Update README.md

README.md

@@ -35,7 +35,7 @@ Key Features:
 
 Install the latest version of diffusers
 ```
-pip install git+https://github.com/huggingface/diffusers
+pip install -U diffusers
 ```
 
 The following contains a code snippet illustrating how to use the model to generate images based on text prompts:
@@ -47,10 +47,8 @@ import torch
 
 from diffusers import QwenImageEditPipeline
 
-pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit")
+pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=torch.bfloat16).to("cuda")
 print("pipeline loaded")
-pipeline.to(torch.bfloat16)
-pipeline.to("cuda")
 pipeline.set_progress_bar_config(disable=None)
 image = Image.open("./input.png").convert("RGB")
 prompt = "Change the rabbit's color to purple, with a flash light background."
@@ -63,14 +61,17 @@ inputs = {
     "num_inference_steps": 50,
 }
 
-with torch.inference_mode():
-    output = pipeline(**inputs)
-    output_image = output.images[0]
-    output_image.save("output_image_edit.png")
-    print("image saved at", os.path.abspath("output_image_edit.png"))
-
+output = pipeline(**inputs)
+output_image = output.images[0]
+output_image.save("output_image_edit.png")
+print("image saved at", os.path.abspath("output_image_edit.png"))
 ```
 
+You can explore further optimization options in Diffusers by checking out the links below:
+
+* [Accelerating inference](https://huggingface.co/docs/diffusers/main/en/optimization/fp16)
+* [Reducing memory consumption](https://huggingface.co/docs/diffusers/main/en/optimization/memory)
+
 ## Showcase
 One of the highlights of Qwen-Image-Edit lies in its powerful capabilities for semantic and appearance editing. Semantic editing refers to modifying image content while preserving the original visual semantics. To intuitively demonstrate this capability, let's take Qwen's mascot—Capybara—as an example:
 ![Capibara](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/edit_en/幻灯片3.JPG#center)