Create new file

app.py

@@ -0,0 +1,46 @@
+import os
+
+os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion")
+os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP")
+
+import argparse
+from functools import partial
+from pathlib import Path
+import sys
+sys.path.append('./cloob-latent-diffusion')
+sys.path.append('./cloob-latent-diffusion/cloob-training')
+sys.path.append('./cloob-latent-diffusion/latent-diffusion')
+sys.path.append('./cloob-latent-diffusion/taming-transformers')
+sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch')
+from omegaconf import OmegaConf
+from PIL import Image
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torchvision import transforms
+from torchvision.transforms import functional as TF
+from tqdm import trange
+from CLIP import clip
+from cloob_training import model_pt, pretrained
+import ldm.models.autoencoder
+from diffusion import sampling, utils
+import train_latent_diffusion as train
+from huggingface_hub import hf_hub_url, cached_download
+import random
+
+# Download the model files
+checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt"))
+ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt"))
+ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml"))
+
+# Define a few utility functions
+
+
+def parse_prompt(prompt, default_weight=3.):
+    if prompt.startswith('http://') or prompt.startswith('https://'):
+        vals = prompt.rsplit(':', 2)
+        vals = [vals[0] + ':' + vals[1], *vals[2:]]
+    else:
+        vals = prompt.rsplit(':', 1)
+    vals = vals + ['', default_weight][len(vals):]
+    return vals[0], float(vals[1])