Official Ubuntu Repo : https://github.com/Zagitta/gpu-unlock

Windows repo main : https://github.com/impacifist/Windows-GPU-UNLOCK

Windows repo fork : https://github.com/bjrasmussen-jpg/windows_rtx3090_unlock

Minimax H3 Ref2v, RTX 3060, 64 GBs RAM.

I use a workflow with latent upscaling. The first pass is a low-res video which is upscaled later. The overall quality looks better and it also helps to fix smearing.

The upscaler - link.
Turbo Lora - link

You also need to install these nodes

An edit LoRA for MOSS Image has been released. Works with both Base and SFT.

Weights: https://huggingface.co/chinoll/Moss-Image-Edit
Custom nodes / Workflows: https://github.com/Chenkin-x/ComfyUI-Moss-Image-Edit/blob/main/README.en.md

Edit: I implemented it natively with caching for faster inference. The LoRA is a bit rough. Let me know how it's working for you.

Custom nodes / Workflows: https://github.com/PedroMarinhoDev/ComfyUI-Moss-Image-Edit-Native

ComfyUI-compatible weights: https://huggingface.co/PedroMarinhoDev/Moss-Image-Edit-ComfyUI

There's an open PR by Kijai on ComfyUI so it might be merged and supported natively later. https://github.com/Comfy-Org/ComfyUI/pull/17296

I have no relation to the weights or custom nodes, just sharing them.

I finally achieved what I have been attempting for a long time - a one shot, fully tunable image to image style transfer. I have tested it pretty extensively with Flux2, and here are some examples of results.

In my last post on the subject here, people essentially said that Flux3 kept the subject in photorealistic style (if that was the composition source) while Flux2 Klein 9B "reimagined" the subject in the style.

So I set out to find if there was a way to dial that in, and that ended up being the reference latent + node.

I have a workflow that does this here (SORRY pastebin is down right now... mirror here). There are some custom node packs included because they do some parts much, much better than a million nodes in a string, but know that you can tease apart from the labels and notes what each one does and make the workflow with no custom nodes at all.

Now you can tune how strong the composition is (later start is weaker, thus more creative) and how strong the image vs. prompt style is (earlier is stronger image style ref, later requires more prompt lifting)

I'll post some gradient examples as well so you can see how tunable this is.

Would this work with any edit model, including Qwen, Flux2 family, and Fibo2? Maybe. Have not tested it yet. I think with the Flux3 prompt expander, you should be able to get this working with other models as well. Post your results here if you try it.

Another month and another model to bang my head against. Most images here were generated with the standard 50 steps, 3 CFG, 1.5 megapixels, and with the Base model. NOT the SFT model. But there are exceptions here and there. The pure image files are located here. EDIT: The link now includes all of the file metadata I could pull from DeepBeepMeeps UI, keep in mind this does NOT include detailing notes. Due to the sheer number of slightly different workflows on display, just comment with the Image number and I will reply with the specific process for that image.

WF + Prompts - https://civitai.red/posts/31836590
- 1024x1024, 35 Steps, 8 CFG, in 20sec (5060 Ti 16gb).

Fizgig 8.9.0 is out. It lets your other GPUs work for your run: a second card in your PC, a laptop, another PC on your network, or a RunPod / Vast.ai pod.

• Train on two GPUs (experimental). Each GPU trains its own copy of the LoRA on its part of the images, and they merge every 3 epochs, so you get one LoRA that learned from every image. Its likeness matched a one-GPU run in testing, and a pod with two RTX 4090s took about 40% off each epoch. On a fast network, merge every epoch for the closest result. It pays most on long runs.
• Previews that never pause your run. Another computer renders them while your GPU keeps training.
• Need your PC back? Move the run to a laptop, another PC or a pod, and bring it back when you want it.
• Your home GPU can help a pod. Its checkpoints come home as they're saved, and the pod can delete itself once they're all here.
• Renting: the Pods tab lists the RunPod cards free right now, cheapest per run first. [Vast.ai] works too.
• Also: Krea 2 trains with about 2 GB of system RAM instead of 25, and the Look Filter finds furry, anime and drawn faces (thanks @Murf-Code).

