dkt/utils/__init__.py

import torch, warnings, glob, os
import numpy as np
from PIL import Image
from einops import repeat, reduce
from typing import Optional, Union
from dataclasses import dataclass
from modelscope import snapshot_download
import numpy as np
from PIL import Image
from typing import Optional


class BasePipeline(torch.nn.Module):

    def __init__(
        self,
        device="cuda", torch_dtype=torch.float16,
        height_division_factor=64, width_division_factor=64,
        time_division_factor=None, time_division_remainder=None,
    ):
        super().__init__()
        # The device and torch_dtype is used for the storage of intermediate variables, not models.
        self.device = device
        self.torch_dtype = torch_dtype
        # The following parameters are used for shape check.
        self.height_division_factor = height_division_factor
        self.width_division_factor = width_division_factor
        self.time_division_factor = time_division_factor
        self.time_division_remainder = time_division_remainder
        self.vram_management_enabled = False
        
        
    def to(self, *args, **kwargs):
        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
        if device is not None:
            self.device = device
        if dtype is not None:
            self.torch_dtype = dtype
        super().to(*args, **kwargs)
        return self


    def check_resize_height_width(self, height, width, num_frames=None):
        # Shape check
        if height % self.height_division_factor != 0:
            height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
            print(f"height % {self.height_division_factor} != 0. We round it up to {height}.")
        if width % self.width_division_factor != 0:
            width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor
            print(f"width % {self.width_division_factor} != 0. We round it up to {width}.")
        if num_frames is None:
            return height, width
        else:
            if num_frames % self.time_division_factor != self.time_division_remainder:
                num_frames = (num_frames + self.time_division_factor - 1) // self.time_division_factor * self.time_division_factor + self.time_division_remainder
                print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.")
            return height, width, num_frames


    def preprocess_image(self, image, torch_dtype=None, device=None, pattern="B C H W", min_value=-1, max_value=1):
        # Transform a PIL.Image to torch.Tensor
        image = torch.Tensor(np.array(image, dtype=np.float32))
        image = image.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device)
        image = image * ((max_value - min_value) / 255) + min_value
        image = repeat(image, f"H W C -> {pattern}", **({"B": 1} if "B" in pattern else {}))
        return image


    def preprocess_video(self, video, torch_dtype=None, device=None, pattern="B C T H W", min_value=-1, max_value=1):
        # Transform a list of PIL.Image to torch.Tensor
        video = [self.preprocess_image(image, torch_dtype=torch_dtype, device=device, min_value=min_value, max_value=max_value) for image in video]
        video = torch.stack(video, dim=pattern.index("T") // 2)
        return video


    def vae_output_to_image(self, vae_output, pattern="B C H W", min_value=-1, max_value=1):
        # Transform a torch.Tensor to PIL.Image
        if pattern != "H W C":
            vae_output = reduce(vae_output, f"{pattern} -> H W C", reduction="mean")
        image = ((vae_output - min_value) * (255 / (max_value - min_value))).clip(0, 255)
        image = image.to(device="cpu", dtype=torch.uint8)
        image = Image.fromarray(image.numpy())
        return image


    def vae_output_to_video(self, vae_output, pattern="B C T H W", min_value=-1, max_value=1):
        # Transform a torch.Tensor to list of PIL.Image
        if pattern != "T H W C":
            vae_output = reduce(vae_output, f"{pattern} -> T H W C", reduction="mean")
        video = [self.vae_output_to_image(image, pattern="H W C", min_value=min_value, max_value=max_value) for image in vae_output]
        return video


    def load_models_to_device(self, model_names=[]):
        if self.vram_management_enabled:
            # offload models
            for name, model in self.named_children():
                if name not in model_names:
                    if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
                        for module in model.modules():
                            if hasattr(module, "offload"):
                                module.offload()
                    else:
                        model.cpu()
            torch.cuda.empty_cache()
            # onload models
            for name, model in self.named_children():
                if name in model_names:
                    if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
                        for module in model.modules():
                            if hasattr(module, "onload"):
                                module.onload()
                    else:
                        model.to(self.device)


    def generate_noise(self, shape, seed=None, rand_device="cpu", rand_torch_dtype=torch.float32, device=None, torch_dtype=None):
        # Initialize Gaussian noise
        generator = None if seed is None else torch.Generator(rand_device).manual_seed(seed)
        noise = torch.randn(shape, generator=generator, device=rand_device, dtype=rand_torch_dtype)
        noise = noise.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device)
        return noise


    def enable_cpu_offload(self):
        warnings.warn("`enable_cpu_offload` will be deprecated. Please use `enable_vram_management`.")
        self.vram_management_enabled = True
        
        
    def get_vram(self):
        return torch.cuda.mem_get_info(self.device)[1] / (1024 ** 3)
    
    
    def freeze_except(self, model_names):
        for name, model in self.named_children():
            if name in model_names:
                model.train()
                model.requires_grad_(True)
            else:
                model.eval()
                model.requires_grad_(False)


