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Update README.md

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@@ -16,21 +16,14 @@ Here we give an example model fine-tuned using 5 images of a cat downloaded from
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  ## Example code of inference
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  ```python
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- import os
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- import sys
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- import torch
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- os.system("git clone https://github.com/adobe-research/custom-diffusion")
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- sys.path.append("custom-diffusion")
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  from diffusers import StableDiffusionPipeline
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- from src import diffuser_training
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  device = 'cuda'
 
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  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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  pipe = pipe.to(device)
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- weight_path = 'custom_diffusion_cat.bin'
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- diffuser_training.load_model(pipe.text_encoder, pipe.tokenizer, pipe.unet, weight_path, '<new1>')
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  prompt = "<new1> cat swimming in a pool"
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  images = pipe(prompt, num_inference_steps=200, guidance_scale=6., eta=1.).images
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  ```
 
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  ## Example code of inference
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  ```python
 
 
 
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  from diffusers import StableDiffusionPipeline
 
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  device = 'cuda'
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+ model_id = 'nupurkmr9/custom_diffusion_cat/cat'
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  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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  pipe = pipe.to(device)
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  prompt = "<new1> cat swimming in a pool"
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  images = pipe(prompt, num_inference_steps=200, guidance_scale=6., eta=1.).images
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  ```