Hi, first of all, a huge thanks to you!
Qwen gives me very good results with simple and medium-complexity images. However, I noticed one issue when there are multiple instances of the same object in a room.
For example, if there are four sofas and I ask it to replace the sofas with chairs, its behavior is not very consistent. Sometimes, it removes all the sofas and places only one chair. If I explicitly prompt it to replace all the sofas and place a chair in each of the four positions, the viewpoints/angles of the generated chairs are often not correct, even when I provide 3–4 reference images of the chair. Overall, the performance seems very good, but this is the main limitation I have noticed so far: when there are multiple instances of the same object in an image, achieving accurate one-to-one replacement while preserving the original position and viewpoint is difficult.
Here is my other issue:
I’m trying to optimize the inference time.
Current setup:
GPU: NVIDIA A100-SXM4-80GB
PyTorch: 2.13.0+cu130
CUDA: 13.0
Precision: BF16
Inference steps: 40
true_cfg_scale: 4.0
guidance_scale: 1.0
Product-consistency LoRA: FractalAIResearch/Kalaido-qwenedit-lora (optional)
VRAM usage: ~54–55 GB
Currently, a single image takes around 6–7 minutes without optimization. After using torch.compile, the first generation takes around 6–7 minutes, but subsequent generations take around 2 minutes.
My target is to get the inference time down to seconds (ideally ~30–60 seconds) while keeping 40 inference steps and similar output quality.
Is this kind of inference speed achievable with Qwen-Image-Edit-2511 on an A100 80GB? Are there recommended optimizations, faster/quantized variants, or inference techniques that can bring the generation time into the seconds range without reducing the steps?