(
animation_pipeline,
# motion module
motion_module_path = "",
motion_module_lora_configs = [],
# image layers
dreambooth_model_path = "",
lora_model_path = "",
lora_alpha = 0.8,
)
| 92 | return ddim_latents |
| 93 | |
| 94 | def load_weights( |
| 95 | animation_pipeline, |
| 96 | # motion module |
| 97 | motion_module_path = "", |
| 98 | motion_module_lora_configs = [], |
| 99 | # image layers |
| 100 | dreambooth_model_path = "", |
| 101 | lora_model_path = "", |
| 102 | lora_alpha = 0.8, |
| 103 | ): |
| 104 | # 1.1 motion module |
| 105 | unet_state_dict = {} |
| 106 | if motion_module_path != "": |
| 107 | print(f"load motion module from {motion_module_path}") |
| 108 | motion_module_state_dict = torch.load(motion_module_path, map_location="cpu") |
| 109 | motion_module_state_dict = motion_module_state_dict["state_dict"] if "state_dict" in motion_module_state_dict else motion_module_state_dict |
| 110 | unet_state_dict.update({name: param for name, param in motion_module_state_dict.items() if "motion_modules." in name}) |
| 111 | |
| 112 | missing, unexpected = animation_pipeline.unet.load_state_dict(unet_state_dict, strict=False) |
| 113 | assert len(unexpected) == 0 |
| 114 | del unet_state_dict |
| 115 | |
| 116 | if dreambooth_model_path != "": |
| 117 | print(f"load dreambooth model from {dreambooth_model_path}") |
| 118 | if dreambooth_model_path.endswith(".safetensors"): |
| 119 | dreambooth_state_dict = {} |
| 120 | with safe_open(dreambooth_model_path, framework="pt", device="cpu") as f: |
| 121 | for key in f.keys(): |
| 122 | dreambooth_state_dict[key] = f.get_tensor(key) |
| 123 | elif dreambooth_model_path.endswith(".ckpt"): |
| 124 | dreambooth_state_dict = torch.load(dreambooth_model_path, map_location="cpu") |
| 125 | |
| 126 | # 1. vae |
| 127 | converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, animation_pipeline.vae.config) |
| 128 | animation_pipeline.vae.load_state_dict(converted_vae_checkpoint) |
| 129 | # 2. unet |
| 130 | converted_unet_checkpoint = convert_ldm_unet_checkpoint(dreambooth_state_dict, animation_pipeline.unet.config) |
| 131 | animation_pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False) |
| 132 | # 3. text_model |
| 133 | animation_pipeline.text_encoder = convert_ldm_clip_checkpoint(dreambooth_state_dict) |
| 134 | del dreambooth_state_dict |
| 135 | |
| 136 | if lora_model_path != "": |
| 137 | print(f"load lora model from {lora_model_path}") |
| 138 | assert lora_model_path.endswith(".safetensors") |
| 139 | lora_state_dict = {} |
| 140 | with safe_open(lora_model_path, framework="pt", device="cpu") as f: |
| 141 | for key in f.keys(): |
| 142 | lora_state_dict[key] = f.get_tensor(key) |
| 143 | |
| 144 | animation_pipeline = convert_lora(animation_pipeline, lora_state_dict, alpha=lora_alpha) |
| 145 | del lora_state_dict |
| 146 | |
| 147 | |
| 148 | for motion_module_lora_config in motion_module_lora_configs: |
| 149 | path, alpha = motion_module_lora_config["path"], motion_module_lora_config["alpha"] |
| 150 | print(f"load motion LoRA from {path}") |
| 151 |
nothing calls this directly
no test coverage detected