xsl
ce445b64a6
部署修复： - torch.load 增加 weights_only=False patch，兼容 PyTorch 2.6+ 加载旧权重 - OSS 改为懒加载，本地用 output_format=base64 无需配凭证即可启动 - 补全被 gitignore 误排除的必需代码：core/models/layers/data、models/layers/data、keypoints/lib - webui 训练命令 --xformers 改 --sdpa（修复 xformers 无 CUDA 支持报错） 功能调整： - hair_grow_service 端口改 8899、preview 路由修复（send_file） - list_hairstyles 增加发型白名单，测试页只展示当前5个发型 新增脚本： - train_lora_parallel.py：直接调 kohya 并行训练 LoRA（绕过 photo_service 串行限制） - train_hairstyles_parallel.py / train_batch_stepC.py：批量训练辅助脚本 - scripts/sync_data_to_server.sh：大文件断点续传到云服务器 文档： - docs/换发型集成文档.md：换发型完整流程、服务架构、资源依赖、训练方法、集成步骤
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
# ------------------------------------------------------------------------------
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# Copyright (c) Microsoft
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# Licensed under the MIT License.
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# Written by Bin Xiao (Bin.Xiao@microsoft.com)
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# ------------------------------------------------------------------------------
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from yacs.config import CfgNode as CN
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# pose_resnet related params
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POSE_RESNET = CN()
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POSE_RESNET.NUM_LAYERS = 50
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POSE_RESNET.DECONV_WITH_BIAS = False
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POSE_RESNET.NUM_DECONV_LAYERS = 3
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POSE_RESNET.NUM_DECONV_FILTERS = [256, 256, 256]
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POSE_RESNET.NUM_DECONV_KERNELS = [4, 4, 4]
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POSE_RESNET.FINAL_CONV_KERNEL = 1
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POSE_RESNET.PRETRAINED_LAYERS = ['*']
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# pose_multi_resoluton_net related params
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POSE_HIGH_RESOLUTION_NET = CN()
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POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*']
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POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64
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POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1
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POSE_HIGH_RESOLUTION_NET.STAGE2 = CN()
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POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1
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POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2
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POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4]
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POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64]
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POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC'
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POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM'
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POSE_HIGH_RESOLUTION_NET.STAGE3 = CN()
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POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1
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POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3
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POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4]
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POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128]
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POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC'
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POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM'
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POSE_HIGH_RESOLUTION_NET.STAGE4 = CN()
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POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1
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POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4
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POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4]
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POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256]
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POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC'
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POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM'
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MODEL_EXTRAS = {
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'pose_resnet': POSE_RESNET,
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'pose_high_resolution_net': POSE_HIGH_RESOLUTION_NET,
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}
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