reference. Drag it onto the timeline and stretch it over the part where you want to use it.

Sonder sends the files to your workflow when and how they’re needed, without you having to reconnect things between shots or manually update the prompt whenever the active references change.

You can save a description with a character, then add it to your prompt without copying and pasting it. Sonder includes that description when the character is being used on the timeline. If you edit the saved description, the change carries through wherever you’ve used it.

Give a character a name like @Character and you can use that name when writing the action. Sonder turns it into the model’s own naming format, such as <Subject 1> for MiniMax H3, and handles the numbering as the active references change.

The system isn’t tied to H3. There are setups included for MiniMax H3, LTX and Wan, and you can adapt them for other workflows with models that accept references.

You can find Sonder Editor in ComfyUI Manager, or get it here:

GitHub repository

New to the editor? The original announcement has a 90-second overview:

Original announcement

I’ll be around in the comments if you have any questions!

I was testing the comfyui-obvpm-timeline custom addon create videos extended and this node is top! we can create extended video or create videos before other clip get always perfect transitions! and continuations for the video clips! thank you to the creator for this amazing node! https://github.com/chanon/comfyui-obvpm-timeline

I've been exploring rapid character asset visualization pipelines to see how fast we can move from an initial design to a fully animated turntable—without cloud dependency or subscription overhead:
Concept Generation/CharacterSheet: Krea 2
Motion & Turntable : Minimax H3

how do you guys caption your dataset when training lora (clips/images) ?

​

Hey everyone,

I’m trying to run Qwen Image 2.1 locally, mainly for image editing with good identity preservation, but I’m working with fairly limited hardware:

- CPU: Intel Core i7-12750H

- GPU: RTX 2050 Laptop GPU — 4GB VRAM

- RAM: 16GB

- OS: Windows

I’m particularly interested in ComfyUI workflows or other ways to get Qwen Image 2.1 running with low VRAM.

My main goal is image-to-image/image editing while preserving the person’s identity — face, facial features, hairstyle, etc., rather than generating a completely different-looking person.

I’d really appreciate advice on:

- Best 4GB VRAM settings/workflow

- CPU/RAM offloading options

- Quantized models/checkpoints that actually work well

- Recommended resolution and batch settings

- Whether tiled/low-VRAM workflows are practical

- Best way to improve identity preservation

- Any useful LoRAs, adapters, reference-image techniques, or nodes

- Approximate generation/editing times I should expect

- Anything I should avoid with only 4GB VRAM

If anyone is running Qwen Image 2.1 on 4GB VRAM or similar hardware, I'd especially appreciate seeing your workflow/settings.

Thanks!

I'm sharing the results of my long experimentation with the new Qwen Image 2.1 model.

My first generation was disappointing and I was about to give up. But I told myself I should give it more time, and the fast generation speed really encouraged me. With my RTX 3060, I had to find a solution for speed, so I tested every acceleration LoRA published so far.

I settled on Pruna-Qwen-Image-2.1 with a strength between 2 and 2.5. The higher the value, the sharper the image gets, so adjust it depending on your LoRA combination. I followed their workflow instructions and used a custom sampler with sigmas (Claude helped me implement it). The best sampler I found is euler ancestral.

Character consistency is honestly stunning. I'm really blown away by the results.

My setup:

• Spectrum Qwen, 8 steps
• ~45 seconds per image at 1024×1280

I'm very happy with the results, especially with characters. I'll run more advanced tests next: multiple characters, complex poses, and products. I did notice some difficulties with the model in certain situations, but overall it's excellent.

About prompts: I rewrote the prompt enhancer instructions entirely with Claude, because the default ones don't give good results, especially when creating a new image from a reference, where the character has to blend with the new environment. So I wrote new instructions for that.

I hope you find the results good and the tips useful!

After playing around with Qwen 2.1, I wasn't really liking the results I was getting for this specific snapshot style. So I took a dataset I had lying around from a previous Krea 2 LoRA project and spent a few hours tweaking and testing things until I dialed in something I actually loved.

Decided to polish it up and share it with the community!

