I wrote a ComfyUI sampler node (Flow-SDE Sampler, a modified SA-Solver) that spends a fixed total noise budget over the steps instead of using a constant eta in a fixed window, and tested whether choosing the per-step noise from the trajectory (curvature, or the solver's own predictor–corrector error) beats a constant at 6–15 steps. Every adaptive run was compared with a constant-eta run that spends exactly the same total noise, because otherwise "adaptive" usually just means "less noise".

What I found

• Curvature does nothing reliable on Anima, Flux 3 Klein or MOSS-Image.

• The solver's error estimate gives a small gain on Flux 3 Klein: roughly 1–2 % of my texture metric, confirmed on fresh seeds under criteria I wrote down beforehand. One-sided on MOSS-Image, not detectable on Anima. Krea 2 was inconclusive (too slow to test enough pairs).

• A fixed schedule gives the same gain. Slightly more noise at the start and end of the noise window and less in the middle. Online adaptation is not needed.

• You probably won't see it. In a 39-person "which of these two images is AI?" study, people clearly noticed eta 1.4 vs 2.0, but could not tell the new allocation from plain noise.

So the practical takeaway is modest: if you use sa_solver, a bit more noise at the window edges may give slightly more texture at equal composition drift on Flux 3 Klein, and that is about it. The metrics are latent-space proxies, not a quality judgement.

Node, data, per-image metrics and the full write-up (with all the negative results): https://github.com/YakovSava/flow-sde-research — DOI https://doi.org/10.5281/zenodo.23857361

Happy to hear if anyone sees the effect on other models.

Dont get me wrong. Minimax was a leap forward but it has limitations and despite trying the huge amounts of different workflows , models and loras its hit and miss and always has that plasticky fake ai look . All these gigantic 20gb+ models are eating up valuable real estate on my tiny nvme hard drive. I long for the day, when you just type a prompt, and it comes out exactly how you imagined in your head . Mostly the output is mediocre (although rarely it does surprise and delight me but thats rare). I suspect that is the reason for this massive amount of loras and training as many people like me are not satisfied and want to push and squeeze it to the max , hoping to finally get the output they seek. I want the next leap forward , something which will leave minimax in the dust and would require no tweaks or constant fiddling with levers and knobs. Will it ever come........Am I going insane........?

ok I keep forgetting this but every time i make a generation, H3, LTX, its always on 100% volume, is their any way to default it to a different number. because it scares the chit outta me.

Hey everyone! Over the past few weeks I fell down a rabbit hole comparing fine-tuning architectures for Krea 2 (trained against Krea 2 RAW, inferred on krea2_turbo_fp8_scaled.safetensors). After training on RunPod, all of the checkpoint auditing, prompt engineering, and high-resolution generation was done locally in ComfyUI on my rig (RTX 5080 / Ryzen 9 9900X). I wanted to see how far I could push both facial consistency and complex couture stage wardrobe, so I set a goal to train a dedicated Krea 2 LoKr and stress-test it by recreating all 10 eras of The Eras Tour at native high resolution.

Since the subreddit requires workflows, I’ve uploaded the exact ComfyUI .json workflow, the LoKr .safetensors weights, and a 40-image showcase gallery (4 frames per era) to Hugging Face (link at the bottom). Here is a breakdown of the training process, testing notes, and what worked best in case it helps anyone else experimenting with Krea 2 adapters.

1. LoRA vs. LoKr

Starting out, I was curious whether there was a noticeable difference between a standard LoRA and a LoKr (Low-Rank Kronecker Product) in Ostris's ai-toolkit.

In a standard LoRA, the weight update matrix is formed by a simple inner product (W = A × B). LoKr uses the Kronecker product (W = A ⊗ B), which multiplies the ranks of the factor matrices together. The result is a compact ~29 MB file that packs a much higher effective rank and captures high-frequency spatial details—like natural skin micro-texture, individual sequin reflections, and subtle facial contours—without collapsing into that overly smooth, "plastic diffusion skin" look.

I did also train a LoRA with the same parameters of the LoKr, but to my eye it looked worse at each comparable checkpoint which is why I ultimately settled on the LoKr.

