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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_perturb_images(
prompt,
seed,
num_inference_steps,
num_images,
perturbation_size,
progress=gr.Progress(),
):
text_embeddings = get_text_embeddings(prompt)
latents_x = generate_latents(seed)
scale_x = torch.cos(
torch.linspace(0, 2, num_images) * torch.pi * perturbation_size / 4
).to(torch_device)
noise_x = torch.tensordot(scale_x, latents_x, dims=0)
progress(0)
images = []
images.append(
(
generate_images(latents_x, text_embeddings, num_inference_steps),
"{}".format(1),
)
)
for i in range(num_images):
np.random.seed(i)
progress(i / (num_images))
latents_y = generate_latents(np.random.randint(0, 100000))
scale_y = torch.sin(
torch.linspace(0, 2, num_images) * torch.pi * perturbation_size / 4
).to(torch_device)
noise_y = torch.tensordot(scale_y, latents_y, dims=0)
noise = noise_x + noise_y
image = generate_images(
noise[num_images - 1], text_embeddings, num_inference_steps
)
images.append((image, "{}".format(i + 2)))
fname = "perturbations"
tab_config = {
"Tab": "Perturbations",
"Prompt": prompt,
"Number of Perturbations": num_images,
"Perturbation Size": perturbation_size,
"Number of Inference Steps per Image": num_inference_steps,
"Seed": seed,
}
export_as_zip(images, fname, tab_config)
return images, f"outputs/{fname}.zip"
__all__ = ["display_perturb_images"]
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