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Update README.md

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@@ -11,7 +11,6 @@ Data: https://huggingface.co/datasets/peter-sushko/RealEdit
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  <img src="https://peter-sushko.github.io/RealEdit/static/images/teaser.svg"/>
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-
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  **There are 2 ways to run inference: either via Diffusers or original InstructPix2Pix pipeline.**
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  ## Option 1: With 🧨Diffusers:
@@ -22,10 +21,8 @@ Install diffusers, transformers library:
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  # Install required libraries
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  pip install torch==2.7.0 diffusers==0.33.1 transformers==4.51.3 accelerate==1.6.0 pillow==11.2.1
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  ```
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-
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  Then run:
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-
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  ```python
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  import torch
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  from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
@@ -40,9 +37,7 @@ pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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  pipe.to("cuda")
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  pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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-
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  url = "https://github.com/AyanaBharadwaj/RealEdit/blob/main/example_imgs/simba.jpg"
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-
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  def download_image(url):
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  image = PIL.Image.open(requests.get(url, stream=True).raw)
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  image = PIL.ImageOps.exif_transpose(image)
@@ -51,7 +46,6 @@ def download_image(url):
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  image = download_image(url)
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  prompt = "give him a crown"
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-
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  result = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=2).images[0]
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  result.save("output.png")
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  ```
 
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  <img src="https://peter-sushko.github.io/RealEdit/static/images/teaser.svg"/>
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  **There are 2 ways to run inference: either via Diffusers or original InstructPix2Pix pipeline.**
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  ## Option 1: With 🧨Diffusers:
 
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  # Install required libraries
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  pip install torch==2.7.0 diffusers==0.33.1 transformers==4.51.3 accelerate==1.6.0 pillow==11.2.1
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  ```
 
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  Then run:
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  ```python
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  import torch
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  from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
 
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  pipe.to("cuda")
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  pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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  url = "https://github.com/AyanaBharadwaj/RealEdit/blob/main/example_imgs/simba.jpg"
 
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  def download_image(url):
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  image = PIL.Image.open(requests.get(url, stream=True).raw)
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  image = PIL.ImageOps.exif_transpose(image)
 
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  image = download_image(url)
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  prompt = "give him a crown"
 
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  result = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=2).images[0]
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  result.save("output.png")
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  ```