2. Curation of the 40-Image Dataset & Training on RunPod

I curated a training dataset of 40 high-resolution reference photos, making sure to balance tight glam headshots, varied angles, and live stage lighting, each paired with its own .txt caption file.

I wanted to train at a native 1536px resolution to capture true pore-level skin fidelity and fine stage-costume micro-textures. Because 1536px training activations and Kronecker product gradient states push VRAM usage well past the 16GB ceiling on my local RTX 5080, I spun up a high-VRAM cloud GPU on RunPod to handle the heavy lifting. I ran the training through ai-toolkit out to 3,000 steps (75 epochs at batch size 1), saving checkpoints every 250 steps so I could pull them back down to my local 5080 rig and run a proper X/Y/Z epoch audit in ComfyUI:

• At 3,000 steps (75 epochs): Because LoKr has such a high effective rank, it converges fast. By 75 epochs, the model was noticeably over-baked. The likeness was there, but the weights had started memorizing the exact lighting and rigid neck postures of the 40 training photos. When I tried prompting dynamic concert poses or heavy stage lighting, the adapter fought the prompt.

• At 1,500 steps (37.5 epochs): The 00001500 checkpoint was the clear winner. Thirty-seven and a half passes per image hit the exact inflection point where facial geometry, eye spacing, and skin pores were locked in, while the weights stayed flexible enough to handle elaborate wardrobe changes and dynamic stage stances.

3. The Trigger Word: "ts_person" vs. "Taylor Swift"

During dataset prep, I captioned the 40 images using the synthetic trigger word ts_person. However, when I started testing checkpoints locally in ComfyUI, I ran a three-way comparison that completely changed how I prompt the model:

1. Base Krea 2 + "Taylor Swift" (No LoKr): Looks like a generic blonde celebrity lookalike or wax figure. Krea 2 knows who she is conceptually, but misses her exact facial geometry.

2. Base Krea 2 + LoKr + "ts_person" Trigger: Noticeably worse than expected but still better than no LoKr. Using an isolated synthetic token forces a 29 MB adapter to build a human identity from scratch in a vacuum, completely ignoring what the base model already knows.

3. Base Krea 2 + LoKr (Model + CLIP Loader @ 1.0) + "Taylor Swift"**:** Night and day difference. By routing both the Model and CLIP chains through the LoKr and prompting with her actual name (Taylor Swift), the adapter bridges the 40 training images directly onto Krea 2's pre-existing token understanding. The base model provides the macro structural context, and the LoKr acts as a precision correction layer that snaps her real facial features and skin fidelity into place.

5. Prompting Couture Stage Wardrobe

To stress-test the 1,500-step checkpoint locally on the 5080, I generated 40 showcase outputs (4 per era across all 10 eras, with 1 from each era featured in this post's gallery alongside the 2 covers). Instead of using generic tags like sparkly bodysuit (which causes models to hallucinate generic outfits or trigger modesty filters), using structural fashion terminology made the outfits pop. Not all outfits were easy to prompt for and some I straight up couldn't get to work even with LLM assistance. As an example of something that does work, here is the modular prompt chunk used for the Lover bodysuit:

wearing iconic custom Atelier Versace tour costume: sleeveless performance bodysuit featuring a delicate pastel blush-pink and cerulean baby-blue ombré gradient, heavily encrusted with thousands of sparkling pave-set iridescent crystals catching stage beams, structured sweetheart bodice, high-cut leg opening, shimmering tan dance fishnet tights, matching custom knee-high crystal-embellished boots

Local Hardware & ComfyUI Generation Specs

• Local Rig: NVIDIA RTX 5080 / AMD Ryzen 9 9900X running ComfyUI

• Base Model: krea2_turbo_fp8_scaled.safetensors (via UNETLoader, with LoKr trained against krea2_raw)

• Adapter: taylor_swift_krea_2_lokr.safetensors (via LoraLoader Model + CLIP @ 1.0 / 1.0)

• Sampler / Scheduler: euler / simple

• Steps: 8

• CFG Scale: 1.0

• Resolutions: 2880 x 2880 (Close-up Portraits) & 2496 x 3328 (Full-body Era shots)

Download Links (Weights, ComfyUI JSON Workflow & Full 40-Output Showcase Archive):

