u/shootthesound

Happy July 4th Weekend! - A lot of my own photography included too this week as I flew across the water to Arizona to take some photos. All the high res and rest of the images (reddit compresses multi image posts) and crops on https://UltrawideWallpapers.net - Pete

https://github.com/shootthesound/Fizgig

- INT8 fast inference (on by default) — previews and the whole workbench run an int8 matmul instead of fp8 on both Klein and Krea 2: faster, near-identical quality. Biggest win on RTX 30-series (which have no fast fp8); 1.29X boost on 40/50-series. It's previews only — your saved LoRA is always exact — and it's the same VRAM as fp8, so it stacks with block swap. Toggle off any time.

- Full feature parity on Krea 2 vs the Klein feature set**:**

• graceful pause/resume of training so you can stop and play rocket league without any downside
• Context LoRA (train a new LoRA on top of a frozen, live one to make them compatible),
• Next Sample over rides during training to change prompt, res, seed and reference.
• Lora Block editing and exploration Prompt/Seed Travel Gifs and Mp4s.

Lots of new features but the key ones are Outpainting, Super resolution Upscaling and a lot of QOL fixes to the UI. https://github.com/shootthesound/ComfyUI-Angelo
Regarding the upscaling, Apart from the method shown in the clip where you drop in an image, I recommend exploring generating an image, hitting the 2x upscale (non-ai) and then the Quick Photo Refine (with or without 'Lite' mode selected)

Latest drop is here - Part 2 of my low poly collection I started last week. All the high res and rest of the images (reddit compresses multi image posts) and crops on https://UltrawideWallpapers.net - Pete

Latest drop is here - Mostly low poly vibes this week. Hope you are having a great Weekend! All the high res and rest of the images (reddit compresses multi image posts) and crops on https://UltrawideWallpapers.net - Pete

Latest drop is here - really geared for OLED this one. Hope you are having a great Weekend! All the high res and rest of the images (reddit compresses multi image posts) and crops on https://UltrawideWallpapers.net - Pete

Few QOL updates including next up thumbnails and improved browse and file filtering.

https://github.com/shootthesound/ComfyUI-SequentialImageLoader

Latest drop is here - 57 this week - and its 8bit week/pixel art vibes. Hope you are having a great Weekend! All the high res and rest of the images (reddit compresses multi image posts) and crops on https://UltrawideWallpapers.net - Pete

There are lots of great but akward sequential image loader nodes. Tied to make one that's a little more friendly for everyday use. It does not do everything, but that's the point!
https://github.com/shootthesound/ComfyUI-SequentialImageLoader

I've been working on and off on a very dedicated Klein 9b training, Lora surgery and gamed Lora exploration experience called Fizgig. Everything is tuned for Klein 9b.
Training is fast and light: on the fp8 Base DiT it runs the frozen-base matmuls in fp8 on the tensor cores for about 1.5× faster steps (RTX 40/50-series), and the fp8 model stays resident at ~9.6 GB so a full 9B LoRA trains comfortably on a 16 GB card.

It also includes things most trainers skip — Context LoRA training (learn a new LoRA on top of an existing frozen one so they coexist: a face that sits on a style, an outfit that drapes over a character), bilingual captions for richer convergence, distilled 4-step previews that match reality, a self-tuning adaptive learning rate, and pause/resume that frees your GPU mid-run and picks up exactly where it left off, full optimizer state and no quality regression — so you can fire up Rocket League without sacrificing your training run.
In the Image Prep Tab, if you choose Auto prep Face crops, it will make extra tight face crops from wider images as additional datapoints in your data set (It even supports gender targeting). Having a tight image alongside a wider shot of a character adds enormous value to the dataset. However if your originals are already small, these can be too small, so its best done with a raw dataset. One last training note, the training defaults to 0.25mp (circa 512x512) training, these get resized in cache on training. Your data set can be higher to give you freedom to experiment, and your dataset does not need to be square.

But the real reason is what happens after training. Fizgig is a workbench, not just a trainer: fix a broken LoRA block-by-block in the Repair Studio with no retraining, explore new variations like a game in LoRA the Explorer, profile exactly which blocks carry style vs identity vs detail, and extract a LoRA down to a smaller rank or a specific block range — all in one app, each tool reading the others' output.

Its free and open source. This latest version has been targeted on Speed improvements for all (the fp8 base mode is my own personal default now) and 16GB Card optimisations. P.S. I recommend starting with the 'Old Reliable' pre-set on the training tab, and then after that try the rank Rank 8 version of the preset. Its easy to forget a lot of the old Rank 16 wisdom arrived when our models had a lot less parameters.

https://github.com/shootthesound/Fizgig

EDIT: added a 4bit base model mode (quantises fp8 or bf16 base in realtime to NF4) for easy training on under 16gb vram cards without swapping blocks. Just done a couple of test runs and looks great.