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train_inference.py
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train_inference.py
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import os
import math
import random
import sys
import matplotlib.pyplot as plt
import imageio
import numpy as np
from PIL import Image, ImageSequence
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from einops import rearrange
from typing import Any, Callable, Dict, List, Optional, Union, Tuple
from tqdm.auto import tqdm
import click
from omegaconf import OmegaConf
import yaml
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import to_tensor
import torch.fft as fft
from pytorch_wavelets import DWT1DForward, DWT1DInverse
from diffusers import DDPMScheduler, DDIMScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler
from diffusers import IFSuperResolutionPipeline, DiffusionPipeline, VideoToVideoSDPipeline
from diffusers.utils import export_to_video, randn_tensor
from diffusers.optimization import get_scheduler
from showone.models import UNet3DConditionModel
from showone.pipelines import TextToVideoIFPipeline, TextToVideoIFInterpPipeline, TextToVideoIFSuperResolutionPipeline
from showone.pipelines.pipeline_t2v_base_pixel import tensor2vid
from showone.pipelines.pipeline_t2v_sr_pixel_cond import TextToVideoIFSuperResolutionPipeline_Cond
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from data.dataset import ImageSequenceDataset
from common.image_util import make_grid, annotate_image, save_images_as_gif
from common.util import get_time_string, get_function_args, ddim_inversion
logger = get_logger(__name__)
def collate_fn(examples):
batch = {
"prompt_ids": torch.cat([example["prompt_ids"] for example in examples], dim=0),
"images": torch.stack([example["images"] for example in examples]),
}
return batch
def log_train_samples(
train_dataloader,
save_path,
num_batch: int = 4):
train_samples = []
for idx, batch in enumerate(train_dataloader):
if idx >= num_batch:
break
train_samples.append(batch["images"])
train_samples = torch.cat(train_samples).numpy()
train_samples = rearrange(train_samples, "b c f h w -> b f h w c")
train_samples = (train_samples * 0.5 + 0.5).clip(0, 1)
train_samples = numpy_to_pil(train_samples)
train_samples = [make_grid(images, cols=1) for images in zip(*train_samples)]
save_images_as_gif(train_samples, save_path)
def numpy_to_pil(images):
pil_images = []
for sequence in images:
pil_images.append(TextToVideoIFPipeline.numpy_to_pil(sequence))
return pil_images
def w_low_freq_local(height, width, delta=0.05, base=1.):
rows = torch.arange(height, dtype=torch.float32)
cols = torch.arange(width, dtype=torch.float32)
rows, cols = torch.meshgrid(rows, cols)
coefficient_matrix = (rows - height / 2)**2 + (cols - width / 2)**2
w_low_freq = ((height/2) ** 2 + (width/2) ** 2) ** delta - coefficient_matrix ** delta + base
return w_low_freq
def sma_local(images, v0hat, accelerator, delta=0.05, base=1.):
b,c,f,h,w = images.shape
img_residuals = torch.abs(images[:, :, 1:, :, :] - images[:, :, :-1, :, :])
fft_img_residuals = fft.fftn(img_residuals.float(), dim=(-2, -1))
fft_img_residuals = fft.fftshift(fft_img_residuals, dim=(-2, -1))
magnitude_img_residuals = torch.abs(fft_img_residuals)
phase_img_residuals = torch.angle(fft_img_residuals)
v0hat_residuals = torch.abs(v0hat[:, :, 1:, :, :] - v0hat[:, :, :-1, :, :])
fft_v0hat_residuals = fft.fftn(v0hat_residuals.float(), dim=(-2, -1))
fft_v0hat_residuals = fft.fftshift(fft_v0hat_residuals, dim=(-2, -1))
magnitude_v0hat_residuals = torch.abs(fft_v0hat_residuals)
phase_v0hat_residuals = torch.angle(fft_v0hat_residuals)