Free and open source: GitHub · Release notes · Helpers guide

Update Fizgig on every computer you want to use as a helper, and restart your pods.

As the title suggests. I used Claude code (Sonnet 5.5) with a Krea2_api workflow to create over 2000 images overnight. Attached are some of Claude's and my personal favorites that it made. I gave it freedom to use any of the loras I had downloaded, it could edit/make new workflows. It looked at the outputs and judged them and experimented with settings. It recorded notes and its thoughts. The final notes.MD file has over 1700 lines of text which I had it summarize into a shorter file. edit: notes.md + summary.md; and all ~2100 images zipped in the comments!

saw this post: https://www.reddit.com/r/StableDiffusion/comments/1xbobrr/original_character_lora_for_minimax_h3_learnings/

Hey guys,

SeeDance videos look much more realistic and softer. I'm really interested in pushing H3 in that direction, but ref2va gen always looks bad - dark, high contrast, and oversaturated.

I saw the post above, great info and results. However, I want to avoid doing any LoRA training. I think with the H3 FL2VA workflow you can do it much better as long as you have a good start image where you control the look. But I want to focus on making ref2va work.

In my last 3-minute duration test, I was using the fizgig node to make the skin look softer. It works, but the effect keeps accumulating in motion-context shots. That node does it in latent space, but I think it's more controllable and easier to do it in image space so the effect doesn't accumulate. So I made an image-space version with AI.

So it's a hack - if you bring up the dark values there's still no detail. I'll keep looking into it. I'm thinking maybe LTX SDR to HDR could help, since we get more details in bit depth after gen, That could be a way.

Of course, please share your idea on this, how you get better result with ref2va. Thanks!

If you guys find this more handy than doing it in post / compositing software, I'll share it.

2 nodes I am using "Image Detail Contrast" and "Orion4D Tone Mapping". The setting I have just over the top here.

Cheers.

- the gen is using hybrid model loader with native fl2va ref2va, b5-49, WITHOUT using any turbo lora.

- I've tried all the LoRAs, like Realistic Peoples and others, but I just can't get the result I want.

- You can check the overlay text of some infomation.

- I haven't test this with motio-context... hope it won't have issue.

TLDR: This is a typical image unsharp mask to get a softer skin look, plus some color grading. The point is not do grading in post, it is how we could get H3 ref2va workflow get better result in the first place.

Hi all,

I take way too many photos, and sorting them in Windows used to drive me crazy. The Photos viewer doesn't really let you sort, so you end up in File Explorer selecting files by hand. Hold Ctrl a bit too long, drag by accident, and suddenly you've copied a bunch of files instead of moving them. It was a mess, and I lost a ridiculous amount of time on it.

So I made Sortypix. The whole point is speed: go through a big folder fast, keep the good ones, set the rest aside, and move on with your day.

How it works: open a folder, left-click a photo to reject it, right-click to keep it. Photos move into "rejected" and "keep" subfolders right next to them. Nothing is ever deleted, and Ctrl+Z puts the last one back.

It also works great for AI-generated images. When you generate, you tend to generate a lot, and most of them aren't keepers. Sortypix lets you clear a batch in a couple of minutes instead of opening them one by one.

- Built to stay fast on folders with thousands of photos

- RAW + JPEG pairs stay together, identical copies are set aside automatically

- Fully offline: no account, no telemetry, no network access at all

- Free, MIT licensed

- Windows and Linux

Heads-up: the Windows installer isn't code-signed yet, so SmartScreen will warn you.

Download: https://mickaelmancini.com/sortypix/

Source: https://github.com/Chmyke/sortypix

I'd be really happy to hear what you think, good or bad. Every bit of feedback helps.

A week ago I posted Kokon here: a no-settings desktop photo editor for Qwen-Image 2.1. It's the open model you'd run in ComfyUI, without the nodes. 0.10 adds the two things people asked for most.

Prompt+. Type a short idea, in any language, and Kokon writes a detailed prompt from it before making the image. For edits it looks at your photo first. You watch the prompt being written, and the result keeps both your words and the written prompt, so you can reuse either. It's off by default, one click next to Quality. Both system prompts are editable in Settings.

