Add model weights

GDPOSR/datasets/params_GDPO.yml

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+scale: 4
+color_jitter_prob: 0.0
+gray_prob: 0.0
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.3, 1.5]
+gaussian_noise_prob: 0.5
+noise_range: [1, 15]
+poisson_scale_range: [0.05, 2.0]
+gray_noise_prob: 0.4
+jpeg_range: [60, 95]
+
+# the second degradation process
+second_phase_prob: 1.0
+second_blur_prob: 0.5
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.6, 1.2]
+gaussian_noise_prob2: 0.5
+noise_range2: [1, 12]
+poisson_scale_range2: [0.05, 1.0]
+gray_noise_prob2: 0.4
+jpeg_range2: [60, 100]
+
+kernel_info:
+    blur_kernel_size: 21
+    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob: 0.1
+    blur_sigma: [0.2, 3]
+    betag_range: [0.5, 4]
+    betap_range: [1, 2]
+
+    blur_kernel_size2: 21
+    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob2: 0.1
+    blur_sigma2: [0.2, 1.5]
+    betag_range2: [0.5, 4]
+    betap_range2: [1, 2]
+
+    final_sinc_prob: 0.8

GDPOSR/datasets/realesrgan.py

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+import os
+import numpy as np
+import cv2
+import glob
+import math
+import yaml
+import random
+from collections import OrderedDict
+import torch
+import torch.nn.functional as F
+
+from basicsr.data.transforms import augment
+from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
+from basicsr.utils import DiffJPEG, USMSharp, img2tensor, tensor2img
+from basicsr.utils.img_process_util import filter2D
+from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
+from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation,
+                                               normalize, rgb_to_grayscale)
+
+cur_path = os.path.dirname(os.path.abspath(__file__))
+
+def ordered_yaml():
+    """Support OrderedDict for yaml.
+
+    Returns:
+        yaml Loader and Dumper.
+    """
+    try:
+        from yaml import CDumper as Dumper
+        from yaml import CLoader as Loader
+    except ImportError:
+        from yaml import Dumper, Loader
+
+    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
+
+    def dict_representer(dumper, data):
+        return dumper.represent_dict(data.items())
+
+    def dict_constructor(loader, node):
+        return OrderedDict(loader.construct_pairs(node))
+
+    Dumper.add_representer(OrderedDict, dict_representer)
+    Loader.add_constructor(_mapping_tag, dict_constructor)
+    return Loader, Dumper
+
+def opt_parse(opt_path):
+    with open(opt_path, mode='r') as f:
+        Loader, _ = ordered_yaml()
+        opt = yaml.load(f, Loader=Loader)  # ignore_security_alert_wait_for_fix RCE
+
+    return opt
+
+class RealESRGAN_degradation(object):
+    def __init__(self, opt_name='params_realesrgan.yml', device='cpu'):
+        opt_path = f'{cur_path}/{opt_name}'
+        self.opt = opt_parse(opt_path)
+        self.device = device #torch.device('cpu')
+        optk = self.opt['kernel_info']       
+
+        # blur settings for the first degradation
+        self.blur_kernel_size = optk['blur_kernel_size']
+        self.kernel_list = optk['kernel_list']
+        self.kernel_prob = optk['kernel_prob']
+        self.blur_sigma = optk['blur_sigma']
+        self.betag_range = optk['betag_range']
+        self.betap_range = optk['betap_range']
+        self.sinc_prob = optk['sinc_prob']
+
+        # blur settings for the second degradation
+        self.blur_kernel_size2 = optk['blur_kernel_size2']
+        self.kernel_list2 = optk['kernel_list2']
+        self.kernel_prob2 = optk['kernel_prob2']
+        self.blur_sigma2 = optk['blur_sigma2']
+        self.betag_range2 = optk['betag_range2']
+        self.betap_range2 = optk['betap_range2']
+        self.sinc_prob2 = optk['sinc_prob2']
+
+        # a final sinc filter
+        self.final_sinc_prob = optk['final_sinc_prob']
+
+        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
+        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
+        self.pulse_tensor[10, 10] = 1
+
+        self.jpeger = DiffJPEG(differentiable=False).to(self.device)
+        self.usm_shaper = USMSharp().to(self.device)
+    
+    def color_jitter_pt(self, img, brightness, contrast, saturation, hue):
+        fn_idx = torch.randperm(4)
+        for fn_id in fn_idx:
+            if fn_id == 0 and brightness is not None:
+                brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
+                img = adjust_brightness(img, brightness_factor)
+
+            if fn_id == 1 and contrast is not None:
+                contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
+                img = adjust_contrast(img, contrast_factor)
+
+            if fn_id == 2 and saturation is not None:
+                saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
+                img = adjust_saturation(img, saturation_factor)
+
+            if fn_id == 3 and hue is not None:
+                hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
+                img = adjust_hue(img, hue_factor)
+        return img
+
+    def random_augment(self, img_gt):
+        # random horizontal flip
+        img_gt, status = augment(img_gt, hflip=True, rotation=False, return_status=True)
+        """
+        # random color jitter 
+        if np.random.uniform() < self.opt['color_jitter_prob']:
+            jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32)
+            img_gt = img_gt + jitter_val
+            img_gt = np.clip(img_gt, 0, 1)    
+
+        # random grayscale
+        if np.random.uniform() < self.opt['gray_prob']:
+            #img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY)
+            img_gt = cv2.cvtColor(img_gt, cv2.COLOR_RGB2GRAY)
+            img_gt = np.tile(img_gt[:, :, None], [1, 1, 3])
+        """
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_gt = img2tensor([img_gt], bgr2rgb=False, float32=True)[0].unsqueeze(0)
+        return img_gt
+
+    def random_kernels(self):
+        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
+        kernel_size = random.choice(self.kernel_range)
+        if np.random.uniform() < self.sinc_prob:
+            # this sinc filter setting is for kernels ranging from [7, 21]
+            if kernel_size < 13:
+                omega_c = np.random.uniform(np.pi / 3, np.pi)
+            else:
+                omega_c = np.random.uniform(np.pi / 5, np.pi)
+            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
+        else:
+            kernel = random_mixed_kernels(
+                    self.kernel_list,
+                    self.kernel_prob,
+                    kernel_size,
+                    self.blur_sigma,
+                    self.blur_sigma, [-math.pi, math.pi],
+                    self.betag_range,
+                    self.betap_range,
+                    noise_range=None)
+        # pad kernel
+        pad_size = (21 - kernel_size) // 2
+        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
+
+        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
+        kernel_size = random.choice(self.kernel_range)
+        if np.random.uniform() < self.sinc_prob2:
+            if kernel_size < 13:
+                omega_c = np.random.uniform(np.pi / 3, np.pi)
+            else:
+                omega_c = np.random.uniform(np.pi / 5, np.pi)
+            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
+        else:
+            kernel2 = random_mixed_kernels(
+                self.kernel_list2,
+                self.kernel_prob2,
+                kernel_size,
+                self.blur_sigma2,
+                self.blur_sigma2, [-math.pi, math.pi],
+                self.betag_range2,
+                self.betap_range2,
+                noise_range=None)
+
+        # pad kernel
+        pad_size = (21 - kernel_size) // 2
+        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
+
+        # ------------------------------------- sinc kernel ------------------------------------- #
+        if np.random.uniform() < self.final_sinc_prob:
+            kernel_size = random.choice(self.kernel_range)
+            omega_c = np.random.uniform(np.pi / 3, np.pi)
+            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
+            sinc_kernel = torch.FloatTensor(sinc_kernel)
+        else:
+            sinc_kernel = self.pulse_tensor
+
+        kernel = torch.FloatTensor(kernel)
+        kernel2 = torch.FloatTensor(kernel2) 
+
+        return kernel, kernel2, sinc_kernel
+
+    @torch.no_grad()
+    def degrade_process(self, img_gt, resize_bak=False):
+        img_gt = self.random_augment(img_gt)
+        kernel1, kernel2, sinc_kernel = self.random_kernels()
+        img_gt, kernel1, kernel2, sinc_kernel = img_gt.to(self.device), kernel1.to(self.device), kernel2.to(self.device), sinc_kernel.to(self.device)
+        #img_gt = self.usm_shaper(img_gt) # shaper gt
+        ori_h, ori_w = img_gt.size()[2:4]
+
+        #scale_final = random.randint(4, 16)
+        scale_final = 4
+
+        # ----------------------- The first degradation process ----------------------- #
+        # blur
+        out = filter2D(img_gt, kernel1)
+        # random resize
+        updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
+        if updown_type == 'up':
+            scale = np.random.uniform(1, self.opt['resize_range'][1])
+        elif updown_type == 'down':
+            scale = np.random.uniform(self.opt['resize_range'][0], 1)
+        else:
+            scale = 1
+        mode = random.choice(['area', 'bilinear', 'bicubic'])
+        out = F.interpolate(out, scale_factor=scale, mode=mode)
+        # noise
+        gray_noise_prob = self.opt['gray_noise_prob']
+        if np.random.uniform() < self.opt['gaussian_noise_prob']:
+            out = random_add_gaussian_noise_pt(
+                out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
+        else:
+            out = random_add_poisson_noise_pt(
+                out,
+                scale_range=self.opt['poisson_scale_range'],
+                gray_prob=gray_noise_prob,
+                clip=True,
+                rounds=False)
+        # JPEG compression
+        jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
+        out = torch.clamp(out, 0, 1)
+        out = self.jpeger(out, quality=jpeg_p)
+
+        # ----------------------- The second degradation process ----------------------- #
+        # blur
+        if self.opt['second_phase_prob'] > random.random():
+            # print('----------------------- The second degradation process -----------------------')
+            if np.random.uniform() < self.opt['second_blur_prob']:
+                out = filter2D(out, kernel2)
+            # random resize
+            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
+            if updown_type == 'up':
+                scale = np.random.uniform(1, self.opt['resize_range2'][1])
+            elif updown_type == 'down':
+                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
+            else:
+                scale = 1
+            mode = random.choice(['area', 'bilinear', 'bicubic'])
+            out = F.interpolate(
+                out, size=(int(ori_h / scale_final * scale), int(ori_w / scale_final * scale)), mode=mode)
+            # noise
+            gray_noise_prob = self.opt['gray_noise_prob2']
+            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
+                out = random_add_gaussian_noise_pt(
+                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
+            else:
+                out = random_add_poisson_noise_pt(
+                    out,
+                    scale_range=self.opt['poisson_scale_range2'],
+                    gray_prob=gray_noise_prob,
+                    clip=True,
+                    rounds=False)
+
+        # JPEG compression + the final sinc filter
+        # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
+        # as one operation.
+        # We consider two orders:
+        #   1. [resize back + sinc filter] + JPEG compression
+        #   2. JPEG compression + [resize back + sinc filter]
+        # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
+        if np.random.uniform() < 0.5:
+            # resize back + the final sinc filter
+            mode = random.choice(['area', 'bilinear', 'bicubic'])
+            out = F.interpolate(out, size=(ori_h // scale_final, ori_w // scale_final), mode=mode)
+            out = filter2D(out, sinc_kernel)
+            # JPEG compression
+            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
+            out = torch.clamp(out, 0, 1)
+            out = self.jpeger(out, quality=jpeg_p)
+        else:
+            # JPEG compression
+            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
+            out = torch.clamp(out, 0, 1)
+            out = self.jpeger(out, quality=jpeg_p)
+            # resize back + the final sinc filter
+            mode = random.choice(['area', 'bilinear', 'bicubic'])
+            out = F.interpolate(out, size=(ori_h // scale_final, ori_w // scale_final), mode=mode)
+            out = filter2D(out, sinc_kernel)
+
+        if np.random.uniform() < self.opt['gray_prob']:
+            out = rgb_to_grayscale(out, num_output_channels=1)
+
+        if np.random.uniform() < self.opt['color_jitter_prob']:
+            brightness = self.opt.get('brightness', (0.5, 1.5))
+            contrast = self.opt.get('contrast', (0.5, 1.5))
+            saturation = self.opt.get('saturation', (0, 1.5))
+            hue = self.opt.get('hue', (-0.1, 0.1))
+            out = self.color_jitter_pt(out, brightness, contrast, saturation, hue)
+            
+        out1 = out
+        img_lq_noresize = torch.clamp((out1 * 255.0).round(), 0, 255) / 255.
+
+        if resize_bak:
+            mode = random.choice(['area', 'bilinear', 'bicubic'])
+            out = F.interpolate(out, size=(ori_h, ori_w), mode=mode)
+        # clamp and round
+        img_lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
+
+        return img_gt, img_lq, img_lq_noresize

GDPOSR/diffusermodels/autoencoder_kl.py

@@ -0,0 +1,560 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalVAEMixin
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    Attention,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.modeling_outputs import AutoencoderKLOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.autoencoders.vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
+
+class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
+    r"""
+    A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
+        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
+            Tuple of downsample block types.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
+            Tuple of upsample block types.
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
+            Tuple of block output channels.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
+        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
+        scaling_factor (`float`, *optional*, defaults to 0.18215):
+            The component-wise standard deviation of the trained latent space computed using the first batch of the
+            training set. This is used to scale the latent space to have unit variance when training the diffusion
+            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
+            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
+            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
+            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
+        force_upcast (`bool`, *optional*, default to `True`):
+            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
+            can be fine-tuned / trained to a lower range without loosing too much precision in which case
+            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
+        up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
+        block_out_channels: Tuple[int] = (64,),
+        layers_per_block: int = 1,
+        act_fn: str = "silu",
+        latent_channels: int = 4,
+        norm_num_groups: int = 32,
+        sample_size: int = 32,
+        scaling_factor: float = 0.18215,
+        force_upcast: float = True,
+    ):
+        super().__init__()
+
+        # pass init params to Encoder
+        self.encoder = Encoder(
+            in_channels=in_channels,
+            out_channels=latent_channels,
+            down_block_types=down_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            act_fn=act_fn,
+            norm_num_groups=norm_num_groups,
+            double_z=True,
+        )
+
+        # pass init params to Decoder
+        self.decoder = Decoder(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            up_block_types=up_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            norm_num_groups=norm_num_groups,
+            act_fn=act_fn,
+        )
+
+        self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
+        self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
+
+        self.use_slicing = False
+        self.use_tiling = False
+
+        # only relevant if vae tiling is enabled
+        self.tile_sample_min_size = self.config.sample_size
+        sample_size = (
+            self.config.sample_size[0]
+            if isinstance(self.config.sample_size, (list, tuple))
+            else self.config.sample_size
+        )
+        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
+        self.tile_overlap_factor = 0.25
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (Encoder, Decoder)):
+            module.gradient_checkpointing = value
+
+    def enable_tiling(self, use_tiling: bool = True):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.use_tiling = use_tiling
+
+    def disable_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.enable_tiling(False)
+
+    def enable_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    @property
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor, _remove_lora=_remove_lora)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor, _remove_lora=True)
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.FloatTensor, return_dict: bool = True
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        """
+        Encode a batch of images into latents.
+
+        Args:
+            x (`torch.FloatTensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+                The latent representations of the encoded images. If `return_dict` is True, a
+                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
+        """
+        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
+            return self.tiled_encode(x, return_dict=return_dict)
+
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self.encoder(x)
+
+        moments = self.quant_conv(h.to(dtype=self.quant_conv.weight.dtype))
+        posterior = DiagonalGaussianDistribution(moments)
+
+        if not return_dict:
+            return (posterior,)
+
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
+        if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
+            return self.tiled_decode(z, return_dict=return_dict)
+
+        z = self.post_quant_conv(z)
+        dec = self.decoder(z)
+
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)
+
+    @apply_forward_hook
+    def decode(
+        self, z: torch.FloatTensor, return_dict: bool = True, generator=None
+    ) -> Union[DecoderOutput, torch.FloatTensor]:
+        """
+        Decode a batch of images.
+
+        Args:
+            z (`torch.FloatTensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+
+        """
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z).sample
+
+        if not return_dict:
+            return (decoded,)
+
+        return DecoderOutput(sample=decoded)
+
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[2], b.shape[2], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
+        return b
+
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
+        return b
+
+    def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
+        r"""Encode a batch of images using a tiled encoder.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
+        steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
+        different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
+        tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
+        output, but they should be much less noticeable.
+
+        Args:
+            x (`torch.FloatTensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
+                If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
+                `tuple` is returned.
+        """
+        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_latent_min_size - blend_extent
+
+        # Split the image into 512x512 tiles and encode them separately.
+        rows = []
+        for i in range(0, x.shape[2], overlap_size):
+            row = []
+            for j in range(0, x.shape[3], overlap_size):
+                tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
+                tile = self.encoder(tile)
+                tile = self.quant_conv(tile)
+                row.append(tile)
+            rows.append(row)
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=3))
+
+        moments = torch.cat(result_rows, dim=2)
+        posterior = DiagonalGaussianDistribution(moments)
+
+        if not return_dict:
+            return (posterior,)
+
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
+        r"""
+        Decode a batch of images using a tiled decoder.
+
+        Args:
+            z (`torch.FloatTensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_sample_min_size - blend_extent
+
+        # Split z into overlapping 64x64 tiles and decode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, z.shape[2], overlap_size):
+            row = []
+            for j in range(0, z.shape[3], overlap_size):
+                tile = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
+                tile = self.post_quant_conv(tile)
+                decoded = self.decoder(tile)
+                row.append(decoded)
+            rows.append(row)
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=3))
+
+        dec = torch.cat(result_rows, dim=2)
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+    ) -> Union[DecoderOutput, torch.FloatTensor]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): Input sample.
+            sample_posterior (`bool`, *optional*, defaults to `False`):
+                Whether to sample from the posterior.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
+        """
+        x = sample
+        posterior = self.encode(x).latent_dist
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z).sample
+
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
+        key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+
+
+    def merge_and_unload(
+        self, progressbar: bool = False, safe_merge: bool = False, adapter_names: Optional[list[str]] = None
+    ) -> torch.nn.Module:
+
+        return self._unload_and_optionally_merge(
+            progressbar=progressbar, safe_merge=safe_merge, adapter_names=adapter_names
+        )
+
+    def _unload_and_optionally_merge(
+        self,
+        merge=True,
+        progressbar: bool = False,
+        safe_merge: bool = False,
+        adapter_names: Optional[list[str]] = None,
+    ):
+        from tqdm import tqdm
+        from peft.tuners.tuners_utils import onload_layer
+        from peft.utils import _get_submodules, ModulesToSaveWrapper
+
+        key_list = [key for key, _ in self.named_modules() if "lora_" not in key]
+        desc = "Unloading " + ("and merging " if merge else "") + "model"
+        for key in tqdm(key_list, disable=not progressbar, desc=desc):
+            try:
+                parent, target, target_name = _get_submodules(self, key)
+            except AttributeError:
+                continue
+            with onload_layer(target):
+                if hasattr(target, "base_layer"):
+                    if merge:
+                        target.merge(safe_merge=safe_merge, adapter_names=adapter_names)
+                    self._replace_module(parent, target_name, target.get_base_layer(), target)
+                elif isinstance(target, ModulesToSaveWrapper):
+                    # save any additional trainable modules part of `modules_to_save`
+                    new_module = target.modules_to_save[target.active_adapter]
+                    if hasattr(new_module, "base_layer"):
+                        # check if the module is itself a tuner layer
+                        if merge:
+                            new_module.merge(safe_merge=safe_merge, adapter_names=adapter_names)
+                        new_module = new_module.get_base_layer()
+                    setattr(parent, target_name, new_module)
+
+        return self
+
+    def _replace_module(self, parent, child_name, new_module, child):
+        setattr(parent, child_name, new_module)
+        # It's not necessary to set requires_grad here, as that is handled by
+        # _mark_only_adapters_as_trainable
+
+        # child layer wraps the original module, unpack it
+        if hasattr(child, "base_layer"):
+            child = child.base_layer
+
+        if not hasattr(new_module, "base_layer"):
+            new_module.weight = child.weight
+            if hasattr(child, "bias"):
+                new_module.bias = child.bias
+
+        if getattr(child, "state", None) is not None:
+            if hasattr(new_module, "base_layer"):
+                new_module.base_layer.state = child.state
+            else:
+                new_module.state = child.state
+            new_module.to(child.weight.device)
+
+        # dispatch to correct device
+        for name, module in new_module.named_modules():
+            if ("lora_" in name) or ("ranknum" in name):
+                weight = child.qweight if hasattr(child, "qweight") else child.weight
+                module.to(weight.device)

