import os
import imageio
import importlib
from omegaconf import OmegaConf
from typing import Union
from safetensors import safe_open
from tqdm import tqdm
import numpy as np
import torch
import torchvision
import torch.distributed as dist
from scipy.interpolate import PchipInterpolator
from einops import rearrange
from utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from utils.convert_lora_safetensor_to_diffusers import convert_lora, load_diffusers_lora
from modules.flow_controlnet import FlowControlNetModel
from modules.image_controlnet import ImageControlNetModel
def zero_rank_print(s):
if (not dist.is_initialized()) and (dist.is_initialized() and dist.get_rank() == 0): print("### " + s)
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=2, fps=8):
videos = rearrange(videos, "b c t h w -> t b c h w")
outputs = []
for x in videos:
x = torchvision.utils.make_grid(x, nrow=n_rows)
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
outputs.append(x)
os.makedirs(os.path.dirname(path), exist_ok=True)
imageio.mimsave(path, outputs, fps=fps, loop=0)
# DDIM Inversion
@torch.no_grad()
def init_prompt(prompt, pipeline):
uncond_input = pipeline.tokenizer(
[""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
text_input = pipeline.tokenizer(
[prompt],
padding="max_length",
max_length=pipeline.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
context = torch.cat([uncond_embeddings, text_embeddings])
return context
def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
timestep, next_timestep = min(
timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
alpha_prod_t_next = ddim_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(latents, t, context, unet):
noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
@torch.no_grad()
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
context = init_prompt(prompt, pipeline)
uncond_embeddings, cond_embeddings = context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in tqdm(range(num_inv_steps)):
t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
latent = next_step(noise_pred, t, latent, ddim_scheduler)
all_latent.append(latent)
return all_latent
@torch.no_grad()
def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
return ddim_latents
def load_weights(
animation_pipeline,
# motion module
motion_module_path = "",
motion_module_lora_configs = [],
# domain adapter
adapter_lora_path = "",
adapter_lora_scale = 1.0,
# image layers
dreambooth_model_path = "",
lora_model_path = "",
lora_alpha = 0.8,
):
# motion module
unet_state_dict = {}
if motion_module_path != "":
print(f"load motion module from {motion_module_path}")
motion_module_state_dict = torch.load(motion_module_path, map_location="cpu")
motion_module_state_dict = motion_module_state_dict["state_dict"] if "state_dict" in motion_module_state_dict else motion_module_state_dict
unet_state_dict.update({name: param for name, param in motion_module_state_dict.items() if "motion_modules." in name})
unet_state_dict.pop("animatediff_config", "")
missing, unexpected = animation_pipeline.unet.load_state_dict(unet_state_dict, strict=False)
assert len(unexpected) == 0
del unet_state_dict
# base model
if dreambooth_model_path != "":
print(f"load dreambooth model from {dreambooth_model_path}")
if dreambooth_model_path.endswith(".safetensors"):
dreambooth_state_dict = {}
with safe_open(dreambooth_model_path, framework="pt", device="cpu") as f:
for key in f.keys():
dreambooth_state_dict[key] = f.get_tensor(key)
elif dreambooth_model_path.endswith(".ckpt"):
dreambooth_state_dict = torch.load(dreambooth_model_path, map_location="cpu")
# 1. vae
converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, animation_pipeline.vae.config)
for key in list(converted_vae_checkpoint.keys()):
if 'mid_block' in key:
if 'key' in key:
new_key = key.replace('key', 'to_k')
elif 'query' in key:
new_key = key.replace('query', 'to_q')
elif 'value' in key:
new_key = key.replace('value', 'to_v')
elif 'proj_attn' in key:
new_key = key.replace('proj_attn', 'to_out.0')
else: new_key=False
if new_key:
converted_vae_checkpoint[new_key] = converted_vae_checkpoint[key]
del converted_vae_checkpoint[key]
m, u = animation_pipeline.vae.load_state_dict(converted_vae_checkpoint, strict=False)
print(f"dreambooth vae: {u}")
# 2. unet
converted_unet_checkpoint = convert_ldm_unet_checkpoint(dreambooth_state_dict, animation_pipeline.unet.config)
m,u = animation_pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False)
# 3. text_model
animation_pipeline.text_encoder = convert_ldm_clip_checkpoint(dreambooth_state_dict)
del dreambooth_state_dict
# lora layers
if lora_model_path != "":
print(f"load lora model from {lora_model_path}")
assert lora_model_path.endswith(".safetensors")
lora_state_dict = {}
with safe_open(lora_model_path, framework="pt", device="cpu") as f:
for key in f.keys():
lora_state_dict[key] = f.get_tensor(key)
animation_pipeline = convert_lora(animation_pipeline, lora_state_dict, alpha=lora_alpha)
del lora_state_dict
# domain adapter lora
if adapter_lora_path != "":
print(f"load domain lora from {adapter_lora_path}")
domain_lora_state_dict = torch.load(adapter_lora_path, map_location="cpu")
domain_lora_state_dict = domain_lora_state_dict["state_dict"] if "state_dict" in domain_lora_state_dict else domain_lora_state_dict
domain_lora_state_dict.pop("animatediff_config", "")
animation_pipeline = load_diffusers_lora(animation_pipeline, domain_lora_state_dict, alpha=adapter_lora_scale)
# motion module lora
for motion_module_lora_config in motion_module_lora_configs:
