DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py

""" OpenAI pretrained model functions

Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""

import os
import warnings
from typing import List, Optional, Union

import torch

from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype
from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url

__all__ = ["list_openai_models", "load_openai_model"]


def list_openai_models() -> List[str]:
    """Returns the names of available CLIP models"""
    return list_pretrained_models_by_tag('openai')


def load_openai_model(
        name: str,
        precision: Optional[str] = None,
        device: Optional[Union[str, torch.device]] = None,
        jit: bool = True,
        cache_dir: Optional[str] = None,
):
    """Load a CLIP model

    Parameters
    ----------
    name : str
        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
    precision: str
        Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'.
    device : Union[str, torch.device]
        The device to put the loaded model
    jit : bool
        Whether to load the optimized JIT model (default) or more hackable non-JIT model.
    cache_dir : Optional[str]
        The directory to cache the downloaded model weights

    Returns
    -------
    model : torch.nn.Module
        The CLIP model
    preprocess : Callable[[PIL.Image], torch.Tensor]
        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
    """
    if device is None:
        device = "cuda" if torch.cuda.is_available() else "cpu"
    if precision is None:
        precision = 'fp32' if device == 'cpu' else 'fp16'

    if get_pretrained_url(name, 'openai'):
        model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir)
    elif os.path.isfile(name):
        model_path = name
    else:
        raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}")

    try:
        # loading JIT archive
        model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
        state_dict = None
    except RuntimeError:
        # loading saved state dict
        if jit:
            warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
            jit = False
        state_dict = torch.load(model_path, map_location="cpu")

    if not jit:
        # Build a non-jit model from the OpenAI jitted model state dict
        cast_dtype = get_cast_dtype(precision)
        try:
            model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype)
        except KeyError:
            sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
            model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype)

        # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use
        model = model.to(device)
        if precision.startswith('amp') or precision == 'fp32':
            model.float()
        elif precision == 'bf16':
            convert_weights_to_lp(model, dtype=torch.bfloat16)

        return model

    # patch the device names
    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]

    def patch_device(module):
        try:
            graphs = [module.graph] if hasattr(module, "graph") else []
        except RuntimeError:
            graphs = []

        if hasattr(module, "forward1"):
            graphs.append(module.forward1.graph)

        for graph in graphs:
            for node in graph.findAllNodes("prim::Constant"):
                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
                    node.copyAttributes(device_node)

    model.apply(patch_device)
    patch_device(model.encode_image)
    patch_device(model.encode_text)

    # patch dtype to float32 (typically for CPU)
    if precision == 'fp32':
        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
        float_node = float_input.node()

        def patch_float(module):
            try:
                graphs = [module.graph] if hasattr(module, "graph") else []
            except RuntimeError:
                graphs = []

            if hasattr(module, "forward1"):
                graphs.append(module.forward1.graph)

            for graph in graphs:
                for node in graph.findAllNodes("aten::to"):
                    inputs = list(node.inputs())
                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
                        if inputs[i].node()["value"] == 5:
                            inputs[i].node().copyAttributes(float_node)

        model.apply(patch_float)
        patch_float(model.encode_image)
        patch_float(model.encode_text)
        model.float()

    # ensure image_size attr available at consistent location for both jit and non-jit
    model.visual.image_size = model.input_resolution.item()
    return model