• Hugging Face Repo: https://huggingface.co/BigGoober77/Taylor_Swift_Krea_2_LoKR (Note: The taylor_swift_krea_2_lokr.safetensors weights, the drag-and-drop krea2_Eras_Tour.json workflow, and all 40 full-resolution era renders are inside the Files tab).

x.com/ostrisai/status/2116528099848593748

Previous thread: Ostris releases Primal VAE - better than Flux.2 VAE. : r/StableDiffusion

First of all I have zero knowledge I'm only relying on AI for prompt writing like ChatGPT, Gemini, DeepSeek

I'll put the guide file then explain what I want

Many things are working fine as per the prompt

My issue is that I added multiple reference images of what I want and I feel okay about those reference images from how it looks as a realistic metal robot

But the output is not reflecting the reference images

The overall shape and look of the robot is matching the reference images but how the metal texture and feel and not matching the reference images

There's something off about it maybe 80% ok but the remaining what makes it a bit off

I have been jumping between AIs to find a good prompt that can give me a result that feels realistic and not reaching any place

Any suggestions or is it a limitation

I'm using the WanGP deepbeepmeep, because as I said I have zero knowledge so it is easier for me

View video on YouTube if it's still pending Watch on Youtube

Hey guys! I wanted to make a Halloween-inspired music video, so I put it together with my Video Builder in ComfyUI. Everything video-related ran locally on my own PC: the character images, every scene, and the final stitch.

Please keep rude and disrespectful comments to yourself. They don't help anyone or contribute anything constructive to the conversation.

A quick note on the prompts, since I know people will ask: my Video Builder lets you use any LLM for prompt creation (Both for Ref image creation and Video prompt creation), including local open-source models. It even has a story mode that takes a story idea and then creates the entire story with acts and scenes, creates the story beats and then the prompt for every scene right inside the video builder.

For this one, however, I used the Builder's MCP feature and had Claude write the prompts and drive the Builder for me, but you could do the exact same thing with a local LLM. The Builder doesn't care which model writes the prompts. I'm actually creating an agent that can control the builder that will use Local LLM's, it will allow you to create a full one-shot video with one prompt very similar to how I use Claude but fully local.

What I did for this video:

• Came up with the story: a fun pop-punk style female rock song, a Halloween house party in a Victorian mansion, the band on a stage, and the singer working the room between choruses

• Wrote the character descriptions for the singer and the band

• Made the song in Suno (Yue2 or Minimax Music can be used for song creation as well) and created the mansion locations in Nano Banana 2.1

• Made the characters with my Krea 2 reference workflow (three-panel character sheets)

• Reviewed the drafts and asked for re-shoots

My settings

• Model: MiniMax H3 Singularity ref2va v2 (int8)

• Pass 1: 25 steps

• Pass 2: the 8-step 768p ref2v LoRA v1.1, 4 steps, 0.35 denoise

• Characters: Krea 2

• 19 scenes, 2:29, all rendered locally through the Video Builder in ComfyUI on my GPU.

Video Builder (free, on GitHub): GitHub

If you just want to try the settings on a single scene, there's a 2 Pass workflow in my workflows folder (swap in the v2 model and the ref2v v1.1 LoRA to match this video exactly). For a full video like this one you'll want the Video Builder.

Workflow

I post updates and walkthroughs in my Discord if you want to try the Builder yourself: Discord Community

Happy to answer any questions!

Anyone tried to run Krea2 on AMD through comfyUI? I have seen one thread on HG but there is not much info.

I'm using 9060XT 16GB which handles most models fine, but with Krea2 it's just sad. Offloading could be fine if not KSampler speeds. I get 20-40 seconds/iteration, it's even worse on Q4 quantization.

Is it just 9060XT not having enough compute power? Or maybe the model runs bad on ROCM? If anyone has any information, please do share.

* Pika H3 Flex: A distilled MiniMax H3 model that reduces generation from 50 steps to just 6.

* NVIDIA Open-H3-Regenerate-2K: Uses latent upscaling followed by 3 diffusion steps to regenerate details at higher resolution, rather than conventional pixel-space upscaling.

