qwenimage-ncnn-vulkan added support for Qwen-Image-2.1 4-step LoRA and ControlNet
It runs faster than pytorch-rocm or sd.cpp on my RX9060XT machine
Hope my tool proves helpful to you :)
https://t.co/RtbbDs72jR
Visualizing to learn model internals in the agentic era can be surprisingly low-friction.
I had Codex one-shot architecture notes earlier for several recent OSS models—DeepSeek V4/V4.1, GLM 5.3, and Kimi K3—specifically from a kernel-development perspective: tensor contracts, exact shapes/layouts, attention variants, KV-cache representation, precision choices, dataflow, and operator boundaries.
Sharing in case they’re useful to others:
https://t.co/HViLxaaJdj
Qwen-Image-2.1 full BF16 running with 2GB VRAM
ncnn/Vulkan for NVIDIA/AMD/Intel/Apple
No CUDA/PyTorch/Python
Portable executable
Text-to-image & Image editing
Up to 10 reference images
Transparent RGBA generation
Dynamic output resolution
Batch generation
https://t.co/mTiok6Qija