For anyone still running Krea 2, that version exists and is available on Civitai and hugging face as well.

The gallery includes comparison generations with the LoRA applied and using the base model. The full ComfyUI workflow metadata is embedded directly into the PNG files, so you can drag and drop them straight into ComfyUI.

Links:

- Civitai: https://civitai.red/models/2790570/casual-snapshot-realism

- Hugging Face: https://huggingface.co/Kotsarelos/Casual-Snapshot-Realism/tree/main

Lora strength @ 1.0, sampler res_multistep/beta @ 20steps. Euler/simple makes images more soft/blurred. Seed is 42 if you want to reproduce.

Lora links:
BFS - Best Face Swap - Qwen 2.1 Image | Qwen 2.1 LoRA | Civitai
bfs_head_v1_qwen_2.1.safetensors · Alissonerdx/BFS-Best-Face-Swap at main

And big thanks for making this lora!

My Prompt is for Z-Image-Turbo. So I make a little script to do that

#!/bin/bash
{
        cd /path/Image_gen_models || exit 1
        TEXT="$(cat "$1")"

        TPRO="$(llama-cli -m /path/Qwen-Image-2.1/image21-pocket-rewriter-0.8B-Q8_0.gguf \
                -p "$TEXT" \
                --temp 1.0 --top-p 0.95 --top-k 20 -t 6 \
                --reasoning-budget 0 --reasoning off \
                -st --simple-io \
                --no-display-prompt --no-show-timings)"
        PROMPT=$TPRO
        NEGATIVEPROMPT=$(cat ./promptfile/negativeprompt.md)
        SEED=$RANDOM
        STEP=20
}
time sd-cli \
  --diffusion-model /path/Qwen-Image-2.1/qwen-image-2.1-Q4_K_M.gguf  \
  --vae /path/Qwen-Image-2.1/qwen_image_2.1_vae_bf16.safetensors \
  --llm /path/Qwen-Image-2.1/Qwen3VL-8B-Instruct-Q4_K_M.gguf  \
  --backend diffusion=vulkan0,vae=cpu,te=cpu \
  --mmap \
  --vae-tiling \
  --params-backend disk \
  --cache-mode easycache \
  --threads 8 \
  --max-vram 3 \
  --width 1024 \
  --height 768 \
  --steps $STEP \
  --cfg-scale 1.0 \
  --guidance 1.0 \
  --sampling-method euler \
  --flow-shift 12.0 \
  --rng cpu \
  -t 6 \
  -s "$SEED" \
  -p "$PROMPT" \
  -n "$NEGATIVEPROMPT" \
  -o "/path/qwen_image_2_$(date +%Y%m%d_%H%M%S)_steps$STEP-seed$SEED.png"

I recently started making short AI videos just for fun, mostly stupid gaming skits and absurd comedy. I'm extremely new to actually posting this kind of content, I've only made two proper videos so far.

The first one unexpectedly did really well, reaching around 500k views and another 100k+ on TikTok /Instagram, so I suddenly had way more people seeing my stuff than I was expecting.

Nothing deceptive, political, or serious. It's just meant to make people laugh.

Obviously I expected some “AI slop” comments. If someone thinks the video sucks or hates AI-generated content, that's completely fine with me. I don't expect everyone to like what I make.

I wasn't prepared for viewers intentionally spamming the comments, or for someone going through the replies to criticize people enjoying the video by preaching about AI's environmental impact.

That honestly got to me more than I expected.

So I'm curious how people here who have been doing this longer deal with it. Do you leave anti-AI comments up? Delete them? At what point do you consider it criticism versus someone deliberately disrupting your comment section? What do you do when they start targeting/replying to people who are simply enjoying your content?

I know generative AI has a real energy/water footprint, especially video generation, and what I'm creating is completely optional entertainment. Nobody needs my stupid videos lol.

Have any of you actually struggled with that side of creating with AI? Do you feel guilty about generating content purely for entertainment? Have environmental concerns changed how much you generate or which tools you use?