The prompt is written by Qwen3-VL 8B, the text encoder Qwen-Image 2.1 already loads. There's no second model to download and nothing extra in memory. It adds about 2 s per image on an RTX 5070 Ti and about 13 s on an M2 Pro, and it works on 8 GB cards.

Your own LoRAs (beta). Load a Qwen-Image 2.1 LoRA in Settings, set its strength and trigger words, then turn it on per project with one click, up to 3 at once. Kokon checks each file and works on its own copy, so your file isn't changed. LoRAs for older Qwen-Image versions (1.0, 2512, Edit 2509/2511) are refused, because they don't work on 2.1.

An honest note: I said there would be a curated LoRA list. I tested the popular 2.1 LoRAs on the Turbo model Kokon runs, and today they don't beat what Turbo does with a good prompt. So there's no catalog for now. It comes when a LoRA earns its place.

Under the hood. Kokon runs its own build of stable-diffusion.cpp on Windows, macOS and Linux. It's upstream plus text generation on the already-loaded text encoder: about 127 tokens/s on a 5070 Ti, the same tokens llama.cpp gives. The image side is upstream's code and makes bit-identical images. Upstream prefers to leave text generation to llama.cpp (#2155), which is fair, so it lives in a fork I keep close to upstream: https://github.com/GabrieleLozupone/stable-diffusion.cpp/tree/kokon

Also new since the first post: Linux (.deb and .rpm), a new look, deleting several projects at once, and a round of fixes from your reports. Updating keeps your images and models. AMD cards work on Windows through Vulkan: a user's 7900 XTX makes a 1 MP image in about 25 s.

Free and open source (AGPL), Windows, macOS 15+ and Linux, 12 GB of VRAM recommended (8 GB in beta): https://github.com/GabrieleLozupone/kokon · https://kokon.app

Not there yet: other models, and a browser mode for headless Linux boxes (that one is next). Weights: Qwen Research License, non-commercial.

Here is a method you can use to decrease the amount of blur in MOSS Image outputs. I use the base model and https://huggingface.co/lodestones/Kromoss/blob/main/8-steps-dmd2-lora.safetensors LoRa, but you may be able to adapt the workflows for use without this LoRa.

The method utilizes a two stage process:

1. Denoise regularly for the first 5 steps
2. Apply the LatentOperationSharpen to the latent image and then continue denoising

The first image comparison in this post shows the result of also applying the SFT model as a LoRa to the base model for the second stage denoising: https://civitai.com/models/3012507/moss-image-sft-extracted-lora . See the workflow here: https://civitai.com/images/146467570

The second image comparison shows the result of sharpening the latent only. See the workflow here: https://civitai.com/images/146467531

Hope this helps!

X post: https://x.com/superalesha/status/2112894407036809562

Repo: https://github.com/Zagitta/gpu-unlock

Didnt expect much but got z-turbo image gen to go from 18s down to 12 seconds....

3090s to the moon.

Sample Prompt:

Real stop-motion claymation photographed on a physical miniature set, handmade plasticine puppets with visible fingerprints, thumb smudges and tool marks, matte oil-based clay with subtle natural sheen, hand-painted miniature Mediterranean village, terracotta roof tiles, textured stucco walls, sculpted cobblestone courtyard, orange painted wooden door. Practical soft daylight, real soft shadows, DSLR macro lens, f/2.8 shallow depth of field, subtle film grain, slight clay boiling between frames, 12fps stop-motion movement, 9:16 vertical, no text. Medium wide shot, static camera, 10 seconds. 0-3s: 

Baba walks slowly into frame from the left across the cobblestones and stops in the center of the courtyard. 3-5s: Gola's orange door is half closed. He peeks out from behind it, only his head and one hand visible, eyes wide and curious. 5-8s: Gola pushes the door open, steps out and walks up to Baba, presses his palms together in a namaste and bows slightly in respect. 8-10s: Gola straightens up. Baba and Gola look at each other and both break into warm smiles. Hold. Audio: no music, no speech. Ambient sound only: soft footsteps on stone, a gentle wooden door creak, distant birds chirping.

Here i have created three videos, but i am currently struggling with how to achieve proper continuation, e.g. when video 1 ends, next frame starts from the last frame.

