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inference_t2i.py
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import os
os.environ["TOKENIZERS_PARALLELISM"] = "true"
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
import wandb
from models import Showo, MAGVITv2, get_mask_chedule
from training.prompting_utils import UniversalPrompting, create_attention_mask_predict_next
from training.utils import get_config, flatten_omega_conf, image_transform
from transformers import AutoTokenizer
import torch.nn.functional as F
import json
def get_vq_model_class(model_type):
if model_type == "magvitv2":
return MAGVITv2
else:
raise ValueError(f"model_type {model_type} not supported.")
if __name__ == '__main__':
config = get_config()
resume_wandb_run = config.wandb.resume
run_id = config.wandb.get("run_id", None)
if run_id is None:
resume_wandb_run = False
run_id = wandb.util.generate_id()
config.wandb.run_id = run_id
wandb_config = {k: v for k, v in flatten_omega_conf(config, resolve=True)}
wandb.init(
project="demo",
name=config.experiment.name + '_t2i' + f'_{config.mode}',
config=wandb_config,
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(config.model.showo.llm_model_path, padding_side="left")
uni_prompting = UniversalPrompting(tokenizer, max_text_len=config.dataset.preprocessing.max_seq_length,
special_tokens=("<|soi|>", "<|eoi|>", "<|sov|>", "<|eov|>", "<|t2i|>", "<|mmu|>", "<|t2v|>", "<|v2v|>", "<|lvg|>"),
ignore_id=-100, cond_dropout_prob=config.training.cond_dropout_prob)
vq_model = get_vq_model_class(config.model.vq_model.type)
vq_model = vq_model.from_pretrained(config.model.vq_model.vq_model_name).to(device)
vq_model.requires_grad_(False)
vq_model.eval()
model = Showo.from_pretrained(config.model.showo.pretrained_model_path).to(device)
model.eval()
mask_token_id = model.config.mask_token_id
# load from users passed arguments
if config.get("validation_prompts_file", None) is not None:
config.dataset.params.validation_prompts_file = config.validation_prompts_file
config.training.batch_size = config.batch_size
config.training.guidance_scale = config.guidance_scale
config.training.generation_timesteps = config.generation_timesteps
# load from users passed arguments
if config.mode == 'inpainting':
prompt = [config.prompt] * config.batch_size
inpainting_image = Image.open(config.image_path).convert("RGB")
inpainting_mask = Image.open(config.inpainting_mask_path).convert("L")
inpainting_image = image_transform(inpainting_image, resolution=config.dataset.params.resolution).to(device)
inpainting_mask = image_transform(inpainting_mask, resolution=config.dataset.params.resolution, normalize=False)
# record original image and inpainting mask
images = torch.clamp(
(torch.stack([inpainting_image, inpainting_mask.repeat(3, 1, 1).to(device)], dim=0) + 1.0) / 2.0,
min=0.0, max=1.0)
images *= 255.0
images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)
pil_images = [Image.fromarray(image) for image in images]
labels = ['original image', 'inpainting mask']
wandb_images = [wandb.Image(image, caption=labels[i]) for i, image in enumerate(pil_images)]
inpainting_image = inpainting_image.unsqueeze(0).repeat(config.training.batch_size, 1, 1, 1)
inpainting_mask = inpainting_mask.unsqueeze(0).to(device)
inpainting_mask = F.interpolate(inpainting_mask, size=config.dataset.params.resolution // 16, mode='bicubic')
inpainting_mask = inpainting_mask.repeat(config.training.batch_size, 1, 1, 1)
inpainting_mask[inpainting_mask < 0.5] = 0
inpainting_mask[inpainting_mask >= 0.5] = 1
inpainting_mask = inpainting_mask.reshape(config.training.batch_size, -1)
inpainting_mask = inpainting_mask.to(torch.bool)
inpainting_image_tokens = vq_model.get_code(inpainting_image) + len(uni_prompting.text_tokenizer)
inpainting_image_tokens[inpainting_mask] = mask_token_id
input_ids, _ = uni_prompting((prompt, inpainting_image_tokens), 't2i_gen')
if config.training.guidance_scale > 0:
uncond_input_ids, _ = uni_prompting(([''] * len(prompt), inpainting_image_tokens), 't2i_gen')
attention_mask = create_attention_mask_predict_next(torch.cat([input_ids, uncond_input_ids], dim=0),
pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
rm_pad_in_image=True)
else:
attention_mask = create_attention_mask_predict_next(input_ids,
pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
rm_pad_in_image=True)
uncond_input_ids = None
if config.get("mask_schedule", None) is not None:
schedule = config.mask_schedule.schedule
args = config.mask_schedule.get("params", {})
mask_schedule = get_mask_chedule(schedule, **args)
else:
mask_schedule = get_mask_chedule(config.training.get("mask_schedule", "cosine"))
with torch.no_grad():
gen_token_ids = model.t2i_generate(
input_ids=input_ids,
uncond_input_ids=uncond_input_ids,
attention_mask=attention_mask,
guidance_scale=config.training.guidance_scale,
temperature=config.training.get("generation_temperature", 1.0),
timesteps=config.training.generation_timesteps,
noise_schedule=mask_schedule,
noise_type=config.training.get("noise_type", "mask"),
seq_len=config.model.showo.num_vq_tokens,
uni_prompting=uni_prompting,
config=config,
)
gen_token_ids = torch.clamp(gen_token_ids, max=config.model.showo.codebook_size - 1, min=0)
images = vq_model.decode_code(gen_token_ids)
images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
images *= 255.0
images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)
pil_images = [Image.fromarray(image) for image in images]
# import ipdb
# ipdb.set_trace()
wandb_images.extend([wandb.Image(image, caption=prompt[i]) for i, image in enumerate(pil_images)])
wandb.log({"generated_images": wandb_images}, step=0)
elif config.mode == 'extrapolation':
prompt = [p for p in config.prompt.split(" *** ") if len(p) != 0]
extra_direction = [d for d in config.extra_direction.split(" *** ") if len(d) != 0]
print(prompt, extra_direction)
W = config.dataset.params.resolution // 16
for id, (prt, direction) in enumerate(zip(prompt, extra_direction)):
prt = [prt] * config.training.batch_size
if id == 0:
extrapolation_image = Image.open(config.image_path).convert("RGB")
extrapolation_image = image_transform(extrapolation_image,
resolution=config.dataset.params.resolution).to(device)
B, _, _ = extrapolation_image.shape
extrapolation_image = extrapolation_image.unsqueeze(0)
extrapolation_image_tokens = vq_model.get_code(extrapolation_image) + len(uni_prompting.text_tokenizer)
extrapolation_image_tokens = extrapolation_image_tokens.reshape(1,
config.dataset.params.resolution // 16,
config.dataset.params.resolution // 16)
extrapolation_image_tokens = extrapolation_image_tokens.repeat(config.training.batch_size, 1, 1)
else:
extrapolation_image_tokens = gen_token_ids + len(uni_prompting.text_tokenizer)
image_left_part = extrapolation_image_tokens[:, :, :-(W//2-config.offset)] - len(uni_prompting.text_tokenizer)
image_right_part = extrapolation_image_tokens[:, :, W//2-config.offset:] - len(uni_prompting.text_tokenizer)
image_up_part = extrapolation_image_tokens[:, :-(W//2-config.offset), :] - len(uni_prompting.text_tokenizer)
image_down_part = extrapolation_image_tokens[:, W//2-config.offset:, :] - len(uni_prompting.text_tokenizer)
if direction in ['left', 'right']:
extrapolation_mask = torch.zeros((config.training.batch_size,
config.dataset.params.resolution // 16,
config.dataset.params.resolution // 16 // 2 + config.offset),
dtype=torch.int64, device=device) + mask_token_id
else:
extrapolation_mask = torch.zeros((config.training.batch_size,
config.dataset.params.resolution // 16 // 2 + config.offset,
config.dataset.params.resolution // 16),
dtype=torch.int64, device=device) + mask_token_id
if direction == 'left':
extrapolation_image_tokens = torch.cat(
[extrapolation_mask, extrapolation_image_tokens[:, :, :W//2-config.offset]], dim=-1)
elif direction == 'right':
extrapolation_image_tokens = torch.cat(
[extrapolation_image_tokens[:, :, -(W//2-config.offset):], extrapolation_mask], dim=-1)
elif direction == 'up':
extrapolation_image_tokens = torch.cat(
[extrapolation_mask, extrapolation_image_tokens[:, :W // 2 - config.offset, :]], dim=-2)
else:
extrapolation_image_tokens = torch.cat(
[extrapolation_image_tokens[:, -(W // 2 - config.offset):, :], extrapolation_mask], dim=-2)
extrapolation_image_tokens = extrapolation_image_tokens.reshape(config.training.batch_size, -1)
input_ids, _ = uni_prompting((prt, extrapolation_image_tokens), 't2i_gen')
if config.training.guidance_scale > 0:
uncond_input_ids, _ = uni_prompting(([''] * len(prt), extrapolation_image_tokens), 't2i_gen')
