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run_videop2p.py
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run_videop2p.py
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# Adapted from https://github.com/google/prompt-to-prompt/blob/main/null_text_w_ptp.ipynb
import os
from typing import Optional, Union, Tuple, List, Callable, Dict
from tqdm.notebook import tqdm
import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
import torch.nn.functional as nnf
import numpy as np
import abc
import ptp_utils
import seq_aligner
import shutil
from torch.optim.adam import Adam
from PIL import Image
from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer
from einops import rearrange
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
import cv2
import argparse
from omegaconf import OmegaConf
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
MY_TOKEN = ''
LOW_RESOURCE = False
NUM_DDIM_STEPS = 50
GUIDANCE_SCALE = 7.5
MAX_NUM_WORDS = 77
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# need to adjust sometimes
mask_th = (.3, .3)
def main(
pretrained_model_path: str,
image_path: str,
prompt: str,
prompts: Tuple[str],
eq_params: Dict,
save_name: str,
is_word_swap: bool,
blend_word: Tuple[str] = None,
cross_replace_steps: float = 0.2,
self_replace_steps: float = 0.5,
video_len: int = 8,
fast: bool = False,
mixed_precision: str = 'fp32',
):
output_folder = os.path.join(pretrained_model_path, 'results')
if fast:
save_name_1 = os.path.join(output_folder, 'inversion_fast.gif')
save_name_2 = os.path.join(output_folder, '{}_fast.gif'.format(save_name))
else:
save_name_1 = os.path.join(output_folder, 'inversion.gif')
save_name_2 = os.path.join(output_folder, '{}.gif'.format(save_name))
if blend_word:
blend_word = (((blend_word[0],), (blend_word[1],)))
eq_params = dict(eq_params)
prompts = list(prompts)
cross_replace_steps = {'default_': cross_replace_steps,}
weight_dtype = torch.float32
if mixed_precision == "fp16":
weight_dtype = torch.float16
elif mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Load the tokenizer
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(
pretrained_model_path,
subfolder="text_encoder",
).to(device, dtype=weight_dtype)
vae = AutoencoderKL.from_pretrained(
pretrained_model_path,
subfolder="vae",
).to(device, dtype=weight_dtype)
unet = UNet3DConditionModel.from_pretrained(
pretrained_model_path, subfolder="unet"
).to(device)
ldm_stable = TuneAVideoPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
).to(device)
try:
ldm_stable.disable_xformers_memory_efficient_attention()
except AttributeError:
print("Attribute disable_xformers_memory_efficient_attention() is missing")
tokenizer = ldm_stable.tokenizer # Tokenizer of class: [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
# A tokenizer breaks a stream of text into tokens, usually by looking for whitespace (tabs, spaces, new lines).
class LocalBlend:
def get_mask(self, maps, alpha, use_pool):
k = 1
maps = (maps * alpha).sum(-1).mean(2)
if use_pool:
maps = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
mask = nnf.interpolate(maps, size=(x_t.shape[3:]))
mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask = mask.gt(self.th[1-int(use_pool)])
mask = mask[:1] + mask
return mask
def __call__(self, x_t, attention_store, step):
self.counter += 1
if self.counter > self.start_blend:
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers.shape[0], -1, 8, 16, 16, MAX_NUM_WORDS) for item in maps]
maps = torch.cat(maps, dim=2)
mask = self.get_mask(maps, self.alpha_layers, True)
if self.substruct_layers is not None:
maps_sub = ~self.get_mask(maps, self.substruct_layers, False)
mask = mask * maps_sub
mask = mask.float()
mask = mask.reshape(-1, 1, mask.shape[-3], mask.shape[-2], mask.shape[-1])
x_t = x_t[:1] + mask * (x_t - x_t[:1])
return x_t
def __init__(self, prompts: List[str], words: [List[List[str]]], substruct_words=None, start_blend=0.2, th=(.3, .3)):
alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
alpha_layers[i, :, :, :, :, ind] = 1
if substruct_words is not None:
substruct_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, substruct_words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
substruct_layers[i, :, :, :, :, ind] = 1
self.substruct_layers = substruct_layers.to(device)
else:
self.substruct_layers = None
self.alpha_layers = alpha_layers.to(device)
self.start_blend = int(start_blend * NUM_DDIM_STEPS)
self.counter = 0
self.th=th
class EmptyControl:
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
def __call__(self, attn, is_cross: bool, place_in_unet: str):
return attn
class AttentionControl(abc.ABC):
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return self.num_att_layers if LOW_RESOURCE else 0
@abc.abstractmethod
def forward (self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= self.num_uncond_att_layers:
if LOW_RESOURCE:
attn = self.forward(attn, is_cross, place_in_unet)
else:
h = attn.shape[0]
attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
class SpatialReplace(EmptyControl):
def step_callback(self, x_t):
if self.cur_step < self.stop_inject:
b = x_t.shape[0]
x_t = x_t[:1].expand(b, *x_t.shape[1:])
return x_t
def __init__(self, stop_inject: float):
super(SpatialReplace, self).__init__()
self.stop_inject = int((1 - stop_inject) * NUM_DDIM_STEPS)
class AttentionStore(AttentionControl):
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [],
"down_self": [], "mid_self": [], "up_self": []}