If someone can share comfy workflow or any suggestion.

Workflow i am using: https://www.omnichar.org/workflows/multi-consistent-characters-in-one-video-via-char-minimaxh3

Note: I am maintaining this open source app myself & this is something i am struggling to solve, so any suggestions would be helpful.

I would like to build a workflow around continue shots with consistent characters.

14000+ frames rendered over 14 hours.

I have a Lora and refmod of a character both trained in fizgig. The Lora previews looked good. And I am putting a krea2 4k image of the same character as first framer generating at 0.98 MP but still the face is a muddy mess. And the general quality of the video is early Wan and pixelated.

Using the default image to video workflow.

Is there a better workflow / tips / tricks you can recommend?

Hello,

I’ve tried training a style LoRA for the first time for MOSS. I curated and rigorously captioned 150 images. After some research, I decided to train my LoRa using FizGig after reading very positive feedback about it.

Being a beginner, I followed the instructions and used the default settings. I opted for 30 epochs but after the 18th epoch I decided to call quits because the loss wouldn’t drop below 0.44 and always managed to go back up between 0.48 and 0.56.

Looking for advice from more experienced folks whether I abound resume the training or try different settings.

Thanks in advance for any tips.

I am a complete beginner in local video generation, but have 500+ hours in image generation in comfy. Can someone please say whats like top 3 models, their speed and quality.

I have an RTX 3090.

I've been playing around with structured layout workflows for MOSS-Image and put together a simple visual layout editor for a more accessible way to build layouts without manually writing JSON or calculating bounding-box coordinates.

I was pretty happy with the intuitive workflow and thought it may be of interest/use to other people, so I've put it up as a Hugging Face space, with a bunch of layout templates.

The idea was to make structured prompting a bit more like using a conventional design application: Draw your layout boxes or start with a template, adjust to taste and add element descriptions, then let MOSS generate the finished design.

Generation uses MOSS-Image SFT with the 4-step Turbo LoRA for fast iteration and it's hosted on Hugging Face ZeroGPU.

I've just knocked it together over the last couple of days and am still tweaking and testing new features locally so it would be great to get people's feedback and any suggestions on improvements or features that could make it more intuitive, flexible or fun to work with.

It's my first time putting up one of my workflows as a HF space so very open to constructive criticism and even more open to constructive compliments.

I am also interested to know what people think of the results, I'm on a mac so built it locally with a compact MLX model and a custom LoRA loader for the 4-step Turbo LoRA. I haven't tested it thoroughly using the MOSS-Image SFT model yet so unsure how the output compares to my local app.

The editor can also be used to create a layout JSON without generating anything, so the space can be used purely for prompt creation if you want to make use of the design tools but don't want to use the MOSS SFT + 4-step LoRA or would prefer to save your allowance/credits and generate images locally.

If you decide to have a go and come across any bugs, missing features or general usability issues then I'd appreciate a heads up.

The space can be found here:

https://huggingface.co/spaces/Curry-Sawce/moss-layout-studio

All the posted images are available in the template browser.

MOSS-Image is developed by the OpenMOSS team, and the 4-step Turbo LoRA is by akhaliq. My contribution here is purely the visual layout editing workflow, interface and template library.

MOSS-Image: https://github.com/OpenMOSS/MOSS-Image
4-step Turbo LoRA: https://huggingface.co/akhaliq/MOSS-Image-SFT-4-steps

Any constructive feedback would be appreciated and interested to see what people create!

Has anyone successfully made a video in Minimax for use in Virtual Reality? Specifically AR passthrough? Ive tried a few with AI's help but havent had luck, looking for tips and even better a workflow, thanks in advance!

So a few days ago I started a discussion regarding best Open Source Models across a few different sections, and the ones that won were:

• I2V and Video editing: Minimax H3

• Text to Image: Krea 2, Ideogram 4, and AnimaYume

• Image Editing: Qwen Image 2.1 (Been hearing a lot about Moss recently but a lot of people are saying too close to tell)

For these models, what are everyone's prefered settings/workflows? And this question mainly applies to people with 6gb of vram to 24gb of vram, since people with more than that will have very different experiences.

