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Create app.py

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  1. app.py +201 -0
app.py ADDED
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+ import os
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+
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+ os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion")
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+ os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP")
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+
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+ import argparse
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+ from functools import partial
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+ from pathlib import Path
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+ import sys
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+ sys.path.append('./cloob-latent-diffusion')
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+ sys.path.append('./cloob-latent-diffusion/cloob-training')
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+ sys.path.append('./cloob-latent-diffusion/latent-diffusion')
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+ sys.path.append('./cloob-latent-diffusion/taming-transformers')
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+ sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch')
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+ from omegaconf import OmegaConf
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+ from PIL import Image
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+ import torch
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+ from torch import nn
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+ from torch.nn import functional as F
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+ from torchvision import transforms
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+ from torchvision.transforms import functional as TF
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+ from tqdm import trange
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+ from CLIP import clip
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+ from cloob_training import model_pt, pretrained
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+ import ldm.models.autoencoder
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+ from diffusion import sampling, utils
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+ import train_latent_diffusion as train
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+ from huggingface_hub import hf_hub_url, cached_download
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+ import random
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+
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+ # Download the model files
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+ checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt"))
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+ ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt"))
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+ ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml"))
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+
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+ # Define a few utility functions
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+
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+ def parse_prompt(prompt, default_weight=3.):
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+ if prompt.startswith('http://') or prompt.startswith('https://'):
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+ vals = prompt.rsplit(':', 2)
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+ vals = [vals[0] + ':' + vals[1], *vals[2:]]
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+ else:
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+ vals = prompt.rsplit(':', 1)
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+ vals = vals + ['', default_weight][len(vals):]
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+ return vals[0], float(vals[1])
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+
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+
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+ def resize_and_center_crop(image, size):
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+ fac = max(size[0] / image.size[0], size[1] / image.size[1])
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+ image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
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+ return TF.center_crop(image, size[::-1])
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+
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+
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+ # Load the models
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+ device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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+ print('Using device:', device)
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+ print('loading models')
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+
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+ # autoencoder
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+ ae_config = OmegaConf.load(ae_config_path)
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+ ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params)
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+ ae_model.eval().requires_grad_(False).to(device)
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+ ae_model.load_state_dict(torch.load(ae_model_path))
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+ n_ch, side_y, side_x = 4, 32, 32
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+
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+ # diffusion model
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+ model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084))
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+ model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
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+ model = model.to(device).eval().requires_grad_(False)
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+
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+ # CLOOB
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+ cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs')
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+ cloob = model_pt.get_pt_model(cloob_config)
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+ checkpoint = pretrained.download_checkpoint(cloob_config)
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+ cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint))
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+ cloob.eval().requires_grad_(False).to(device)
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+
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+
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+ # The key function: returns a list of n PIL images
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+ def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15,
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+ method='plms', eta=None):
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+ zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device)
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+ target_embeds, weights = [zero_embed], []
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+
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+ for prompt in prompts:
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+ txt, weight = parse_prompt(prompt)
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+ target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float())
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+ weights.append(weight)
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+
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+ for prompt in images:
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+ path, weight = parse_prompt(prompt)
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+ img = Image.open(utils.fetch(path)).convert('RGB')
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+ clip_size = cloob.config['image_encoder']['image_size']
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+ img = resize_and_center_crop(img, (clip_size, clip_size))
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+ batch = TF.to_tensor(img)[None].to(device)
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+ embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1)
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+ target_embeds.append(embed)
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+ weights.append(weight)
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+
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+ weights = torch.tensor([1 - sum(weights), *weights], device=device)
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+
