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update infer script

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This commit is contained in:
Yuwei Guo
2023-09-25 11:39:18 +08:00
parent ae04222ba0
commit 069dd9432f
2 changed files with 94 additions and 0 deletions
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68
animatediff/utils/util.py
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@@ -7,8 +7,11 @@ import torch
import torchvision
import torch.distributed as dist
from safetensors import safe_open
from tqdm import tqdm
from einops import rearrange
from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora, convert_motion_lora_ckpt_to_diffusers
def zero_rank_print(s):
@@ -87,3 +90,68 @@ def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
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 = [],
# image layers
dreambooth_model_path = "",
lora_model_path = "",
lora_alpha = 0.8,
):
# 1.1 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})
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")
# 1. vae
converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, animation_pipeline.vae.config)
animation_pipeline.vae.load_state_dict(converted_vae_checkpoint)
# 2. unet
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)
# 3. text_model
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