GDPOSR/diffusermodels/unet_2d_condition.py

@@ -0,0 +1,1280 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils  import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    Attention,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    ImageHintTimeEmbedding,
+    ImageProjection,
+    ImageTimeEmbedding,
+    PositionNet,
+    TextImageProjection,
+    TextImageTimeEmbedding,
+    TextTimeEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unet_2d_blocks import (
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    UNetMidBlock2DSimpleCrossAttn,
+    get_down_block,
+    get_up_block,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet2DConditionOutput(BaseOutput):
+    """
+    The output of [`UNet2DConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+
+
+class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
+            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+       reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
+            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+            Dimension for the timestep embeddings.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
+        *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
+        *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+            otherwise.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: Union[int, Tuple[int]] = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        dropout: float = 0.0,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: Union[int, Tuple[int]] = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
+        encoder_hid_dim: Optional[int] = None,
+        encoder_hid_dim_type: Optional[str] = None,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        resnet_skip_time_act: bool = False,
+        resnet_out_scale_factor: int = 1.0,
+        time_embedding_type: str = "positional",
+        time_embedding_dim: Optional[int] = None,
+        time_embedding_act_fn: Optional[str] = None,
+        timestep_post_act: Optional[str] = None,
+        time_cond_proj_dim: Optional[int] = None,
+        conv_in_kernel: int = 3,
+        conv_out_kernel: int = 3,
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        attention_type: str = "default",
+        class_embeddings_concat: bool = False,
+        mid_block_only_cross_attention: Optional[bool] = None,
+        cross_attention_norm: Optional[str] = None,
+        addition_embed_type_num_heads=64,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
+            for layer_number_per_block in transformer_layers_per_block:
+                if isinstance(layer_number_per_block, list):
+                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            if mid_block_only_cross_attention is None:
+                mid_block_only_cross_attention = only_cross_attention
+
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if mid_block_only_cross_attention is None:
+            mid_block_only_cross_attention = False
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                dropout=dropout,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                attention_type=attention_type,
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                cross_attention_dim=cross_attention_dim[-1],
+                attention_head_dim=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                skip_time_act=resnet_skip_time_act,
+                only_cross_attention=mid_block_only_cross_attention,
+                cross_attention_norm=cross_attention_norm,
+            )
+        elif mid_block_type == "UNetMidBlock2D":
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                num_layers=0,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                add_attention=False,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = (
+            list(reversed(transformer_layers_per_block))
+            if reverse_transformer_layers_per_block is None
+            else reverse_transformer_layers_per_block
+        )
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resolution_idx=i,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                dropout=dropout,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if norm_num_groups is not None:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+            )
+
+            self.conv_act = get_activation(act_fn)
+
+        else:
+            self.conv_norm_out = None
+            self.conv_act = None
+
+        conv_out_padding = (conv_out_kernel - 1) // 2
+        self.conv_out = nn.Conv2d(
+            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
+        )
+
+        if attention_type in ["gated", "gated-text-image"]:
+            positive_len = 768
+            if isinstance(cross_attention_dim, int):
+                positive_len = cross_attention_dim
+            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
+                positive_len = cross_attention_dim[0]
+
+            feature_type = "text-only" if attention_type == "gated" else "text-image"
+            self.position_net = PositionNet(
+                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
+            )
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor, _remove_lora=_remove_lora)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor, _remove_lora=True)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def enable_freeu(self, s1, s2, b1, b2):
+        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
+
+        The suffixes after the scaling factors represent the stage blocks where they are being applied.
+
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
+        are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+        Args:
+            s1 (`float`):
+                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+                mitigate the "oversmoothing effect" in the enhanced denoising process.
+            s2 (`float`):
+                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+                mitigate the "oversmoothing effect" in the enhanced denoising process.
+            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+        """
+        for i, upsample_block in enumerate(self.up_blocks):
+            setattr(upsample_block, "s1", s1)
+            setattr(upsample_block, "s2", s2)
+            setattr(upsample_block, "b1", b1)
+            setattr(upsample_block, "b2", b2)
+
+    def disable_freeu(self):
+        """Disables the FreeU mechanism."""
+        freeu_keys = {"s1", "s2", "b1", "b2"}
+        for i, upsample_block in enumerate(self.up_blocks):
+            for k in freeu_keys:
+                if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
+                    setattr(upsample_block, k, None)
+
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
+        key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        r"""
+        The [`UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
+                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
+                through the `self.time_embedding` layer to obtain the timestep embeddings.
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
+                A tuple of tensors that if specified are added to the residuals of down unet blocks.
+            mid_block_additional_residual: (`torch.Tensor`, *optional*):
+                A tensor that if specified is added to the residual of the middle unet block.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+            down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+                additional residuals to be added to UNet long skip connections from down blocks to up blocks for
+                example from ControlNet side model(s)
+            mid_block_additional_residual (`torch.Tensor`, *optional*):
+                additional residual to be added to UNet mid block output, for example from ControlNet side model
+            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        for dim in sample.shape[-2:]:
+            if dim % default_overall_up_factor != 0:
+                # Forward upsample size to force interpolation output size.
+                forward_upsample_size = True
+                break
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
+            sample = torch.cat([sample, hint], dim=1)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
+            encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 2.5 GLIGEN position net
+        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
+            cross_attention_kwargs = cross_attention_kwargs.copy()
+            gligen_args = cross_attention_kwargs.pop("gligen")
+            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
+
+        # 3. down
+        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+
+        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
+        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
+        is_adapter = down_intrablock_additional_residuals is not None
+        # maintain backward compatibility for legacy usage, where
+        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
+        #       but can only use one or the other
+        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
+            deprecate(
+                "T2I should not use down_block_additional_residuals",
+                "1.3.0",
+                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
+                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
+                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
+                standard_warn=False,
+            )
+            down_intrablock_additional_residuals = down_block_additional_residuals
+            is_adapter = True
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+                if is_adapter and len(down_intrablock_additional_residuals) > 0:
+                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)
+                if is_adapter and len(down_intrablock_additional_residuals) > 0:
+                    sample += down_intrablock_additional_residuals.pop(0)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = down_block_res_sample + down_block_additional_residual
+                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
+                sample = self.mid_block(
+                    sample,
+                    emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = self.mid_block(sample, emb)
+
+            # To support T2I-Adapter-XL
+            if (
+                is_adapter
+                and len(down_intrablock_additional_residuals) > 0
+                and sample.shape == down_intrablock_additional_residuals[0].shape
+            ):
+                sample += down_intrablock_additional_residuals.pop(0)
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    upsample_size=upsample_size,
+                    scale=lora_scale,
+                )
+
+        # 6. post-process
+        if self.conv_norm_out:
+            sample = self.conv_norm_out(sample)
+            sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet2DConditionOutput(sample=sample)
+
+        
+    def merge_and_unload(
+        self, progressbar: bool = False, safe_merge: bool = False, adapter_names: Optional[list[str]] = None
+    ) -> torch.nn.Module:
+
+        return self._unload_and_optionally_merge(
+            progressbar=progressbar, safe_merge=safe_merge, adapter_names=adapter_names
+        )
+
+    def _unload_and_optionally_merge(
+        self,
+        merge=True,
+        progressbar: bool = False,
+        safe_merge: bool = False,
+        adapter_names: Optional[list[str]] = None,
+    ):
+        from tqdm import tqdm
+        from peft.tuners.tuners_utils import onload_layer
+        from peft.utils import _get_submodules, ModulesToSaveWrapper
+
+        key_list = [key for key, _ in self.named_modules() if "lora_" not in key]
+        desc = "Unloading " + ("and merging " if merge else "") + "model"
+        for key in tqdm(key_list, disable=not progressbar, desc=desc):
+            try:
+                parent, target, target_name = _get_submodules(self, key)
+            except AttributeError:
+                continue
+            with onload_layer(target):
+                if hasattr(target, "base_layer"):
+                    if merge:
+                        target.merge(safe_merge=safe_merge, adapter_names=adapter_names)
+                    self._replace_module(parent, target_name, target.get_base_layer(), target)
+                elif isinstance(target, ModulesToSaveWrapper):
+                    # save any additional trainable modules part of `modules_to_save`
+                    new_module = target.modules_to_save[target.active_adapter]
+                    if hasattr(new_module, "base_layer"):
+                        # check if the module is itself a tuner layer
+                        if merge:
+                            new_module.merge(safe_merge=safe_merge, adapter_names=adapter_names)
+                        new_module = new_module.get_base_layer()
+                    setattr(parent, target_name, new_module)
+
+        return self
+
+    def _replace_module(self, parent, child_name, new_module, child):
+        setattr(parent, child_name, new_module)
+        # It's not necessary to set requires_grad here, as that is handled by
+        # _mark_only_adapters_as_trainable
+
+        # child layer wraps the original module, unpack it
+        if hasattr(child, "base_layer"):
+            child = child.base_layer
+
+        if not hasattr(new_module, "base_layer"):
+            new_module.weight = child.weight
+            if hasattr(child, "bias"):
+                new_module.bias = child.bias
+
+        if getattr(child, "state", None) is not None:
+            if hasattr(new_module, "base_layer"):
+                new_module.base_layer.state = child.state
+            else:
+                new_module.state = child.state
+            new_module.to(child.weight.device)
+
+        # dispatch to correct device
+        for name, module in new_module.named_modules():
+            if ("lora_" in name) or ("ranknum" in name):
+                weight = child.qweight if hasattr(child, "qweight") else child.weight
+                module.to(weight.device)

GDPOSR/inferences/test.py

@@ -0,0 +1,107 @@
+import os
+import argparse
+import numpy as np
+from PIL import Image
+import torch
+from torchvision import transforms
+import torchvision.transforms.functional as F
+import sys
+sys.path.append("GDPOSR")
+from modelfile.GDPOSR import GDPOSRTest
+from my_utils.wavelet_color_fix import adain_color_fix, wavelet_color_fix
+import glob
+sys.path.append('./')
+from ram.models.ram_lora import ram
+from ram import inference_ram as inference
+tensor_transforms = transforms.Compose([
+                transforms.ToTensor(),
+            ])
+
+ram_transforms = transforms.Compose([
+            transforms.Resize((384, 384)),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+
+def get_validation_prompt(args, image, model, device='cuda'):
+    validation_prompt = ""
+    lq = tensor_transforms(image).unsqueeze(0).to(device)
+    lq = ram_transforms(lq)
+    captions = inference(lq, model)
+    validation_prompt = f"{captions[0]}, {args.prompt},"
+    
+    return validation_prompt
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_image', type=str, default="", help='path to the input image')
+    parser.add_argument('--model_name', type=str, default='realsr', help='name of the pretrained model to be used')
+    parser.add_argument('--pretrained_path', type=str, default='', help='path to a model state dict to be used')
+    parser.add_argument('--output_dir', type=str, default='', help='the directory to save the output')
+    parser.add_argument('--seed', type=int, default=42, help='Random seed to be used')
+    parser.add_argument("--process_size", type=int, default=512)
+    parser.add_argument("--upscale", type=int, default=4)
+    parser.add_argument("--align_method", type=str, choices=['wavelet', 'adain', 'nofix'], default='adain')
+    parser.add_argument("--pretrained_model_name_or_path", type=str, default="")
+    parser.add_argument('--ram_ft_path', type=str, default=None) # 
+    parser.add_argument('--prompt', type=str, default='', help='positive prompts')
+    parser.add_argument('--negprompt', type=str, default='', help='negative prompts')
+    parser.add_argument("--time_step", type=int, default=1)
+    parser.add_argument("--time_step_noise", type=int, default=1)
+    args = parser.parse_args()
+
+    # initialize the model
+    model = GDPOSRTest(args)
+    model.set_eval()
+
+    if os.path.isdir(args.input_image):
+        image_names = sorted(glob.glob(f'{args.input_image}/*.png'))
+    else:
+        image_names = [args.input_image]
+
+    print("=== use ram ===")
+    model_vlm = ram(pretrained='./ckp/ram_swin_large_14m.pth',
+            pretrained_condition=args.ram_ft_path,
+            image_size=384,
+            vit='swin_l')
+    model_vlm.eval()
+    model_vlm.to("cuda")
+
+    # make the output dir
+    os.makedirs(args.output_dir, exist_ok=True)
+    print(f'There are {len(image_names)} images.')
+    for image_name in image_names:
+        
+        # make sure that the input image is a multiple of 8
+        input_image = Image.open(image_name).convert('RGB')
+        ori_width, ori_height = input_image.size
+        rscale = args.upscale
+        resize_flag = False
+        if ori_width < args.process_size//rscale or ori_height < args.process_size//rscale:
+            scale = (args.process_size//rscale)/min(ori_width, ori_height)
+            input_image = input_image.resize((int(scale*ori_width), int(scale*ori_height)))
+            resize_flag = True
+        input_image = input_image.resize((input_image.size[0]*rscale, input_image.size[1]*rscale))
+
+        new_width = input_image.width - input_image.width % 8
+        new_height = input_image.height - input_image.height % 8
+        input_image = input_image.resize((new_width, new_height), Image.LANCZOS)
+        bname = os.path.basename(image_name)
+
+        # get caption
+        validation_prompt = get_validation_prompt(args, input_image, model_vlm)
+        # translate the image
+        with torch.no_grad():
+            c_t = F.to_tensor(input_image).unsqueeze(0).cuda()*2-1
+            output_image = model(c_t, positive_prompt=[validation_prompt])
+            output_pil = transforms.ToPILImage()(output_image[0].cpu() * 0.5 + 0.5)
+            if args.align_method == 'adain':
+                output_pil = adain_color_fix(target=output_pil, source=input_image)
+            elif args.align_method == 'wavelet':
+                output_pil = wavelet_color_fix(target=output_pil, source=input_image)
+            else:
+                pass
+            if resize_flag:
+                output_pil.resize((int(args.upscale*ori_width), int(args.upscale*ori_height)))
+
+        output_pil.save(os.path.join(args.output_dir, bname))

GDPOSR/losses/grpo.py

@@ -0,0 +1,52 @@
+import pyiqa
+
+from basicsr.utils import img2tensor, tensor2img
+from torch.utils import data as data
+import glob
+import numpy as np
+import math
+import random
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Normal
+import sys
+
+class AdaptiveReward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        device = torch.device("cuda")
+        self.iqa_psnr = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)
+        self.iqa_maniqa = pyiqa.create_metric('maniqa', device=device)
+        self.iqa_musiq = pyiqa.create_metric('musiq', device=device)
+
+    def normalize_tensor(self, tensor):
+
+        min_val = tensor.min()
+        max_val = tensor.max()
+        
+        normalized_tensor = (tensor - min_val) / (max_val - min_val)
+        return normalized_tensor
+
+    def forward(self, x, y, fedilty_ratio, detail_ratio):
+        x = x * 0.5 + 0.5
+        y = y * 0.5 + 0.5
+        b,gs,c,h,w=x.shape
+        reward = torch.zeros([b,gs])
+        for i in range(b):
+            fedilty_i = fedilty_ratio[i]
+            detail_i = detail_ratio[i]
+            x_i = x[i]
+            y_i = y[i] 
+            psnr_result = self.normalize_tensor(self.iqa_psnr(x_i, y_i))
+            musiq_result = self.normalize_tensor(self.iqa_musiq(x_i).squeeze(1))
+            maniqa_result = self.normalize_tensor(self.iqa_maniqa(x_i).squeeze(1))
+
+            reward_i = fedilty_i*psnr_result + detail_i*0.5*(maniqa_result+musiq_result)
+            combined_mean = torch.mean(reward_i)
+            combined_std = reward_i.std(unbiased=True) 
+            reward_i = (reward_i - combined_mean) / (combined_std+1e-8)
+            reward[i] = reward_i        
+        
+        return reward.detach()
+

GDPOSR/mergelora.py

@@ -0,0 +1,72 @@
+import os
+import requests
+import sys
+import copy
+import random
+import time
+import glob
+import math
+import yaml
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+from peft import LoraConfig
+from types import SimpleNamespace
+
+import sys
+sys.path.append("./")
+from diffusermodels.autoencoder_kl import AutoencoderKL as AutoencoderKLMerge
+from diffusermodels.unet_2d_condition import UNet2DConditionModel as UNet2DConditionModelMerge
+
+
+def UNetMergeLoRA(basemodel_path='', trainedmodel_path='', savepath='', savename=''):
+
+    loraweight = torch.load(trainedmodel_path)
+    # vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
+    unet = UNet2DConditionModelMerge.from_pretrained(basemodel_path, subfolder="unet")
+
+    # load unet lora
+    lora_conf_encoder = LoraConfig(r=loraweight["rank_unet"], init_lora_weights="gaussian", target_modules=loraweight["unet_lora_encoder_modules"])
+    lora_conf_decoder = LoraConfig(r=loraweight["rank_unet"], init_lora_weights="gaussian", target_modules=loraweight["unet_lora_decoder_modules"])
+    lora_conf_others = LoraConfig(r=loraweight["rank_unet"], init_lora_weights="gaussian", target_modules=loraweight["unet_lora_others_modules"])
+    unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
+    unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
+    unet.add_adapter(lora_conf_others, adapter_name="default_others")
+    for n, p in unet.named_parameters():
+        if "lora" in n or "conv_in" in n:
+            p.data.copy_(loraweight["state_dict_unet"][n])
+
+    unet.set_adapter(['default_encoder', 'default_decoder', 'default_others'])
+    unet = unet.merge_and_unload()
+    unet.save_pretrained(os.path.join(savepath, savename))
+
+def VAEMergeLoRA(basemodel_path='', trainedmodel_path='', savepath='', savename=''):
+
+    loraweight = torch.load(trainedmodel_path)
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
+
+    # load vae lora
+    vae_lora_conf_encoder = LoraConfig(r=loraweight["rank_vae"], init_lora_weights="gaussian", target_modules=loraweight["vae_lora_encoder_modules"])
+    vae_lora_conf_decoder = LoraConfig(r=loraweight["rank_vae"], init_lora_weights="gaussian", target_modules=loraweight["vae_lora_decoder_modules"])
+    vae.add_adapter(vae_lora_conf_encoder, adapter_name="default_encoder")
+    vae.add_adapter(vae_lora_conf_decoder, adapter_name="default_decoder")
+    for n, p in vae.named_parameters():
+        if "lora" in n:
+            p.data.copy_(loraweight["state_dict_vae"][n])
+
+    vae.set_adapter(['default_encoder'])
+    vae = vae.merge_and_unload()
+    vae.save_pretrained(os.path.join(savepath, savename))
+
+unetbasemodel_path=''
+unettrainedmodel_path=''
+unetsavepath=''
+unetsavename=''
+UNetMergeLoRA(unetbasemodel_path, unettrainedmodel_path, unetsavepath, unetsavename)
+
+vaebasemodel_path=''
+vaetrainedmodel_path=''
+vaesavepath=''
+vaesavename=''
+VAEMergeLoRA(vaebasemodel_path, vaetrainedmodel_path, vaesavepath, vaesavename)

