Wanted to share some learnings we had optimizing and deploying Qwen-Image-Edit at scale to replace Nano-Banana. The goal was to generate a product catalogue of 1.2m images, which would have cost $46k with Nano-Banana or GPT-Image-Edit.
Qwen-Image-Edit being Apache 2.0 allows you to fine-tune and apply a few tricks like compilation, lightning lora and quantization to cut costs.
The base model takes ~15s to generate an image which would mean we would need 1,200,000*15/60/60=5,000 compute hours.
Compilation of the PyTorch graph + applying a lightning LoRA cut inference down to ~4s per image which resulted in ~1,333 compute hours.
I'm a big fan of open source models, so wanted to share the details in case it inspires you to own your own weights in the future.
gschoeni|4 months ago
Qwen-Image-Edit being Apache 2.0 allows you to fine-tune and apply a few tricks like compilation, lightning lora and quantization to cut costs.
The base model takes ~15s to generate an image which would mean we would need 1,200,000*15/60/60=5,000 compute hours.
Compilation of the PyTorch graph + applying a lightning LoRA cut inference down to ~4s per image which resulted in ~1,333 compute hours.
I'm a big fan of open source models, so wanted to share the details in case it inspires you to own your own weights in the future.