r/StableDiffusion

[ I'm posting this on behalf of u/LodestoneRock since for some reason his post on this sub keep getting auto removed by Reddit. ]

Hey everyone, First of all, thank you to everyone for supporting this project, and to the Krea team who made the best uncompromised base model. Unlike the previous run, where I had to un-distill the model, the Krea team kindly provided us with their real base model. The training is much cheaper than the previous Chroma run. Though cheap is relative, it’s still costing me about ~10K RTX pro 6000 hours (3+ months on 4xGPU machine) running locally, this includes a bunch of failed experiments and bugs during training. Also thank you to ComfyOrg for the 2 RTX pro 6000 donations ♥️

The Base Krea is also not collapsed which allows me to do OPD (RL) directly ontop of it. As a result, you will get a good generation out of the box. Here are some results:

You can download them here: https://huggingface.co/lodestones/Kroma

(Civitai link soon)

So what’s next?

There are several stale projects like my pixel space run chroma radiance and zeta chroma, but with the limited compute, I have decided to not continue them for now since compute is at a premium atm. Right now the focus is to create a better dataset (where about 90% of the quality comes from) while continuing to add more knowledge to kroma in the background. I do want to tackle video generation models like h3 in the near future once the dataset curation is done, but fine tuning the video model has astronomical compute cost so I'm looking for funding for more hardware to train a model. I firmly believe that hardware funding donations help a ton because we can indefinitely do more experiments in a sustainable way without having to worry about rental cost and psychological burden of “burned money” on failed experiments. I have a target of getting 4 more RTX pro 6000 but that’s like 60 grand in total which is an insane amount of money! So I don’t expect to reach this goal immediately, it's more about chipping in one card at a time at the pace of the accumulated donations. Every contribution helps, no matter the amount, this project simply isn’t possible without your support!

My discord: discord.gg/SQVcWVbqKx

[ I'll let Lodestone post his support and donation links in a comment. ]

Euler / Simple / 8 steps / CFG = 1.0

Since Qwen 2.1 Turbo dropped suddenly right after my last taste test, figured might as well do one for it as well.

Instructions:

Each image is either Krea2-Turbo or Qwen 2.1 Turbo, using the exact same prompt.

You do not know which one is which, it could be either A or B on each individual image.

Pick your favorite based on preference and list them.

You can also guess which one of each is Krea2 or Qwen 2.1 Turbo.

The answer key will be posted 24 hours after posting.

Technical Setup

This time all images were generated at 2MP, using 8 steps. Krea2-Turbo was the fp8 model and text encoder, but Qwen was BF16 for both model and text encoder.

Krea2 used Euler-Simple, where as the still less understood Qwen Turbo I went with Linear Quadratic (I know there is an official sigma curve for it, but its close enough) and since Euler seems also not be a good sampler, I went with the tried and trusted Heun, which gave comparable inference times to Krea2 (Qwen 2.1 Turbo is about twice as fast as Krea2-Turbo on Euler).

I did test (and have in the past) different samplers and schedulers in Krea2 but overall they generally seem to all be equal, so I did not differ from the standard Euler-Simple.

Prompts:

All prompts are the same for both models. They were generated in ChatGPT, expanding off basic ideas. Some of the expanded prompts were not great and were tweaked manually when both models demonstrated consistent inconsistencies with the prompt idea, and reran on both models. Each prompt was ran up to 10 times to find a suitable generation.

The prompts for each image are here: https://paste.sh/yDAHasri#TveZa0FgYZ-UJ89Exlvpbelm

I’ve been playing with orbit-style videos from video models (lately Minimax H3 in ComfyUI) and kept thinking they’d make great input for Gaussian Splatting. The catch was that the usual pipeline means installing COLMAP, a trainer, CUDA and so on. That’s a lot of friction just to try one idea.

So I built Gausify Orbit: https://orbit.gausify.com/

You drop in your video and the whole thing runs locally in your browser on WebGPU:

• Pulls frames from the video
• Optional background removal with RMBG-1.4 (BRIA), running through ONNX Runtime Web
• Camera poses from a small structure-from-motion step
• Trains the splat with Brush
• Preview the result and export it as PLY or SOG

No Python, no CUDA, no account. Your video never leaves your machine. The models download once, and after that the app works offline too. If you want to show someone a result, you can share a temporary private link that opens in a viewer (it even supports WebXR).