w_low_freq = w_low_freq_local(h, w, delta=delta, base=base).to(accelerator.device).reshape(1,1,1,h,w)
loss_sma_mag = torch.mean(torch.abs(magnitude_img_residuals.float() - magnitude_v0hat_residuals.float()) * w_low_freq)
loss_sma_phase = torch.mean(torch.abs(phase_img_residuals.float() - phase_v0hat_residuals.float()) * w_low_freq)
loss_sma_local = loss_sma_mag + loss_sma_phase
return loss_sma_local
def sma_global(images, v0hat, wavelet_type='haar', num_levels=3, ld_levels=[1., 1., 1., 1.]):
b,c,f,h,w = images.shape
images = images.permute(0, 1, 3, 4, 2).reshape(b, c*h*w, f).float()
v0hat = v0hat.permute(0, 1, 3, 4, 2).reshape(b, c*h*w, f).float()
img_residuals = torch.abs(images[:, :, 1:] - images[:, :, :-1])
v0hat_residuals = torch.abs(v0hat[:, :, 1:] - v0hat[:, :, :-1])
dwt = DWT1DForward(wave=wavelet_type, J=num_levels).cuda()
images_l, images_h = dwt(img_residuals)
v0hat_l, v0hat_h = dwt(v0hat_residuals)
l1_loss = 0.0
l1_loss += torch.abs(images_l - v0hat_l).mean() * ld_levels[0]
for i, (c1, c2) in enumerate(zip(images_h, v0hat_h)):
l1_loss += torch.abs(c1 - c2).mean() * ld_levels[i + 1]
return l1_loss
class SampleLogger:
def __init__(
self,
prompts: List[str],
clip_length: int,
logdir: str,
subdir: str = "validations",
num_samples_per_prompt: int = 1,
sample_seeds: List[int] = None,
num_inference_steps: int = 75,
guidance_scale: float = 9.0,
annotate: bool = False,
annotate_size: int = 6,
make_grid: bool = True,
grid_column_size: int = 2,
height: int = 40,
width: int = 64,) -> None:
self.prompts = prompts
self.clip_length = clip_length
self.guidance_scale = guidance_scale
self.num_inference_steps = num_inference_steps
self.height = height
self.width = width
if sample_seeds is None:
max_num_samples_per_prompt = int(1e5)
if num_samples_per_prompt > max_num_samples_per_prompt:
raise ValueError
sample_seeds = torch.randint(0, max_num_samples_per_prompt, (num_samples_per_prompt,))
sample_seeds = sorted(sample_seeds.numpy().tolist())
self.sample_seeds = sample_seeds
self.logdir = os.path.join(logdir, subdir)
os.makedirs(self.logdir)
self.annotate = annotate
self.annotate_size = annotate_size
self.make_grid = make_grid
self.grid_column_size = grid_column_size
def log_sample_images(
self, pipeline: TextToVideoIFPipeline, device: torch.device, step: int, inv_images: Optional[torch.FloatTensor]=None,
):
samples_all = []
save_dir = os.path.join(self.logdir, f"step_{step}")
os.makedirs(save_dir, exist_ok=True)
for idx, prompt in enumerate(tqdm(self.prompts, desc="Generating sample images")):
for seed in self.sample_seeds:
prompt_embeds, negative_embeds = pipeline.encode_prompt(prompt)
sequence = pipeline(
inv_images=inv_images,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=torch.manual_seed(seed),
num_inference_steps=self.num_inference_steps,
num_frames=self.clip_length,
guidance_scale=self.guidance_scale,
num_images_per_prompt=1,
height=self.height,
width=self.width,
output_type="pt",
).frames
imageio.mimsave(
f"{save_dir}/{idx}_base.gif",
tensor2vid(sequence.clone()),
fps=2)
# if self.make_grid:
# samples_all = [make_grid(images, cols=2) for images in zip(*samples_all)]
# #save_images_as_gif(samples_all, os.path.join(save_dir, "grid_base.gif"))
# imageio.mimsave( os.path.join(save_dir, "grid_base.gif"), fps=2 )
def train(
pretrained_t2v_path: str = "showlab/show-1-base",
ddim_inv_scheduler_path: str = "/mnt/ssd8/hyeonho/stable-diffusion-v1-5/scheduler",
vid_name: str = "some-video-name",
exp_name: str = "",
gradient_accumulation_steps: int = 1,
mixed_precision: Optional[str] = "fp16",
gradient_checkpointing: bool = True,
scale_lr: bool = False,
