videogeneratorbytheacc / utils /common_utils.py
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import ast
import gc
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from diffusers.models.attention_processor import AttnProcessor2_0
from diffusers.models.attention import BasicTransformerBlock
from decord import VideoReader
import wandb
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
def is_attn(name):
return "attn1" or "attn2" == name.split(".")[-1]
def set_processors(attentions):
for attn in attentions:
attn.set_processor(AttnProcessor2_0())
def set_torch_2_attn(unet):
optim_count = 0
for name, module in unet.named_modules():
if is_attn(name):
if isinstance(module, torch.nn.ModuleList):
for m in module:
if isinstance(m, BasicTransformerBlock):
set_processors([m.attn1, m.attn2])
optim_count += 1
if optim_count > 0:
print(f"{optim_count} Attention layers using Scaled Dot Product Attention.")
# From LatentConsistencyModel.get_guidance_scale_embedding
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (`int`, *optional*, defaults to 512):
dimension of the embeddings to generate
dtype:
data type of the generated embeddings
Returns:
`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(
f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
)
return x[(...,) + (None,) * dims_to_append]
# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
scaled_timestep = timestep_scaling * timestep
c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
return c_skip, c_out
# Compare LCMScheduler.step, Step 4
def get_predicted_original_sample(
model_output, timesteps, sample, prediction_type, alphas, sigmas
):
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
if prediction_type == "epsilon":
pred_x_0 = (sample - sigmas * model_output) / alphas
elif prediction_type == "sample":
pred_x_0 = model_output
elif prediction_type == "v_prediction":
pred_x_0 = alphas * sample - sigmas * model_output
else:
raise ValueError(
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
f" are supported."
)
return pred_x_0
# Based on step 4 in DDIMScheduler.step
def get_predicted_noise(
model_output, timesteps, sample, prediction_type, alphas, sigmas
):
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
if prediction_type == "epsilon":
pred_epsilon = model_output
elif prediction_type == "sample":
pred_epsilon = (sample - alphas * model_output) / sigmas
elif prediction_type == "v_prediction":
pred_epsilon = alphas * model_output + sigmas * sample
else:
raise ValueError(
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
f" are supported."
)
return pred_epsilon
# From LatentConsistencyModel.get_guidance_scale_embedding
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (`int`, *optional*, defaults to 512):
dimension of the embeddings to generate
dtype:
data type of the generated embeddings
Returns:
`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(
f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
)
return x[(...,) + (None,) * dims_to_append]
# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
scaled_timestep = timestep_scaling * timestep
c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
return c_skip, c_out
# Compare LCMScheduler.step, Step 4
def get_predicted_original_sample(
model_output, timesteps, sample, prediction_type, alphas, sigmas
):
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
if prediction_type == "epsilon":
pred_x_0 = (sample - sigmas * model_output) / alphas
elif prediction_type == "sample":
pred_x_0 = model_output
elif prediction_type == "v_prediction":
pred_x_0 = alphas * sample - sigmas * model_output
else:
raise ValueError(
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
f" are supported."
)
return pred_x_0
# Based on step 4 in DDIMScheduler.step
def get_predicted_noise(
model_output, timesteps, sample, prediction_type, alphas, sigmas
):
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
if prediction_type == "epsilon":
pred_epsilon = model_output
elif prediction_type == "sample":
pred_epsilon = (sample - alphas * model_output) / sigmas
elif prediction_type == "v_prediction":
pred_epsilon = alphas * model_output + sigmas * sample
else:
raise ValueError(
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
f" are supported."