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+ torch.manual_seed(seed)
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+
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+ def cfg_model_fn(x, t):
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+ n = x.shape[0]
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+ n_conds = len(target_embeds)
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+ x_in = x.repeat([n_conds, 1, 1, 1])
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+ t_in = t.repeat([n_conds])
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+ clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
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+ vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
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+ v = vs.mul(weights[:, None, None, None, None]).sum(0)
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+ return v
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+
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+ def run(x, steps):
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+ if method == 'ddpm':
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+ return sampling.sample(cfg_model_fn, x, steps, 1., {})
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+ if method == 'ddim':
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+ return sampling.sample(cfg_model_fn, x, steps, eta, {})
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+ if method == 'prk':
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+ return sampling.prk_sample(cfg_model_fn, x, steps, {})
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+ if method == 'plms':
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+ return sampling.plms_sample(cfg_model_fn, x, steps, {})
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+ if method == 'pie':
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+ return sampling.pie_sample(cfg_model_fn, x, steps, {})
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+ if method == 'plms2':
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+ return sampling.plms2_sample(cfg_model_fn, x, steps, {})
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+ assert False
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+
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+ batch_size = n
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+ x = torch.randn([n, n_ch, side_y, side_x], device=device)
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+ t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
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+ steps = utils.get_spliced_ddpm_cosine_schedule(t)
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+ pil_ims = []
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+ for i in trange(0, n, batch_size):
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+ cur_batch_size = min(n - i, batch_size)
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+ out_latents = run(x[i:i+cur_batch_size], steps)
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+ outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device))
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+ for j, out in enumerate(outs):
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+ pil_ims.append(utils.to_pil_image(out))
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+
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+ return pil_ims
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+
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+
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+ import gradio as gr
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+
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+ def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
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+ if seed == None :
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+ seed = random.randint(0, 10000)
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+ print( prompt, im_prompt, seed, n_steps)
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+ prompts = [prompt]
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+ im_prompts = []
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+ if im_prompt != None:
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+ im_prompts = [im_prompt]
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+ pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
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+ return pil_ims[0]
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+
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+ iface = gr.Interface(fn=gen_ims,
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+ inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"),
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+ #gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0),
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+ gr.inputs.Textbox(label="Text prompt"),
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+ gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
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+ #gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps")
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+ ],
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+ outputs=[gr.outputs.Image(type="pil", label="Generated Image")],
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+ examples=[
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+ ["Impressionism, oil on canvas"],
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+ ["Futurism, in the style of Wassily Kandinsky"],
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+ ["Art Nouveau, in the style of John Singer Sargent"],
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+ ["Surrealism, in the style of Edgar Degas"],
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+ ["Expressionism, in the style of Wassily Kandinsky"],
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+ ["Futurism, in the style of Egon Schiele"],
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+ ["Neoclassicism, in the style of Gustav Klimt"],
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+ ["Cubism, in the style of Gustav Klimt"],
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+ ["Op Art, in the style of Marc Chagall"],
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+ ["Romanticism, in the style of M.C. Escher"],
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+ ["Futurism, in the style of M.C. Escher"],
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+ ["Abstract Art, in the style of M.C. Escher"],
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+ ["Mannerism, in the style of Paul Klee"],
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+ ["Romanesque Art, in the style of Leonardo da Vinci"],
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+ ["High Renaissance, in the style of Rembrandt"],
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+ ["Magic Realism, in the style of Gustave Dore"],
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+ ["Realism, in the style of Jean-Michel Basquiat"],
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+ ["Art Nouveau, in the style of Paul Gauguin"],
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+ ["Avant-garde, in the style of Pierre-Auguste Renoir"],
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+ ["Baroque, in the style of Edward Hopper"],
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+ ["Post-Impressionism, in the style of Wassily Kandinsky"],
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+ ["Naturalism, in the style of Rene Magritte"],
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+ ["Constructivism, in the style of Paul Cezanne"],
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+ ["Abstract Expressionism, in the style of Henri Matisse"],
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+ ["Pop Art, in the style of Vincent van Gogh"],
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+ ["Futurism, in the style of Wassily Kandinsky"],
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+ ["Futurism, in the style of Zdzislaw Beksinski"],
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+ ['Surrealism, in the style of Salvador Dali'],
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+ ["Aaron Wacker, oil on canvas"]
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+ ],
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+ title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:',
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+ description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts",
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+ article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..'
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+
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+ )
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+ iface.launch(enable_queue=True) # , debug=True for colab debugging