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from diffusers import DiffusionPipeline |
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import gradio as gr |
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import torch |
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import math |
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orig_start_prompt = "a photograph of an adult lion" |
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orig_end_prompt = "a photograph of a lion cub" |
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if torch.cuda.is_available(): |
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device = "cuda" |
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dtype = torch.float16 |
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else: |
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device = "cpu" |
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dtype = torch.bfloat16 |
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pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=dtype, custom_pipeline='unclip_text_interpolation') |
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pipe.to(device) |
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def unclip_text_interpolation( |
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start_prompt, |
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end_prompt, |
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steps, |
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seed |
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): |
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generator = torch.Generator() |
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generator.manual_seed(seed) |
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output = pipe(start_prompt, end_prompt, steps, enable_sequential_cpu_offload=False, generator=generator) |
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return output.images |
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inputs = [ |
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gr.Textbox(lines=2, default=orig_start_prompt, label="Start Prompt"), |
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gr.Textbox(lines=2, default=orig_end_prompt, label="End Prompt"), |
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gr.Slider(minimum=2, maximum=12, default=5, step=1, label="Steps"), |
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gr.Number(0, label="Seed", precision=0) |
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] |
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output = gr.Gallery( |
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label="Generated images", show_label=False, elem_id="gallery" |
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).style(grid=[2], height="auto") |
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examples = [ |
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[orig_start_prompt, orig_end_prompt, 5, 42], |
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["a photo of a landscape in winter","a photo of a landscape in fall", 5, 20], |
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["a photo of a victorian house", "a photo of a modern house", 5, 20] |
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] |
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title = "UnClip Text Interpolation Pipeline" |
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demo_app = gr.Interface( |
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fn=unclip_text_interpolation, |
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inputs=inputs, |
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outputs=output, |
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title=title, |
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theme='huggingface', |
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examples=examples, |
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cache_examples=False |
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) |
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demo_app.launch(debug=True, enable_queue=True) |