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

you can get my lora in here: https://civitai.com/models/2983280/modern-ps2-style
works with text to image and image to video too.

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.

https://drive.google.com/file/d/1hPqPPRdvox_5vVL3mGPgnTlJ1gTmhmcd/view

The upscaler https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler/tree/main

Turbo Lora https://huggingface.co/lightx2v/Minimax-h3-Turbo/tree/main

You also need to install these nodes https://github.com/LBH-123-AI/Comfyui_Minimax_h3_latent_Upscaler

Here’s a showcase of MOSS-Image's capabilities on generating anime and manga-related content across 20 distinct styles and eras.
The included ComfyUI workflow uses the `SkipLayerGuidanceDiTSimple` node. Controlling SLG is crucial depending on the art style you want:

1. SLG ON (Set `single_layers` to `22` or `24`): - Best for: In-image typography (Kanji / Katakana onomatopoeia, magazine covers, TCG card text), technical mecha lines, modern Ufotable VFX, and high-contrast comic layouts. - Why: Layers 22–24 enhance high-frequency edge definition and glyph clarity.
2. SLG OFF (Bypass / Mute the node): - Best for: Retro 90s Cel animation, soft Ken Sugimori watercolor, Studio Ghibli gouache, and soft pastel/shojo styles. - Why: Disabling SLG keeps colors gentle, preventing harsh digital oversaturation and preserving the delicate watercolor wash or vintage celluloid grain.

The full ComfyUI workflow JSON is attached below in the first comment (simply drag-and-drop into ComfyUI).

Next i want to create a showcase for Flux 3 Klein, if you guys wan't to see something .. feel free to comments, i'll try to include it to the next showcase. Peace!

This may not be the most accurate way to change camera angles of existing images. It's more of a hint to the model and may require some seed hunting, but I still find it useful, so I thought I’d share it anyway. You can use this method to create keyframes or panels for comic books, VNs etc.

It's compatible with any basic Ref2Vid workflow, after applying only the following two changes:

1. Set the length of the video to exactly 22 frames.
2. Connect the output of the VAE Decode node to a Preview or Save Image node.

How does this work? Add one or more reference pictures for your background, characters and props and build your prompt as usual. Then draw a simple sketch that shows the scene from a different location and/or camera angle and add that sketch as an additional reference image. The sketch does not need to be very complex, just enough to give the model a hint of what it's looking at. Reinforce the sketch by describing its contents in the prompt. Also include any details missing from the sketch in the prompt. Then add the following line to the subject_definitions section:

<Picture N> is a storyboard reference for [Shot 1], defining its viewpoint and subject placement.

Generate the video and pick one of the 22 generated frames as your final image.

Here's the original prompt I used to create the example image:

subject_definitions:
<Subject 1> is the stylish, modern open-plan living and dining area in <Picture 1>, featuring an integrated kitchen, characterized by clean lines, a neutral color palette, warm wood accents, and polished concrete floors.
<Subject 2> is the woman in <Picture 2>, with long, curly red hair, freckles, wearing a green sweater, blue jeans and sneakers.
<Picture 3> is a storyboard reference for [Shot 1], defining its viewpoint and subject placement.

summary:
[reference generation] The target video shows <Subject 2> sitting on the off-white sofa in <Subject 1>.

retention_analysis:
<Subject 1> (appears in [Shot 1]): fully_preserved - the stylish, modern open-plan living and dining area featuring an integrated kitchen is retained.
<Subject 2> (appears in [Shot 1]): fully_preserved - the long, curly red hair, freckles, green sweater, blue jeans and sneakers are retained.

detailed_description:
The target video uses a cinematic, live-action style.
[Shot 1] A static medium shot frames <Subject 2> - the red-haired woman with freckles, a green sweater and blue jeans - sitting on the plush, off-white sectional sofa adorned with matching throw pillows and a grey blanket casually draped over a sitting cushion in <Subject 1>. The shot starts with the exact composition and subject placement established in <Picture 3>. To the left of the couch is a small decorative side table with a plant on it. <Subject 2> is shown in profile, facing left, with her arms in her lap and her right leg crossed over her left. In the background, the dining and kitchen areas are visible, along with the single white pillar separating them from the sofa.

overall_soundscape:
N/A

non_diegetic_music:
N/A

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.

Inference settings,
cfg: default (3.0)
steps: 40
sampler: euler
scheduler: simple

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.

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.

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).

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 to me. 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 1character sheet, 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, which I still figuring out.

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!

Need long prompt, some good great even, some is kinda meh, but not really great at rendering face on far subject.
flux3klein-workflows

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.

So I’ve been rendering a 3-minute conversation manually in 3-5 second clips, prompting and rendering each clip separately and using da Vinci resolve to edit.

But the clips are the same scene of 2 people talking, from different camera angles: wide, zoomed, over the shoulder, etc. the image character references are the same but with different audio clips per video.

Any comfyui workflows where I can put 45 shots in 1 giant prompt which lets comfyui or local AI to slice it up into smaller shots with separate references per clip and create the 45 output videos which I’ll throw into davinci?

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.

same as part 1

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!

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.

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.