GDPOSR/modelfile/GDPOSR.py

@@ -0,0 +1,623 @@
+import os
+import requests
+import sys
+import copy
+import random
+import time
+import glob
+import math
+import yaml
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+from peft import LoraConfig
+from types import SimpleNamespace
+from transformers import AutoTokenizer, CLIPTextModel
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler
+from diffusers.utils.peft_utils import set_weights_and_activate_adapters
+from diffusers.utils.import_utils import is_xformers_available
+
+def make_1step_sched(pretrained_model_path):
+    noise_scheduler_1step = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
+    noise_scheduler_1step.set_timesteps(1, device="cuda")
+    noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
+    return noise_scheduler_1step
+
+def find_filepath(directory, filename):
+    matches = glob.glob(f"{directory}/**/{filename}", recursive=True)
+    return matches[0] if matches else None
+
+
+def read_yaml(file_path):
+    with open(file_path, 'r') as file:
+        data = yaml.safe_load(file)
+    return data
+
+def initialize_vae(rank, return_lora_module_names=False, pretrained_model_name_or_path=None):
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
+    vae.requires_grad_(False)
+    vae.train()
+    
+    l_target_modules_encoder, l_target_modules_decoder, l_modules_others = [], [], []
+    l_grep = ["conv1","conv2","conv_in", "conv_shortcut",
+        "conv", "conv_out", "to_k", "to_q", "to_v", "to_out.0",
+    ]
+    for n, p in vae.named_parameters():
+        if "bias" in n or "norm" in n: continue
+        for pattern in l_grep:
+            if pattern in n and ("encoder" in n):
+                l_target_modules_encoder.append(n.replace(".weight",""))
+                break
+            elif pattern in n and ("decoder" in n):
+                l_target_modules_decoder.append(n.replace(".weight",""))
+                break
+            elif ('quant_conv' in n) and ('post_quant_conv' not in n):
+                l_target_modules_encoder.append(n.replace(".weight",""))
+                break
+            elif 'post_quant_conv' in n:
+                l_target_modules_decoder.append(n.replace(".weight",""))
+                break
+            elif pattern in n:
+                l_modules_others.append(n.replace(".weight",""))
+                break
+    lora_conf_encoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_encoder)
+    lora_conf_decoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_decoder)
+    vae.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
+    vae.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
+    # vae.set_adapter(["default_encoder", "default_decoder"])
+    if return_lora_module_names:
+        return vae, l_target_modules_encoder, l_target_modules_decoder, l_modules_others
+    else:
+        return vae
+
+def initialize_unet(rank, return_lora_module_names=False, pretrained_model_name_or_path=None):
+    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
+    unet.requires_grad_(False)
+    unet.train()
+
+    l_target_modules_encoder, l_target_modules_decoder, l_modules_others = [], [], []
+    l_grep = ["to_k", "to_q", "to_v", "to_out.0", "conv", "conv1", "conv2", "conv_in", "conv_shortcut", "conv_out", "proj_out", "proj_in", "ff.net.2", "ff.net.0.proj"]
+    for n, p in unet.named_parameters():
+        if "bias" in n or "norm" in n: continue
+        for pattern in l_grep:
+            if pattern in n and ("down_blocks" in n or "conv_in" in n):
+                l_target_modules_encoder.append(n.replace(".weight",""))
+                break
+            elif pattern in n and "up_blocks" in n:
+                l_target_modules_decoder.append(n.replace(".weight",""))
+                break
+            elif pattern in n:
+                l_modules_others.append(n.replace(".weight",""))
+                break
+    lora_conf_encoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_encoder)
+    lora_conf_decoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_decoder)
+    lora_conf_others = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_modules_others)
+    unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
+    unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
+    unet.add_adapter(lora_conf_others, adapter_name="default_others")
+    if return_lora_module_names:
+        return unet, l_target_modules_encoder, l_target_modules_decoder, l_modules_others
+    else:
+        return unet
+
+def initialize_unet_sr(rank, return_lora_module_names=False, pretrained_model_name_or_path=None, args=None):
+    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
+    if args.use_lr_concat_lr_999noise:
+        new_conv_in = torch.nn.Conv2d(8, 320, 3, 1, 1)
+        new_conv_in.weight.data[:, :4, ...] = unet.conv_in.weight.data
+        new_conv_in.weight.data[:, -4:, ...] = unet.conv_in.weight.data
+        new_conv_in.bias.data = unet.conv_in.bias.data
+        unet.conv_in = new_conv_in
+    unet.requires_grad_(False)
+    unet.train()
+
+    l_target_modules_encoder, l_target_modules_decoder, l_modules_others = [], [], []
+    l_grep = ["to_k", "to_q", "to_v", "to_out.0", "conv", "conv1", "conv2", "conv_in", "conv_shortcut", "conv_out", "proj_out", "proj_in", "ff.net.2", "ff.net.0.proj"]
+    for n, p in unet.named_parameters():
+        if "bias" in n or "norm" in n: continue
+        for pattern in l_grep:
+            if pattern in n and ("down_blocks" in n or "conv_in" in n):
+                l_target_modules_encoder.append(n.replace(".weight",""))
+                break
+            elif pattern in n and "up_blocks" in n:
+                l_target_modules_decoder.append(n.replace(".weight",""))
+                break
+            elif pattern in n:
+                l_modules_others.append(n.replace(".weight",""))
+                break
+    lora_conf_encoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_encoder)
+    lora_conf_decoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_decoder)
+    lora_conf_others = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_modules_others)
+    unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
+    unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
+    unet.add_adapter(lora_conf_others, adapter_name="default_others")
+    if return_lora_module_names:
+        return unet, l_target_modules_encoder, l_target_modules_decoder, l_modules_others
+    else:
+        return unet
+
+class VSD(torch.nn.Module):
+    def __init__(self, args, accelerator):
+        super().__init__() 
+
+        self.tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+        self.text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+        self.sched = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+        self.args = args
+
+        weight_dtype = torch.float32
+        if accelerator.mixed_precision == "fp16":
+            weight_dtype = torch.float16
+        elif accelerator.mixed_precision == "bf16":
+            weight_dtype = torch.bfloat16
+
+        self.vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+        self.unet_fix = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+        self.unet_update, self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others =\
+                initialize_unet(rank=args.lora_rank_unet_vsd, pretrained_model_name_or_path=args.pretrained_model_name_or_path, return_lora_module_names=True)
+        self.lora_rank_unet = args.lora_rank_unet_vsd
+
+        if args.enable_xformers_memory_efficient_attention:
+            if is_xformers_available():
+                self.unet_fix.enable_xformers_memory_efficient_attention()
+                self.unet_update.enable_xformers_memory_efficient_attention()
+            else:
+                raise ValueError("xformers is not available, please install it by running `pip install xformers`")
+
+        if args.gradient_checkpointing:
+            self.unet_fix.enable_gradient_checkpointing()
+            self.unet_update.enable_gradient_checkpointing()
+
+        self.text_encoder.to(accelerator.device, dtype=weight_dtype)
+        self.unet_fix.to(accelerator.device, dtype=weight_dtype)
+        self.unet_update.to(accelerator.device)
+        self.vae.to(accelerator.device)
+        
+        self.text_encoder.requires_grad_(False)
+        self.vae.requires_grad_(False)
+        self.unet_fix.requires_grad_(False)
+
+    def set_eval(self):
+        self.unet_fix.eval()
+        self.unet.eval()
+        self.unet_update.eval()
+
+    def set_train(self):
+        self.unet_update.train()
+        for n, _p in self.unet_update.named_parameters():
+            if "lora" in n:
+                _p.requires_grad = True
+
+    def forward(self, c_t, prompt=None, neg_prompt_tokens=None, prompt_tokens=None, deterministic=True, r=1.0, noise_map=None, args=None):
+
+        caption_enc = self.text_encoder(prompt_tokens)[0]
+        neg_caption_enc = self.text_encoder(neg_prompt_tokens)[0]
+
+        encoded_control = self.vae.encode(c_t).latent_dist.sample() * self.vae.config.scaling_factor
+        model_pred = self.unet(encoded_control, self.timesteps, encoder_hidden_states=caption_enc.to(torch.float32),).sample
+        x_denoised = self.sched.step(model_pred, self.timesteps, encoded_control, return_dict=True).prev_sample
+
+        output_image = (self.vae.decode(x_denoised / self.vae.config.scaling_factor).sample).clamp(-1, 1)
+
+        return output_image, caption_enc, neg_caption_enc
+
+    def forward_latent(self, model, latents, timestep, prompt_embeds):
+        
+        noise_pred = model(
+        latents,
+        timestep=timestep,
+        encoder_hidden_states=prompt_embeds,
+        ).sample
+
+        return noise_pred
+
+    def compute_lora_loss(self, latents_pred, prompt_embeds, args):
+
+        latents_pred = latents_pred.detach()
+        prompt_embeds = prompt_embeds.detach()
+        noise = torch.randn_like(latents_pred)
+        bsz = latents_pred.shape[0]
+        timesteps = torch.randint(0, self.sched.config.num_train_timesteps, (bsz,), device=latents_pred.device)
+        timesteps = timesteps.long()
+        noisy_latents = self.sched.add_noise(latents_pred, noise, timesteps)
+        disc_pred = self.forward_latent(
+            self.unet_update,
+            timestep=timesteps,
+            latents=noisy_latents,
+            prompt_embeds=prompt_embeds
+        )
+        if args.snr_gamma_vsd is None:
+            loss_d = F.mse_loss(disc_pred.float(), noise.float(), reduction="mean")
+        else:
+            # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
+            # Since we predict the noise instead of x_0, the original formulation is slightly changed.
+            # This is discussed in Section 4.2 of the same paper.
+            snr = compute_snr(self.sched, timesteps)
+            if self.sched.config.prediction_type == "v_prediction":
+                # Velocity objective requires that we add one to SNR values before we divide by them.
+                snr = snr + 1
+            mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
+
+            loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
+            loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
+            loss_d = loss.mean()
+
+        return loss_d
+
+    def eps_to_mu(self, scheduler, model_output, sample, timesteps):
+        alphas_cumprod = scheduler.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
+        alpha_prod_t = alphas_cumprod[timesteps]
+        while len(alpha_prod_t.shape) < len(sample.shape):
+            alpha_prod_t = alpha_prod_t.unsqueeze(-1)
+        beta_prod_t = 1 - alpha_prod_t
+        pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+        return pred_original_sample
+
+    def distribution_matching_loss(
+        self,
+        real_model,
+        fake_model,
+        noise_scheduler,
+        latents,
+        prompt_embeds,
+        negative_prompt_embeds,
+        args,
+    ):
+        bsz = latents.shape[0]
+        min_dm_step = int(noise_scheduler.config.num_train_timesteps * args.min_dm_step_ratio)
+        max_dm_step = int(noise_scheduler.config.num_train_timesteps * args.max_dm_step_ratio)
+
+        timestep = torch.randint(min_dm_step, max_dm_step, (bsz,), device=latents.device).long()
+        noise = torch.randn_like(latents)
+        noisy_latents = noise_scheduler.add_noise(latents, noise, timestep)
+
+        with torch.no_grad():
+            noise_pred = self.forward_latent(
+                fake_model,
+                latents=noisy_latents,
+                timestep=timestep,
+                prompt_embeds=prompt_embeds.float(),
+            )
+            pred_fake_latents = self.eps_to_mu(noise_scheduler, noise_pred, noisy_latents, timestep)
+
+            noisy_latents_input = torch.cat([noisy_latents] * 2)
+            timestep_input = torch.cat([timestep] * 2)
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+            noise_pred = self.forward_latent(
+                real_model,
+                latents=noisy_latents_input.to(dtype=torch.float16),
+                timestep=timestep_input,
+                prompt_embeds=prompt_embeds.to(dtype=torch.float16),
+            )
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + args.cfg_vsd * (noise_pred_text - noise_pred_uncond)
+            noise_pred.to(dtype=torch.float32)
+
+            pred_real_latents = self.eps_to_mu(noise_scheduler, noise_pred, noisy_latents, timestep)
+
+        weighting_factor = torch.abs(latents - pred_real_latents).mean(dim=[1, 2, 3], keepdim=True)
+
+        grad = (pred_fake_latents - pred_real_latents) / weighting_factor
+        loss = F.mse_loss(latents, self.stopgrad(latents - grad))
+        return loss
+
+    def stopgrad(self, x):
+        return x.detach()
+
+    def save_model(self, outf):
+        sd = {}
+        sd["unet_lora_encoder_modules"], sd["unet_lora_decoder_modules"], sd["unet_lora_others_modules"] =\
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others
+        sd["rank_unet"] = self.lora_rank_unet
+        sd["state_dict_unet"] = {k: v for k, v in self.unet.state_dict().items() if "lora" in k}
+        torch.save(sd, outf)
+
+class NAOSD(torch.nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+        self.text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder").cuda()
+        self.sched = make_1step_sched(args.pretrained_model_name_or_path)
+        self.sched2 = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+        self.args = args
+        self.device =  torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+        vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+        unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+        
+        if args.pretrained_path is None:
+            vae, lora_vae_modules_encoder, lora_vae_modules_decoder, lora_vae_others =\
+                 initialize_vae(rank=args.lora_rank_vae, pretrained_model_name_or_path=args.pretrained_model_name_or_path, return_lora_module_names=True)
+            unet, lora_unet_modules_encoder, lora_unet_modules_decoder, lora_unet_others =\
+                 initialize_unet_sr(rank=args.lora_rank_unet, pretrained_model_name_or_path=args.pretrained_model_name_or_path, return_lora_module_names=True, args=args)
+            self.lora_rank_unet = args.lora_rank_unet
+            self.lora_rank_vae = args.lora_rank_vae
+            self.lora_vae_modules_encoder, self.lora_vae_modules_decoder, self.lora_vae_others = \
+                lora_vae_modules_encoder, lora_vae_modules_decoder, lora_vae_others
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others = \
+                lora_unet_modules_encoder, lora_unet_modules_decoder, lora_unet_others
+        
+        self.unet, self.vae = unet, vae
+        
+        if args.pretrained_path is not None:
+            print('==================================> loading pre-trained weight')
+            sd = torch.load(args.pretrained_path)
+            self.load_ckpt_from_state_dict(sd)
+            self.lora_rank_unet = sd['rank_unet']
+            self.lora_rank_vae = sd['rank_vae']
+            self.lora_vae_modules_encoder, self.lora_vae_modules_decoder, self.lora_vae_others = \
+                sd['vae_lora_encoder_modules'], sd['vae_lora_decoder_modules'], sd['vae_lora_others_modules']
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others = \
+                sd['unet_lora_encoder_modules'], sd['unet_lora_decoder_modules'], sd['unet_lora_others_modules']
+
+        self.unet, self.vae = self.unet.cuda(), self.vae.cuda()
+        self.timesteps = torch.tensor([args.time_step], device="cuda").long()
+        self.timestepsnoise = torch.tensor([args.time_step_noise], device="cuda").long()
+        self.text_encoder.requires_grad_(False)
+
+    def set_eval(self):
+        self.unet.eval()
+        self.vae.eval()
+        self.unet.requires_grad_(False)
+        self.vae.requires_grad_(False)
+
+    def set_train(self):
+        self.unet.train()
+        self.vae.train()
+        for n, _p in self.unet.named_parameters():
+            if "lora" in n:
+                _p.requires_grad = True
+        self.unet.conv_in.requires_grad_(True)
+        for n, _p in self.vae.named_parameters():
+            if "lora" in n:
+                _p.requires_grad = True
+
+    def encode_prompt(self, prompt):
+        with torch.no_grad():
+            text_input_ids = self.tokenizer(
+                prompt, max_length=self.tokenizer.model_max_length,
+                padding="max_length", truncation=True, return_tensors="pt"
+            ).input_ids
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(self.text_encoder.device),
+            )[0]
+        return prompt_embeds
+
+    def forward(self, c_t, positive_prompt=None, negative_prompt=None, args=None):
+        caption_enc = self.encode_prompt(positive_prompt)
+        neg_caption_enc = self.encode_prompt(negative_prompt)
+        encoded_control = self.vae.encode(c_t).latent_dist.sample() * self.vae.config.scaling_factor
+        noise = torch.randn_like(encoded_control)
+        encoded_control = self.sched2.add_noise(encoded_control, noise, self.timestepsnoise)
+
+        model_pred = self.unet(encoded_control, self.timesteps, encoder_hidden_states=caption_enc.to(torch.float32),).sample
+        x_denoised = self.sched.step(model_pred, self.timesteps, encoded_control, return_dict=True).prev_sample
+        output_image = self.vae.decode(x_denoised / self.vae.config.scaling_factor).sample
+        output_image = output_image.clamp(-1, 1)
+
+        return output_image, x_denoised, caption_enc, neg_caption_enc, noise
+
+    def save_model(self, outf):
+        sd = {}
+        sd["vae_lora_encoder_modules"], sd["vae_lora_decoder_modules"], sd["vae_lora_others_modules"] =\
+            self.lora_vae_modules_encoder, self.lora_vae_modules_decoder, self.lora_vae_others 
+        sd["unet_lora_encoder_modules"], sd["unet_lora_decoder_modules"], sd["unet_lora_others_modules"] =\
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others
+        sd["rank_unet"] = self.lora_rank_unet
+        sd["rank_vae"] = self.lora_rank_vae
+        sd["state_dict_unet"] = {k: v for k, v in self.unet.state_dict().items() if "lora" in k or "conv_in" in k}
+        sd["state_dict_vae"] = {k: v for k, v in self.vae.state_dict().items() if "lora" in k or "skip" in k}
+        torch.save(sd, outf)
+    
+    def load_ckpt_from_state_dict(self, sd):
+        # load unet lora
+        lora_conf_encoder = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_encoder_modules"])
+        lora_conf_decoder = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_decoder_modules"])
+        lora_conf_others = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_others_modules"])
+        self.unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
+        self.unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
+        self.unet.add_adapter(lora_conf_others, adapter_name="default_others")
+        for n, p in self.unet.named_parameters():
+            if "lora" in n or "conv_in" in n:
+                p.data.copy_(sd["state_dict_unet"][n])
+
+        # load vae lora
+        vae_lora_conf_encoder = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_encoder_modules"])
+        vae_lora_conf_decoder = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_decoder_modules"])
+        self.vae.add_adapter(vae_lora_conf_encoder, adapter_name="default_encoder")
+        self.vae.add_adapter(vae_lora_conf_decoder, adapter_name="default_decoder")
+        for n, p in self.vae.named_parameters():
+            if "lora" in n:
+                p.data.copy_(sd["state_dict_vae"][n])
+
+class GDPOSR(torch.nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+        self.text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder").cuda()
+        self.sched = make_1step_sched(args.pretrained_model_name_or_path)
+        self.sched2 = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+        self.args = args
+        self.device =  torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+        vae = AutoencoderKL.from_pretrained(args.basemodel_path, subfolder="vae")
+        unet = UNet2DConditionModel.from_pretrained(args.basemodel_path, subfolder="unet")
+        ref_unet = UNet2DConditionModel.from_pretrained(args.basemodel_path, subfolder="unet")
+        
+        if args.pretrained_path is None:
+            print('==================================> randomly initiate the weight')
+            unet, lora_unet_modules_encoder, lora_unet_modules_decoder, lora_unet_others =\
+                 initialize_unet_sr(rank=args.lora_rank_unet, pretrained_model_name_or_path=args.basemodel_path, return_lora_module_names=True, args=args)
+            self.lora_rank_unet = args.lora_rank_unet
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others = \
+                lora_unet_modules_encoder, lora_unet_modules_decoder, lora_unet_others
+        
+        self.unet, self.vae = unet, vae
+        
+        if args.pretrained_path is not None:
+            print('==================================> loading pre-trained weight')
+            sd = torch.load(args.pretrained_path)
+            self.load_ckpt_from_state_dict(sd)
+            self.lora_rank_unet = sd['rank_unet']
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others = \
+                sd['unet_lora_encoder_modules'], sd['unet_lora_decoder_modules'], sd['unet_lora_others_modules']
+
+        self.unet, self.vae = self.unet.cuda(), self.vae.cuda()
+        self.ref_unet = ref_unet.cuda()
+        self.timesteps = torch.tensor([args.time_step], device="cuda").long()
+        self.timestepsnoise = torch.tensor([args.time_step_noise], device="cuda").long()
+        self.text_encoder.requires_grad_(False)
+
+    def set_eval(self):
+        self.unet.eval()
+        self.vae.eval()
+        self.ref_unet.eval()
+        self.unet.requires_grad_(False)
+        self.vae.requires_grad_(False)
+        self.ref_unet.requires_grad_(False)
+
+    def set_train(self):
+        self.unet.train()
+        self.vae.train()
+        for n, _p in self.unet.named_parameters():
+            if "lora" in n:
+                _p.requires_grad = True
+        for n, _p in self.ref_unet.named_parameters():
+                _p.requires_grad = False
+
+    def encode_prompt(self, prompt):
+        with torch.no_grad():
+            text_input_ids = self.tokenizer(
+                prompt, max_length=self.tokenizer.model_max_length,
+                padding="max_length", truncation=True, return_tensors="pt"
+            ).input_ids
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(self.text_encoder.device),
+            )[0]
+        return prompt_embeds
+
+    def forward(self, c_t, positive_prompt=[''], negative_prompt=[''], args=None):
+        caption_enc = self.encode_prompt(positive_prompt)
+        neg_caption_enc = self.encode_prompt(negative_prompt)
+        with torch.no_grad():
+            encoded_control = self.vae.encode(c_t).latent_dist.sample() * self.vae.config.scaling_factor
+        encoded_control_ref = encoded_control
+        noise = torch.randn_like(encoded_control)
+        encoded_control = self.sched2.add_noise(encoded_control, noise, self.timestepsnoise)
+
+        model_pred = self.unet(encoded_control, self.timesteps, encoder_hidden_states=caption_enc.to(torch.float32),).sample
+        x_denoised = self.sched.step(model_pred, self.timesteps, encoded_control, return_dict=True).prev_sample
+        output_image = self.vae.decode(x_denoised / self.vae.config.scaling_factor).sample
+        output_image = output_image.clamp(-1, 1)
+
+        with torch.no_grad():
+            encoded_control_ref = self.sched2.add_noise(encoded_control_ref, noise, self.timestepsnoise)
+            ref_model_pred = self.ref_unet(encoded_control_ref, self.timesteps, encoder_hidden_states=caption_enc.to(torch.float32),).sample
+            ref_x_denoised = self.sched.step(ref_model_pred, self.timesteps, encoded_control_ref, return_dict=True).prev_sample
+            ref_output_image = self.vae.decode(ref_x_denoised / self.vae.config.scaling_factor).sample
+            ref_output_image = ref_output_image.clamp(-1, 1)
+
+        return output_image, x_denoised, model_pred, caption_enc, neg_caption_enc, noise, ref_output_image, ref_x_denoised, ref_model_pred
+    
+    def GDPOReference(self, c_t, positive_prompt=[''], negative_prompt=[''], args=None, groupsize=6):
+        
+        with torch.no_grad():
+            
+            caption_enc = self.encode_prompt(positive_prompt).unsqueeze(1)
+            encoded_control = self.vae.encode(c_t).latent_dist.sample() * self.vae.config.scaling_factor
+            b,c,h,w=encoded_control.shape
+            encoded_control = encoded_control.unsqueeze(1)
+            caption_enc = caption_enc.repeat(1,groupsize,1,1)
+            encoded_control = encoded_control.repeat(1, groupsize, 1, 1, 1)
+            noise = torch.randn_like(encoded_control)
+            output_image = torch.zeros_like(c_t).unsqueeze(1).repeat(1,groupsize,1,1,1)
+            x_denoised = torch.zeros_like(noise)
+            model_pred = torch.zeros_like(noise)
+            for i in range(b):
+                encoded_control_i = self.sched2.add_noise(encoded_control[i], noise[i], self.timestepsnoise)
+                # print(encoded_control.shape, caption_enc.shape, self.timesteps.shape)
+                model_pred_i = self.ref_unet(encoded_control_i, self.timesteps, encoder_hidden_states=caption_enc[i],).sample
+                x_denoised_i = self.sched.step(model_pred_i, self.timesteps, encoded_control_i, return_dict=True).prev_sample
+                output_image_i = self.vae.decode(x_denoised_i / self.vae.config.scaling_factor).sample
+                output_image_i = output_image_i.clamp(-1, 1)
+                output_image[i] = output_image_i
+                x_denoised[i] = x_denoised_i
+                model_pred[i] = model_pred_i
+
+        return output_image, x_denoised, model_pred
+    
+    def save_model(self, outf):
+        sd = {}
+        sd["unet_lora_encoder_modules"], sd["unet_lora_decoder_modules"], sd["unet_lora_others_modules"] =\
+            self.lora_unet_modules_encoder, self.lora_unet_modules_decoder, self.lora_unet_others
+        sd["rank_unet"] = self.lora_rank_unet
+        sd["state_dict_unet"] = {k: v for k, v in self.unet.state_dict().items() if "lora" in k or "conv_in" in k}
+        torch.save(sd, outf)
+    
+    def load_ckpt_from_state_dict(self, sd):
+        # load unet lora
+        lora_conf_encoder = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_encoder_modules"])
+        lora_conf_decoder = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_decoder_modules"])
+        lora_conf_others = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_others_modules"])
+        self.unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
+        self.unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
+        self.unet.add_adapter(lora_conf_others, adapter_name="default_others")
+        for n, p in self.unet.named_parameters():
+            if "lora" in n or "conv_in" in n:
+                p.data.copy_(sd["state_dict_unet"][n])
+
+class GDPOSRTest(torch.nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+        self.text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder").cuda()
+        self.sched = make_1step_sched(args.pretrained_model_name_or_path)
+        self.sched2 = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+        self.args = args
+        self.device =  torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+        vae = AutoencoderKL.from_pretrained(args.pretrained_path, subfolder="vae")
+        unet = UNet2DConditionModel.from_pretrained(args.pretrained_path, subfolder="unet")
+                
+        self.unet, self.vae = unet, vae
+        self.unet, self.vae = self.unet.cuda(), self.vae.cuda()
+        self.timesteps = torch.tensor([args.time_step], device="cuda").long()
+        self.timestepsnoise = torch.tensor([args.time_step_noise], device="cuda").long()
+        self.text_encoder.requires_grad_(False)
+
+    def set_eval(self):
+        self.unet.eval()
+        self.vae.eval()
+        self.unet.requires_grad_(False)
+        self.vae.requires_grad_(False)
+
+    def encode_prompt(self, prompt):
+        with torch.no_grad():
+            text_input_ids = self.tokenizer(
+                prompt, max_length=self.tokenizer.model_max_length,
+                padding="max_length", truncation=True, return_tensors="pt"
+            ).input_ids
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(self.text_encoder.device),
+            )[0]
+        return prompt_embeds
+
+    def forward(self, c_t, positive_prompt=['']):
+        
+        caption_enc = self.encode_prompt(positive_prompt)
+        encoded_control = self.vae.encode(c_t).latent_dist.sample() * self.vae.config.scaling_factor
+        noise = torch.randn_like(encoded_control)
+        encoded_control = self.sched2.add_noise(encoded_control, noise, self.timestepsnoise)
+
+        model_pred = self.unet(encoded_control, self.timesteps, encoder_hidden_states=caption_enc.to(torch.float32),).sample
+        x_denoised = self.sched.step(model_pred, self.timesteps, encoded_control, return_dict=True).prev_sample
+        output_image = self.vae.decode(x_denoised / self.vae.config.scaling_factor).sample
+        output_image = output_image.clamp(-1, 1)
+        
+
+        return output_image

