colomi
0eb61f3e60
包含： - hair_service_sd: 换发型/换发色算法服务 (端口 8801) - photo_service: LoRA 训练调度服务 (端口 32678) - stable-diffusion-webui: SD WebUI 推理服务 (端口 57860) - kohya_ss_home: 训练环境代码 - meidaojia: 监控测试脚本 - setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查) - start_all_services.sh: 启动3个服务 - configure.ini.template: 路径模板化 (BASE_DIR自动推导) - conda_envs/py310.yml: py310 环境定义 大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除， 由网盘单独上传。
145 lines
4.7 KiB
Python
Executable File
145 lines
4.7 KiB
Python
Executable File
"""
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Tiny AutoEncoder for Stable Diffusion
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(DNN for encoding / decoding SD's latent space)
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https://github.com/madebyollin/taesd
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"""
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import os
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import torch
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import torch.nn as nn
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from modules import devices, paths_internal, shared
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sd_vae_taesd_models = {}
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def conv(n_in, n_out, **kwargs):
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return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
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class Clamp(nn.Module):
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@staticmethod
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def forward(x):
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return torch.tanh(x / 3) * 3
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class Block(nn.Module):
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def __init__(self, n_in, n_out):
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super().__init__()
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self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
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self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
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self.fuse = nn.ReLU()
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def forward(self, x):
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return self.fuse(self.conv(x) + self.skip(x))
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def decoder(latent_channels=4):
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return nn.Sequential(
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Clamp(), conv(latent_channels, 64), nn.ReLU(),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
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Block(64, 64), conv(64, 3),
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)
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def encoder(latent_channels=4):
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return nn.Sequential(
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conv(3, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
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conv(64, latent_channels),
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)
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class TAESDDecoder(nn.Module):
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latent_magnitude = 3
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latent_shift = 0.5
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def __init__(self, decoder_path="taesd_decoder.pth", latent_channels=None):
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"""Initialize pretrained TAESD on the given device from the given checkpoints."""
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super().__init__()
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if latent_channels is None:
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latent_channels = 16 if "taesd3" in str(decoder_path) else 4
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self.decoder = decoder(latent_channels)
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self.decoder.load_state_dict(
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torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
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class TAESDEncoder(nn.Module):
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latent_magnitude = 3
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latent_shift = 0.5
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def __init__(self, encoder_path="taesd_encoder.pth", latent_channels=None):
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"""Initialize pretrained TAESD on the given device from the given checkpoints."""
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super().__init__()
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if latent_channels is None:
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latent_channels = 16 if "taesd3" in str(encoder_path) else 4
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self.encoder = encoder(latent_channels)
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self.encoder.load_state_dict(
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torch.load(encoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
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def download_model(model_path, model_url):
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if not os.path.exists(model_path):
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os.makedirs(os.path.dirname(model_path), exist_ok=True)
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print(f'Downloading TAESD model to: {model_path}')
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torch.hub.download_url_to_file(model_url, model_path)
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def decoder_model():
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if shared.sd_model.is_sd3:
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model_name = "taesd3_decoder.pth"
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elif shared.sd_model.is_sdxl:
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model_name = "taesdxl_decoder.pth"
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else:
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model_name = "taesd_decoder.pth"
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loaded_model = sd_vae_taesd_models.get(model_name)
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if loaded_model is None:
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model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
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download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
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if os.path.exists(model_path):
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loaded_model = TAESDDecoder(model_path)
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loaded_model.eval()
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loaded_model.to(devices.device, devices.dtype)
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sd_vae_taesd_models[model_name] = loaded_model
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else:
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raise FileNotFoundError('TAESD model not found')
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return loaded_model.decoder
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def encoder_model():
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if shared.sd_model.is_sd3:
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model_name = "taesd3_encoder.pth"
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elif shared.sd_model.is_sdxl:
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model_name = "taesdxl_encoder.pth"
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else:
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model_name = "taesd_encoder.pth"
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loaded_model = sd_vae_taesd_models.get(model_name)
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if loaded_model is None:
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model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
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download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
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if os.path.exists(model_path):
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loaded_model = TAESDEncoder(model_path)
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loaded_model.eval()
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loaded_model.to(devices.device, devices.dtype)
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sd_vae_taesd_models[model_name] = loaded_model
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else:
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raise FileNotFoundError('TAESD model not found')
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return loaded_model.encoder
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