attention_mask = create_attention_mask_predict_next(torch.cat([input_ids, uncond_input_ids], dim=0),
pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
rm_pad_in_image=True)
else:
attention_mask = create_attention_mask_predict_next(input_ids,
pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
rm_pad_in_image=True)
uncond_input_ids = None
if config.get("mask_schedule", None) is not None:
schedule = config.mask_schedule.schedule
args = config.mask_schedule.get("params", {})
mask_schedule = get_mask_chedule(schedule, **args)
else:
mask_schedule = get_mask_chedule(config.training.get("mask_schedule", "cosine"))
with torch.no_grad():
gen_token_ids = model.t2i_generate(
input_ids=input_ids,
uncond_input_ids=uncond_input_ids,
attention_mask=attention_mask,
guidance_scale=config.training.guidance_scale,
temperature=config.training.get("generation_temperature", 1.0),
timesteps=config.training.generation_timesteps,
noise_schedule=mask_schedule,
noise_type=config.training.get("noise_type", "mask"),
seq_len=config.model.showo.num_vq_tokens,
uni_prompting=uni_prompting,
config=config,
)
gen_token_ids = torch.clamp(gen_token_ids, max=config.model.showo.codebook_size - 1, min=0)
gen_token_ids = gen_token_ids.reshape(config.training.batch_size,
config.dataset.params.resolution // 16,
config.dataset.params.resolution // 16)
if direction == 'left':
gen_token_ids = torch.cat([gen_token_ids, image_right_part], dim=-1)
elif direction == 'right':
gen_token_ids = torch.cat([image_left_part, gen_token_ids], dim=-1)
elif direction == 'up':
gen_token_ids = torch.cat([gen_token_ids, image_down_part], dim=-2)
else:
gen_token_ids = torch.cat([image_left_part, gen_token_ids], dim=-2)
_, h, w = gen_token_ids.shape
gen_token_ids = gen_token_ids.reshape(config.training.batch_size, -1)
images = vq_model.decode_code(gen_token_ids, shape=(h, w))
images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
images *= 255.0
images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)
pil_images = [Image.fromarray(image) for image in images]
wandb_images = [wandb.Image(image, caption=' '.join(prompt)) for i, image in enumerate(pil_images)]
wandb.log({"generated_images": wandb_images}, step=0)
elif config.mode == 't2i':
with open('datasets/journeydb/initial_data.json', 'r') as f:
datas = json.load(f)
for i in tqdm(range(len(datas))):
output_dir = os.path.join("datasets/journeydb/generated_images", f'{datas[i]["id"]}')
os.makedirs(output_dir, exist_ok=True)
prompts = [datas[i]["caption"]] * 16
image_tokens = torch.ones((len(prompts), config.model.showo.num_vq_tokens),
dtype=torch.long, device=device) * mask_token_id
input_ids, _ = uni_prompting((prompts, image_tokens), 't2i_gen')
if config.training.guidance_scale > 0:
uncond_input_ids, _ = uni_prompting(([''] * len(prompts), image_tokens), 't2i_gen')
attention_mask = create_attention_mask_predict_next(torch.cat([input_ids, uncond_input_ids], dim=0),
pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
rm_pad_in_image=True)
else:
attention_mask = create_attention_mask_predict_next(input_ids,
pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
rm_pad_in_image=True)
uncond_input_ids = None
if config.get("mask_schedule", None) is not None:
schedule = config.mask_schedule.schedule
args = config.mask_schedule.get("params", {})
mask_schedule = get_mask_chedule(schedule, **args)
else:
mask_schedule = get_mask_chedule(config.training.get("mask_schedule", "cosine"))
with torch.no_grad():
gen_token_ids = model.t2i_generate(
input_ids=input_ids,
uncond_input_ids=uncond_input_ids,
attention_mask=attention_mask,
guidance_scale=config.training.guidance_scale,
temperature=config.training.get("generation_temperature", 1.0),
timesteps=config.training.generation_timesteps,
noise_schedule=mask_schedule,
noise_type=config.training.get("noise_type", "mask"),
seq_len=config.model.showo.num_vq_tokens,
uni_prompting=uni_prompting,
config=config,
)
gen_token_ids = torch.clamp(gen_token_ids, max=config.model.showo.codebook_size - 1, min=0)
images = vq_model.decode_code(gen_token_ids)
images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
images *= 255.0
images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)
for j, image in enumerate(images):
save_path = os.path.join(output_dir, f"{j}.png")
Image.fromarray(image).save(save_path)