GDPOSR/my_utils/mask.py

@@ -0,0 +1,126 @@
+import cv2
+import math
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+def calculate_complexity_degree(img, weight=1.0):
+    try:
+        # img = (img + 1.0) / 2.0 # (0, 1)
+        h, w = img.shape
+        # img = cv2.resize(img, dsize=(256,256))
+        img = cv2.resize(img, dsize=(64,64))
+        sobel_x = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3, borderType=cv2.BORDER_REPLICATE) # (-4, 4)
+        sobel_y = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3, borderType=cv2.BORDER_REPLICATE)
+        edge_magnitude = np.sqrt(sobel_x ** 2 + sobel_y ** 2) # [-4*2**0.5, 4*2**0.5]
+        hist, _ = np.histogram(edge_magnitude, bins=512, range=(-5.5, 5.5))
+        hist = hist / (hist.sum()+1e-5)
+        entropy = -np.sum(hist * np.log2(hist + 1e-10)) 
+
+        complexity_degree = entropy**weight*h*w
+    except Exception as e:
+        print(f"img: {img.shape}")
+    return complexity_degree
+
+def create_complexity_matrix(gray_img, patch_size=60):
+    """
+    Divide a grayscale image into patches and calculate the complexity of each patch 
+    to generate a complexity matrix.
+
+    Parameters:
+        gray_img: Input grayscale image (NumPy array).
+        patch_size: Size of each patch (default: 6x6).
+        
+    Returns:
+        complexity_matrix: The complexity matrix (same size as the input image).
+    """
+
+    h, w = gray_img.shape
+    complexity_matrix = np.zeros((h, w))
+    
+    rows = h // patch_size
+    cols = w // patch_size
+    
+    for i in range(rows):
+        for j in range(cols):
+            patch = gray_img[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size]
+            
+            complexity = calculate_complexity_degree(patch)
+            
+            complexity_matrix[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size] = complexity
+    
+    if rows * patch_size < h:
+        for j in range(cols):
+            patch = gray_img[rows*patch_size:, j*patch_size:(j+1)*patch_size]
+            complexity = calculate_complexity_degree(patch)
+            complexity_matrix[rows*patch_size:, j*patch_size:(j+1)*patch_size] = complexity
+    
+    if cols * patch_size < w:
+        for i in range(rows):
+            patch = gray_img[i*patch_size:(i+1)*patch_size, cols*patch_size:]
+            complexity = calculate_complexity_degree(patch)
+            complexity_matrix[i*patch_size:(i+1)*patch_size, cols*patch_size:] = complexity
+    
+    if rows * patch_size < h and cols * patch_size < w:
+        patch = gray_img[rows*patch_size:, cols*patch_size:]
+        complexity = calculate_complexity_degree(patch)
+        complexity_matrix[rows*patch_size:, cols*patch_size:] = complexity
+    
+    return complexity_matrix
+
+def binarize_complexity_matrix(complexity_matrix, threshold=50):
+    """
+    Binarize the complexity matrix.
+
+    Parameters:
+        complexity_matrix: The complexity matrix.
+        threshold: The threshold value. Elements greater than this value are set to 1, 
+                and elements less than or equal to it are set to 0.
+                
+    Returns:
+        binary_matrix: The binarized matrix.
+        fidelity_zero_ratio: The proportion of the fidelity region (ratio of zeros).
+        detail_one_ratio: The proportion of the detail region (ratio of ones).
+    """    
+    binary_matrix = np.zeros_like(complexity_matrix, dtype=np.uint8)
+    binary_matrix[complexity_matrix > threshold] = 1
+    
+    unique_values = np.unique(binary_matrix)
+    if not np.all(np.isin(unique_values, [0, 1])):
+        raise ValueError("The input matrix must be a binary matrix containing only 0 and 1.")
+    
+    total_elements = binary_matrix.size
+    
+    zero_count = np.count_nonzero(binary_matrix == 0)
+    one_count = np.count_nonzero(binary_matrix == 1)
+    
+    fedilty_zero_ratio = round((zero_count / total_elements), 2)
+    detail_one_ratio = round((one_count / total_elements), 2)
+
+    return binary_matrix, fedilty_zero_ratio, detail_one_ratio
+
+def extract_and_dilate_edges(gray, threshold1=100, threshold2=200, dilation_size=3, downscale_factor=8):
+    
+    edges = cv2.Canny(gray, threshold1, threshold2)
+    kernel = np.ones((dilation_size, dilation_size), np.uint8)
+    dilated_edges = cv2.dilate(edges, kernel, iterations=1)
+
+    h, w = dilated_edges.shape
+    new_h, new_w = h // downscale_factor, w // downscale_factor
+    downsampled_edges = cv2.resize(dilated_edges, (new_w, new_h), interpolation=cv2.INTER_AREA)
+    downsampled_edges = downsampled_edges/255.0
+    
+    _, downsampled_edges_mask = cv2.threshold(downsampled_edges, 0.498, 1.0, cv2.THRESH_BINARY)
+
+    return downsampled_edges_mask
+
+if __name__ == '__main__':
+    img = cv2.imread("DrealSR/test_HR/DSC_1412_x1.png")
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    gray_img = gray_img/255
+
+    complexity_matrix = create_complexity_matrix(gray_img, patch_size=10)
+    print(complexity_matrix.shape)
+    binary_matrix, fedilty_zero_ratio, detail_one_ratio = binarize_complexity_matrix(complexity_matrix, threshold=50)
+    print(fedilty_zero_ratio, detail_one_ratio)
+    print(binary_matrix.shape)

