Tested on M1 Max: Diffusers stretches an 832×1216 reference to 832×1248, making figures 2.7% narrower. Setting output_resolution=1006 cut whole-image drift to about 0.1px.
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: SeFi-Image turbo runs on MPS in bf16 at 13–47s/image; fake text and face artifacts only clear up on 5B RL at 50 steps, 18 min/image.
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.
Krea 2 Turbo bf16 renders in ~3.5 min on a Mac (M1 Max 64GB, ComfyUI). fp8 is rejected on MPS, Raw NaNs to black at 47 min, and NSFW behavior gets tested. Working settings and VRAM notes for Apple Silicon.
Tested Boogu-Image-0.1 on an M1 Max 64GB ComfyUI: the fp8 build is rejected by MPS, so bf16 is mandatory, and Turbo runs ~70s per 1024px image. Notes on photoreal vs anime, bilingual text, and where NSFW stops.
A hands-on log of running Qwen-Scope's Sparse Autoencoder locally on M1 Max 64GB with Qwen3-8B-Base, extracting feature IDs that discriminate between Japanese, English, code, and Chinese from a single middle layer.
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.
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.
Flash-MoE is a C/Metal inference engine that runs Qwen3.5-397B-A17B on a MacBook Pro M3 Max at 4.36 tokens/s. With expert streaming from SSD and hand-written Metal shaders, it fits the 209GB model into a 48GB memory budget.
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.