lr: float = 3e-5, # TRY: either 1e-5 or 3e-5 or 1e-4
train_batch_size: int = 1,
adam_weight_decay: float=1e-2,
adam_epsilon: float=1e-8,
logdir: str = "outputs",
lr_scheduler: str = "constant", # ["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"]
lr_warmup_steps: int = 0,
train_steps: int = 500,
validation_steps: int = 100,
train_dataset: Dict = None,
validation_sample_logger: Dict = None,
seed: int = 12345,
max_grad_norm: float = 1.0,
num_inv_steps: int = 30,
threshold: float = 0.2,
trainable_modules: Tuple = (".to_q", ".to_k", ".to_v"),
save_config: bool = True,
sr2_strength: float = 0.8,
sr2_steps: int = 50,
ld_global: float = 0.5,
ld_local: float = 0.2,
num_levels: int = 2,
ld_levels: List[float] = [1., 1., 1.],
):
vid_name = vid_name
time_str = get_time_string()
logdir = os.path.join(logdir, vid_name, f'{time_str}{exp_name}')
os.makedirs(logdir, exist_ok=True)
# save config file
if save_config:
inference_prompts = [ f"{i}: " + p for i,p in enumerate(validation_sample_logger["prompts"]) ]
config_dict = OmegaConf.create(
{
"train_steps": train_steps,
"lr" : lr,
"num_inv_steps":num_inv_steps,
"threshold":threshold,
"trainable_modules": trainable_modules,
"seed": seed,
"train_prompt":train_dataset["prompt"],
"inference_prompts":inference_prompts,
"sr2_strength": sr2_strength,
"sr2_steps": sr2_steps,
"ld_global": ld_global,
"ld_local": ld_local,
"num_levels": num_levels,
"ld_levels": ld_levels
})
OmegaConf.save(config_dict, os.path.join(logdir, "config.yaml"))
if seed is not None:
set_seed(seed)
torch.manual_seed(seed)
# configure accelerator
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
mixed_precision=mixed_precision,
)
weight_dtype = torch.float16
if True:
# Load [key-frame generation] pipeline #
pipe_base = TextToVideoIFPipeline.from_pretrained(
pretrained_t2v_path,
torch_dtype=torch.float16,
variant="fp16"
)
pipe_base.enable_model_cpu_offload()
print("key-frame generation pipeline loaded\n")
# = = = = = = = = = = = = = = = = = = #
# Load [frame interpolation] pipeline #
pretrained_model_path = "showlab/show-1-interpolation" # hf: "showlab/show-1-interpolation"
pipe_interp_1 = TextToVideoIFInterpPipeline.from_pretrained(
pretrained_model_path, torch_dtype=torch.float16, variant="fp16")
pipe_interp_1.enable_model_cpu_offload()
print("frame interpolation loaded\n")
# = = = = = = = = = = = = = = = = = = #
# Load [super resolution 1-1 (pixel space)] pipeline #
pretrained_model_path = "DeepFloyd/IF-II-L-v1.0" # hf: "DeepFloyd/IF-II-L-v1.0"
pipe_sr_1_image = IFSuperResolutionPipeline.from_pretrained(
pretrained_model_path, text_encoder=None, variant="fp16", torch_dtype=torch.float16)
pipe_sr_1_image.enable_model_cpu_offload()
print("image sr loaded loaded\n")
# = = = = = = = = = = = = = = = = = = #
# Load [super resolution 1-2 (pixel space)] pipeline #
pretrained_model_path = "showlab/show-1-sr1" # hf: "showlab/show-1-sr1"
pipe_sr_1_cond = TextToVideoIFSuperResolutionPipeline_Cond.from_pretrained(
pretrained_model_path, torch_dtype=torch.float16)
pipe_sr_1_cond.enable_model_cpu_offload()
print("spatial sr1 (pixel-based) loaded\n")
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = #
# Load [super resolution 2 (latent space)] pipeline #
pretrained_model_path = "showlab/show-1-sr2" # hf: "showlab/show-1-sr2"
pipe_sr_2 = VideoToVideoSDPipeline.from_pretrained(
pretrained_model_path, torch_dtype=torch.float16)
pipe_sr_2.enable_model_cpu_offload()
pipe_sr_2.enable_vae_slicing()
print("spatial sr2 (latent-based) loaded\n")
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = #
# Load [key-frame generation] modules #
pipe_base_components = pipe_base.components