GDPOSR/my_utils/training_utils_realsr.py

@@ -0,0 +1,305 @@
+import os
+import cv2
+import random
+import argparse
+import json
+import glob
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+import torchvision.transforms.functional as F
+
+import sys
+sys.path.append('./')
+from GDPOSR.my_utils.mask import create_complexity_matrix, binarize_complexity_matrix, extract_and_dilate_edges
+from GDPOSR.datasets.realesrgan import RealESRGAN_degradation
+
+
+def parse_args_realsr_training(input_args=None):
+    """
+    Parses command-line arguments used for configuring an paired session (pix2pix-Turbo).
+    This function sets up an argument parser to handle various training options.
+
+    Returns:
+    argparse.Namespace: The parsed command-line arguments.
+   """ 
+    parser = argparse.ArgumentParser()
+    # args for grpo training
+    parser.add_argument("--groupsize", default=6, type=int) 
+    parser.add_argument("--time_min", default=150, type=int) 
+    parser.add_argument("--time_max", default=350, type=int) 
+    parser.add_argument("--updatestep", default=4000, type=int)
+    parser.add_argument("--patchsize", default=125, type=int)
+    parser.add_argument("--beta_dpo", default=0.25, type=float)
+    parser.add_argument("--klloss", default=1.0, type=float)
+    parser.add_argument("--grpoloss", default=1.0, type=float)
+    # args for the vsd training
+    parser.add_argument("--positive_prompt", type=str, default='')
+    parser.add_argument("--negative_prompt", type=str, default='')
+    parser.add_argument("--lambda_vsd", default=1.0, type=float)
+    parser.add_argument("--lambda_vsd_lora", default=1.0, type=float)
+    parser.add_argument("--lambda_klloss", default=0.0, type=float)
+    parser.add_argument("--min_dm_step_ratio", default=0.02, type=float) 
+    parser.add_argument("--max_dm_step_ratio", default=0.98, type=float)
+    parser.add_argument("--cfg_vsd", default=7.5, type=float) 
+    parser.add_argument("--cfg_csd", default=7.5, type=float) 
+    parser.add_argument("--snr_gamma_vsd", default=None)
+    parser.add_argument("--lora_rank_unet_vsd", default=8, type=int) 
+    parser.add_argument("--pretrained_model_name_or_path_vsd", default='', type=str)
+    parser.add_argument("--basemodel_path", default='', type=str)
+    
+    # args for the loss function
+    parser.add_argument("--gan_disc_type", default="vagan_clip")
+    parser.add_argument("--gan_loss_type", default="multilevel_sigmoid_s")
+    parser.add_argument("--lambda_gan", default=0.2, type=float)
+    parser.add_argument("--lambda_lpips", default=2, type=float)
+    parser.add_argument("--lambda_l2", default=1.0, type=float)
+
+    # dataset options
+    parser.add_argument("--dataset_folder", default='', type=str)
+    parser.add_argument("--testdataset_folder", default='', type=str)
+    parser.add_argument("--train_image_prep", default="resized_crop_512", type=str)
+    parser.add_argument("--test_image_prep", default="resized_crop_512", type=str)
+    parser.add_argument("--null_text_ratio", default=1., type=float)
+
+    # validation eval args
+    parser.add_argument("--eval_freq", default=500, type=int)
+    parser.add_argument("--track_val_fid", default=False, action="store_true")
+    parser.add_argument("--num_samples_eval", type=int, default=100, help="Number of samples to use for all evaluation")
+
+    parser.add_argument("--viz_freq", type=int, default=100, help="Frequency of visualizing the outputs.")
+    parser.add_argument("--tracker_project_name", type=str, default="train_pix2pix_turbo", help="The name of the wandb project to log to.")
+    parser.add_argument('--tiled_size', type=int, default=768)
+    parser.add_argument('--tiled_overlap', type=int, default=256)
+
+
+    # details about the model architecture
+    parser.add_argument("--pretrained_model_name_or_path", default='', type=str)
+    parser.add_argument("--revision", type=str, default=None,)
+    parser.add_argument("--variant", type=str, default=None,)
+    parser.add_argument("--cliptextmodule", type=str, default=None,) 
+    parser.add_argument("--upsampler", type=str, default=None,)
+    parser.add_argument("--tokenizer_name", type=str, default=None)
+    parser.add_argument("--lora_rank_unet", default=8, type=int) 
+    parser.add_argument("--lora_rank_unet2", default=0, type=int)
+    parser.add_argument("--lora_rank_vae", default=4, type=int) 
+    parser.add_argument("--time_step", default=999, type=int)
+    parser.add_argument("--time_step_noise", default=250, type=int)
+    parser.add_argument("--pretrained_path", default=None, type=str)
+    parser.add_argument("--pretrained_unet_path", default=None, type=str) 
+    parser.add_argument("--pretrained_vae_path", default=None, type=str)
+    parser.add_argument("--stage2", default=None, type=str)
+    parser.add_argument("--stage3", default=None, type=str)
+
+
+    # training details
+    parser.add_argument("--output_dir", default='experience/OSSR_vaeEcLora_ntr1_vsd_ntr0_nostage_clip_test')
+    parser.add_argument("--cache_dir", default=None,)
+    parser.add_argument("--seed", type=int, default=123, help="A seed for reproducible training.")
+    parser.add_argument("--resolution", type=int, default=512,)
+    parser.add_argument("--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader.")
+    parser.add_argument("--num_training_epochs", type=int, default=10)
+    parser.add_argument("--max_train_steps", type=int, default=10_000,)
+    parser.add_argument("--checkpointing_steps", type=int, default=500,)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=2, help="Number of updates steps to accumulate before performing a backward/update pass.",)
+    parser.add_argument("--gradient_checkpointing", action="store_true",)
+    parser.add_argument("--learning_rate", type=float, default=5e-5)
+    parser.add_argument("--lr_scheduler", type=str, default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument("--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler.")
+    parser.add_argument("--lr_num_cycles", type=int, default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+
+    parser.add_argument("--dataloader_num_workers", type=int, default=0,)
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--ema_decay", type=float, default=0.999, help="EMA decay rate for model parameters.")
+    parser.add_argument("--allow_tf32", action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument("--report_to", type=str, default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--mixed_precision", type=str, default="fp16", choices=["no", "fp16", "bf16"],)
+    parser.add_argument("--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers.")
+    parser.add_argument("--set_grads_to_none", action="store_true",)
+
+    parser.add_argument("--logging_dir", type=str, default="logs")
+    parser.add_argument("--use_online_deg", action="store_true",)
+    parser.add_argument("--deg_file_path", default="params_pasd.yml", type=str)
+    parser.add_argument("--align_method", type=str, choices=['wavelet', 'adain', 'nofix'], default='adain')
+
+    # vae lora
+    parser.add_argument("--use_vae_encode_lora", action="store_true",)
+    parser.add_argument("--use_vae_decode_lora", action="store_true",)
+
+    # use_lr_999noise
+    parser.add_argument("--use_lr_999noise", action="store_true",)
+    parser.add_argument("--use_lr_concat_lr_999noise", action="store_true",)
+
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    # # only for debug
+    # args.enable_xformers_memory_efficient_attention = True
+    # args.use_online_deg = True
+    # args.use_lr_concat_lr_999noise = True
+
+    return args
+
+
+def build_transform(image_prep):
+    """
+    Constructs a transformation pipeline based on the specified image preparation method.
+
+    Parameters:
+    - image_prep (str): A string describing the desired image preparation
+
+    Returns:
+    - torchvision.transforms.Compose: A composable sequence of transformations to be applied to images.
+    """
+    if image_prep == "resized_crop_512":
+        T = transforms.Compose([
+            transforms.Resize(512, interpolation=transforms.InterpolationMode.LANCZOS),
+            transforms.CenterCrop(512),
+        ])
+    elif image_prep == "resize_286_randomcrop_256x256_hflip":
+        T = transforms.Compose([
+            transforms.Resize((286, 286), interpolation=Image.LANCZOS),
+            transforms.RandomCrop((256, 256)),
+            transforms.RandomHorizontalFlip(),
+        ])
+    elif image_prep in ["resize_256", "resize_256x256"]:
+        T = transforms.Compose([
+            transforms.Resize((256, 256), interpolation=Image.LANCZOS)
+        ])
+    elif image_prep in ["resize_512", "resize_512x512"]:
+        T = transforms.Compose([
+            transforms.Resize((512, 512), interpolation=Image.LANCZOS)
+        ])
+    elif image_prep == "no_resize":
+        T = transforms.Lambda(lambda x: x)
+    return T
+
+
+class PairedSROnlineDataset(torch.utils.data.Dataset):
+    def __init__(self, dataset_folder, split, image_prep, deg_file_path=None, image_size=512, args=None):
+        super().__init__()
+        self.split = split
+        self.args = args
+        clip_mean = [0.48145466, 0.4578275, 0.40821073]
+        clip_std = [0.26862954, 0.26130258, 0.27577711]
+        self.clip_normalize = transforms.Normalize(mean=clip_mean, std=clip_std)
+
+        if split == 'train':
+            self.gt_folder = os.path.join(dataset_folder, "gt")
+            self.gt_list = []
+            self.gt_list += glob.glob(os.path.join(self.gt_folder, '*.png'))
+
+            self.T = build_transform(image_prep)
+            self.split = split
+
+            self.degradation = RealESRGAN_degradation(deg_file_path, device='cpu')
+            self.crop_preproc = transforms.Compose([
+                transforms.RandomCrop(image_size),
+                transforms.RandomHorizontalFlip(),
+            ])
+        elif split == 'test':
+            dataset_folder = args.testdataset_folder 
+            self.input_folder = os.path.join(dataset_folder, "test_SR_bicubic")
+            self.output_folder = os.path.join(dataset_folder, "test_HR")
+            
+            self.lr_list = []
+            self.gt_list = []
+            self.lr_list += glob.glob(os.path.join(self.input_folder, '*.png'))
+            self.gt_list += glob.glob(os.path.join(self.output_folder, '*.png'))
+
+            self.T = build_transform(image_prep)
+            self.split = split
+            assert len(self.lr_list) == len(self.gt_list)
+
+    def __len__(self):
+        return len(self.gt_list)
+
+    def __getitem__(self, idx):
+
+        if self.split == 'train':
+            gt_img = Image.open(self.gt_list[idx]).convert('RGB')
+            gt_img = self.crop_preproc(gt_img)
+
+            output_t, img_t, img_t_noresize = self.degradation.degrade_process(np.asarray(gt_img)/255., resize_bak=True)
+            output_t_0 = output_t
+            output_t, img_t, img_t_noresize = output_t.squeeze(0), img_t.squeeze(0), img_t_noresize.squeeze(0)
+
+            img_t = F.normalize(img_t, mean=[0.5], std=[0.5])
+            img_t_noresize = F.normalize(img_t_noresize, mean=[0.5], std=[0.5])
+            # output images scaled to -1,1
+            output_t = F.normalize(output_t, mean=[0.5], std=[0.5])
+
+            # 
+            output_t_0 = output_t_0.permute(0,2,3,1).contiguous()
+            gray_gt1 = 255 * output_t_0.squeeze(0).cpu().numpy()
+            gray_gt1 = gray_gt1.astype(np.uint8)
+            gray_gt_img_org = cv2.cvtColor(gray_gt1, cv2.COLOR_BGR2GRAY)
+            # gray_gt_img_org = cv2.cvtColor(cv2.imread(self.gt_list[idx]), cv2.COLOR_BGR2GRAY)
+            gray_gt_img = gray_gt_img_org/255
+            complexity_matrix = create_complexity_matrix(gray_gt_img, patch_size=10)
+            binary_matrix, fedilty_zero_ratio, detail_one_ratio = binarize_complexity_matrix(complexity_matrix, threshold=50)
+            downsampled_edges_mask = extract_and_dilate_edges(gray_gt_img_org, threshold1=100, threshold2=200, dilation_size=3, downscale_factor=8)
+            complexity_matrix = torch.tensor(complexity_matrix).unsqueeze(0)
+            binary_matrix = torch.tensor(binary_matrix).unsqueeze(0)
+            fedilty_zero_ratio = torch.tensor(fedilty_zero_ratio)
+            detail_one_ratio = torch.tensor(detail_one_ratio)
+            downsampled_edges_mask = torch.tensor(downsampled_edges_mask)
+
+            return {
+                "HR": output_t,
+                "LR": img_t,
+                "negative_prompt": self.args.negative_prompt,
+                'fedilty_ratio': fedilty_zero_ratio,
+                'detail_ratio': detail_one_ratio,
+            }
+            
+        elif self.split == 'test':
+            input_img = Image.open(self.lr_list[idx]).convert('RGB')
+            input_img_noresize = Image.open(self.gt_list[idx].replace('test_HR/','test_LR/')).convert('RGB')
+            output_img = Image.open(self.gt_list[idx]).convert('RGB')
+
+            # input images scaled to -1, 1
+            img_t = self.T(input_img)
+            img_t = F.to_tensor(img_t)
+
+            img_t_noresize = self.T(input_img_noresize)
+            img_t_noresize = F.to_tensor(img_t_noresize)
+            
+            img_t = F.normalize(img_t, mean=[0.5], std=[0.5])
+            img_t_noresize = F.normalize(img_t_noresize, mean=[0.5], std=[0.5])
+            # output images scaled to -1,1
+            output_t = self.T(output_img)
+            output_t = F.to_tensor(output_t)
+            output_t = F.normalize(output_t, mean=[0.5], std=[0.5])
+
+            return {
+                "HR": output_t,
+                "LR": img_t,
+                "negative_prompt": self.args.negative_prompt,
+                "base_name": os.path.basename(self.lr_list[idx]),
+            }

GDPOSR/my_utils/wavelet_color_fix.py

@@ -0,0 +1,119 @@
+'''
+# --------------------------------------------------------------------------------
+#   Color fixed script from Li Yi (https://github.com/pkuliyi2015/sd-webui-stablesr/blob/master/srmodule/colorfix.py)
+# --------------------------------------------------------------------------------
+'''
+
+import torch
+from PIL import Image
+from torch import Tensor
+from torch.nn import functional as F
+
+from torchvision.transforms import ToTensor, ToPILImage
+
+def adain_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply adaptive instance normalization
+    result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def wavelet_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply wavelet reconstruction
+    result_tensor = wavelet_reconstruction(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def calc_mean_std(feat: Tensor, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.reshape(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().reshape(b, c, 1, 1)
+    feat_mean = feat.reshape(b, c, -1).mean(dim=2).reshape(b, c, 1, 1)
+    return feat_mean, feat_std
+
+def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor):
+    """Adaptive instance normalization.
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+def wavelet_blur(image: Tensor, radius: int):
+    """
+    Apply wavelet blur to the input tensor.
+    """
+    # input shape: (1, 3, H, W)
+    # convolution kernel
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125, 0.25, 0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    # add channel dimensions to the kernel to make it a 4D tensor
+    kernel = kernel[None, None]
+    # repeat the kernel across all input channels
+    kernel = kernel.repeat(3, 1, 1, 1)
+    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
+    # apply convolution
+    output = F.conv2d(image, kernel, groups=3, dilation=radius)
+    return output
+
+def wavelet_decomposition(image: Tensor, levels=5):
+    """
+    Apply wavelet decomposition to the input tensor.
+    This function only returns the low frequency & the high frequency.
+    """
+    high_freq = torch.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
+    """
+    Apply wavelet decomposition, so that the content will have the same color as the style.
+    """
+    # calculate the wavelet decomposition of the content feature
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+    # calculate the wavelet decomposition of the style feature
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+    # reconstruct the content feature with the style's high frequency
+    return content_high_freq + style_low_freq

GDPOSR/train/train_GDPOSR.py

@@ -0,0 +1,233 @@
+import os
+import gc
+import lpips
+import clip
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+import copy
+
+import diffusers
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.optimization import get_scheduler
+
+import wandb
+from cleanfid.fid import get_folder_features, build_feature_extractor, fid_from_feats
+import sys
+sys.path.append("GDPOSR")
+from modelfile.GDPOSR import GDPOSR as GDPOSRModel
+from my_utils.training_utils_realsr import parse_args_realsr_training, PairedSROnlineDataset  
+
+from pathlib import Path
+from accelerate.utils import set_seed, ProjectConfiguration
+from accelerate import DistributedDataParallelKwargs
+
+sys.path.append('GDPOSR')
+from GDPOSR.my_utils.wavelet_color_fix import adain_color_fix, wavelet_color_fix
+from diffusers.training_utils import compute_snr
+from diffusers import DDPMScheduler, AutoencoderKL
+from GDPOSR.losses.grpo import AdaptiveReward as RewardFunction
+
+from ram.models.ram_lora import ram
+from ram import inference_ram as inference
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+        kwargs_handlers=[ddp_kwargs],
+    )
+
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    if accelerator.is_main_process:
+        os.makedirs(os.path.join(args.output_dir, "checkpoints"), exist_ok=True)
+        os.makedirs(os.path.join(args.output_dir, "eval"), exist_ok=True)
+
+    net_pix2pix = GDPOSRModel(args)
+    net_pix2pix.set_train()
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            net_pix2pix.unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available, please install it by running `pip install xformers`")
+
+    if args.gradient_checkpointing:
+        net_pix2pix.unet.enable_gradient_checkpointing()
+
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    net_lpips = lpips.LPIPS(net='vgg').cuda()
+    net_lpips.requires_grad_(False)
+    net_ARF = RewardFunction()
+    net_ARF.requires_grad_(False)
+
+    # # set adapter
+    net_pix2pix.unet.set_adapter(['default_encoder', 'default_decoder', 'default_others'])
+
+    # make the optimizer
+    layers_to_opt = []
+    for n, _p in net_pix2pix.unet.named_parameters():
+        if "lora" in n:
+            assert _p.requires_grad
+            layers_to_opt.append(_p)
+
+    optimizer = torch.optim.AdamW(layers_to_opt, lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,)
+    lr_scheduler = get_scheduler(args.lr_scheduler, optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
+        num_cycles=args.lr_num_cycles, power=args.lr_power,)
+
+    # make the dataloader
+    dataset_train = PairedSROnlineDataset(dataset_folder=args.dataset_folder, image_prep=args.train_image_prep, split="train", deg_file_path=args.deg_file_path, args=args)
+    dataset_val = PairedSROnlineDataset(dataset_folder=args.dataset_folder, image_prep=args.test_image_prep, split="test", deg_file_path=args.deg_file_path, args=args)
+    dl_train = torch.utils.data.DataLoader(dataset_train, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers)
+    dl_val = torch.utils.data.DataLoader(dataset_val, batch_size=1, shuffle=False, num_workers=0)
+
+    # init RAM
+    ram_transforms = transforms.Compose([
+        transforms.Resize((384, 384)),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    RAM = ram(pretrained='./ckp/ram_swin_large_14m.pth',
+            pretrained_condition=None,
+            image_size=384,
+            vit='swin_l')
+    RAM.eval()
+    RAM.to("cuda", dtype=torch.float16)
+
+    # Prepare everything with our `accelerator`.
+    net_pix2pix, optimizer, dl_train, lr_scheduler = accelerator.prepare(
+        net_pix2pix, optimizer, dl_train, lr_scheduler
+    )
+    net_lpips, net_ARF = accelerator.prepare(net_lpips, net_ARF)
+    # renorm with image net statistics
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    progress_bar = tqdm(range(0, args.max_train_steps), initial=0, desc="Steps",
+        disable=not accelerator.is_local_main_process,)
+
+    # start the training loop
+    global_step = 0
+    for epoch in range(0, args.num_training_epochs):
+        for step, batch in enumerate(dl_train):
+            with accelerator.accumulate(net_pix2pix):
+                x_src = batch["LR"]
+                x_tgt = batch["HR"]
+                fedilty_ratio = batch["fedilty_ratio"]
+                detail_ratio = batch["detail_ratio"]
+
+                B, C, H, W = x_src.shape
+                # image description
+                x_tgt_ram = ram_transforms(x_tgt*0.5+0.5)
+                caption_r = inference(x_tgt_ram.to(dtype=torch.float16), RAM)
+                with torch.no_grad():
+                    positive_prompt = []
+                    negative_prompt = []
+                    for i in range(B):
+                        ram_image = x_tgt[i,:,:,:].unsqueeze(0)
+                        x_tgt_ram = ram_transforms(ram_image*0.5+0.5)
+                        caption = inference(x_tgt_ram.to(dtype=torch.float16), RAM)
+                        positive_prompt.append(f'{caption[0]}, {args.positive_prompt}')
+                        negative_prompt.append(args.negative_prompt)
+                # generate some samples
+                if torch.cuda.device_count() > 1:
+                    sample_images, _, _ = net_pix2pix.module.GDPOReference(x_src, positive_prompt=positive_prompt, negative_prompt=negative_prompt, args=args, groupsize=args.groupsize)
+                else:
+                    sample_images, _, _ = net_pix2pix.GDPOReference(x_src, positive_prompt=positive_prompt, negative_prompt=negative_prompt, args=args, groupsize=args.groupsize)
+                # select winning and losing samples:
+                x_tgt_re = x_tgt.unsqueeze(1).repeat(1,args.groupsize,1,1,1)
+                rewards = net_ARF(sample_images, x_tgt_re, fedilty_ratio, detail_ratio)
+                rewards = rewards.cuda()
+                b_sample, g_sample, c_sample, h_sample, w_sample = sample_images.shape
+                x_src_wl = sample_images.view(b_sample*g_sample, c_sample, h_sample, w_sample)
+                ps_wl = []
+                nps_wl = []
+                for i in range(args.groupsize):
+                    ps_wl += positive_prompt
+                    nps_wl += negative_prompt
+                # forward pass
+                x_tgt_pred, latents_pred, model_pred, prompt_embeds, neg_prompt_embeds, noise, ref_output_image, ref_x_denoised, ref_model_pred = net_pix2pix(x_src_wl, positive_prompt=ps_wl, negative_prompt=nps_wl, args=args)
+                # GDPO
+                model_losses = (model_pred - noise).pow(2).mean(dim=[1,2,3])
+                # b_model, c_model, h_model, w_model = model_losses.shape
+                model_losses = model_losses.view(b_sample, g_sample)
+                model_losses = rewards * model_losses
+                model_diff = model_losses.sum(1)
+                # model_losses_w, model_losses_l = model_losses.chunk(2)
+                ref_losses = (ref_model_pred - noise).pow(2).mean(dim=[1,2,3])
+                ref_losses = ref_losses.view(b_sample, g_sample)
+                ref_losses = rewards * ref_losses
+                ref_diff = ref_losses.sum(1)
+                scale_term = -0.5 * 5000 
+                inside_term = scale_term * (model_diff - ref_diff)
+                implicit_acc = (inside_term > 0).sum().float() / inside_term.size(0)
+                gdpo_loss = -1 * F.logsigmoid(inside_term).mean()
+                loss = gdpo_loss
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(layers_to_opt, args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
+
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                if accelerator.is_main_process:
+                    logs = {}
+                    # log all the losses
+                    logs["loss"] = gdpo_loss.detach().item()
+                    progress_bar.set_postfix(**logs)
+
+                    # checkpoint the model
+                    if global_step % args.checkpointing_steps == 1:
+                        outf = os.path.join(args.output_dir, "checkpoints", f"model_{global_step}.pkl")
+                        accelerator.unwrap_model(net_pix2pix).save_model(outf)
+
+                    accelerator.log(logs, step=global_step)
+
+
+if __name__ == "__main__":
+    args = parse_args_realsr_training()
+    main(args)

