(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, infer=False, **kwargs)

(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs)

(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs)

(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, model_manager=None, **kwargs)

(self, file_path="", model_names=[], model_classes=[])

(self, file_path="", state_dict={}, model_names=[], model_classes=[], model_resource=None)

(self, file_path="", state_dict={}, model_names=[], model_classes=[], extra_kwargs={})

(self,model_manager:ModelManager,extender_classes=[])

(self, model_manager: ModelManager, refiner_classes=[])

(file_path, torch_dtype=None)

(self, file_path="", state_dict={})

(self, file_path="", state_dict={})

(self, file_path="", state_dict={})

(self, file_path="", state_dict={})

(self)

(self)

(self, images)

(module, name, buffer, persistent=True)

(module, name, param)

(self, timestep, sample, sample_stablized)

(self, imgs, deterministic=False)

(folder, extensions)

( model, device_id, param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=t

()

()

(self, frames)

(tokenizer, prompt, max_length=None)

(self, sample, noise, timestep)

(self, timestep)

(self, x, freqs)

(self, x: torch.Tensor, timestep: torch.Tensor, context: torch.Tensor,

(self, vae_output)

(self, vae_output)

(*args, **kwargs)