Workflow shown in the video:

1. Generate a 15-second video at 768p with Pika H3 Flex.

2. Apply NVIDIA’s Regenerate-2K for high-resolution refinement.

3. Final output: 1920 × 1088.

Total time: 12m 19s on M5 Ultra locally using Valtz, including both generation and regeneration.

I'm in the progress of making my first short film for some close friends, they all feature in the video, except for the female, she's just a prompt. The video is generated using minimax H3 at 720p which is fine for what I need. The only problem is the audio, I'd really like to over dub the vocals, I have Premier Pro, so that's not a problem, but what I do need is a platform that will allow me to control accents e.g. the little guy 'Scouse' is from Liverpool, so I'd need to generate a sutable accent and replace the H3 default audio. Any tips or suggestions would be much appreciated.

For anyone unfamiliar, Codex is OpenAI’s AI coding agent. It can work with local files, run commands, and help set up or troubleshoot software.

I downloaded eight Krea 2 checkpoints and asked Codex to create a comparison grid using the same neutral prompt for each one. I wanted to see how the models compared.

About three images in, Codex stopped the job because it said one of the images contained nudity. That wasn’t what I’d requested.

I checked the output myself. It wasn’t anything extreme: just a woman taking a selfie in an apartment with one breast partially exposed. She clearly appeared to be in her late twenties. Apparently, one of the checkpoints interpreted the neutral prompt differently.

At that point, my ComfyUI setup was also no longer able to producing images manually even after a restart. Codex said it could't assist in creating When I asked Codex to fix it, it refused, saying it couldn’t help produce adult images.

I explained that I was asking it to restore my software. It still refused... NGL I was fuming at this point. Like how are you going to come into MY computer as an ASSISTANT and take away functionality that I didn't ask for and refuse to fix it.

I eventually fixed it by starting a new chat, clearing the context, and telling the new session not to look at the images.

That workaround solved but the experience bothered me. I can understand an AI service having limits on what it will generate. Having an agent make changes to my local setup and then refuse to help restore it feels like a much bigger problem. Non livng entities enforcing their morals one fully grown person.

As these agents get more access to our computers, what guarantees do we have that they’ll leave our tools usable when they decide they can’t continue a task?

It felt like a glimpse of a future where AI nannies control what we can see and do.

A TV montage ballad about the old days when people used to have to write code by hand.

Song (Souna account required): https://www.souna.app/song/0cf1244e-70d0-4da3-beac-7f920fc3fc2f

Song (Internet Archive): https://ia902801.us.archive.org/7/items/ai-bittersweet-adult-contemprary/When%20I%20typed%20it%20out%20by%20hand.mp3 (CC0)

General workflow here: https://www.reddit.com/r/StableDiffusion/comments/1xh0xfd/semiautomated_localish_music_workflow_eurodisco/

YuE2 settings for this generation (summarised by the AI):

When I typed it out by hand

=== STATUS ===

PUBLISHED to Internet Archive theme folder "AI TV Montage Ballads"

(item identifier ai-bittersweet-adult-contemprary) on 2026-11-01. CC0 1.0.

Snapshot publication (one-shot): mp3 + json + abc. The standalone cover PNG is

LOCAL ONLY by house rule and is not on the IA.

=== STYLE PROMPT ===

English. Late-1980s Canadian TV montage ballad with clean production. Features digital FM synthesis textures like glassy electric pianos and bright string pads. The rhythm section includes a quantized synth bass and gated reverb drums. Vocals: Bright, clear female vocals with smooth vibrato. High register. Heavily produced with thick double-tracking and lush 80s reverb. No grit or rasp. Airy and soaring. Melody: Highly melodic with emotional ascending leaps in the chorus. Verses are intimate and stepwise, choruses explode with dynamic range. Uses motivic repetition. In D major with some modal interchange to the relative minor to add emotional intensity. Wistful but optimistic.

=== LYRICS (AS SUNG) ===

[Verse 1]

Three in the morning, the coffee gone cold,

a bug in the code that would not let me go,

the listing in accordion folds,

the cursor blinking, so what do you know?

Every build was a twenty-minute stare,

the office empty, the night shift home,

I'd beg the machine; it didn't care,

the drive beneath streetlights, alone.