GDPOSR/train/train_NAOSD.py

@@ -0,0 +1,256 @@
+import os
+import gc
+import lpips
+import clip
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+import copy
+
+import diffusers
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.optimization import get_scheduler
+
+import wandb
+from cleanfid.fid import get_folder_features, build_feature_extractor, fid_from_feats
+import sys
+sys.path.append("GDPOSR")
+from modelfile.GDPOSR import VSD, NAOSD
+from my_utils.training_utils_realsr import parse_args_realsr_training, PairedSROnlineDataset  
+
+from pathlib import Path
+from accelerate.utils import set_seed, ProjectConfiguration
+from accelerate import DistributedDataParallelKwargs
+
+sys.path.append('GDPOSR')
+from GDPOSR.my_utils.wavelet_color_fix import adain_color_fix, wavelet_color_fix
+from diffusers.training_utils import compute_snr
+from diffusers import DDPMScheduler, AutoencoderKL
+
+from ram.models.ram_lora import ram
+from ram import inference_ram as inference
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+        kwargs_handlers=[ddp_kwargs],
+    )
+
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    if accelerator.is_main_process:
+        os.makedirs(os.path.join(args.output_dir, "checkpoints"), exist_ok=True)
+        os.makedirs(os.path.join(args.output_dir, "eval"), exist_ok=True)
+
+    net_pix2pix = NAOSD(args)
+    net_pix2pix.set_train()
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            net_pix2pix.unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available, please install it by running `pip install xformers`")
+
+    if args.gradient_checkpointing:
+        net_pix2pix.unet.enable_gradient_checkpointing()
+
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    # init vsd model
+    net_disc = VSD(args=args, accelerator=accelerator)
+    net_disc.set_train()
+
+    net_lpips = lpips.LPIPS(net='vgg').cuda()
+    net_lpips.requires_grad_(False)
+
+    # # set adapter
+    if args.use_vae_encode_lora and (not args.use_vae_decode_lora):
+        print('==== Use Lora at VAE Encoder ====')
+        net_pix2pix.vae.set_adapter(['default_encoder'])
+    elif (not args.use_vae_encode_lora) and args.use_vae_decode_lora:
+        print('==== Use Lora at VAE Decoder ====')
+        net_pix2pix.vae.set_adapter(['default_decoder'])
+    elif args.use_vae_encode_lora and args.use_vae_decode_lora:
+        print('==== Use Lora at VAE En&Decoder ====')
+        net_pix2pix.vae.set_adapter(['default_encoder', 'default_decoder'])
+    else:
+        print('==== Use Fix VAE ====')
+        net_pix2pix.vae.disable_adapters()
+    net_pix2pix.unet.set_adapter(['default_encoder', 'default_decoder', 'default_others'])
+
+    # make the optimizer
+    layers_to_opt = []
+    for n, _p in net_pix2pix.unet.named_parameters():
+        if "lora" in n:
+            assert _p.requires_grad
+            layers_to_opt.append(_p)
+    layers_to_opt += list(net_pix2pix.unet.conv_in.parameters())
+    for n, _p in net_pix2pix.vae.named_parameters():
+        if "lora" in n:
+            # assert _p.requires_grad
+            layers_to_opt.append(_p)
+
+    optimizer = torch.optim.AdamW(layers_to_opt, lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,)
+    lr_scheduler = get_scheduler(args.lr_scheduler, optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
+        num_cycles=args.lr_num_cycles, power=args.lr_power,)
+
+    layers_to_opt_disc = []
+    for n, _p in net_disc.unet_update.named_parameters():
+        if "lora" in n:
+            assert _p.requires_grad
+            layers_to_opt_disc.append(_p)
+    optimizer_disc = torch.optim.AdamW(layers_to_opt_disc, lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,)
+    lr_scheduler_disc = get_scheduler(args.lr_scheduler, optimizer=optimizer_disc,
+            num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+            num_training_steps=args.max_train_steps * accelerator.num_processes,
+            num_cycles=args.lr_num_cycles, power=args.lr_power)
+
+    # make the dataloader
+    dataset_train = PairedSROnlineDataset(dataset_folder=args.dataset_folder, image_prep=args.train_image_prep, split="train", deg_file_path=args.deg_file_path, args=args)
+    dataset_val = PairedSROnlineDataset(dataset_folder=args.dataset_folder, image_prep=args.test_image_prep, split="test", deg_file_path=args.deg_file_path, args=args)
+    dl_train = torch.utils.data.DataLoader(dataset_train, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers)
+    dl_val = torch.utils.data.DataLoader(dataset_val, batch_size=1, shuffle=False, num_workers=0)
+
+    # init RAM
+    ram_transforms = transforms.Compose([
+        transforms.Resize((384, 384)),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    RAM = ram(pretrained='./ckp/ram_swin_large_14m.pth',
+            pretrained_condition=None,
+            image_size=384,
+            vit='swin_l')
+    RAM.eval()
+    RAM.to("cuda", dtype=torch.float16)
+
+    # Prepare everything with our `accelerator`.
+    net_pix2pix, net_disc, optimizer, optimizer_disc, dl_train, lr_scheduler, lr_scheduler_disc = accelerator.prepare(
+        net_pix2pix, net_disc, optimizer, optimizer_disc, dl_train, lr_scheduler, lr_scheduler_disc
+    )
+    net_lpips = accelerator.prepare(net_lpips)
+    # renorm with image net statistics
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    progress_bar = tqdm(range(0, args.max_train_steps), initial=0, desc="Steps",
+        disable=not accelerator.is_local_main_process,)
+
+    # start the training loop
+    global_step = 0
+    for epoch in range(0, args.num_training_epochs):
+        for step, batch in enumerate(dl_train):
+            l_acc = [net_pix2pix, net_disc]
+            with accelerator.accumulate(*l_acc):
+                x_src = batch["LR"]
+                x_tgt = batch["HR"]
+                B, C, H, W = x_src.shape
+                # image description
+                x_tgt_ram = ram_transforms(x_tgt*0.5+0.5)
+                caption_r = inference(x_tgt_ram.to(dtype=torch.float16), RAM)
+                with torch.no_grad():
+                    positive_prompt = []
+                    negative_prompt = []
+                    for i in range(B):
+                        ram_image = x_tgt[i,:,:,:].unsqueeze(0)
+                        x_tgt_ram = ram_transforms(ram_image*0.5+0.5)
+                        caption = inference(x_tgt_ram.to(dtype=torch.float16), RAM)
+                        positive_prompt.append(f'{caption[0]}, {args.positive_prompt}')
+                        negative_prompt.append(args.negative_prompt)
+                # forward pass
+                x_tgt_pred, latents_pred, prompt_embeds, neg_prompt_embeds, noise = net_pix2pix(x_src, positive_prompt=positive_prompt, negative_prompt=negative_prompt, args=args)
+                # Reconstruction loss
+                loss_l2 = F.mse_loss(x_tgt_pred.float(), x_tgt.float(), reduction="mean") * args.lambda_l2
+                loss_lpips = net_lpips(x_tgt_pred.float(), x_tgt.float()).mean() * args.lambda_lpips
+                loss = loss_l2 + loss_lpips
+                # KL loss
+                if torch.cuda.device_count() > 1:
+                    loss_kl = net_disc.module.distribution_matching_loss(net_disc.module.unet_fix, net_disc.module.unet_update, net_disc.module.sched, latents_pred, prompt_embeds, neg_prompt_embeds, args, ) * args.lambda_vsd
+                else:
+                    loss_kl = net_disc.distribution_matching_loss(net_disc.unet_fix, net_disc.unet_update, net_disc.sched, latents_pred, prompt_embeds, neg_prompt_embeds, args, ) * args.lambda_vsd
+                loss = loss + loss_kl
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(layers_to_opt, args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
+
+                """
+                Disc loss: let lora model closed to generator 
+                """
+                if torch.cuda.device_count() > 1:
+                    loss_d = net_disc.module.compute_lora_loss(latents_pred, prompt_embeds, args)*args.lambda_vsd_lora
+                else:
+                    loss_d = net_disc.compute_lora_loss(latents_pred, prompt_embeds, args)*args.lambda_vsd_lora
+                accelerator.backward(loss_d)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(net_disc.parameters(), args.max_grad_norm)
+                optimizer_disc.step()
+                lr_scheduler_disc.step()
+                optimizer_disc.zero_grad(set_to_none=args.set_grads_to_none)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                if accelerator.is_main_process:
+                    logs = {}
+                    # log all the losses
+                    logs["loss_d"] = loss_d.detach().item()
+                    logs["loss_kl"] = loss_kl.detach().item()
+                    logs["loss_l2"] = loss_l2.detach().item()
+                    logs["loss_lpips"] = loss_lpips.detach().item()
+                    progress_bar.set_postfix(**logs)
+
+                    # checkpoint the model
+                    if global_step % args.checkpointing_steps == 1:
+                        outf = os.path.join(args.output_dir, "checkpoints", f"model_{global_step}.pkl")
+                        accelerator.unwrap_model(net_pix2pix).save_model(outf)
+
+
+                    accelerator.log(logs, step=global_step)
+
+
+if __name__ == "__main__":
+    args = parse_args_realsr_training()
+    main(args)

ram/__init__.py

@@ -0,0 +1,2 @@
+from .inference import inference_tag2text, inference_ram, inference_ram_openset
+from .transform import get_transform

ram/configs/condition_config.json

@@ -0,0 +1,3 @@
+{
+    "nf": 64
+  }

ram/configs/med_config.json

@@ -0,0 +1,21 @@
+{
+    "architectures": [
+      "BertModel"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-12,
+    "max_position_embeddings": 512,
+    "model_type": "bert",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 30524,
+    "encoder_width": 768,
+    "add_cross_attention": true   
+  }

ram/configs/q2l_config.json

@@ -0,0 +1,22 @@
+{
+    "architectures": [
+      "BertModel"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-12,
+    "max_position_embeddings": 512,
+    "model_type": "bert",
+    "num_attention_heads": 4,
+    "num_hidden_layers": 2,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 30522,
+    "encoder_width": 768,
+    "add_cross_attention": true,
+    "add_tag_cross_attention": false
+  }

ram/configs/swin/config_swinB_384.json

@@ -0,0 +1,9 @@
+{
+    "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth",
+    "vision_width": 1024,
+    "image_res": 384,
+    "window_size": 12,
+    "embed_dim": 128,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 4, 8, 16, 32 ]
+  }

ram/configs/swin/config_swinL_384.json

@@ -0,0 +1,9 @@
+{
+    "ckpt": "pretrain_model/swin_large_patch4_window12_384_22k.pth",
+    "vision_width": 1536,
+    "image_res": 384,
+    "window_size": 12,
+    "embed_dim": 192,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 6, 12, 24, 48 ]
+  }

ram/configs/swin/config_swinL_444.json

@@ -0,0 +1,9 @@
+{
+    "ckpt": "pretrain_model/swin_large_patch4_window12_384_22k.pth",
+    "vision_width": 1536,
+    "image_res": 444,
+    "window_size": 12,
+    "embed_dim": 192,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 6, 12, 24, 48 ]
+  }

