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115 lines
4.8 KiB
115 lines
4.8 KiB
# -*- coding: utf-8 -*-
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import os
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from glob import glob
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from os.path import join
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import torch
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import torchvision
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import transformers
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import more_itertools
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import numpy as np
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import matplotlib.pyplot as plt
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from tqdm.auto import tqdm
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from . import utils
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def generate_images(text, tokenizer, dalle, vae, top_k, top_p, images_num, image_prompts=None, temperature=1.0, bs=8,
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seed=None, use_cache=True):
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# TODO docstring
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if seed is not None:
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utils.seed_everything(seed)
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vocab_size = dalle.get_param('vocab_size')
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text_seq_length = dalle.get_param('text_seq_length')
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image_seq_length = dalle.get_param('image_seq_length')
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total_seq_length = dalle.get_param('total_seq_length')
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device = dalle.get_param('device')
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text = text.lower().strip()
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input_ids = tokenizer.encode_text(text, text_seq_length=text_seq_length)
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pil_images, scores = [], []
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for chunk in more_itertools.chunked(range(images_num), bs):
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chunk_bs = len(chunk)
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with torch.no_grad():
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attention_mask = torch.tril(torch.ones((chunk_bs, 1, total_seq_length, total_seq_length), device=device))
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out = input_ids.unsqueeze(0).repeat(chunk_bs, 1).to(device)
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has_cache = False
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sample_scores = []
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if image_prompts is not None:
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prompts_idx, prompts = image_prompts.image_prompts_idx, image_prompts.image_prompts
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prompts = prompts.repeat(chunk_bs, 1)
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for idx in tqdm(range(out.shape[1], total_seq_length)):
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idx -= text_seq_length
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if image_prompts is not None and idx in prompts_idx:
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out = torch.cat((out, prompts[:, idx].unsqueeze(1)), dim=-1)
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else:
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logits, has_cache = dalle(out, attention_mask,
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has_cache=has_cache, use_cache=use_cache, return_loss=False)
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logits = logits[:, -1, vocab_size:]
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logits /= temperature
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filtered_logits = transformers.top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
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probs = torch.nn.functional.softmax(filtered_logits, dim=-1)
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sample = torch.multinomial(probs, 1)
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sample_scores.append(probs[torch.arange(probs.size(0)), sample.transpose(0, 1)])
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out = torch.cat((out, sample), dim=-1)
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codebooks = out[:, -image_seq_length:]
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images = vae.decode(codebooks)
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pil_images += utils.torch_tensors_to_pil_list(images)
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scores += torch.cat(sample_scores).sum(0).detach().cpu().numpy().tolist()
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return pil_images, scores
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def super_resolution(pil_images, realesrgan):
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result = []
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for pil_image in pil_images:
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with torch.no_grad():
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sr_image = realesrgan.predict(np.array(pil_image))
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result.append(sr_image)
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return result
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def cherry_pick_by_clip(pil_images, text, ruclip, ruclip_processor, device='cpu', count=4):
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with torch.no_grad():
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inputs = ruclip_processor(text=text, images=pil_images)
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for key in inputs.keys():
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inputs[key] = inputs[key].to(device)
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outputs = ruclip(**inputs)
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sims = outputs.logits_per_image.view(-1).softmax(dim=0)
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items = []
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for index, sim in enumerate(sims.cpu().numpy()):
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items.append({'img_index': index, 'cosine': sim})
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items = sorted(items, key=lambda x: x['cosine'], reverse=True)[:count]
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top_pil_images = [pil_images[x['img_index']] for x in items]
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top_scores = [x['cosine'] for x in items]
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return top_pil_images, top_scores
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def show(pil_images, nrow=4, save_dir=None, show=True):
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"""
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:param pil_images: list of images in PIL
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:param nrow: number of rows
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:param save_dir: dir for separately saving of images, example: save_dir='./pics'
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"""
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if save_dir is not None:
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os.makedirs(save_dir, exist_ok=True)
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count = len(glob(join(save_dir, 'img_*.png')))
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for i, pil_image in enumerate(pil_images):
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pil_image.save(join(save_dir, f'img_{count+i}.png'))
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imgs = torchvision.utils.make_grid(utils.pil_list_to_torch_tensors(pil_images), nrow=nrow)
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if not isinstance(imgs, list):
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imgs = [imgs.cpu()]
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fix, axs = plt.subplots(ncols=len(imgs), squeeze=False, figsize=(14, 14))
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for i, img in enumerate(imgs):
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img = img.detach()
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img = torchvision.transforms.functional.to_pil_image(img)
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if save_dir is not None:
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count = len(glob(join(save_dir, 'group_*.png')))
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img.save(join(save_dir, f'group_{count+i}.png'))
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if show:
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axs[0, i].imshow(np.asarray(img))
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axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
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if show:
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fix.show()
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plt.show()
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