base_tokenizer = pipe_base_components["tokenizer"]
base_text_encoder = pipe_base_components["text_encoder"]
#base_feature_extractor = pipe_base_components["feature_extractor"]
base_unet = pipe_base_components["unet"].to(dtype=torch.float32)
base_scheduler = pipe_base_components["scheduler"]
# freeze all
base_text_encoder.requires_grad_(False)
base_unet.requires_grad_(False)
# unfreeze all projection layers of Temporal Attentions
for name, module in base_unet.named_modules():
if "temp_attentions" in name and name.endswith( tuple(trainable_modules) ):
for params in module.parameters():
params.requires_grad = True
if gradient_checkpointing:
base_unet.enable_gradient_checkpointing()
if scale_lr:
lr = ( lr * gradient_accumulation_steps * train_batch_size * accelerator.num_processes )
params_to_optimize = base_unet.parameters()
optimizer = torch.optim.AdamW(
params_to_optimize,
lr=lr,
betas=(0.9, 0.999),
weight_decay=adam_weight_decay,
eps=adam_epsilon,
)
# load train dataset
prompt_ids = base_tokenizer(
train_dataset["prompt"],
padding="max_length",
max_length=77,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
).input_ids[0]
train_dataset = ImageSequenceDataset( **train_dataset, prompt_ids = prompt_ids )
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=False,
collate_fn=collate_fn,
)
train_sample_save_path = os.path.join(logdir, "train_samples.gif")
log_train_samples(train_dataloader, train_sample_save_path)
lr_scheduler = get_scheduler(
lr_scheduler,
optimizer=optimizer,
num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
num_training_steps=train_steps * gradient_accumulation_steps,
)
# accelerator.prepare
base_unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
base_unet, optimizer, train_dataloader, lr_scheduler
)
accelerator.register_for_checkpointing(lr_scheduler)
base_text_encoder.to(accelerator.device)
base_text_encoder.eval()
# ddim inv scheduler
if num_inv_steps > 0:
ddim_inv_scheduler = DDIMScheduler.from_pretrained(ddim_inv_scheduler_path)
ddim_inv_scheduler.set_timesteps(num_inv_steps)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("video") # , config=vars(args))
# [Train]
total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Instantaneous batch size per device = {train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {train_steps}")
step = 0
if validation_sample_logger is not None and accelerator.is_main_process:
validation_sample_logger = SampleLogger(**validation_sample_logger, logdir=logdir)
progress_bar = tqdm(
range(step, train_steps),
disable=not accelerator.is_local_main_process,
)
progress_bar.set_description("Training Steps [KFG]")
def make_data_yielder(dataloader):
while True:
for batch in dataloader:
yield batch
accelerator.wait_for_everyone()
train_data_yielder = make_data_yielder(train_dataloader)
sampled_timesteps = torch.randint(0, base_scheduler.config.num_train_timesteps, (train_batch_size, train_steps), device=accelerator.device)
prompt_embeds, _ = pipe_base.encode_prompt(train_dataset.prompt)
alphas = base_scheduler.alphas_cumprod.to(accelerator.device)
while step < train_steps:
batch = next(train_data_yielder)
base_unet.train()
with accelerator.accumulate(base_unet):
images = batch["images"].to(dtype=weight_dtype)
b,c,f,h,w = images.shape # (1, 3, 8, 40, 64)
noise = torch.randn_like(images)
timestep = sampled_timesteps[:, step]
timestep.long()
# add noise to the x_0 (forward diffusion)
noisy_intermediate_images = base_scheduler.add_noise(images, noise, timestep)
# predict noise residual
model_pred = base_unet(
noisy_intermediate_images,
timestep,
encoder_hidden_states=prompt_embeds,
).sample
