# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. """LPIPS loss. Adapted from: github.com/CompVis/stable-diffusion/ldm/modules/losses/contperceptual.py. """ import hashlib import os from collections import namedtuple import requests import torch import torch.distributed as dist import torch.nn as nn import torch.utils.checkpoint as checkpoint from loguru import logger as logging from torchvision import models from tqdm import tqdm from cosmos_predict1.utils.distributed import is_rank0 _TORCH_HOME = os.getenv("TORCH_HOME", "/mnt/workspace/.cache/torch") _URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"} _CKPT_MAP = {"vgg_lpips": "vgg.pth"} _MD5_MAP = {"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"} def _download(url, local_path, chunk_size=1024): os.makedirs(os.path.split(local_path)[0], exist_ok=True) with requests.get(url, stream=True) as r: total_size = int(r.headers.get("content-length", 0)) with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: with open(local_path, "wb") as f: for data in r.iter_content(chunk_size=chunk_size): if data: f.write(data) pbar.update(chunk_size) def _md5_hash(path): with open(path, "rb") as f: content = f.read() return hashlib.md5(content).hexdigest() def _get_ckpt_path(name, root, check=False): assert name in _URL_MAP path = os.path.join(root, _CKPT_MAP[name]) if not os.path.exists(path) or (check and not _md5_hash(path) == _MD5_MAP[name]): logging.info("Downloading {} model from {} to {}".format(name, _URL_MAP[name], path)) _download(_URL_MAP[name], path) md5 = _md5_hash(path) assert md5 == _MD5_MAP[name], md5 return path class LPIPS(nn.Module): def __init__(self, checkpoint_activations: bool = False): super().__init__() self.scaling_layer = ScalingLayer() self.chns = [64, 128, 256, 512, 512] # vg16 features self.net = vgg16(pretrained=True, requires_grad=False, checkpoint_activations=checkpoint_activations) if dist.is_initialized() and not is_rank0(): dist.barrier() self.load_from_pretrained() if dist.is_initialized() and is_rank0(): dist.barrier() for param in self.parameters(): param.requires_grad = False def load_from_pretrained(self, name="vgg_lpips"): ckpt = _get_ckpt_path(name, f"{_TORCH_HOME}/hub/checkpoints") self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) logging.info("Loaded pretrained LPIPS loss from {}".format(ckpt)) @classmethod def from_pretrained(cls, name="vgg_lpips"): if name != "vgg_lpips": raise NotImplementedError model = cls() ckpt = _get_ckpt_path(name) model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) return model def forward(self, input, target): in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) outs0, outs1 = self.net(in0_input), self.net(in1_input) feats0, feats1, diffs = {}, {}, {} for kk in range(len(self.chns)): feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 res = [diffs[kk].mean([1, 2, 3], keepdim=True) for kk in range(len(self.chns))] val = res[0] for l in range(1, len(self.chns)): val += res[l] return val class ScalingLayer(nn.Module): def __init__(self): super(ScalingLayer, self).__init__() self.register_buffer("shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None], persistent=False) self.register_buffer("scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None], persistent=False) def forward(self, inp): return (inp - self.shift) / self.scale def normalize_tensor(x, eps=1e-10): norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True)) return x / (norm_factor + eps) class vgg16(torch.nn.Module): def __init__(self, requires_grad=False, pretrained=True, checkpoint_activations: bool = False): super(vgg16, self).__init__() vgg_pretrained_features = models.vgg16(pretrained=pretrained).features self.checkpoint_activations = checkpoint_activations self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() self.N_slices = 5 for x in range(4): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(4, 9): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(9, 16): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(16, 23): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(23, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): if self.checkpoint_activations: h = checkpoint.checkpoint(self.slice1, X, use_reentrant=False) else: h = self.slice1(X) h_relu1_2 = h if self.checkpoint_activations: h = checkpoint.checkpoint(self.slice2, h, use_reentrant=False) else: h = self.slice2(h) h_relu2_2 = h if self.checkpoint_activations: h = checkpoint.checkpoint(self.slice3, h, use_reentrant=False) else: h = self.slice3(h) h_relu3_3 = h if self.checkpoint_activations: h = checkpoint.checkpoint(self.slice4, h, use_reentrant=False) else: h = self.slice4(h) h_relu4_3 = h if self.checkpoint_activations: h = checkpoint.checkpoint(self.slice5, h, use_reentrant=False) else: h = self.slice5(h) h_relu5_3 = h vgg_outputs = namedtuple("VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]) out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) return out