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""" |
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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(): |
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return nn.Sequential( |
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Clamp(), conv(4, 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(): |
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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, 4), |
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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"): |
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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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self.decoder = decoder() |
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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"): |
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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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self.encoder = encoder() |
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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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model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "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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model_name = "taesdxl_encoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "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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