Delete app.py

app.py

@@ -1,201 +0,0 @@
-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])
-
-
-def resize_and_center_crop(image, size):
-    fac = max(size[0] / image.size[0], size[1] / image.size[1])
-    image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
-    return TF.center_crop(image, size[::-1])
-
-
-# Load the models
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-print('Using device:', device)
-print('loading models')
-
-# autoencoder
-ae_config = OmegaConf.load(ae_config_path)
-ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params)
-ae_model.eval().requires_grad_(False).to(device)
-ae_model.load_state_dict(torch.load(ae_model_path))
-n_ch, side_y, side_x = 4, 32, 32
-
-# diffusion model
-model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084))
-model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
-model = model.to(device).eval().requires_grad_(False)
-
-# CLOOB
-cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs')
-cloob = model_pt.get_pt_model(cloob_config)
-checkpoint = pretrained.download_checkpoint(cloob_config)
-cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint))
-cloob.eval().requires_grad_(False).to(device)
-
-
-# The key function: returns a list of n PIL images
-def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15,
-             method='plms', eta=None):
-  zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device)
-  target_embeds, weights = [zero_embed], []
-
-  for prompt in prompts:
-      txt, weight = parse_prompt(prompt)
-      target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float())
-      weights.append(weight)
-
-  for prompt in images:
-      path, weight = parse_prompt(prompt)
-      img = Image.open(utils.fetch(path)).convert('RGB')
-      clip_size = cloob.config['image_encoder']['image_size']
-      img = resize_and_center_crop(img, (clip_size, clip_size))
-      batch = TF.to_tensor(img)[None].to(device)
-      embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1)
-      target_embeds.append(embed)
-      weights.append(weight)
-
-  weights = torch.tensor([1 - sum(weights), *weights], device=device)
-
-  torch.manual_seed(seed)
-
-  def cfg_model_fn(x, t):
-      n = x.shape[0]
-      n_conds = len(target_embeds)
-      x_in = x.repeat([n_conds, 1, 1, 1])
-      t_in = t.repeat([n_conds])
-      clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
-      vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
-      v = vs.mul(weights[:, None, None, None, None]).sum(0)
-      return v
-
-  def run(x, steps):
-      if method == 'ddpm':
-          return sampling.sample(cfg_model_fn, x, steps, 1., {})
-      if method == 'ddim':
-          return sampling.sample(cfg_model_fn, x, steps, eta, {})
-      if method == 'prk':
-          return sampling.prk_sample(cfg_model_fn, x, steps, {})
-      if method == 'plms':
-          return sampling.plms_sample(cfg_model_fn, x, steps, {})
-      if method == 'pie':
-          return sampling.pie_sample(cfg_model_fn, x, steps, {})
-      if method == 'plms2':
-          return sampling.plms2_sample(cfg_model_fn, x, steps, {})
-      assert False
-
-  batch_size = n
-  x = torch.randn([n, n_ch, side_y, side_x], device=device)
-  t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
-  steps = utils.get_spliced_ddpm_cosine_schedule(t)
-  pil_ims = []
-  for i in trange(0, n, batch_size):
-      cur_batch_size = min(n - i, batch_size)
-      out_latents = run(x[i:i+cur_batch_size], steps)
-      outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device))
-      for j, out in enumerate(outs):
-          pil_ims.append(utils.to_pil_image(out))
-
-  return pil_ims
-  
-  
-import gradio as gr
-
-def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
-  if seed == None :
-    seed = random.randint(0, 10000)
-  print( prompt, im_prompt, seed, n_steps)
-  prompts = [prompt]
-  im_prompts = []
-  if im_prompt != None:
-    im_prompts = [im_prompt]
-  pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
-  return pil_ims[0]
-
-iface = gr.Interface(fn=gen_ims, 
-  inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"),
-          #gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0),
-          gr.inputs.Textbox(label="Text prompt"),
-          gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
-          #gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps")
-          ], 
-  outputs=[gr.outputs.Image(type="pil", label="Generated Image")],
-  examples=[
-  ["Impressionism, oil on canvas"],
-  ["Futurism, in the style of Wassily Kandinsky"], 
-  ["Art Nouveau, in the style of John Singer Sargent"], 
-  ["Surrealism, in the style of Edgar Degas"], 
-  ["Expressionism, in the style of Wassily Kandinsky"], 
-  ["Futurism, in the style of Egon Schiele"], 
-  ["Neoclassicism, in the style of Gustav Klimt"], 
-  ["Cubism, in the style of Gustav Klimt"], 
-  ["Op Art, in the style of Marc Chagall"], 
-  ["Romanticism, in the style of M.C. Escher"], 
-  ["Futurism, in the style of M.C. Escher"], 
-  ["Abstract Art, in the style of M.C. Escher"], 
-  ["Mannerism, in the style of Paul Klee"], 
-  ["Romanesque Art, in the style of Leonardo da Vinci"], 
-  ["High Renaissance, in the style of Rembrandt"], 
-  ["Magic Realism, in the style of Gustave Dore"], 
-  ["Realism, in the style of Jean-Michel Basquiat"], 
-  ["Art Nouveau, in the style of Paul Gauguin"], 
-  ["Avant-garde, in the style of Pierre-Auguste Renoir"], 
-  ["Baroque, in the style of Edward Hopper"], 
-  ["Post-Impressionism, in the style of Wassily Kandinsky"], 
-  ["Naturalism, in the style of Rene Magritte"], 
-  ["Constructivism, in the style of Paul Cezanne"], 
-  ["Abstract Expressionism, in the style of Henri Matisse"], 
-  ["Pop Art, in the style of Vincent van Gogh"], 
-  ["Futurism, in the style of Wassily Kandinsky"], 
-  ["Futurism, in the style of Zdzislaw Beksinski"], 
-  ['Surrealism, in the style of Salvador Dali'], 
-  ["Aaron Wacker, oil on canvas"]
-  ],
-  title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:',
-  description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts",
-  article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..'
-
-)
-iface.launch(enable_queue=True) # , debug=True for colab debugging