Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,743 Bytes
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import torch
import spaces
import numpy as np
import gradio as gr
from src.util.base import *
from src.util.params import *
@spaces.GPU()
def display_circular_images(
prompt, seed, num_inference_steps, num_images, start_degree, end_degree, progress=gr.Progress()
):
np.random.seed(seed)
num_images += 1
text_embeddings = get_text_embeddings(prompt)
latents_x = generate_latents(seed)
latents_y = generate_latents(seed * np.random.randint(0, 100000))
scale_x = torch.cos(
torch.linspace(start_degree, end_degree, num_images) * torch.pi / 180
).to(torch_device)
scale_y = torch.sin(
torch.linspace(start_degree, end_degree, num_images) * torch.pi / 180
).to(torch_device)
noise_x = torch.tensordot(scale_x, latents_x, dims=0)
noise_y = torch.tensordot(scale_y, latents_y, dims=0)
noise = noise_x + noise_y
progress(0)
images = []
for i in range(num_images):
progress(i / num_images)
image = generate_images(noise[i], text_embeddings, num_inference_steps)
images.append((image, str(start_degree + i*(end_degree-start_degree)/(num_images-1))))
progress(1, desc="Exporting as gif")
export_as_gif(images, filename="circular.gif")
fname = "circular"
tab_config = {
"Tab": "Circular",
"Prompt": prompt,
"Number of Steps around the Circle": num_images,
"Start Proportion of Circle": start_degree,
"End Proportion of Circle": end_degree,
"Number of Inference Steps per Image": num_inference_steps,
"Seed": seed,
}
export_as_zip(images, fname, tab_config)
return images, "outputs/circular.gif", f"outputs/{fname}.zip"
__all__ = ["display_circular_images"]
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