)
return pred_epsilon
def param_optim(model, condition, extra_params=None, is_lora=False, negation=None):
extra_params = extra_params if len(extra_params.keys()) > 0 else None
return {
"model": model,
"condition": condition,
"extra_params": extra_params,
"is_lora": is_lora,
"negation": negation,
}
def create_optim_params(name="param", params=None, lr=5e-6, extra_params=None):
params = {"name": name, "params": params, "lr": lr}
if extra_params is not None:
for k, v in extra_params.items():
params[k] = v
return params
def create_optimizer_params(model_list, lr):
import itertools
optimizer_params = []
for optim in model_list:
model, condition, extra_params, is_lora, negation = optim.values()
# Check if we are doing LoRA training.
if is_lora and condition and isinstance(model, list):
params = create_optim_params(
params=itertools.chain(*model), extra_params=extra_params
)
optimizer_params.append(params)
continue
if is_lora and condition and not isinstance(model, list):
for n, p in model.named_parameters():
if "lora" in n:
params = create_optim_params(n, p, lr, extra_params)
optimizer_params.append(params)
continue
# If this is true, we can train it.
if condition:
for n, p in model.named_parameters():
should_negate = "lora" in n and not is_lora
if should_negate:
continue
params = create_optim_params(n, p, lr, extra_params)
optimizer_params.append(params)
return optimizer_params
def handle_trainable_modules(
model, trainable_modules=None, is_enabled=True, negation=None
):
acc = []
unfrozen_params = 0
if trainable_modules is not None:
unlock_all = any([name == "all" for name in trainable_modules])
if unlock_all:
model.requires_grad_(True)
unfrozen_params = len(list(model.parameters()))
else:
model.requires_grad_(False)
for name, param in model.named_parameters():
for tm in trainable_modules:
if all([tm in name, name not in acc, "lora" not in name]):
param.requires_grad_(is_enabled)
acc.append(name)
unfrozen_params += 1
def huber_loss(pred, target, huber_c=0.001):
loss = torch.sqrt((pred.float() - target.float()) ** 2 + huber_c**2) - huber_c
return loss.mean()
@torch.no_grad()
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
"""
for targ, src in zip(target_params, source_params):
src_to_dtype = src.to(targ.dtype)
targ.detach().mul_(rate).add_(src_to_dtype, alpha=1 - rate)
def log_validation_video(pipeline, args, accelerator, save_fps):
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
validation_prompts = [
"An astronaut riding a horse.",
"Darth vader surfing in waves.",
"Robot dancing in times square.",
"Clown fish swimming through the coral reef.",
"A child excitedly swings on a rusty swing set, laughter filling the air.",
"With the style of van gogh, A young couple dances under the moonlight by the lake.",
"A young woman with glasses is jogging in the park wearing a pink headband.",
"Impressionist style, a yellow rubber duck floating on the wave on the sunset",
"Wolf, turns its head, in the wild",
"Iron man, walks, on the moon, 8k, high detailed, best quality",
"With the style of low-poly game art, A majestic, white horse gallops gracefully",
"a rabbit, low-poly game art style",
]
video_logs = []
if getattr(args, "use_motion_cond", False):
use_motion_cond = True
else:
use_motion_cond = False
for _, prompt in enumerate(validation_prompts):
if use_motion_cond:
motin_gs_unit = (args.motion_gs_max - args.motion_gs_min) / 2
for i in range(3):
with torch.autocast("cuda"):
videos = pipeline(
prompt=prompt,
frames=args.n_frames,
num_inference_steps=8,
num_videos_per_prompt=1,
fps=args.fps,
use_motion_cond=True,
motion_gs=motin_gs_unit * i,
lcm_origin_steps=args.num_ddim_timesteps,
generator=generator,
)
videos = (videos.clamp(-1.0, 1.0) + 1.0) / 2.0
videos = (
(videos * 255)
.to(torch.uint8)
