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Update README.md

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@@ -27,7 +27,7 @@ This model card focuses on the model associated with the Stable Diffusion model,
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  pages = {10684-10695}
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  }
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- ## Usage examples
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  ```bash
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  pip install --upgrade diffusers transformers scipy
@@ -40,15 +40,20 @@ huggingface-cli login
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  Running the pipeline with the default PLMS scheduler:
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  ```python
 
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  from torch import autocast
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  from diffusers import StableDiffusionPipeline
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- model_id = "CompVis/stable-diffusion-v1-3-diffusers"
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- pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to("cuda")
 
 
 
 
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  prompt = "a photograph of an astronaut riding a horse"
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  with autocast("cuda"):
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- image = pipe(prompt, guidance_scale=7)["sample"][0] # image here is in PIL format
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  image.save(f"astronaut_rides_horse.png")
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  ```
@@ -171,43 +176,6 @@ This model card focuses on the model associated with the Stable Diffusion model,
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  pages = {10684-10695}
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  }
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- ## Usage examples
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-
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- ```bash
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- pip install --upgrade diffusers transformers scipy
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- ```
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-
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- Run this command to log in with your HF Hub token if you haven't before:
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- ```bash
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- huggingface-cli login
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- ```
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-
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- Running the pipeline with the default PLMS scheduler:
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- ```python
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- from torch import autocast
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- from diffusers import StableDiffusionPipeline
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-
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- model_id = "CompVis/stable-diffusion-v1-3-diffusers"
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- pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to("cuda")
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-
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- prompt = "a photograph of an astronaut riding a horse"
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- with autocast("cuda"):
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- image = pipe(prompt, guidance_scale=7)["sample"][0] # image here is in PIL format
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-
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- image.save(f"astronaut_rides_horse.png")
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- ```
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-
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- To swap out the noise scheduler, pass it to `from_pretrained`:
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-
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- ```python
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- from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
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-
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- model_id = "CompVis/stable-diffusion-v1-3-diffusers"
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- # Use the K-LMS scheduler here instead
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- scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
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- pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True).to("cuda")
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- ```
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-
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  # Uses
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  ## Direct Use
 
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  pages = {10684-10695}
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  }
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+ ## Examples
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  ```bash
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  pip install --upgrade diffusers transformers scipy
 
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  Running the pipeline with the default PLMS scheduler:
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  ```python
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+ import torch
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  from torch import autocast
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  from diffusers import StableDiffusionPipeline
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+ model_id = "CompVis/stable-diffusion-v1-4"
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+ device = "cuda"
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+
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+ generator = torch.Generator(device=device).manual_seed(0)
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+ pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)
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+ pipe = pipe.to(device)
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  prompt = "a photograph of an astronaut riding a horse"
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  with autocast("cuda"):
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+ image = pipe(prompt)["sample"][0] # image here is in PIL format
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  image.save(f"astronaut_rides_horse.png")
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  ```
 
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  pages = {10684-10695}
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  }
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  # Uses
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  ## Direct Use