# How can I replace an object using a reference image while preserving its exact design? > Source: > Published: 2026-09-07 07:12:21+00:00 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?