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| 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==1.6.5 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=[["An iceberg, oil on canvas"],["A martian landscape, in the style of Monet"], ['A peaceful meadow, pastel crayons'], ["A painting of a vase of flowers"], ["A ship leaving the port in the summer, oil on canvas"]], | |
| title='Generate art from text prompts :', | |
| description="By typing a text prompt or providing an image prompt, and pressing submit you can generate images based on this prompt. The model was trained on images from the [WikiArt](https://huggingface.co/datasets/huggan/wikiart) dataset, comprised mostly of paintings.", | |
| article = 'The model is a distilled version of a cloob-conditioned latent diffusion model fine-tuned on the WikiArt dataset. You can find more information on this model on the [model card](https://huggingface.co/huggan/distill-ccld-wa). The student model training and this demo were done by [@gigant](https://huggingface.co/gigant). The teacher model was trained by [@johnowhitaker](https://huggingface.co/johnowhitaker)' | |
| ) | |
| iface.launch(enable_queue=True) # , debug=True for colab debugging |