Since it all runs on your GPU, how fast it goes depends on your hardware. You also need a browser with WebGPU (recent Chrome or Edge work best). AI videos aren’t perfectly 3D-consistent either, so some generations reconstruct much better than others. Slow, steady orbits around one centered subject give the best results.

Tip for better results: I’ve been using this LoRA by pablodawson to get cleaner orbits out of Minimax H3: https://huggingface.co/pablodawson/MiniMax-H3-360-Orbit-LoRA

It makes the camera move in a much smoother, more consistent 360° orbit around the subject. That helps a lot here, because the reconstruction step relies on the camera following a predictable path. In my tests, videos made with it got better camera poses and noticeably sharper splats than plain prompting. Highly recommend it if you’re generating orbit videos for this.

ComfyUI Workflow: https://github.com/obraia/ComfyUI/blob/main/workflows/minimax-h3-gausify-orbit.json

Hey everyone!

I'm an AI art creator, and for a while now I've been wanting a better way to organize character prompts, find inspiration, and experiment with different scenes without having to dig through a bunch of different sources.

So I decided to build my own little website called Senpai's Waifu Library.

It started as something I was making just for myself, but I figured if I was finding it useful, other people might too. So I decided to make it public!

The website includes:

• A growing library of anime characters and their prompts
• Reusable scene prompts you can mix with different characters
• Search and filtering to make finding things easier
• Easy-to-copy prompts for your own generations

It's completely free, and I'm still working on adding more characters, tools, and features as I go.

I'm not a professional web developer, so this has also been a pretty fun learning experience for me.

I'd genuinely love to hear what you think! Especially if there's something you'd like to see added or improved.

Link

https://github.com/r34vtraining/Longshot_Studio (don't forget to download the 2 other custom nodes for this to work, you will find them under requirements)

After more back and forth with Claude for optimization and UI change, here is the latest update of Long Shot Studio.

1. Added multi-take, queue up batch renders and go back and review all the different takes once they are finished.
2. Timeline now has all the different takes you can switch around and review. All takes are saved in mp4 and latent.
3. RTX VSR Pro updated to upscale 360 frames in 12 seconds (for final output or preview), you can set chunk size to slightly decrease upscale time.
4. all project gets their own input and output subfolders
5. Studio Version Number added to top left
6. model tree is easier to read

EDIT:

1. added ability to queue shots within project and across multiple projects
2. auto-restart comfy if queue encounters OOM error
3. added first and last frame anchor function

Just draw your composition and let Minimax figure it out instead of using references from the internet

Side Note:

I noticed when trying to replace people, the Model struggles a lot when it is physically not straightforward to fit something in.

Like when your hair has a tied bun behind your head, Minimax struggles to lie you on the floor. You have to tell Minimax to untie the hair.

Which gets really difficult when it is very complex with a lot of minor conflicts between the original and replacement subject

Fizgig, my free open-source LoRA trainer, now does music.

Train ACE-Step 1.5 XL LoRAs on your own songs and play every checkpoint in the gallery as it trains.
You can also make slider LoRAs that dial between two styles, like calm to energetic, without needing any songs.

Repair Studio works like a DAW for AI music, with per-block sliders, covers, repaint and extend, and LoRA mixing, and saving out wavs of the music you create in it.

A new I'm feeling lucky button connected to the Repair Studio which works ludicrously well with the DAW-like mode for Ace-Step for exploration purposes.

To get started there's a free and fully credited chiptune dataset and LoRA - I strongly encourage reading the tips I've included in it - starting training this way will make everything super clear: https://huggingface.co/datasets/ShootTheSound/Fizgig-ACE-Step-Chiptune-Starter

Release notes: https://github.com/shootthesound/Fizgig/releases/tag/v7.3.0

EDIT: I forgot to say that Music to Music is supported combining Ace Steps own Cover and sound alike modes with a new denoise feature that's similar to what many of us in the AI imaging scene are used to.