@dataclass
class ModelConfig:
    path: Union[str, list[str]] = None
    model_id: str = None
    origin_file_pattern: Union[str, list[str]] = None
    download_resource: str = "ModelScope"
    offload_device: Optional[Union[str, torch.device]] = None
    offload_dtype: Optional[torch.dtype] = None
    local_model_path: str = None
    skip_download: bool = False

    def download_if_necessary(self, use_usp=False):
        if self.path is None:
            # Check model_id and origin_file_pattern
            if self.model_id is None:
                raise ValueError(f"""No valid model files. Please use `ModelConfig(path="xxx")` or `ModelConfig(model_id="xxx/yyy", origin_file_pattern="zzz")`.""")
            
            # Skip if not in rank 0
            if use_usp:
                import torch.distributed as dist
                skip_download = self.skip_download or dist.get_rank() != 0
            else:
                skip_download = self.skip_download
                
            # Check whether the origin path is a folder
            if self.origin_file_pattern is None or self.origin_file_pattern == "":
                self.origin_file_pattern = ""
                allow_file_pattern = None
                is_folder = True
            elif isinstance(self.origin_file_pattern, str) and self.origin_file_pattern.endswith("/"):
                allow_file_pattern = self.origin_file_pattern + "*"
                is_folder = True
            else:
                allow_file_pattern = self.origin_file_pattern
                is_folder = False
            
            # Download
            if self.local_model_path is None:
                self.local_model_path = "./models"
            if not skip_download:
                downloaded_files = glob.glob(self.origin_file_pattern, root_dir=os.path.join(self.local_model_path, self.model_id))
                snapshot_download(
                    self.model_id,
                    local_dir=os.path.join(self.local_model_path, self.model_id),
                    allow_file_pattern=allow_file_pattern,
                    ignore_file_pattern=downloaded_files,
                    local_files_only=False
                )
            
            # Let rank 1, 2, ... wait for rank 0
            if use_usp:
                import torch.distributed as dist
                dist.barrier(device_ids=[dist.get_rank()])
                
            # Return downloaded files
            if is_folder:
                self.path = os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern)
            else:
                self.path = glob.glob(os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern))
            if isinstance(self.path, list) and len(self.path) == 1:
                self.path = self.path[0]



class PipelineUnit:
    def __init__(
        self,
        seperate_cfg: bool = False,
        take_over: bool = False,
        input_params: tuple[str] = None,
        input_params_posi: dict[str, str] = None,
        input_params_nega: dict[str, str] = None,
        onload_model_names: tuple[str] = None
    ):
        self.seperate_cfg = seperate_cfg
        self.take_over = take_over
        self.input_params = input_params
        self.input_params_posi = input_params_posi
        self.input_params_nega = input_params_nega
        self.onload_model_names = onload_model_names


    def process(self, pipe: BasePipeline, inputs: dict, positive=True, **kwargs) -> dict:
        raise NotImplementedError("`process` is not implemented.")



class PipelineUnitRunner:
    def __init__(self):
        pass

    def __call__(self, unit: PipelineUnit, pipe: BasePipeline, inputs_shared: dict, inputs_posi: dict, inputs_nega: dict) -> tuple[dict, dict]:
        if unit.take_over:
            # Let the pipeline unit take over this function.
            inputs_shared, inputs_posi, inputs_nega = unit.process(pipe, inputs_shared=inputs_shared, inputs_posi=inputs_posi, inputs_nega=inputs_nega)
        elif unit.seperate_cfg:
            # Positive side
            processor_inputs = {name: inputs_posi.get(name_) for name, name_ in unit.input_params_posi.items()}
            if unit.input_params is not None:
                for name in unit.input_params:
                    processor_inputs[name] = inputs_shared.get(name)
            processor_outputs = unit.process(pipe, **processor_inputs)
            inputs_posi.update(processor_outputs)
            # Negative side
            if inputs_shared["cfg_scale"] != 1:
                processor_inputs = {name: inputs_nega.get(name_) for name, name_ in unit.input_params_nega.items()}
                if unit.input_params is not None:
                    for name in unit.input_params:
                        processor_inputs[name] = inputs_shared.get(name)
                processor_outputs = unit.process(pipe, **processor_inputs)
                inputs_nega.update(processor_outputs)
            else:
                inputs_nega.update(processor_outputs)
        else:
            processor_inputs = {name: inputs_shared.get(name) for name in unit.input_params}
            processor_outputs = unit.process(pipe, **processor_inputs)
            inputs_shared.update(processor_outputs)
        return inputs_shared, inputs_posi, inputs_nega