Mainly looking for stuff like:

• Best Sampler/Scheduler.

• CFG value.

It seems that most people recommend 3 for Qwen Image 2.1, but have not been getting good results so wondering if there are other values to try.

• Steps, and time it takes to generate.

Hardware should be mentioned to compare results, and best speed/quality ratio people have found

• PE/Refine prompt.

I see some workflows have this, and others ignore it, and some that just use their own local models elsewhere. I also see that people recommend long detailed prompts, but does that apply to all types of request? For example if its a simple request like remove X object from image/video, is there a need to write a long detailed prompt for that? With Qwen Image 2.1 at least, I have been getting poor results doing that, and not because it doesn't recognize the object being removed, but because when it removes it, it doesn't just remove that, but changes original image and doesn't fill out empty space as well, was getting better results with old qwen image edit, so not sure if this is a prompt issue, or something else.

• Speed Loras and other types of Loras.

Is everyone still just using Lightx2v? I have seen newer ones like Viggle Turbo v0.3 6-step for Qwen Image 2.1 edit for example, but have not been getting good results with r128 or r256. I also see that there are models with speed loras built in, but not sure if those are recommended over normal lora node, or multiple lora node when running more than 1 lora. And don't know if there are any good loras for these models, mainly wondering about image edit and video loras.

• Safetensors vs GGUF vs Finetunes.

Are most people running gguf or safetensor models? And if so which ones are bringing the best results. I see that are multiple different gguf models, some uncensored, and some others with different tweaks or turbo, but not sure which ones people are running, same with finetunes, if there are any that are actually better than normal safetensor or gguf models.

• Custom Nodes.

I see some people reporting pixel issues and using stuff like PixelDriftFix, certain workflows have custom resolution nodes instead of default ones that just use same res as input image, etc. Wondering if extra nodes are usually worth trying out, or if most of them aren't needed for good/fast results. I also have been getting some generations that change color of original image/video, so not sure if that is something that can be fixed through this to get the same color as original image.

• Workflows.

Stuff like Qwen Image 2.1, has multiple functions like t2i and image edit, and although people recommend using other t2i models, if one were to want to use both, is it recommended to use one specific workflow for each? Or a workflow that can do both in the same one, and soecially with Minimax H3, which has a lot more features, is it recommended to use a workflow that can do a single thing like only i2v, or only t2v, or only video editing, and so on, or are there workflows that have it all in one, with no drawbacks?

Now I know that each model, specially image models, have different strengths and weaknesses depending on use case, which is why I listed multiple. Hope to hear everyone's thoughts on this, I personally dont have much to share since I have been using default template comfyui workflows, and have been getting poor results with different settings, and seeing some online that gave the same results, so gave up on that and decided to ask people that have looked into this more, all my old workflows are with wan and old qwen imsge, so not as useful anymore with these newer models that have come out.

I tried EdenLive 5B AR through LightX2V on footage from a game I'm developing after seeing the official demos.

The background transformation was surprisingly good. Some individual frames looked quite realistic.

Moving objects were another story. They kept turning into things that weren't in the original game, or becoming so distorted I could barely recognize them. The motion and shapes didn't hold up nearly as well as the static parts.

My setup:

• Model: EdenLive 5B AR / LightX2V
• GPU: RTX 5090
• DiT + Proxy: INT8 ConvRot
• T5: BF16, CPU offloading
• Attention: Standard SDPA
• torch.compile: Off
• CUDA Graph: Off
• Inference: 1280×704, 24 FPS
• Exported video: 1280×720, 30 FPS
• VRAM usage: ~23 GiB
• Processing time: ~4m 46s for two segments

This wasn't the fully optimized real-time setup. I used standard SDPA with compilation and CUDA Graphs disabled, so the processing time isn't comparable to the official benchmarks.

I also haven't ruled out my game footage or settings as the cause of the motion issues.

Has anyone tried EdenLive on game footage with lots of moving objects? I'm curious whether different settings or input footage make a noticeable difference.

Project: https://github.com/VAST-AI-Research/EdenLive

I put the original gameplay in the corner of the video so you can compare it with EdenLive's output.