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Update README.md

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  1. README.md +4 -4
README.md CHANGED
@@ -54,7 +54,7 @@ You can use the model both with the [🧨Diffusers library](https://github.com/h
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  from diffusers import VersatileDiffusionTextToImagePipeline
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  import torch
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- pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("diffusers/vd-official-test", torch_dtype=torch.float16)
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  pipe.remove_unused_weights()
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  pipe = pipe.to("cuda")
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@@ -75,7 +75,7 @@ url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
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  response = requests.get(url)
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  image = Image.open(BytesIO(response.content)).convert("RGB")
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- pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("diffusers/vd-official-test", torch_dtype=torch.float16)
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  pipe = pipe.to("cuda")
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  generator = torch.Generator(device="cuda").manual_seed(0)
@@ -84,7 +84,7 @@ image.save("./car_variation.png")
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  ```
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  #### Dual-guided generation
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  ```py
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- from diffusers import VersatileDiffusionImageVariationPipeline
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  import torch
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  import requests
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  from io import BytesIO
@@ -97,7 +97,7 @@ response = requests.get(url)
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  image = Image.open(BytesIO(response.content)).convert("RGB")
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  text = "a red car in the sun"
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- pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("diffusers/vd-official-test", torch_dtype=torch.float16)
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  pipe.remove_unused_weights()
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  pipe = pipe.to("cuda")
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  from diffusers import VersatileDiffusionTextToImagePipeline
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  import torch
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+ pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
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  pipe.remove_unused_weights()
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  pipe = pipe.to("cuda")
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  response = requests.get(url)
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  image = Image.open(BytesIO(response.content)).convert("RGB")
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+ pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
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  pipe = pipe.to("cuda")
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  generator = torch.Generator(device="cuda").manual_seed(0)
 
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  ```
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  #### Dual-guided generation
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  ```py
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+ from diffusers import VersatileDiffusionDualGuidedPipeline
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  import torch
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  import requests
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  from io import BytesIO
 
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  image = Image.open(BytesIO(response.content)).convert("RGB")
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  text = "a red car in the sun"
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+ pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
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  pipe.remove_unused_weights()
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  pipe = pipe.to("cuda")
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