Diffusers merged our fix for the Qwen-Image 2.1 TaylorSeer and KV cache collision in under 4 days. Notes on AI contribution guidelines, reproduction steps, and test design.
Tested on M1 Max 64GB: the training-free TaylorSeer cache in Diffusers made Qwen-Image 2.1 about 2.7x faster. It crashes with the default KV cache; a small patch fixes it.
Tested on M1 Max 64GB: AnimaLoraToolkit + Anima-Base crashes on torch 2.6.0 (MPS SDPA bug) but runs 300 steps clean on 2.7.1, at ~21 s/step, roughly 9x slower than an RTX 5090.
Tested local Wan video gen on a Radeon 8060S (Strix Halo, 48GB UMA, Windows). ZLUDA can't run stock PyTorch; AMD's TheRock gfx1151 wheel gives native ROCm. FastWan 1.3B in 4min, Wan 14B I2V in 13.6min — VAE decode and 16GB-RAM Segfaults are the real limits.
Hands-on run of trellis-mac (the CUDA-free port of TRELLIS.2) on M1 Max 64GB. Setup via uv with PyTorch 2.11.0 MPS, applied mps_compat.py patches, and recorded actual generation time vs the M4 Pro 24GB 3.5-minute reference, plus where the bottlenecks land on Apple Silicon.
A port that replaces TRELLIS.2's CUDA-only libraries (flash_attn, nvdiffrast, sparse 3D convolution) with pure-PyTorch equivalents and runs Microsoft's 4B image-to-3D model on an M4 Pro in about 3.5 minutes without any NVIDIA GPU.
Based on EE Times' interview with AMD AI Software VP Anush Elangovan, we assess the ROCm vs CUDA ecosystem gap. Includes hands-on experience with ROCm breaking four times on Strix Halo, plus practical guidance on choosing between NVIDIA, AMD, and Apple Silicon.
An update moved inference from FP16 to BF16, and M1-M3 Macs emulate BF16 at half speed. Benchmarks of the regression and the config that lands at 2:30.
Upscaling images loaded via the Load Image node was producing garbled output. Fixed it by addressing the non-contiguous tensor issue — a one-line patch to comfy/utils.py. Added a 2026-04-29 follow-up after a ComfyUI update wiped the patch and the bug came back, with the upstream PyTorch issue and a recurrence-detection snippet.