path, alpha = motion_module_lora_config["path"], motion_module_lora_config["alpha"]
print(f"load motion LoRA from {path}")
motion_lora_state_dict = torch.load(path, map_location="cpu")
motion_lora_state_dict = motion_lora_state_dict["state_dict"] if "state_dict" in motion_lora_state_dict else motion_lora_state_dict
motion_lora_state_dict.pop("animatediff_config", "")
animation_pipeline = load_diffusers_lora(animation_pipeline, motion_lora_state_dict, alpha)
return animation_pipeline
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def load_checkpoint(model_file, model):
if not os.path.isfile(model_file):
raise RuntimeError(f"{model_file} does not exist")
state_dict = torch.load(model_file, map_location="cpu")
global_step = state_dict['global_step'] if "global_step" in state_dict else 0
new_state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
new_state_dict = {k.replace('module.', '') : v for k, v in new_state_dict.items()}
m, u = model.load_state_dict(new_state_dict, strict=False)
return model, global_step, m, u, new_state_dict
def load_model(model, model_path):
if model_path != "":
print(f"init model from checkpoint: {model_path}")
model_ckpt = torch.load(model_path, map_location="cpu")
if "global_step" in model_ckpt: print(f"global_step: {model_ckpt['global_step']}")
state_dict = model_ckpt["state_dict"] if "state_dict" in model_ckpt else model_ckpt
m, u = model.load_state_dict(state_dict, strict=False)
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
assert len(u) == 0
def interpolate_trajectory(points, n_points):
x = [point[0] for point in points]
y = [point[1] for point in points]
t = np.linspace(0, 1, len(points))
fx = PchipInterpolator(t, x)
fy = PchipInterpolator(t, y)
new_t = np.linspace(0, 1, n_points)
new_x = fx(new_t)
new_y = fy(new_t)
new_points = list(zip(new_x, new_y))
return new_points
def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True):
"""Generate a bivariate isotropic or anisotropic Gaussian kernel.
In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.
Args:
kernel_size (int):
sig_x (float):
sig_y (float):
theta (float): Radian measurement.
grid (ndarray, optional): generated by :func:`mesh_grid`,
with the shape (K, K, 2), K is the kernel size. Default: None
isotropic (bool):
Returns:
kernel (ndarray): normalized kernel.
"""
if grid is None:
grid, _, _ = mesh_grid(kernel_size)
if isotropic:
sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
else:
sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
kernel = pdf2(sigma_matrix, grid)
kernel = kernel / np.sum(kernel)
return kernel
def mesh_grid(kernel_size):
"""Generate the mesh grid, centering at zero.
Args:
kernel_size (int):
Returns:
xy (ndarray): with the shape (kernel_size, kernel_size, 2)
xx (ndarray): with the shape (kernel_size, kernel_size)
yy (ndarray): with the shape (kernel_size, kernel_size)
"""
ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.)
xx, yy = np.meshgrid(ax, ax)
xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size,
1))).reshape(kernel_size, kernel_size, 2)
return xy, xx, yy
def pdf2(sigma_matrix, grid):
"""Calculate PDF of the bivariate Gaussian distribution.
Args:
sigma_matrix (ndarray): with the shape (2, 2)
grid (ndarray): generated by :func:`mesh_grid`,
with the shape (K, K, 2), K is the kernel size.
Returns:
kernel (ndarrray): un-normalized kernel.
"""
inverse_sigma = np.linalg.inv(sigma_matrix)
kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2))
return kernel
def sigma_matrix2(sig_x, sig_y, theta):
"""Calculate the rotated sigma matrix (two dimensional matrix).
Args:
sig_x (float):
sig_y (float):
theta (float): Radian measurement.
Returns:
ndarray: Rotated sigma matrix.
"""
d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]])
u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T))
def create_image_controlnet(controlnet_config, unet, controlnet_path=""):
# load controlnet model
controlnet = None
unet.config.num_attention_heads = 8
unet.config.projection_class_embeddings_input_dim = None
controlnet_config = OmegaConf.load(controlnet_config)
controlnet = ImageControlNetModel.from_unet(unet, controlnet_additional_kwargs=controlnet_config.get("controlnet_additional_kwargs", {}))
if controlnet_path != "":
print(f"loading controlnet checkpoint from {controlnet_path} ...")
controlnet_state_dict = torch.load(controlnet_path, map_location="cuda")
if "global_step" in controlnet_state_dict: print(f"global_step: {controlnet_state_dict['global_step']}")
controlnet_state_dict = controlnet_state_dict["state_dict"] if "state_dict" in controlnet_state_dict else controlnet_state_dict
controlnet_state_dict.pop("animatediff_config", "")
controlnet.load_state_dict(controlnet_state_dict)
return controlnet
def create_flow_controlnet(controlnet_config, unet, controlnet_path=""):
# load controlnet model
controlnet = None
unet.config.num_attention_heads = 8
unet.config.projection_class_embeddings_input_dim = None
controlnet_config = OmegaConf.load(controlnet_config)
controlnet = FlowControlNetModel.from_unet(unet, controlnet_additional_kwargs=controlnet_config.get("controlnet_additional_kwargs", {}))
if controlnet_path != "":
print(f"loading controlnet checkpoint from {controlnet_path} ...")
controlnet_state_dict = torch.load(controlnet_path, map_location="cuda")
controlnet_state_dict = controlnet_state_dict["controlnet"] if "controlnet" in controlnet_state_dict else controlnet_state_dict
controlnet_state_dict.pop("animatediff_config", "")
controlnet.load_state_dict(controlnet_state_dict)
return controlnet