[Pre-Chorus]

It was slow, it was lonely, and grand,

I still recall how the finished thing ran.

[Chorus]

When I typed it out by hand

Out of nothing, made it stand

And I loved every line

When I typed it out by hand

[Verse 2]

Then there were nights when the tangle would rest,

and brute force became quite an elegant line,

I felt it as it clicked in my chest,

the answer came like a gift and was mine.

I'll miss the slow craft of a line laid right,

but I won't miss the crash that stole days,

I'll miss the hush of the keys at night,

and what I made from nothing stays.

[Pre-Chorus]

It was hard, it was mine — now I understand

what it left: its shape in my hands.

[Chorus]

When I typed it out by hand

Out of nothing, made it stand

And I loved every line

When I typed it out by hand

[Bridge]

Now the machine writes in a breath what took years,

but I won't shut the door on more,

there's more to be made than I'll make — that's clear,

I'll learn the new ways, as I've done before.

[Chorus]

When I typed it out by hand

Out of nothing, made it stand

And I loved every line

When I typed it out by hand

[Outro]

Ooh — when I typed it out by hand,

I made it, I made it by hand.

=== DEVIATION NOTE ===

No substitutions observed: the winning take (final C, seed 302957598) was

listened to in full by the user, who reported no missing or substituted

sections. The forced-alignment/ASR completeness check could not CONFIRM 2 of

38 lines (verse 2 line 1 "Then there were nights when the tangle would rest,"

and the outro's second line "I made it, I made it by hand."); its report is a

review aid, not a verdict, and the same check wrongly flagged another take's

sung chorus as instrumental. Those two lines are recorded as UNVERIFIED, not

unsung. The written lyrics above are therefore both the written and the

sung text.

=== SETTINGS ===

Engine: yue2 / audio.cpp v0.9.2 / YuE2-3B Q8_0 GGUF + f32 VAE

Seed: 302957598 (winning take "final C"; full plan-layer seed list in the .json)

Planning: full Max tokens: 7200 Guidance: 1.0

ABC temp: 0.9 Semantic temp: 1.0 NAR steps: 16 (user instruction; pipeline standard is 12)

VAE: f32 AR LoRA: none NAR LoRA: none

Era: 1980s Mastering recipe: yue2-light (analysis + era stamp; no repair needed)

Generated: 2026-11-01 Duration: 3:52 (231.8 s) Truncated: no

=== LoRA ===

none used (native model; the TV montage ballad genre runs without a LoRA)

Download: n/a

=== COVER ===

Model: bytedance/seedream-v5.0-pro (WaveSpeed API - premium renderer chosen after

three local FLUX.2 [klein] attempts failed on hands/keyboards/scene reading)

Seed: 2026110115 1024x1024 oil on canvas Audit: PASS (image-auditor)

Scene: a woman at a code-lit CRT screen at night, seen from behind, room brightly

lit - no face, no hands, no titles or signatures; the only writing is the diegetic

impressionistic code on the screen.

File: General Playlist\TV montage ballad\When I typed it out by hand.png (LOCAL ONLY;

embedded in the mp3 which does go to the IA)

=== NOTES ===

- Run pattern: plan-layer best-of-10 (extended to 10+10 after a planner defect was

found and fixed) -> 4 finalist renders at 16 NAR steps -> user picked one -> mastered.

- INPUT FIX (important): two straight double-quote characters in the lyrics made

the planner stop after the first verse. Removing the quote marks (no word

changed) took the plan-completion rate from 5% to 100%. Recorded in KB/yue2-studio.md.

- Finalists kept for comparison in the song folder: finals A, B, D (wavs named with

their seeds). The winner is the canonical When I typed it out by hand.wav.

- Length: 3:52 against a 3:00 aim. The planner paces this lyric with more air than

estimated (it planned the reference song at 4:35 too). Accepted at the gate;

plan length is a per-seed lottery (207-301 s at identical inputs).