def forward(self, attn, is_cross: bool, place_in_unet: str):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= 32 ** 2:
self.step_store[key].append(attn)
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
class AttentionControlEdit(AttentionStore, abc.ABC):
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store, self.cur_step)
return x_t
def replace_self_attention(self, attn_base, att_replace, place_in_unet):
if att_replace.shape[2] <= 32 ** 2:
attn_base = attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
return attn_base
else:
return att_replace
@abc.abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce, place_in_unet)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def __init__(self, prompts, num_steps: int,
cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
self_replace_steps: Union[float, Tuple[float, float]],
local_blend: Optional[LocalBlend]):
super(AttentionControlEdit, self).__init__()
self.batch_size = len(prompts)
self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
if type(self_replace_steps) is float:
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
self.local_blend = local_blend
class AttentionReplace(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
local_blend: Optional[LocalBlend] = None):
super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
class AttentionRefine(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
local_blend: Optional[LocalBlend] = None):
super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
self.mapper, alphas = self.mapper.to(device), alphas.to(device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
class AttentionReweight(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,
local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None):
super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.equalizer = equalizer.to(device)
self.prev_controller = controller
def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
Tuple[float, ...]]):
if type(word_select) is int or type(word_select) is str:
word_select = (word_select,)
equalizer = torch.ones(1, 77)
for word, val in zip(word_select, values):
inds = ptp_utils.get_word_inds(text, word, tokenizer)
equalizer[:, inds] = val
return equalizer
def aggregate_attention(attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int):
out = []
attention_maps = attention_store.get_average_attention()
num_pixels = res ** 2
for location in from_where:
for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
if item.shape[1] == num_pixels:
cross_maps = item.reshape(8, 8, res, res, item.shape[-1])
out.append(cross_maps)
out = torch.cat(out, dim=1)
out = out.sum(1) / out.shape[1]
return out.cpu()
def make_controller(prompts: List[str], is_replace_controller: bool, cross_replace_steps: Dict[str, float], self_replace_steps: float, blend_words=None, equilizer_params=None, mask_th=(.3,.3)) -> AttentionControlEdit:
if blend_words is None:
lb = None
else:
lb = LocalBlend(prompts, blend_word, th=mask_th)
if is_replace_controller:
controller = AttentionReplace(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, local_blend=lb)
else:
controller = AttentionRefine(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, local_blend=lb)
if equilizer_params is not None:
eq = get_equalizer(prompts[1], equilizer_params["words"], equilizer_params["values"])
controller = AttentionReweight(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps, equalizer=eq, local_blend=lb, controller=controller)
return controller
def load_512_seq(image_path, left=0, right=0, top=0, bottom=0, n_sample_frame=video_len, sampling_rate=1):
images = []
for file in sorted(os.listdir(image_path)):
images.append(file)
n_images = len(images)
sequence_length = (n_sample_frame - 1) * sampling_rate + 1
if n_images < sequence_length:
raise ValueError
frames = []
for index in range(n_sample_frame):
p = os.path.join(image_path, images[index])
image = np.array(Image.open(p).convert("RGB"))
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((512, 512)))
frames.append(image)
return np.stack(frames)
class NullInversion:
def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
return prev_sample
def next_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
def get_noise_pred(self, latents, t, is_forward=True, context=None):
latents_input = torch.cat([latents] * 2)
if context is None:
context = self.context
guidance_scale = 1 if is_forward else GUIDANCE_SCALE
noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
if is_forward:
latents = self.next_step(noise_pred, t, latents)
else:
latents = self.prev_step(noise_pred, t, latents)
return latents
@torch.no_grad()
def latent2image(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = self.model.vae.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = (image * 255).astype(np.uint8)
return image
@torch.no_grad()
def latent2image_video(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
latents = latents[0].permute(1, 0, 2, 3)
image = self.model.vae.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
return image
@torch.no_grad()
def image2latent(self, image):
with torch.no_grad():
if type(image) is Image:
image = np.array(image)
if type(image) is torch.Tensor and image.dim() == 4:
latents = image
else:
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device, dtype=weight_dtype)
latents = self.model.vae.encode(image)['latent_dist'].mean
latents = latents * 0.18215
return latents
@torch.no_grad()
def image2latent_video(self, image):
with torch.no_grad():
