latent_DDCM_CCFG.py

import spaces
import torch
import numpy as np
import gradio as gr

from util.file import generate_binary_file, load_numpy_from_binary_bitwise
from latent_utils import generate_ours


@torch.no_grad()
@spaces.GPU(duration=80)
def main(prompt, T, K, K_tilde, model_type='512x512', bitstream=None, avail_models=None,
         progress=gr.Progress(track_tqdm=True)):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    indices = load_numpy_from_binary_bitwise(bitstream, K, T, model_type, T - 1)
    if indices is not None:
        indices = indices.to(device)

    # model, _ = load_model(img_size_to_id[img_size], T, device, float16=True, compile=False)
    model = avail_models[model_type].to(device)

    model.device = device
    model.model.to(device=device)

    model.model.scheduler.device = device

    model.set_timesteps(T, device=device)

    with torch.no_grad():
        x, indices = generate_ours(model,
                                   num_noises=K,
                                   num_noises_to_optimize=K_tilde,
                                   prompt=prompt,
                                   negative_prompt=None,
                                   indices=indices)
    x = (x / 2 + 0.5).clamp(0, 1)
    x = x.detach().cpu().squeeze().numpy()
    x = np.transpose(x, (1, 2, 0))
    torch.cuda.empty_cache()

    if bitstream is None:
        indices = generate_binary_file(indices.numpy(), K, T, model_type)
        return x, indices
    return x