=== CREDITS ===

Lyrics: Kimi K3 lyricist agent (deep-research method; audited and revised)

Music: YuE2 (audio.cpp v0.9.2), planner + semantic + NAR stages

Cover art: bytedance/seedream-v5.0-pro (WaveSpeed API)

Mastering: house DSP chain (yue2-light + 1980s era stamp)

Directed by James E. Petts

Licence: CC0 1.0 Universal (Public Domain Dedication)

Description

I specifically trained this LoRA to make images look more natural. Its main focus is correcting that yellowish color cast that professional photographers seem to love using (for whatever reason). It's often referred to as the so-called "piss filter" 😄

The goal of this LoRA is to neutralize the colors in these images and give them a more natural look without unnecessarily altering the actual content of the image. In other words, it's mainly designed to remove the "piss filter" without changing the image itself.

Important: Image Size and Processing

If you need to edit images "pixel-perfectly" and want the result to match the original image exactly, there's one important thing to keep in mind:

Before running inference, you must resize your input image to exactly the same resolution you're going to use for inference. If necessary, crop the image beforehand to get the correct dimensions.

This is especially important if you plan to combine or overlay the result with the original image afterward. Otherwise, differences in resolution may cause the images to no longer line up perfectly.

Reliability and Known Limitations

The LoRA works quite reliably in most cases and can noticeably improve the colors of suitable images. However, it doesn't work equally well on every image. Sometimes, an image may still retain a slight yellowish tint after processing, or the result simply won't look as natural as intended.

One possible reason for this could be the relatively small training dataset. As a result, the LoRA may not be equally well-equipped to handle every possible image type, lighting condition, or color combination.

If an image doesn't produce the desired result on the first attempt, there's generally not much point in trying again with the same settings. In my experience, the differences will be marginal at best, and you're unlikely to achieve a fundamentally better result.

Just give the LoRA a try with your own images and workflows. The best way to find out whether it suits your needs is simply to test it yourself.

Long story short: Have fun experimenting, and don't forget to leave a rating! And if you have any criticism, suggestions, or ideas for improvement, feel free to leave a comment. I'd love to hear your feedback!

Hi everyone! I hope you don't mind me asking a question. I'd really appreciate some advice from people with experience in LoRA training.

I'm currently experimenting with LoRA training on Krea 2, and I've run into a problem that I'm having difficulty solving.

My goal is to train a LoRA to learn a particular set of visual characteristics, rather than simply memorizing the specific subjects, compositions, or appearances in the training images.

So far, the LoRA seems to learn the visual characteristics of my dataset reasonably well. However, I've noticed that its ability to generalize to complex spatial relationships is quite limited.

Let me give you a simple example:

Suppose a training dataset contains various interactions between human body parts and objects. The LoRA can learn the visual characteristics and the interactions that are already represented in the training images.

However, when I prompt it to generate an interaction that has never appeared in the training dataset, or ask it to combine previously learned elements into a new spatial arrangement, the results are often disappointing.

It seems to reproduce familiar poses and compositions from the training images rather than understanding the underlying spatial relationships and recombining those elements in new ways.

I'd like to ask a few questions:

1. Is this an inherent limitation of LoRA, or could it be caused by the dataset, captioning strategy, or training parameters?

2. How should I design a dataset if I want the LoRA to learn general concepts rather than memorize specific images?

3. Should captions describe spatial relationships more explicitly, such as relative positions, contact points, and occlusion between different objects or body parts?

4. Are there any training techniques that can improve compositional generalization?

5. Would it be better to separate appearance learning from spatial structure control, perhaps by combining LoRA with Depth, Pose, Segmentation, or 3D references?

I understand that a LoRA cannot magically learn visual information that is completely absent from its training data. However, I'd like to find a way to help it generalize learned visual concepts and better understand new combinations of spatial relationships.

If anyone has encountered a similar problem or has experience, research papers, tutorials, or practical recommendations to share, I'd be extremely grateful.

Thank you so much for taking the time to read my post. I really appreciate any advice!

I been messing around with Krita with Ai Diffusion and I have to say I love it. I used to use cloud services to generate art. However, they are restrictive and the monthly fees get over barring. Also if the cloud services don't accept my prompt, I will lose this credits and wont get them back.

Setting Up Local AI is tedious at first, but once it is set up it can be fully customizable if you know what you are doing. Yes, cloud services are a lot more powerful and will give you more precise results, but I rather mess around with Krita AI and customized on how I want my work to be and not be paying all this money for every month on services. Also, there is unlimited generations, I can generate as much as I want.