ram/data/ram_tag_list.txt

@@ -0,0 +1,4585 @@
+3D CG rendering
+3D glasses
+abacus
+abalone
+monastery
+belly
+academy
+accessory
+accident
+accordion
+acorn
+acrylic paint
+act
+action
+action film
+activity
+actor
+adaptation
+add
+adhesive tape
+adjust
+adult
+adventure
+advertisement
+antenna
+aerobics
+spray can
+afro
+agriculture
+aid
+air conditioner
+air conditioning
+air sock
+aircraft cabin
+aircraft model
+air field
+air line
+airliner
+airman
+plane
+airplane window
+airport
+airport runway
+airport terminal
+airship
+airshow
+aisle
+alarm
+alarm clock
+mollymawk
+album
+album cover
+alcohol
+alcove
+algae
+alley
+almond
+aloe vera
+alp
+alpaca
+alphabet
+german shepherd
+altar
+amber
+ambulance
+bald eagle
+American shorthair
+amethyst
+amphitheater
+amplifier
+amusement park
+amusement ride
+anchor
+ancient
+anemone
+angel
+angle
+animal
+animal sculpture
+animal shelter
+animation
+animation film
+animator
+anime
+ankle
+anklet
+anniversary
+trench coat
+ant
+antelope
+antique
+antler
+anvil
+apartment
+ape
+app
+app icon
+appear
+appearance
+appetizer
+applause
+apple
+apple juice
+apple pie
+apple tree
+applesauce
+appliance
+appointment
+approach
+apricot
+apron
+aqua
+aquarium
+aquarium fish
+aqueduct
+arcade
+arcade machine
+arch
+arch bridge
+archaelogical excavation
+archery
+archipelago
+architect
+architecture
+archive
+archway
+area
+arena
+argument
+arm
+armadillo
+armband
+armchair
+armoire
+armor
+army
+army base
+army tank
+array
+arrest
+arrow
+art
+art exhibition
+art gallery
+art print
+art school
+art studio
+art vector illustration
+artichoke
+article
+artifact
+artist
+artists loft
+ash
+ashtray
+asia temple
+asparagus
+asphalt road
+assemble
+assembly
+assembly line
+association
+astronaut
+astronomer
+athlete
+athletic
+atlas
+atm
+atmosphere
+atrium
+attach
+fighter jet
+attend
+attraction
+atv
+eggplant
+auction
+audi
+audio
+auditorium
+aurora
+author
+auto factory
+auto mechanic
+auto part
+auto show
+auto showroom
+car battery
+automobile make
+automobile model
+motor vehicle
+autumn
+autumn forest
+autumn leave
+autumn park
+autumn tree
+avatar
+avenue
+aviator sunglasses
+avocado
+award
+award ceremony
+award winner
+shed
+ax
+azalea
+baboon
+baby
+baby bottle
+baby carriage
+baby clothe
+baby elephant
+baby food
+baby seat
+baby shower
+back
+backdrop
+backlight
+backpack
+backyard
+bacon
+badge
+badger
+badlands
+badminton
+badminton racket
+bag
+bagel
+bagpipe
+baguette
+bait
+baked goods
+baker
+bakery
+baking
+baking sheet
+balance
+balance car
+balcony
+ball
+ball pit
+ballerina
+ballet
+ballet dancer
+ballet skirt
+balloon
+balloon arch
+baseball player
+ballroom
+bamboo
+bamboo forest
+banana
+banana bread
+banana leaf
+banana tree
+band
+band aid
+bandage
+headscarf
+bandeau
+bangs
+bracelet
+balustrade
+banjo
+bank
+bank card
+bank vault
+banknote
+banner
+banquet
+banquet hall
+banyan tree
+baozi
+baptism
+bar
+bar code
+bar stool
+barbecue
+barbecue grill
+barbell
+barber
+barber shop
+barbie
+barge
+barista
+bark
+barley
+barn
+barn owl
+barn door
+barrel
+barricade
+barrier
+handcart
+bartender
+baseball
+baseball base
+baseball bat
+baseball hat
+baseball stadium
+baseball game
+baseball glove
+baseball pitcher
+baseball team
+baseball uniform
+basement
+basil
+basin
+basket
+basket container
+basketball
+basketball backboard
+basketball coach
+basketball court
+basketball game
+basketball hoop
+basketball player
+basketball stadium
+basketball team
+bass
+bass guitar
+bass horn
+bassist
+bat
+bath
+bath heater
+bath mat
+bath towel
+swimwear
+bathrobe
+bathroom
+bathroom accessory
+bathroom cabinet
+bathroom door
+bathroom mirror
+bathroom sink
+toilet paper
+bathroom window
+batman
+wand
+batter
+battery
+battle
+battle rope
+battleship
+bay
+bay bridge
+bay window
+bayberry
+bazaar
+beach
+beach ball
+beach chair
+beach house
+beach hut
+beach towel
+beach volleyball
+lighthouse
+bead
+beagle
+beak
+beaker
+beam
+bean
+bean bag chair
+beanbag
+bear
+bear cub
+beard
+beast
+beat
+beautiful
+beauty
+beauty salon
+beaver
+bed
+bedcover
+bed frame
+bedroom
+bedding
+bedpan
+bedroom window
+bedside lamp
+bee
+beech tree
+beef
+beekeeper
+beeper
+beer
+beer bottle
+beer can
+beer garden
+beer glass
+beer hall
+beet
+beetle
+beige
+clock
+bell pepper
+bell tower
+belt
+belt buckle
+bench
+bend
+bengal tiger
+bento
+beret
+berry
+berth
+beverage
+bib
+bibimbap
+bible
+bichon
+bicycle
+bicycle helmet
+bicycle wheel
+biker
+bidet
+big ben
+bike lane
+bike path
+bike racing
+bike ride
+bikini
+bikini top
+bill
+billard
+billboard
+billiard table
+bin
+binder
+binocular
+biology laboratory
+biplane
+birch
+birch tree
+bird
+bird bath
+bird feeder
+bird house
+bird nest
+birdbath
+bird cage
+birth
+birthday
+birthday cake
+birthday candle
+birthday card
+birthday party
+biscuit
+bishop
+bison
+bit
+bite
+black
+black sheep
+blackberry
+blackbird
+blackboard
+blacksmith
+blade
+blanket
+sports coat
+bleacher
+blender
+blessing
+blind
+eye mask
+flasher
+snowstorm
+block
+blog
+blood
+bloom
+blossom
+blouse
+blow
+hair drier
+blowfish
+blue
+blue artist
+blue jay
+blue sky
+blueberry
+bluebird
+pig
+board
+board eraser
+board game
+boardwalk
+boat
+boat deck
+boat house
+paddle
+boat ride
+bobfloat
+bobcat
+body
+bodyboard
+bodybuilder
+boiled egg
+boiler
+bolo tie
+bolt
+bomb
+bomber
+bonasa umbellu
+bone
+bonfire
+bonnet
+bonsai
+book
+book cover
+bookcase
+folder
+bookmark
+bookshelf
+bookstore
+boom microphone
+boost
+boot
+border
+Border collie
+botanical garden
+bottle
+bottle cap
+bottle opener
+bottle screw
+bougainvillea
+boulder
+bouquet
+boutique
+boutique hotel
+bow
+bow tie
+bow window
+bowl
+bowling
+bowling alley
+bowling ball
+bowling equipment
+box
+box girder bridge
+box turtle
+boxer
+underdrawers
+boxing
+boxing glove
+boxing ring
+boy
+brace
+bracket
+braid
+brain
+brake
+brake light
+branch
+brand
+brandy
+brass
+brass plaque
+bread
+breadbox
+break
+breakfast
+seawall
+chest
+brewery
+brick
+brick building
+wall
+brickwork
+wedding dress
+bride
+groom
+bridesmaid
+bridge
+bridle
+briefcase
+bright
+brim
+broach
+broadcasting
+broccoli
+bronze
+bronze medal
+bronze sculpture
+bronze statue
+brooch
+creek
+broom
+broth
+brown
+brown bear
+brownie
+brunch
+brunette
+brush
+coyote
+brussels sprout
+bubble
+bubble gum
+bubble tea
+bucket cabinet
+shield
+bud
+buddha
+buffalo
+buffet
+bug
+build
+builder
+building
+building block
+building facade
+building material
+lamp
+bull
+bulldog
+bullet
+bullet train
+bulletin board
+bulletproof vest
+bullfighting
+megaphone
+bullring
+bumblebee
+bumper
+roll
+bundle
+bungee
+bunk bed
+bunker
+bunny
+buoy
+bureau
+burial chamber
+burn
+burrito
+bus
+bus driver
+bus interior
+bus station
+bus stop
+bus window
+bush
+business
+business card
+business executive
+business suit
+business team
+business woman
+businessman
+bust
+butcher
+butchers shop
+butte
+butter
+cream
+butterfly
+butterfly house
+button
+buttonwood
+buy
+taxi
+cabana
+cabbage
+cabin
+cabin car
+cabinet
+cabinetry
+cable
+cable car
+cactus
+cafe
+canteen
+cage
+cake
+cake stand
+calculator
+caldron
+calendar
+calf
+call
+phone box
+calligraphy
+calm
+camcorder
+camel
+camera
+camera lens
+camouflage
+camp
+camper
+campfire
+camping
+campsite
+campus
+can
+can opener
+canal
+canary
+cancer
+candle
+candle holder
+candy
+candy bar
+candy cane
+candy store
+cane
+jar
+cannon
+canopy
+canopy bed
+cantaloupe
+cantilever bridge
+canvas
+canyon
+cap
+cape
+cape cod
+cappuccino
+capsule
+captain
+capture
+car
+car dealership
+car door
+car interior
+car logo
+car mirror
+parking lot
+car seat
+car show
+car wash
+car window
+caramel
+card
+card game
+cardboard
+cardboard box
+cardigan
+cardinal
+cargo
+cargo aircraft
+cargo ship
+caribbean
+carnation
+carnival
+carnivore
+carousel
+carp
+carpenter
+carpet
+slipper
+house finch
+coach
+dalmatian
+aircraft carrier
+carrot
+carrot cake
+carry
+cart
+carton
+cartoon
+cartoon character
+cartoon illustration
+cartoon style
+carve
+case
+cash
+cashew
+casino
+casserole
+cassette
+cassette deck
+plaster bandage
+casting
+castle
+cat
+cat bed
+cat food
+cat furniture
+cat tree
+catacomb
+catamaran
+catamount
+catch
+catcher
+caterpillar
+catfish
+cathedral
+cattle
+catwalk
+catwalk show
+cauliflower
+cave
+caviar
+CD
+CD player
+cedar
+ceiling
+ceiling fan
+celebrate
+celebration
+celebrity
+celery
+cello
+smartphone
+cement
+graveyard
+centerpiece
+centipede
+ceramic
+ceramic tile
+cereal
+ceremony
+certificate
+chain
+chain saw
+chair
+chairlift
+daybed
+chalet
+chalice
+chalk
+chamber
+chameleon
+champagne
+champagne flute
+champion
+championship
+chandelier
+changing table
+channel
+chap
+chapel
+character sculpture
+charcoal
+charge
+charger
+chariot
+charity
+charity event
+charm
+graph
+chase
+chassis
+check
+checkbook
+chessboard
+checklist
+cheer
+cheerlead
+cheese
+cheeseburger
+cheesecake
+cheetah
+chef
+chemical compound
+chemist
+chemistry
+chemistry lab
+cheongsam
+cherry
+cherry blossom
+cherry tomato
+cherry tree
+chess
+chestnut
+chicken
+chicken breast
+chicken coop
+chicken salad
+chicken wing
+garbanzo
+chiffonier
+chihuahua
+child
+child actor
+childs room
+chile
+chili dog
+chimney
+chimpanzee
+chinaware
+chinese cabbage
+chinese garden
+chinese knot
+chinese rose
+chinese tower
+chip
+chipmunk
+chisel
+chocolate
+chocolate bar
+chocolate cake
+chocolate chip
+chocolate chip cookie
+chocolate milk
+chocolate mousse
+truffle
+choir
+kitchen knife
+cutting board
+chopstick
+christmas
+christmas ball
+christmas card
+christmas decoration
+christmas dinner
+christmas eve
+christmas hat
+christmas light
+christmas market
+christmas ornament
+christmas tree
+chrysanthemum
+church
+church tower
+cider
+cigar
+cigar box
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+tea pot
+tea set
+teach
+teacher
+teacup
+teal
+team photo
+team presentation
+tear
+technician
+technology
+teddy
+tee
+teenager
+telegraph pole
+zoom lens
+telescope
+television
+television camera
+television room
+television studio
+temperature
+temple
+tempura
+tennis
+tennis court
+tennis match
+tennis net
+tennis player
+tennis racket
+tent
+tequila
+terminal
+terrace
+terrain
+terrarium
+territory
+test
+test match
+test tube
+text
+text message
+textile
+texture
+thanksgiving
+thanksgiving dinner
+theater
+theatre actor
+therapy
+thermometer
+thermos
+thermos bottle
+thermostat
+thicket
+thimble
+thing
+thinking
+thistle
+throne
+throne room
+throw
+throw pillow
+thunder
+thunderstorm
+thyme
+tiara
+tick
+ticket
+ticket booth
+tide pool
+tie
+tiger
+tight
+tile
+tile flooring
+tile roof
+tile wall
+tin
+tinfoil
+tinsel
+tiramisu
+tire
+tissue
+toast
+toaster
+tobacco
+tobacco pipe
+toddler
+toe
+tofu
+toilet bowl
+toilet seat
+toiletry
+tokyo tower
+tomato
+tomato sauce
+tomato soup
+tomb
+tong
+tongs
+tool
+toolbox
+toothbrush
+toothpaste
+toothpick
+topiary garden
+topping
+torch
+tornado
+tortilla
+tortoise
+tote bag
+totem pole
+totoro
+toucan
+touch
+touchdown
+tour
+tour bus
+tour guide
+tourist
+tourist attraction
+tournament
+tow truck
+towel
+towel bar
+tower block
+tower bridge
+town
+town square
+toy
+toy car
+toy gun
+toyshop
+track
+tractor
+trade
+tradition
+traditional
+traffic
+traffic cone
+traffic congestion
+traffic jam
+traffic sign
+trail
+trailer
+trailer truck
+train
+train bridge
+train car
+train interior
+train track
+train window
+trainer
+training
+training bench
+training ground
+trolley
+trampoline
+transformer
+transparency
+travel
+tray
+treadmill
+treat
+tree
+tree branch
+tree farm
+tree frog
+tree house
+tree root
+tree trunk
+trial
+triangle
+triathlon
+tribe
+tributary
+trick
+tricycle
+trim
+trio
+tripod
+trombone
+troop
+trophy
+trophy cup
+tropic
+trout
+truck
+truck driver
+tub
+tube
+tugboat
+tulip
+tuna
+tundra
+tunnel
+turbine
+turkey
+turn
+turnip
+turquoise
+turret
+turtle
+tusk
+tv actor
+tv cabinet
+tv drama
+tv genre
+tv personality
+tv show
+tv sitcom
+tv tower
+twig
+twilight
+twin
+twine
+twist
+type
+type on
+typewriter
+ukulele
+ultraman
+umbrella
+underclothes
+underwater
+unicorn
+uniform
+universe
+university
+up
+urban
+urinal
+urn
+use
+utensil
+utility room
+vacuum
+valley
+valve
+vampire
+van
+vanilla
+vanity
+variety
+vase
+vault
+vector cartoon illustration
+vector icon
+vegetable
+vegetable garden
+vegetable market
+vegetation
+vehicle
+veil
+vein
+velvet
+vending machine
+vendor
+vent
+vespa
+vessel
+vest
+vet
+veteran
+veterinarians office
+viaduct
+video
+video camera
+video game
+videotape
+view mirror
+vigil
+villa
+village
+vine
+vinegar
+vineyard
+violence
+violet
+violin
+violinist
+violist
+vision
+visor
+vodka
+volcano
+volleyball
+volleyball court
+volleyball player
+volunteer
+voyage
+vulture
+waffle
+waffle iron
+wagon
+wagon wheel
+waist
+waiter
+waiting hall
+waiting room
+walk
+walking
+walking cane
+wall clock
+wallpaper
+walnut
+walrus
+war
+warehouse
+warm
+warning sign
+warrior
+warship
+warthog
+wash
+washer
+washing
+washing machine
+wasp
+waste
+waste container
+watch
+water
+water bird
+water buffalo
+water cooler
+water drop
+water feature
+water heater
+water level
+water lily
+water park
+water pipe
+water purifier
+water ski
+water sport
+water surface
+water tower
+watercolor
+watercolor illustration
+watercolor painting
+waterfall
+watering can
+watermark overlay stamp
+watermelon
+waterproof jacket
+waterway
+wave
+wax
+weapon
+wear
+weather
+vane
+web
+webcam
+wedding
+wedding ring
+wedding bouquet
+wedding cake
+wedding couple
+wedding invitation
+wedding party
+wedding photo
+wedding photographer
+wedding photography
+wedding reception
+wedge
+weed
+weight
+weight scale
+welder
+well
+western food
+western restaurant
+wet
+wet bar
+wet suit
+wetland
+wetsuit
+whale
+whale shark
+wheat
+wheat field
+wheel
+wheelchair
+wheelie
+whipped cream
+whisk
+whisker
+whiskey
+whistle
+white
+white house
+white wine
+whiteboard
+wicket
+wide
+wield
+wig
+Wii
+Wii controller
+wild
+wildebeest
+wildfire
+wildflower
+wildlife
+willow
+wind
+wind chime
+wind farm
+wind turbine
+windmill
+window
+window box
+window display
+window frame
+window screen
+window seat
+window sill
+wiper
+windshield
+windy
+wine bottle
+wine cooler
+wine cabinet
+wine cellar
+wine glass
+wine rack
+wine tasting
+winery
+wing
+winter
+winter melon
+winter morning
+winter scene
+winter sport
+winter storm
+wire
+wisteria
+witch
+witch hat
+wok
+wolf
+woman
+wood
+wood duck
+wood floor
+wood wall
+wood-burning stove
+wooden spoon
+woodland
+woodpecker
+woodworking plane
+wool
+job
+work card
+workbench
+worker
+workplace
+workshop
+world
+worm
+worship
+wound
+wrap
+wrap dress
+wrapping paper
+wrestle
+wrestler
+wrinkle
+wristband
+write
+writer
+writing
+writing brush
+writing desk
+yacht
+yak
+yard
+yellow
+yoga
+yoga mat
+yoghurt
+yoke
+yolk
+youth
+youth hostel
+yurt
+zebra
+zebra crossing
+zen garden
+zip
+zipper
+zombie
+zongzi
+zoo

ram/data/ram_tag_list_chinese.txt

@@ -0,0 +1,4585 @@
+三维CG渲染 
+3d眼镜
+算盘 
+鲍鱼 
+修道院 
+肚子 
+学院 
+附件 
+事故 
+手风琴 
+橡子 
+丙烯颜料
+表演
+行动 
+动作电影 
+活动 
+演员 
+改编本
+添加 
+胶带 
+调整 
+成人 
+冒险 
+广告 
+天线 
+有氧运动 
+喷雾罐
+爆炸头
+农业 
+帮助
+空调 
+空调系统
+风向标
+飞机客舱 
+飞机模型 
+机场 
+航线
+客机 
+飞行员 
+飞机 
+飞机窗口 
+机场 
+机场跑道 
+航站楼 
+飞艇 
+航展
+过道 
+警报
+闹钟 
+信天翁 
+唱片
+唱片封面 
+酒精 
+壁龛
+水藻
+胡同/球道
+杏仁 
+芦荟
+高山 
+羊驼 
+字母表
+德国牧羊犬
+圣坛
+琥珀
+救护车 
+秃鹰
+美国短毛猫 
+紫水晶 
+圆形剧场 
+扩音器
+游乐园 
+游乐设施 
+锚 
+古老的 
+海葵 
+天使 
+角 
+动物 
+动物雕塑 
+动物收容所 
+动画片
+动画电影 
+动画师 
+动漫 
+脚踝 
+短袜 
+周年庆
+风衣 
+蚂蚁 
+羚羊 
+古董 
+鹿角 
+铁砧 
+公寓 
+猿 
+应用程序 
+应用图标 
+出现 
+外观 
+开胃菜 
+掌声 
+苹果 
+苹果汁 
+苹果派 
+苹果树 
+苹果酱 
+设备 
+约定
+通道
+杏子
+围裙 
+浅绿色
+水族馆 
+观赏鱼
+渡槽 
+游乐中心
+商场游戏机
+拱门
+拱桥 
+考古现场
+射箭 
+群岛 
+建筑师
+建筑设计
+档案
+拱门 
+地区
+竞技场 
+争论
+手臂 
+穿山甲
+臂章 
+扶手椅 
+衣柜
+盔甲
+军队 
+军事基地 
+坦克 
+阵列
+逮捕 
+箭头 
+艺术 
+艺术展
+美术馆
+艺术印刷品
+艺术学校 
+艺术工作室 
+艺术矢量插图 
+洋蓟 
+文章 
+手工艺品
+艺术家 
+艺术阁楼 
+灰 
+烟灰缸 
+亚洲寺庙 
+芦笋 
+沥青道路
+组装
+集会
+生产流水线
+协会 
+宇航员 
+天文学家 
+运动员 
+运动 
+地图集
+自助取款机 
+大气层
+中庭 
+连接
+战斗机 
+参加 
+吸引力 
+全地形车
+茄子 
+拍卖 
+奥迪汽车
+音频 
+礼堂 
+极光 
+作者 
+汽车厂 
+汽车修理工 
+汽车零件
+车展 
+汽车展厅 
+汽车电池 
+汽车制造
+汽车模型 
+汽车
+秋天 
+秋天的森林 
+秋天的叶子
+秋天的公园 
+秋天的树 
+阿凡达
+林荫大道
+飞行员太阳镜 
+牛油果
+奖品
+颁奖典礼 
+获奖者
+棚
+斧头 
+杜鹃花 
+狒狒 
+婴儿 
+奶瓶 
+婴儿车 
+婴儿衣服 
+小象 
+婴儿食品 
+婴儿座椅
+迎婴派对
+背后/后面
+背景 
+背光 
+背包 
+后院 
+培根 
+徽章 
+獾 
+荒地 
+羽毛球运动
+羽毛球拍 
+袋子
+面包圈
+风笛 
+法棍
+诱饵 
+焙烤食品 
+面包师
+面包店 
+烘焙 
+烤盘 
+平衡 
+平衡车 
+阳台 
+球 
+球池
+芭蕾舞女演员 
+芭蕾舞 
+芭蕾舞演员 
+芭蕾舞裙 
+气球 
+气球拱门 
+棒球手 
+舞厅 
+竹子 
+竹林 
+香蕉 
+香蕉面包 
+香蕉叶子 
+香蕉树 
+乐队 
+创可贴 
+绷带 
+头巾 
+束发带 
+刘海
+手镯 
+栏杆
+五弦琴
+银行 
+银行卡 
+银行金库
+纸币
+横幅/旗帜
+宴会 
+宴会厅 
+榕树
+包子 
+洗礼 
+酒吧 
+条形码 
+高脚凳
+烧烤 
+烧烤架 
+杠铃 
+理发师 
+理发店 
+芭比娃娃 
+驳船 
+咖啡师 
+树皮 
+大麦 
+谷仓 
+仓鸮
+挡光板
+桶 
+路障
+屏障
+手推车 
+酒保 
+棒球 
+棒球基地 
+棒球棒 
+棒球帽 
+棒球场
+棒球比赛 
+棒球手套 
+棒球投手 
+棒球队 
+棒球制服 
+地下室 
+罗勒 
+水盆
+篮子 
+篮子
+篮球 
+篮球篮板 
+篮球教练 
+篮球场 
+篮球比赛 
+篮球框
+篮球运动员 
+篮球馆 
+篮球队 
+贝斯
+低音吉他 
+低音喇叭 
+贝斯手 
+球棒/球拍
+浴室
+水浴加热器
+浴垫 
+浴巾 
+泳装 
+浴袍 
+浴室 
+浴室配件 
+浴室柜 
+浴室门 
+浴室镜子
+浴室水槽 
+卫生纸
+浴室窗户
+蝙蝠侠 
+棒子
+接连猛打/击球员
+电池 
+战斗 
+战绳
+战舰 
+海湾
+海湾大桥 
+凸窗 
+杨梅
+集市 
+海滩 
+沙滩球 
+沙滩椅 
+海滨别墅 
+海滩小屋 
+沙滩毛巾 
+沙滩排球 
+灯塔 
+珠子
+比格犬
+鸟嘴
+烧杯 
+横梁
+豆子
+豆袋椅 
+豆袋 
+熊 
+幼熊 
+胡子 
+野兽 
+击打/击败
+美丽的 
+美丽
+美容院 
+海狸 
+床
+床单
+床架
+卧室 
+床上用品 
+便盆 
+卧室窗户 
+床头灯 
+蜜蜂 
+山毛榉
+牛肉 
+养蜂人 
+蜂鸣器 
+啤酒 
+啤酒瓶 
+啤酒罐 
+啤酒花园 
+啤酒杯 
+啤酒馆
+甜菜 
+甲虫 
+米色 
+时钟 
+甜椒 
+钟楼 
+皮带
+皮带扣 
+长凳
+弯曲 
+孟加拉虎 
+盒饭 
+贝雷帽 
+浆果 
+停泊位 
+饮料 
+围嘴 
+拌饭 
+圣经 
+比熊
+自行车 
+自行车头盔 
+自行车车轮 
+自行车骑士
+坐浴盆 
+大本钟 
+自行车道 
+自行车道 
+自行车赛
+骑车
+比基尼 
+比基尼上衣
+账单
+台球
+广告牌 
+台球台
+垃圾箱
+活页夹
+双筒望远镜 
+生物学实验室 
+双翼飞机 
+桦木 
+桦树 
+鸟 
+鸟池
+喂鸟器 
+鸟舍
+鸟巢 
+鸟池
+鸟笼 
+出生 
+生日 
+生日蛋糕 
+生日蜡烛 
+生日贺卡 
+生日聚会 
+饼干 
+主教 
+野牛 
+钻头
+咬 
+黑色
+黑山羊
+黑莓 
+乌鸦
+黑板
+铁匠 
+叶片/刀片
+毯子/覆盖层
+��动外套 
+看台
+搅拌机 
+祝福 
+窗帘
+眼罩 
+闪光 
+暴风雪 
+块 
+博客 
+血 
+开花
+花
+女装衬衫
+吹
+吹风机
+河豚 
+蓝色
+蓝色艺术家
+蓝松鸦
+蓝天
+蓝莓 
+蓝知更鸟 
+猪 
+板子
+板擦
+棋盘游戏 
+木板路 
+船 
+船甲板
+船屋
+桨 
+乘船 
+浮标
+山猫 
+躯干
+身体冲浪板
+健美运动员 
+水煮鸡蛋 
+锅炉 
+饰扣式领带
+门闩
+炸弹 
+轰炸机 
+披肩榛鸡
+骨骼
+篝火 
+阀盖 
+盆景 
+书 
+书籍封面
+书柜 
+文件夹 
+书签 
+书架
+书店 
+远程拾音器
+推动
+靴子
+边界
+边境牧羊犬 
+植物园 
+瓶 
+瓶盖 
+开瓶器 
+螺旋开瓶器
+三角梅
+巨石
+花束 
+时装店
+精品酒店 
+鞠躬/蝴蝶结
+领结 
+弓形窗
+碗 
+保龄球运动
+保龄球馆 
+保龄球 
+保龄球设备 
+盒子 
+箱形梁桥
+箱龟 
+拳击手 
+内裤 
+拳击 
+拳击手套 
+拳击台
+男孩 
+支撑物
+支架 
+辫子
+大脑 
+刹车 
+刹车灯 
+树枝
+商标
+白兰地 
+黄铜 
+黄铜牌匾 
+面包 
+面包箱 
+休息
+早餐 
+防浪堤 
+胸部
+啤酒厂 
+砖块
+砖建筑物
+墙 
+砖块
+婚纱 
+新娘 
+新郎 
+伴娘 
+桥 
+缰绳 
+公文包 
+明亮的 
+边沿
+钻头
+广播 
+西兰花 
+青铜 
+铜牌 
+青铜雕塑 
+青铜雕像 
+胸针 
+小溪
+扫帚 
+肉汤 
+棕色
+棕熊 
+巧克力蛋糕 
+早午餐 
+浅黑肤色的女人 
+刷子
+郊狼
+包菜
+气泡
+泡泡糖 
+珍珠奶茶
+斗柜 
+盾牌
+芽 
+佛 
+水牛 
+自助餐 
+昆虫
+建造
+建造者
+建筑 
+积木
+建筑立面 
+建筑材料 
+灯 
+牛 
+斗牛犬 
+子弹 
+动车 
+公告栏 
+防弹背心 
+斗牛 
+扩音器 
+斗牛场 
+大黄蜂 
+保险杠 
+卷/地形起伏
+捆
+蹦极 
+双层床 
+地堡/击球
+兔子 
+浮标 
+书桌
+墓室 
+燃烧 
+玉米煎饼 
+公交车
+公交车司机
+公交车内部
+公交车站 
+公交车站 
+公交车窗户
+灌木
+商业
+名片 
+业务主管 
+商务西装
+业务团队 
+女商人
+商人 
+半身像
+屠夫 
+肉铺
+孤峰 
+黄油 
+奶油 
+蝴蝶 
+蝴蝶馆
+按钮 
+梧桐树
+购买
+出租车 
+小屋 
+卷心菜 
+小屋/机舱
+守车
+储藏柜
+橱柜
+电缆 
+缆车 
+仙人掌 
+咖啡馆 
+食堂 
+笼子
+蛋糕 
+蛋糕台
+计算器 
+大锅 
+日历 
+小腿 
+通话
+电话亭
+书法 
+平静的
+摄像机 
+骆驼 
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+相机镜头 
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+露营者 
+篝火 
+露营
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+开罐器 
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+癌症 
+蜡烛 
+烛台 
+糖果 
+块状糖
+柺杖糖 
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+四柱床
+香瓜 
+悬臂桥 
+帆布 
+峡谷 
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+卡布奇诺
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+队长 
+捕获 
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+汽车内饰
+车标
+后视镜
+停车场 
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+车展
+洗车 
+车窗 
+焦糖 
+卡片
+纸牌游戏
+纸板 
+纸板盒 
+羊毛衫
+红衣凤头鸟
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+货运飞机 
+货船 
+加勒比 
+康乃馨 
+狂欢节 
+食肉动物 
+旋转木马 
+鲤鱼 
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+地毯 
+拖鞋 
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+长途客车
+斑点狗
+航空母舰
+胡萝卜 
+胡萝卜蛋糕 
+携带 
+手推车
+纸箱/纸盒
+卡通 
+卡通人物 
+卡通插图 
+卡通风格 
+雕刻 
+容器
+现金 
+腰果 
+赌场 
+砂锅 
+磁带 
+盒式录音机
+石膏绷带
+铸造 
+城堡 
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+猫窝
+猫粮 
+猫器具
+猫架
+地下墓穴 
+双体船 
+美洲狮
+握着/抓着
+捕手 
+毛毛虫 
+鲶鱼 
+教堂 
+牛 
+猫步 
+走秀 
+菜花 
+洞穴 
+鱼子酱 
+光盘
+CD播放器
+雪松 
+天花板 
+吊扇 
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+名人 
+芹菜 
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+手机 
+水泥 
+墓地 
+中心装饰品
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+陶瓷 
+瓷砖 
+麦片 
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+升降椅 
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+木屋
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+婴儿换尿布台
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+皴裂处
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+人物雕塑
+木炭
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+充电器
+战车
+慈善机构
+慈善活动
+魅力
+图表
+追逐
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+检查/支票
+支票簿
+棋盘
+检查表
+欢呼声
+鼓励/啦啦队
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+奶酪汉堡
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+猎豹
+厨师
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+化学
+化学实验室
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+樱桃
+樱花
+樱桃番茄
+樱桃树
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+鹰嘴豆
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+黑猩猩
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+巧克力碎片
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+巧克力慕斯
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+工厂车间
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+小说书
+田野/场地/野外
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+打架
+花样滑冰运动员
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+文件
+档案照片
+文件柜
+填满
+胶片相机
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+电影格式
+电影首映礼
+电影制片人
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+小薄片
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+平
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+空中乘务员
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+触发器
+翻转图
+浮动
+群
+洪水
+地板/地面
+落地扇
+脚垫
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+滑雪鞋 
+皮肤 
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ram/data/ram_tag_list_threshold.txt

