Update README.md

README.md

@@ -66,30 +66,32 @@ pip install git+https://github.com/huggingface/transformers
 To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#diffusers.VersatileDiffusionPipeline)
 
 ```py
+#! pip install git+https://github.com/huggingface/transformers diffusers torch
 from diffusers import VersatileDiffusionPipeline
 import torch
 import requests
 from io import BytesIO
 from PIL import Image
 
-# let's download an initial image
-url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
+pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
 
+# prompt
+prompt = "a red car"
+
+# initial image
+url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
 response = requests.get(url)
 image = Image.open(BytesIO(response.content)).convert("RGB")
 
-pipe = VersatileDiffusionPipeline.from_pretrained(
-    "shi-labs/versatile-diffusion", torch_dtype=torch.float16
-)
-pipe = pipe.to("cuda")
+# text to image
+image = pipe.text_to_image(prompt).images[0]
 
-generator = torch.Generator(device="cuda").manual_seed(0)
-image = pipe.image_variation(image, generator=generator).images[0]
-image.save("./car_variation.png")
+# image variation
+image = pipe.image_variation(image).images[0]
 
-#similarly with 
-# pipe.text_to_image(...)
-# pipe.dual_guided(...)
+# image variation
+image = pipe.dual_guided(prompt, image).images[0]
 ```
 
 ### Task Specific