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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(images, steps, enable_sequential_cpu_offload=False, 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=True | |
) | |
demo_app.launch(debug=True, enable_queue=True) |