Spaces:
Runtime error
Runtime error
File size: 6,590 Bytes
c0fdaf5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
import os
import imageio
import numpy as np
from typing import Union
import torch
import torchvision
import torch.distributed as dist
from safetensors import safe_open
from tqdm import tqdm
from einops import rearrange
from animatelcm.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from animatelcm.utils.convert_lora_safetensor_to_diffusers import convert_lora, convert_motion_lora_ckpt_to_diffusers
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=6, 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)
# 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_path = "",
motion_module_lora_configs = [],
dreambooth_model_path = "",
lora_model_path = "",
lora_alpha = 0.8,
):
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})
missing, unexpected = animation_pipeline.unet.load_state_dict(unet_state_dict, strict=False)
assert len(unexpected) == 0
del unet_state_dict
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")
converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, animation_pipeline.vae.config)
animation_pipeline.vae.load_state_dict(converted_vae_checkpoint)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(dreambooth_state_dict, animation_pipeline.unet.config)
animation_pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False)
animation_pipeline.text_encoder = convert_ldm_clip_checkpoint(dreambooth_state_dict)
del dreambooth_state_dict
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
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
animation_pipeline = convert_motion_lora_ckpt_to_diffusers(animation_pipeline, motion_lora_state_dict, alpha)
return animation_pipeline
|