Oh, the audacity! 🧐 A groundbreaking treatise on GPU optimization reduced to a masterclass in web browsing 101: turn on #JavaScript and #cookies, and maybe the secrets of the universe will reveal themselves. 🚀🔒 Clearly, the real optimization here is finding a website that works. 🙄
https://dl.acm.org/doi/10.1145/3669940.3707274 #GPUOptimization #WebBrowsing #TechHumor #InternetSecrets #HackerNews #ngated

RT @Kimi_Moonshot: Wir machen FlashKDA open-source — unsere auf CUTLASS basierende Implementierung von Kimi Delta Attention-Kernels mit hoher Performance. Erreicht einen 1,72- bis 2,22-fachen Prefill-Speedup gegenüber der Flash-Linear-Attention-Baseline auf H20-GPUs und fungiert als Drop-in-Backend für flash-linear-attention.

mehr auf Arint.info

#AttentionMechanism #DeepLearning #GPUoptimization #LLM #OpenSource #arint_info

https://x.com/Kimi_Moonshot/status/2046607915424034839#m

YES SUCCEEDED!!!

Just rendered an image at 944×1152 (slightly above 1024×1024) using Flux1-Schnell-FP8 on my 6700 XT, and it works! (Image 1 is the Real-ESRGAN 2× upscaled version)

Workflow 1: Sampling (Image 2)

Prompt executed → UNet generates the latent

Step 1 (model load + latent generation) took 419 seconds

Output: Latent tensor saved to disk

Workflow 2 : VAE Decode (Image 3)

Latent loaded → VAE decodes the image

Duration: 7.5 seconds

Advantage: UNet doesn’t need to stay in VRAM → VRAM freed, even on 12 GB GPUs

The problem with the stock LoadLatent Node

Dropdown only shows files if they were produced / annotated by a previous SaveLatent Node

Node is designed to pass latents inside a graph, not load arbitrary files from disk

Purpose: prevents accidentally loading wrong files

Workaround (Image 4)

Edited /ComfyUI/nodes.py, class LoadLatent

Hardcoded latent path → Node now loads directly from disk

Result: Workflow 2 runs instantly, UNet can be unloaded

Timing

Step 1 (model load + latent generation): 419 s

Step 2 (VAE decode): 7.5 s

Result: High-res images on a 12 GB RDNA2 GPU are now possible on Flux1-Schnell-FP8 without ComfyUI crashing! (Image 5 is the original output)

This might actually become my new Flux workflow: render quick 512×512 previews first (which works perfectly on RDNA2 GPUs), sort out the good ones, extract the seed from the PNG metadata, and then re-render only the selected images with the same seed using the split workflow at higher resolutions. This way, high-resolution Flux1-Schnell-FP8 renders become possible on 12 GB RDNA2 GPUs D:

Question at the end: Has anyone ever done this before? Because I have no clue xD

#ComfyUI #flux #Flux1SchnellFP8 #FP8 #AMD #RDNA2 #VAE #AIArt #Pixelfed #HighResolution #GPUOptimization #LatentWorkflow #AIWorkflow #AIHacks #RealESRGAN #Upscale #AIExperiment #CreativeAI #DigitalArt #AICommunity #python #linux #opensource #foss

Lenovo launches GPU Advanced Services, promising up to 30 percent faster AI performance

https://web.brid.gy/r/https://nerds.xyz/2025/09/lenovo-gpu-ai/