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README.md
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@@ -63,6 +63,37 @@ According to our evaluation, Stable Cascade performs best in both prompt alignme
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comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
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aesthetic prompts. Specifically, the comparison was held against Playground v2, SDXL Turbo, SDXL and Würstchen v2.
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## Uses
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comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
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aesthetic prompts. Specifically, the comparison was held against Playground v2, SDXL Turbo, SDXL and Würstchen v2.
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## Code Example
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```python
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import torch
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from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
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device = "cuda"
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dtype = torch.bfloat16
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num_images_per_prompt = 2
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prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=dtype).to(device)
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decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=dtype).to(device)
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prompt = "Anthropomorphic cat dressed as a pilot"
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negative_prompt = ""
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prior_output = prior_pipeline(
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prompt=caption,
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height=1024,
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width=1024,
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negative_prompt=negative_prompt,
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guidance_scale=4.0,
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num_images_per_prompt=num_images_per_prompt,
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)
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decoder_output = decoder_pipeline(
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image_embeddings=prior_output.image_embeddings,
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prompt=caption,
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negative_prompt=negative_prompt,
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guidance_scale=0.0,
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output_type="pil",
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).images
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```
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## Uses
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