if pipe_base.scheduler.config.variance_type not in ["learned", "learned_range"]:
model_pred, _ = model_pred.split(noisy_intermediate_images.shape[1], dim=1)
# Tweedie's denoised estimate
alpha_t = alphas[timestep, None, None, None, None]
v0hat = (noisy_intermediate_images - (1. - alpha_t) ** (0.5) * model_pred) / alpha_t ** (0.5)
assert base_scheduler.config.prediction_type == "epsilon"
target = noise
loss = 0
model_pred_residual = torch.abs(model_pred[:, :, 1:, :, :] - model_pred[:, :, :-1, :, :])
target_residual = torch.abs(target[:, :, 1:, :, :] - target[:, :, :-1, :, :])
loss = loss + (1 - F.cosine_similarity(model_pred_residual, target_residual, dim=2).mean())
loss_sma_local = sma_local(images, v0hat, accelerator, delta=0.05, base=1.)
loss_sma_global = sma_global(images, v0hat, num_levels=num_levels, ld_levels=ld_levels, wavelet_type='haar')
loss = loss + ld_global * loss_sma_global + ld_local * loss_sma_local
# update weights
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(base_unet.parameters(), max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
if accelerator.sync_gradients:
progress_bar.update(1)
step += 1
if accelerator.is_main_process:
if validation_sample_logger is not None and step % validation_steps == 0:
base_unet.eval()
inv_images = None
if num_inv_steps > 0:
inv_images = ddim_inversion(
pipeline=pipe_base,
ddim_scheduler=ddim_inv_scheduler,
video_latent=images,
num_inv_steps=num_inv_steps
)[-1].to(weight_dtype)
for idx, val_prompt in enumerate(validation_sample_logger.prompts):
inference(
pipe_base=pipe_base,
pipe_interp_1=pipe_interp_1,
pipe_sr_1_image=pipe_sr_1_image,
pipe_sr_1_cond=pipe_sr_1_cond,
pipe_sr_2=pipe_sr_2,
inv_images=inv_images,
prompt=val_prompt,
prompt_idx=idx,
seed=seed,
output_dir=os.path.join(validation_sample_logger.logdir, f"step_{step}"),
sr2_strength=sr2_strength,
sr2_steps=sr2_steps,
)
logs = {"loss": loss.detach().item(), "lr":lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=step)
accelerator.end_training()
def inference(pipe_base, pipe_interp_1, pipe_sr_1_image, pipe_sr_1_cond, pipe_sr_2,
inv_images, prompt, prompt_idx, seed, output_dir, sr2_strength, sr2_steps):
negative_prompt = "low resolution, blur"
os.makedirs(output_dir, exist_ok=True)
# text embeds
prompt_embeds, negative_embeds = pipe_base.encode_prompt(prompt)
# - - - - - - - - keyframes generation - - - - - - - - #
video_frames = pipe_base(
inv_images=inv_images,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
num_frames=8,
height=40,
width=64,
num_inference_steps=75,
guidance_scale=9.0,
generator=torch.manual_seed(seed),
output_type="pt").frames
imageio.mimsave(f"{output_dir}/{prompt_idx}_base.gif",
tensor2vid(video_frames.clone()),
fps=2)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# - - CONVERT DTYPE OF video_frames (fp32 -> fp16) - - #
video_frames = video_frames.to(dtype=torch.float16)
# - - - - - - interpolation (2fps -> 7.5fps) - - - - - - #
bsz, channel, num_frames, height, width = video_frames.shape
new_num_frames = 3 * (num_frames - 1) + num_frames
new_video_frames = torch.zeros((bsz, channel, new_num_frames, height, width),
dtype=video_frames.dtype,
device=video_frames.device)
new_video_frames[:, :, torch.arange(0, new_num_frames, 4), ...] = video_frames
from diffusers.utils.torch_utils import randn_tensor
init_noise = randn_tensor((bsz, channel, 5, height, width),
generator=torch.manual_seed(seed),
device=video_frames.device,
dtype=video_frames.dtype)
for i in range(num_frames - 1):
batch_i = torch.zeros((bsz, channel, 5, height, width),
dtype=video_frames.dtype,
device=video_frames.device)
batch_i[:, :, 0, ...] = video_frames[:, :, i, ...]