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(0, 3, 1, 2).to(device).to(device, dtype=weight_dtype)
latents = self.model.vae.encode(image)['latent_dist'].mean
latents = rearrange(latents, "(b f) c h w -> b c f h w", b=1)
latents = latents * 0.18215
return latents
@torch.no_grad()
def init_prompt(self, prompt: str):
uncond_input = self.model.tokenizer(
[""], padding="max_length", max_length=self.model.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
text_input = self.model.tokenizer(
[prompt],
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
self.context = torch.cat([uncond_embeddings, text_embeddings])
self.prompt = prompt
@torch.no_grad()
def ddim_loop(self, latent):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in range(NUM_DDIM_STEPS):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@property
def scheduler(self):
return self.model.scheduler
@torch.no_grad()
def ddim_inversion(self, image):
latent = self.image2latent_video(image)
image_rec = self.latent2image_video(latent)
ddim_latents = self.ddim_loop(latent)
return image_rec, ddim_latents
def null_optimization(self, latents, num_inner_steps, epsilon):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
uncond_embeddings_list = []
latent_cur = latents[-1]
# bar = tqdm(total=num_inner_steps * NUM_DDIM_STEPS)
for i in range(NUM_DDIM_STEPS):
uncond_embeddings = uncond_embeddings.clone().detach()
uncond_embeddings.requires_grad = True
optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))
latent_prev = latents[len(latents) - i - 2]
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)
for j in range(num_inner_steps):
noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)
noise_pred = noise_pred_uncond + GUIDANCE_SCALE * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
loss = nnf.mse_loss(latents_prev_rec, latent_prev)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
# bar.update()
if loss_item < epsilon + i * 2e-5:
break
# for j in range(j + 1, num_inner_steps):
# bar.update()
uncond_embeddings_list.append(uncond_embeddings[:1].detach())
with torch.no_grad():
context = torch.cat([uncond_embeddings, cond_embeddings])
latent_cur = self.get_noise_pred(latent_cur, t, False, context)
# bar.close()
return uncond_embeddings_list
def invert(self, image_path: str, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False):
self.init_prompt(prompt)
ptp_utils.register_attention_control(self.model, None)
image_gt = load_512_seq(image_path, *offsets)
if verbose:
print("DDIM inversion...")
image_rec, ddim_latents = self.ddim_inversion(image_gt)
if verbose:
print("Null-text optimization...")
uncond_embeddings = self.null_optimization(ddim_latents, num_inner_steps, early_stop_epsilon)
return (image_gt, image_rec), ddim_latents[-1], uncond_embeddings
def invert_(self, image_path: str, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False):
self.init_prompt(prompt)
ptp_utils.register_attention_control(self.model, None)
image_gt = load_512_seq(image_path, *offsets)
if verbose:
print("DDIM inversion...")
image_rec, ddim_latents = self.ddim_inversion(image_gt)
if verbose:
print("Null-text optimization...")
return (image_gt, image_rec), ddim_latents[-1], None
def __init__(self, model):
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
set_alpha_to_one=False)
self.model = model
self.tokenizer = self.model.tokenizer
self.model.scheduler.set_timesteps(NUM_DDIM_STEPS)
self.prompt = None
self.context = None
null_inversion = NullInversion(ldm_stable)
###############
# Custom APIs:
ldm_stable.enable_xformers_memory_efficient_attention()
if fast:
(image_gt, image_enc), x_t, uncond_embeddings = null_inversion.invert_(image_path, prompt, offsets=(0,0,0,0), verbose=True)
else:
(image_gt, image_enc), x_t, uncond_embeddings = null_inversion.invert(image_path, prompt, offsets=(0,0,0,0), verbose=True)
##### load uncond #####
# uncond_embeddings_load = np.load(uncond_embeddings_path)
# uncond_embeddings = []
# for i in range(uncond_embeddings_load.shape[0]):
# uncond_embeddings.append(torch.from_numpy(uncond_embeddings_load[i]).to(device))
#######################
##### save uncond #####
# uncond_embeddings = torch.cat(uncond_embeddings)
# uncond_embeddings = uncond_embeddings.cpu().numpy()
#######################
print("Start Video-P2P!")
controller = make_controller(prompts, is_word_swap, cross_replace_steps, self_replace_steps, blend_word, eq_params, mask_th=mask_th)
ptp_utils.register_attention_control(ldm_stable, controller)
generator = torch.Generator(device=device)
with torch.no_grad():
sequence = ldm_stable(
prompts,
generator=generator,
latents=x_t,
uncond_embeddings_pre=uncond_embeddings,
controller = controller,
video_length=video_len,
fast=fast,
).videos
sequence1 = rearrange(sequence[0], "c t h w -> t h w c")
sequence2 = rearrange(sequence[1], "c t h w -> t h w c")
inversion = []
videop2p = []
for i in range(sequence1.shape[0]):
inversion.append( Image.fromarray((sequence1[i] * 255).numpy().astype(np.uint8)) )
videop2p.append( Image.fromarray((sequence2[i] * 255).numpy().astype(np.uint8)) )
inversion[0].save(save_name_1, save_all=True, append_images=inversion[1:], optimize=False, loop=0, duration=250)
videop2p[0].save(save_name_2, save_all=True, append_images=videop2p[1:], optimize=False, loop=0, duration=250)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/videop2p.yaml")
parser.add_argument("--fast", action='store_true')
args = parser.parse_args()
main(**OmegaConf.load(args.config), fast=args.fast)