The other day, when I published information about v1.5.7, I received a request in the comments regarding support for w4a8.

https://www.reddit.com/r/StableDiffusion/comments/1wuj0rj/updated_updated/

At first, I thought it would be straightforward to implement, but when I actually tried it, I found it quite a struggle.

However, I’ve managed to get it working stable, so I’m releasing it as v1.6.6.

https://github.com/ussoewwin/ComfyUI-SeedVR2-VideoUpscaler-with-TensorRT/releases/tag/v1.6.6

https://huggingface.co/ussoewwin/SeedVR2-ConvRot-INT8-and-w4a8

Firstly, as for the advantages of w4a8, benchmark results clearly show that the quality is superior to that of NVFP4. Yet, the size is equivalent to 4 bits.

https://github.com/ussoewwin/ComfyUI-SeedVR2-VideoUpscaler-with-TensorRT/blob/main/benchmark/benchmark%20result.md

On the other hand, what wasn’t as good as I’d hoped was that VRAM consumption was almost on a par with ConvRot INT8 (though, given that w4a8 expands weights to 8 bits, this was to be expected), and that with Distorch2—which I’d gone to such lengths to implement—VRAM usage actually skyrocketed, rendering it unusable.

It’s simply a matter of not using Distorch2, so it isn’t a fatal flaw, mind you.

...
The on/off function for Norm BF16, which was previously implemented in Distorch2 to counteract VRAM spikes, has now also been implemented in the Legacy Loader. Enabling this feature allows you to suppress VRAM spikes by up to approximately 3GB, albeit at the cost of a slight reduction in image quality.

I fine-tuned SD1.5 on Minecraft item textures. You type a prompt and get a 16×16 sprite with transparency that you can drop straight into a resource pack.

SD can't generate 16×16 directly, so I trained on textures upscaled 16× with nearest-neighbour. The model draws on that grid at 256px, and block-averaging brings it back down to 16×16. Transparency is a magenta background that gets keyed out.

It also does sketch → texture (doodle a rough 16×16, img2img finishes it) and material sets from a single design.

Not perfect. Around 10–15% of outputs fill the whole frame, so try a few seeds.

Demo (free, no GPU): https://huggingface.co/spaces/yuuki14202028/minecraft-item-16px-demo

Code: https://github.com/yuuki14202028/pixelgen

Update: I posted this MCP open-source project last week. Since then, I've added video generation and Qwen-Image-2.1 Turbo. No silly ComfyUI here.

https://github.com/hypersniper05/MCP-Image-Generator-Uncensored

Qwen-Image-2.1 Turbo runs on as little as 4GB of VRAM (tested on a GTX 1650; it works, but slow. A newer GPU should be faster). With 8GB of VRAM you get better speeds, and you can even run the video model, Kandinsky 6 Lite. It's a 3B-parameter model, so don't expect quality on par with MiniMax-H3.

I tried to make the installation as painless as possible, and it can use multiple GPUs if you have them.

Here's what your LLM can create for you via the MCP:

Core Qwen-Image-2.1 feature

• Text to image
• Image editing (combine up to 10 images, restore and colorize old photos)
• Text in images
• 360° panoramas

Extra tools for your LLM:

• Watermark removal (LoRA)
• Texture-fix VAE
• 2x / 4x upscaler (ESRGAN, up to 8192 px)
• 360° viewer with Pannellum (the client gets the image plus a ready-to-open 360 viewer link)
• SVG creation with VTracer
• Background removal with BiRefNet
• Transparent PNG creation
• Seamless tiles, including height and normal maps
• 5- to 20-second videos with sound (5-second clips stitched together)
• Transparent video animations from your images (alpha channel)
• GIFs from videos and animations

MCP internal features:

• HTTP Streamable
• Multi-Job queue, streaming jobs 1 at a time for systems with VRAM constraints
• Flexible input: URL, server path, or upload
• Gallery web UI to manage your media
• Start, Stop, Update, and Setup scripts for non-techie users
• 16 Tools in total
• Read more in the link above

All attributions are in the repo. TODO: add an option for MiniMax-H3! and Trellius 2 (Image to 3D). Thanks!