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add diffusers example

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  1. README.md +43 -0
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@@ -60,6 +60,49 @@ The model is intended for research purposes only. Possible research areas and ta
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  Excluded uses are described below.
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  ### Out-of-Scope Use
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  The model was not trained to be factual or true representations of people or events,
 
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  Excluded uses are described below.
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+ ### Diffusers
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+
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+ ```
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+ pip install diffusers transformers accelerate --upgrade
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+ ```
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+
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+ - **Text-to-image**:
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+
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+ SDXL-Turbo does not make use of `guidance_scale` or `negative_prompt`, we disable it with `guidance_scale=0.0`.
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+ Preferably, the model generates images of size 512x512 but higher image sizes work as well.
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+ A **single step** is enough to generate high quality images.
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+
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+ ```py
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+ from diffusers import AutoPipelineForText2Image
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+ import torch
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+
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+ pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
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+ pipe.to("cuda")
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+
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+ prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
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+
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+ image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
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+ ```
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+
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+ - **Image-to-image**:
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+
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+ When using SDXL-Turbo for image-to-image generation, make sure that `num_inference_steps` * `strength` is larger or equal
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+ to 1. The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, *e.g.* 0.5 * 2.0 = 1 step in our example
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+ below.
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+
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+ ```py
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+ from diffusers import AutoPipelineForImage2Image
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+ from diffusers.utils import load_image
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+
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+ pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
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+
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+ init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512))
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+
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+ prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
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+
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+ image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0]
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+ ```
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+
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  ### Out-of-Scope Use
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  The model was not trained to be factual or true representations of people or events,