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| from diffusers import DiffusionPipeline | |
| import gradio as gr | |
| import torch | |
| import math | |
| import PIL | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| dtype = torch.float16 | |
| else: | |
| device = "cpu" | |
| dtype = torch.bfloat16 | |
| pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=dtype, custom_pipeline='unclip_image_interpolation') | |
| pipe.to(device) | |
| def unclip_image_interpolation( | |
| start_image, | |
| end_image, | |
| steps, | |
| seed | |
| ): | |
| generator = torch.Generator() | |
| generator.manual_seed(seed) | |
| images = [start_image, end_image] | |
| output = pipe(image=images, steps=steps, generator=generator) | |
| return output.images | |
| inputs = [ | |
| gr.Image(type="pil"), | |
| gr.Image(type="pil"), | |
| gr.Slider(minimum=2, maximum=12, default=5, step=1, label="Steps"), | |
| gr.Number(0, label="Seed", precision=0) | |
| ] | |
| output = gr.Gallery( | |
| label="Generated images", show_label=False, elem_id="gallery" | |
| ).style(grid=[2], height="auto") | |
| examples = [ | |
| ["starry_night.jpg","dogs.jpg", 5, 20], | |
| ["flowers.jpg", "dogs.jpg", 5, 42], | |
| ["starry_night.jpg","flowers.jpg", 6, 9011] | |
| ] | |
| title = "UnClip Image Interpolation Pipeline" | |
| demo_app = gr.Interface( | |
| fn=unclip_image_interpolation, | |
| inputs=inputs, | |
| outputs=output, | |
| title=title, | |
| theme='huggingface', | |
| examples=examples, | |
| cache_examples=False | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) |