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+walkway
+fisherman
+gas mask
+loch
+youth
+hanger
+dot
+fish
+street market
+animation film
+crime fiction film
+boar
+emblem
+halloween costume
+kangaroo
+couple
+spoon
+squirrel
+neon sign
+sky
+office desk
+beauty salon
+breakwater
+fashion look
+toaster
+author
+news conference
+outdoor
+canoe
+dragon
+tool
+shopping centre
+ladybug
+swimming pool
+landscaping
+ski pole
+red
+truck
+fly
+temple
+level
+sunday
+railroad bridge
+car mirror
+lawn mower
+flute
+aircraft carrier
+fashion menswear london week
+sunshine
+tile floor
+skull
+fossil
+flower arrangement
+diaper
+sea turtle
+cherry blossom
+fireman
+shack
+lens
+waiter
+animal
+basement
+snow
+autumn park
+glass box
+kick
+head
+anniversary
+vine
+back
+paper lantern
+fish tank
+cellphone
+silk
+coral
+notebook
+photo
+gazebo
+ketchup
+driver
+farmer
+bonfire
+chestnut
+photoshoot
+football field
+olive tree
+pheasant
+sandal
+toilet
+fireplace
+music
+deity
+fish market
+fig
+bell
+neck
+grave
+villa
+cyclist
+crate
+grey
+asphalt road
+soccer
+hostel
+municipality
+courthouse
+roof
+end table
+pot
+sedan
+structure
+folk artist
+sport
+sport team
+protest
+syringe
+fashion designer
+jersey
+heart shape
+kayak
+stare
+sit with
+direct
+read
+photograph
+spin
+teach
+laugh
+carve
+grow on
+warm
+watch
+stretch
+smell
+decorate
+shine
+light
+dance
+send
+park
+chase
+collect
+lead
+kiss
+lead to
+lick
+smile
+cheer
+sit
+point
+block
+rock
+drop
+cut
+ski
+wrap
+lose
+serve
+provide
+sleep
+dress
+embrace
+burn
+pack
+stir
+create
+touch
+wash
+stick
+reveal
+shop
+train
+paint
+groom
+hunt
+bloom
+play
+pay
+brush
+shoot
+hold
+picture
+carry
+sip
+contain
+turn
+pour
+pitch
+give
+add
+blow
+look in
+show
+walk
+illuminate
+kneel
+cover
+drag
+post
+present
+fit
+operate
+fish
+race
+write
+deliver
+peel
+push
+run
+sit around
+buy
+jump
+walk on
+attend
+clean
+sell
+ride on
+mount
+host
+dry
+plant
+sing
+row
+shake
+perch
+ride
+fight
+skateboard
+live
+call
+surround
+practice
+play on
+work on
+step
+relax
+hit
+fall in
+flow
+greet
+launch
+wear
+hang on
+drive
+sit in
+break
+learn
+fly
+connect
+display
+locate
+compete
+go for
+sail
+lift
+toast
+help
+run on
+reflect
+pose
+scratch
+frame
+dribble
+herd
+enter
+exit
+place
+inspect
+build
+pick
+fill
+grind
+skate
+offer
+float
+sit by
+stand
+release
+rest
+singe
+climb
+tie
+mark
+lay
+stand around
+capture
+set
+land
+swinge
+run in
+kick
+lean
+head
+sign
+approach
+swim
+close
+crash
+control
+fall
+remove
+repair
+open
+appear
+travel
+load
+miss
+check
+surf
+moor
+smoke
+drink
+board
+seat
+feed
+rise
+sit on
+swing
+grow
+strike
+date
+slide
+share
+graze
+jump in
+lie
+extrude
+roll
+move
+gather
+eat
+pull
+run through
+squeeze
+lay on
+draw
+play with
+wave
+assemble
+perform
+march
+score
+attach
+adjust
+hang
+hug
+sleep on
+throw
+live in
+talk
+pet
+work
+run with
+see
+flip
+catch
+cook
+receive
+celebrate
+look
+classic
+bridal
+indoor
+industrial
+teenage
+mini
+grassy
+aged
+long
+warm
+light
+handsome
+happy
+three
+pregnant
+circular
+urban
+silver
+ceramic
+3d
+green
+blonde
+golden
+dark
+tropical
+ripe
+deep
+fat
+musical
+giant
+medical
+medieval
+bare
+stunning
+bold
+geographical
+huge
+plastic
+foggy
+stormy
+gothic
+biological
+empty
+clear
+antique
+pink
+steep
+brown
+striped
+aerial
+rainy
+cool
+flying
+commercial
+purple
+trendy
+blank
+haired
+dead
+wooden
+flat
+high
+beige
+panoramic
+angry
+dozen
+rural
+solar
+big
+small
+stained
+thick
+many
+fresh
+clean
+strong
+abstract
+crowded
+retro
+dry
+gorgeous
+martial
+modern
+blue
+cloudy
+low
+four
+outdoor
+single
+much
+beautiful
+snowy
+pretty
+new
+short
+sunny
+closed
+rocky
+red
+two
+double
+male
+gray
+five
+colorful
+automotive
+various
+one
+old
+rusty
+tall
+wild
+narrow
+natural
+several
+frozen
+textured
+lush
+young
+hot
+mixed
+white
+float
+quiet
+round
+bright
+religious
+female
+historical
+shiny
+traditional
+tourist
+yellow
+bald
+coastal
+lovely
+little
+broken
+romantic
+wide
+royal
+rich
+open
+cute
+ancient
+cold
+political
+elderly
+gold
+full
+rustic
+metallic
+floral
+sad
+wet
+fancy
+senior
+tiny
+stylish
+large
+frosty
+orange
+transparent
+electronic
+shallow
+scared
+armed
+dirty
+historic
+black
+few
+windy
+some
+square
+ornamental
+sandy
+thin

ram/inference.py

@@ -0,0 +1,46 @@
+'''
+ * The Inference of RAM and Tag2Text Models
+ * Written by Xinyu Huang
+'''
+import torch
+
+
+def inference_tag2text(image, model, input_tag="None"):
+
+    with torch.no_grad():
+        caption, tag_predict = model.generate(image,
+                                              tag_input=None,
+                                              max_length=50,
+                                              return_tag_predict=True)
+
+    if input_tag == '' or input_tag == 'none' or input_tag == 'None':
+        return tag_predict[0], None, caption[0]
+
+    # If user input specified tags:
+    else:
+        input_tag_list = []
+        input_tag_list.append(input_tag.replace(',', ' | '))
+
+        with torch.no_grad():
+            caption, input_tag = model.generate(image,
+                                                tag_input=input_tag_list,
+                                                max_length=50,
+                                                return_tag_predict=True)
+
+        return tag_predict[0], input_tag[0], caption[0]
+
+
+def inference_ram(image, model):
+
+    with torch.no_grad():
+        tags, tags_chinese = model.generate_tag(image)
+
+    return tags[0],tags_chinese[0]
+
+
+def inference_ram_openset(image, model):
+
+    with torch.no_grad():
+        tags = model.generate_tag_openset(image)
+
+    return tags[0]

ram/models/__init__.py

@@ -0,0 +1,2 @@
+from .ram import ram
+from .tag2text import tag2text

ram/models/bert.py

@@ -0,0 +1,1035 @@
+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+ * Based on huggingface code base
+ * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
+'''
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor, device, dtype, nn
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import (
+    ModelOutput,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+from transformers.models.bert.configuration_bert import BertConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class BertEmbeddings_nopos(nn.Module):
+    """Construct the embeddings from word and position embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        # self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+        # self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        
+        self.config = config
+
+    def forward(
+        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        # if position_ids is None:
+            # position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        # if self.position_embedding_type == "absolute":
+        #     position_embeddings = self.position_embeddings(position_ids)
+        #     # print('add position_embeddings!!!!')
+        #     embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word and position embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        
+        self.config = config
+
+    def forward(
+        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            # print('add position_embeddings!!!!')
+            embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, is_cross_attention):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+        
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        if is_cross_attention:
+            self.key = nn.Linear(config.encoder_width, self.all_head_size)
+            self.value = nn.Linear(config.encoder_width, self.all_head_size)
+        else:
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+        self.save_attention = False   
+            
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+        
+    def get_attn_gradients(self):
+        return self.attn_gradients
+    
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+        
+    def get_attention_map(self):
+        return self.attention_map
+    
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention:
+            # print(self.key.weight.shape)
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        past_key_value = (key_layer, value_layer)
+
+        # compatible with higher versions of transformers 
+        if key_layer.shape[0] > query_layer.shape[0]:
+            key_layer = key_layer[:query_layer.shape[0], :, :, :]
+            attention_mask = attention_mask[:query_layer.shape[0], :, :]
+            value_layer = value_layer[:query_layer.shape[0], :, :, :]
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+        
+        if is_cross_attention and self.save_attention:
+            self.save_attention_map(attention_probs)
+            attention_probs.register_hook(self.save_attn_gradients)         
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs_dropped = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs_dropped = attention_probs_dropped * head_mask
+
+        context_layer = torch.matmul(attention_probs_dropped, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config, is_cross_attention=False):
+        super().__init__()
+        self.self = BertSelfAttention(config, is_cross_attention)
+        self.output = BertSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, layer_num):
+        super().__init__()
+        self.config = config
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BertAttention(config)      
+        self.layer_num = layer_num          
+        if self.config.add_cross_attention:
+            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        mode=None,
+    ):
+        
+        if mode == 'tagging':
+            
+            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
+
+            cross_attention_outputs = self.crossattention(
+                hidden_states,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions=output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights  
+
+            present_key_value = cross_attention_outputs[-1]
+
+        else:
+            # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+            self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+            self_attention_outputs = self.attention(
+                hidden_states,
+                attention_mask,
+                head_mask,
+                output_attentions=output_attentions,
+                past_key_value=self_attn_past_key_value,
+            )
+            attention_output = self_attention_outputs[0]
+
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+
+            if mode=='multimodal':
+                assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
+
+                cross_attention_outputs = self.crossattention(
+                    attention_output,
+                    attention_mask,
+                    head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions=output_attentions,
+                )
+                attention_output = cross_attention_outputs[0]
+                outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+
+        outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+        mode='multimodal',
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+        next_decoder_cache = () if use_cache else None
+               
+        for i in range(self.config.num_hidden_layers):
+            layer_module = self.layer[i]
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    mode=mode,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    mode=mode,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = BertConfig
+    base_model_prefix = "bert"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+class BertModel(BertPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+    input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BertEmbeddings(config)
+        
+        self.encoder = BertEncoder(config)
+
+        self.pooler = BertPooler(config) if add_pooling_layer else None
+
+        self.init_weights()
+ 
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    
+    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
+        """
+        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if attention_mask.dim() == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif attention_mask.dim() == 2:
+            # Provided a padding mask of dimensions [batch_size, seq_length]
+            # - if the model is a decoder, apply a causal mask in addition to the padding mask
+            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+            if is_decoder:
+                batch_size, seq_length = input_shape
+
+                seq_ids = torch.arange(seq_length, device=device)
+                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
+                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
+                # causal and attention masks must have same type with pytorch version < 1.3
+                causal_mask = causal_mask.to(attention_mask.dtype)
+   
+                if causal_mask.shape[1] < attention_mask.shape[1]:
+                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
+                    causal_mask = torch.cat(
+                        [
+                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
+                            causal_mask,
+                        ],
+                        axis=-1,
+                    )                     
+
+                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError(
+                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+                    input_shape, attention_mask.shape
+                )
+            )
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        return extended_attention_mask
+    
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        is_decoder=False,
+        mode='multimodal',
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+            device = input_ids.device
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+            device = inputs_embeds.device
+        elif encoder_embeds is not None:    
+            input_shape = encoder_embeds.size()[:-1]
+            batch_size, seq_length = input_shape 
+            device = encoder_embeds.device
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
+
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+            
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
+                                                                                 device, is_decoder)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if encoder_hidden_states is not None:
+            if type(encoder_hidden_states) == list:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
+            else:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            
+            if type(encoder_attention_mask) == list:
+                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
+            elif encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+            else:    
+                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+        
+        if encoder_embeds is None:
+            embedding_output = self.embeddings(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                inputs_embeds=inputs_embeds,
+                past_key_values_length=past_key_values_length,
+            )
+        else:
+            embedding_output = encoder_embeds
+            
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            mode=mode,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class BertLMHeadModel(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        return_logits=False,            
+        is_decoder=True,
+        reduction='mean',
+        mode='multimodal', 
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        Returns:
+        Example::
+            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
+            >>> import torch
+            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+            >>> config = BertConfig.from_pretrained("bert-base-cased")
+            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> prediction_logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            is_decoder=is_decoder,
+            mode=mode,
+        )
+        
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+        # sequence_output.shape torch.Size([85, 30, 768])
+        # prediction_scores.shape torch.Size([85, 30, 30524])
+        # labels.shape torch.Size([85, 30])
+
+
+        if return_logits:
+            return prediction_scores[:, :-1, :].contiguous()  
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) 
+            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            if reduction=='none':
+                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=lm_loss,
+            logits=prediction_scores,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "input_ids": input_ids, 
+            "attention_mask": attention_mask, 
+            "past_key_values": past,
+            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
+            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
+            "is_decoder": True,
+        }
+
+    def _reorder_cache(self, past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+