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