.permute(0, 2, 1, 3, 4)
.cpu()
.numpy()
)
video_logs.append(
{
"validation_prompt": f"GS={i * motin_gs_unit}, {prompt}",
"videos": videos,
}
)
else:
for i in range(2):
with torch.autocast("cuda"):
videos = pipeline(
prompt=prompt,
frames=args.n_frames,
num_inference_steps=4 * (i + 1),
num_videos_per_prompt=1,
fps=args.fps,
use_motion_cond=False,
lcm_origin_steps=args.num_ddim_timesteps,
generator=generator,
)
videos = (videos.clamp(-1.0, 1.0) + 1.0) / 2.0
videos = (
(videos * 255)
.to(torch.uint8)
.permute(0, 2, 1, 3, 4)
.cpu()
.numpy()
)
video_logs.append(
{
"validation_prompt": f"Steps={4 * (i + 1)}, {prompt}",
"videos": videos,
}
)
for tracker in accelerator.trackers:
if tracker.name == "wandb":
formatted_videos = []
for log in video_logs:
videos = log["videos"]
validation_prompt = log["validation_prompt"]
for video in videos:
video = wandb.Video(video, caption=validation_prompt, fps=save_fps)
formatted_videos.append(video)
tracker.log({f"validation": formatted_videos})
del pipeline
gc.collect()
def tuple_type(s):
if isinstance(s, tuple):
return s
value = ast.literal_eval(s)
if isinstance(value, tuple):
return value
raise TypeError("Argument must be a tuple")
def load_model_checkpoint(model, ckpt):
def load_checkpoint(model, ckpt, full_strict):
state_dict = torch.load(ckpt, map_location="cpu", weights_only=True)
if "state_dict" in list(state_dict.keys()):
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict, strict=full_strict)
del state_dict
gc.collect()
return model
load_checkpoint(model, ckpt, full_strict=True)
print(">>> model checkpoint loaded.")
return model
def read_video_to_tensor(
path_to_video, sample_fps, sample_frames, uniform_sampling=False
):
video_reader = VideoReader(path_to_video)
video_fps = video_reader.get_avg_fps()
video_frames = video_reader._num_frame
video_duration = video_frames / video_fps
sample_duration = sample_frames / sample_fps
stride = video_fps / sample_fps
if uniform_sampling or video_duration <= sample_duration:
index_range = np.linspace(0, video_frames - 1, sample_frames).astype(np.int32)
else:
max_start_frame = video_frames - np.ceil(sample_frames * stride).astype(
np.int32
)
if max_start_frame > 0:
start_frame = random.randint(0, max_start_frame)
else:
start_frame = 0
index_range = start_frame + np.arange(sample_frames) * stride
index_range = np.round(index_range).astype(np.int32)
sampled_frames = video_reader.get_batch(index_range).asnumpy()
pixel_values = torch.from_numpy(sampled_frames).permute(0, 3, 1, 2).contiguous()
pixel_values = pixel_values / 255.0
del video_reader
return pixel_values
def calculate_motion_rank_new(tensor_ref, tensor_gen, rank_k=1):
if rank_k == 0:
loss = torch.tensor(0.0, device=tensor_ref.device)
elif rank_k > tensor_ref.shape[-1]:
raise ValueError(
"The value of rank_k cannot be larger than the number of frames"
)
else:
# Sort the reference tensor along the frames dimension
_, sorted_indices = torch.sort(tensor_ref, dim=-1)
# Create a mask to select the top rank_k frames
mask = torch.zeros_like(tensor_ref, dtype=torch.bool)
mask.scatter_(-1, sorted_indices[..., -rank_k:], True)
# Compute the mean squared error loss only on the masked elements
loss = F.mse_loss(tensor_ref[mask].detach(), tensor_gen[mask])
return loss
def compute_temp_loss(attention_prob, attention_prob_example):
temp_attn_prob_loss = []
# 1. Loop though all layers to get the query, key, and Compute the PCA loss
for name in attention_prob.keys():
attn_prob_example = attention_prob_example[name]
attn_prob = attention_prob[name]
module_attn_loss = calculate_motion_rank_new(
attn_prob_example.detach(), attn_prob, rank_k=1
)
temp_attn_prob_loss.append(module_attn_loss)
loss_temp = torch.stack(temp_attn_prob_loss) * 100
loss = loss_temp.mean()
return loss