batch_i[:, :, -1, ...] = video_frames[:, :, i + 1, ...]
batch_i = pipe_interp_1(
pixel_values=batch_i,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
num_frames=batch_i.shape[2],
height=40,
width=64,
num_inference_steps=75,
guidance_scale=4.0,
generator=torch.manual_seed(seed),
output_type="pt",
init_noise=init_noise,
cond_interpolation=True,
).frames
new_video_frames[:, :, i * 4:i * 4 + 5, ...] = batch_i
video_frames = new_video_frames
imageio.mimsave(f"{output_dir}/{prompt_idx}_inter.gif",
tensor2vid(video_frames.clone()),
fps=8)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# - - - - - - - - - - sr1 - - - - - - - - - - #
bsz, channel, num_frames, height, width = video_frames.shape
window_size, stride = 8, 7
if num_frames == 61:
window_size, stride = 7,6
new_video_frames = torch.zeros(
(bsz, channel, num_frames, height * 4, width * 4),
dtype=video_frames.dtype,
device=video_frames.device)
for i in range(0, num_frames - window_size + 1, stride):
batch_i = video_frames[:, :, i:i + window_size, ...]
all_frame_cond = None
if i == 0:
first_frame_cond = pipe_sr_1_image(
image=video_frames[:, :, 0, ...],
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
height=height * 4,
width=width * 4,
num_inference_steps=70,
guidance_scale=4.0,
noise_level=150,
generator=torch.manual_seed(seed),
output_type="pt").images
first_frame_cond = first_frame_cond.unsqueeze(2)
else:
first_frame_cond = new_video_frames[:, :, i:i + 1, ...]
batch_i = pipe_sr_1_cond(image=batch_i,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
first_frame_cond=first_frame_cond,
height=height * 4,
width=width * 4,
num_inference_steps=125,
guidance_scale=7.0,
noise_level=250,
generator=torch.manual_seed(seed),
output_type="pt").frames
new_video_frames[:, :, i:i + window_size, ...] = batch_i
video_frames = new_video_frames
imageio.mimsave(f"{output_dir}/{prompt_idx}_sr1.gif",
tensor2vid(video_frames.clone()),
fps=8)
# - - - - - - - - - - - - - - - - - - - - - - #
# - - - - - - - - - - sr1 - - - - - - - - - - #
video_frames = [
Image.fromarray(frame).resize((576, 320))
for frame in tensor2vid(video_frames.clone())
]
video_frames = pipe_sr_2(prompt,
negative_prompt=negative_prompt,
video=video_frames,
strength=sr2_strength,
num_inference_steps=sr2_steps,
generator=torch.manual_seed(seed),
output_type="pt").frames
imageio.mimsave(f"{output_dir}/{prompt_idx}.gif",
tensor2vid(video_frames.clone()),
fps=8)
# - - - - - - - - - - - - - - - - - - - - - - - #
@click.command()
@click.option("--config", type=str, default="config/car.yml")
@click.option("--exp_name", type=str, default="")
def run(config, exp_name):
param_dict = OmegaConf.load(config)
if not exp_name == "":
param_dict.update({'exp_name': exp_name})
train(**param_dict)
if __name__ == "__main__":
run()