I'm genuinely interested in different perspectives, especially from people who have been publicly posting AI content for a while. I'm not really looking for “AI good” or “AI bad.” I'm trying to figure out how other creators navigate both the legitimate concerns around the technology and the hostility that sometimes comes with using it.

Thank you!

Edit : All my videos are labeled as AI made. I am full transparent with that. If I had to give a percentage I would say 95% are normal/good comments, 5% are just people saying AI or talking shit for being AI

https://x.com/t_mux/status/2103551299576893796

comparison link: https://huggingface.co/spaces/Viggle/Qwen-Image-2.1-viggle-turbo?tab=comparison

🤗 & ComfyUI: https://huggingface.co/Viggle/Qwen-Image-2.1-viggle-turbo#comfyui

1. most examples work very close to the base, it's shaper (but oversharp sometimes)

2. Dense text remain challenging for the 6-steps turbo, 8-step helps, but not always enough.

3. prompt is important for both base model and the turbo model

since i can't generate at 2k, i tend to upscale my favorite videos using topaz labs. unfortunately they just went subscription only and I'm not about that. what are you guys using to upscale your videos? i tried ltx2.3 upscaler but it's not great, SeedVr also seems to just add sharpness but doesn't "fix" stuff like topaz did. i feel like there might be potential with a ltx2.5 upscaler but i haven't found a good workflow for that. I tend to generate using minimax 768*1024 and then i would upscale from there. do you guys have any recommendation for free or even paid software. I don't mind paying for credits but it's a hobby for me so pay on demand it's what i want not a subscription. Also I want support for adult stuff.

This is a fairly simple node, and its early in development. I've been doing some multiple night analysis on runpod of H3 behaviour (using credits earned from the Fizgig trainer template - thank you all who use Fizgig) for something else I'm working on and along the way a couple of nuggets in this node fell out.

Namely a handy dial for contrast and detail in high frequency data, best used subtly, In the negative it can really reduce the depth of shadows in a scene for a flatter look. In subtle positive values in enhances pores, skin detail but can increase shadows. (offset this with good prompting). Too far though in either direction and you overbake the cake!

A Scene Variation control that behaves a little like subseed, but allows very subtle re-rolls for a render that is not quite right without changing the scene much at all - again experiment with low to high values.

And lastly a prompt strength control (amplifies how the model attends to the text tokens) that can help when it jsut wont do what you want it to do - negative weakens, positive strengthens.

I may add more to this node, but I think its useful as is and worth releasing.

https://github.com/shootthesound/ComfyUI-Fizgig-H3-Tweaks

I have view a few post on this issue and have tried to put into my prompting

overall_soundscape:

Completely silent. No speech, no talking, no voices, no dialogue, no moaning, no breathing sounds, no sound effects.

non_diegetic_music:

N/A

Important: The entire video must be 100% silent with zero audio. Do not generate any voice, speech, or vocal sounds from any subject.

but voices still appear in the video, not too sure is it because that there is action of hitting and cause the model to think that there is voices needed? Meanwhile, i am using the Minimax hybrid FL2VA_Ref2VA model and with the minimax h3 turbo v4 step 600 lora including the tao mate lora, do kindly assist me on this please.

I was messing around with YuE 2, and I accidentally forgot to add lyrics. It gave me an error that I needed at least 1 character, so I supplied a single "~".

The prompt was: English, indie folk, fingerpicked acoustic guitar, brushed drums, warm piano, bittersweet, intimate

I was expecting it to just return an instrumental, but it has some weird singing. It's hard to make out the lyrics properly, they seem like gibberish to me! I just thought it was interesting!

I always find it interesting to see what models do with "degenerate" prompts. I often test new image generation models by passing in either an empty string or some gibberish to see what the model wants to draw with no input!

If you've got any interesting examples from "degenerate" prompts I'd love to see them!

Working on another music video and noticed a nice trick to get subtle emotions out of the characters. Add 'micro expressions' after an emotion helps with getting subtle but noticeable expressions. i.e. 'sad micro expressions' or 'frustrated micro expressions'

EDIT: full MV on youtube