Update README.md
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README.md
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@@ -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("
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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("
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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
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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 =
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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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