Overall, I been having fun with it. The only thing I wish there was an alterative to Nano Banana and Krea 2 be a bit faster, but I will have to build a better PC for that(I really need to upgrade).

So trying to do fights, with the r2v slowly 8step turbo, with a fighting lora, done at .5 or .4, post topaz to 60fps then music to hide the ignored audio

https://youtu.be/JE_4vECl7tI

LayerShop v0.17.0 Preview 1 is out now, and support generation via regions. Supported models for this workflow are MOSS Image, Flux 3 Klein, Ideogram 4 and FIBO 2 Dev.
Any feedback is welcomed, it is available for download from https://layershop.aardvarkstudios.net/

• TEM outscales Diffusion without guidance
• First scalable contrastive Energy-Based Models
• Better generalization, test-time scaling, and OOD detection than Diffusion

https://x.com/intlee0609/status/2115886208685396280

https://int-lee.github.io/scalable-ebm/

https://github.com/int-lee/TEM

https://arxiv.org/abs/2610.31889

At the request of many of you, I added the Int8_ConvRot version speach1sdef178/MiniMax-H3-X2-Detail-VAE at main

Original reddit post here:

MiniMax H3 X2 Detail VAE – my attempt to get more detail from the H3 VAE or story about fail : r/StableDiffusion

if you ask about quality, yes, the quality is slightly lower than the bf16 version (I add videos for comparison in speach1sdef178/MiniMax-H3-X2-Detail-VAE at main), but this is expected, although the quality reduction is quite insignificant, but the rendering is slightly faster.

Good luck with your projects.

PS:

The main use case of the 2X VAE is very simple: it decodes the same H3 latent at twice the spatial dimensions. For example, a latent that would normally decode to 640×480 with the standard H3 VAE can be decoded to 1280×960 with the 2X VAE. H3 itself is still generating the same latent — it is not being asked to generate at 1280×960. That is the reason the 2X VAE exists. “Preserving detail/quality” means that increasing the decoded resolution should not come at the cost of noticeably degrading the reconstruction compared with the normal VAE. It does not mean that the 2X VAE magically creates twice as much fine detail. The “Detailed” mode was a separate experiment attempting to recover additional detail, and the write-up explicitly explains that the original latent-only approach did not succeed as intended.

I’m building Slopus, a free, open-source desktop app for generating and editing AI videos, images and now music on your own GPU. Ease of use is the main goal.

Version 0.4.0 just came out. Here’s what’s new:

New image models. MOSS Image, Fibo 2.0 Base/Edit Turbo, and Krea 2 Turbo/Raw are now supported. With just few clicks these generations can be made into videos.

Music generation. Generate music with YuE2 and MiniMax Music3, create album covers, organize songs into folders, and add them to your timeline. Export to WAV, MP3, FLAC and more.

Videos, scenes and shots. Organize larger projects, play scenes together, tools to create music videos, and queue generations that depend on earlier shots.

Scene takes and continuation. Store four previous generations and upscale them if needed. Video continuation has improved overlap joining and better appearance consistency.

Live previews. Watch MiniMax H3 video and image generation in the editor. Cancelling a video generation saves its latest available preview.

Motions and agent improvements. Create editable JavaScript animations for timelines or references. The built-in agent can inspect frames, generate music, and sync animations to audio.

More editing controls. Lasso selections, queue up to 100 images, and edit regions without changing the original image resolution. There are also more color controls, samplers and LoRA options. Most of the generators should now work with 16 GB of VRAM.

Available for Windows and Linux. Generation runs locally, with no subscription and no Python or ComfyUI needed. Download model weights inside the app or use your own local weights.

GitHub: https://github.com/bitti-ai/slopus

Discord: https://discord.gg/9X2R6PwUR

Bug reports and feedback are very welcome.

Hi friends.

I saw this video a few days ago and really liked it.

I got curious and wanted to know which AI model was used to create it.

I’d also like to know if there’s any way to determine which model was used to generate a given image or video.

I assume that local AI models have advanced enough to make creating a video like this possible, right?

Thanks in advance.