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| import os | |
| import torch | |
| from torch import nn | |
| from modules import devices, paths, shared | |
| sd_vae_approx_models = {} | |
| class VAEApprox(nn.Module): | |
| def __init__(self, latent_channels=4): | |
| super(VAEApprox, self).__init__() | |
| self.conv1 = nn.Conv2d(latent_channels, 8, (7, 7)) | |
| self.conv2 = nn.Conv2d(8, 16, (5, 5)) | |
| self.conv3 = nn.Conv2d(16, 32, (3, 3)) | |
| self.conv4 = nn.Conv2d(32, 64, (3, 3)) | |
| self.conv5 = nn.Conv2d(64, 32, (3, 3)) | |
| self.conv6 = nn.Conv2d(32, 16, (3, 3)) | |
| self.conv7 = nn.Conv2d(16, 8, (3, 3)) | |
| self.conv8 = nn.Conv2d(8, 3, (3, 3)) | |
| def forward(self, x): | |
| extra = 11 | |
| x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2)) | |
| x = nn.functional.pad(x, (extra, extra, extra, extra)) | |
| for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]: | |
| x = layer(x) | |
| x = nn.functional.leaky_relu(x, 0.1) | |
| return x | |
| def download_model(model_path, model_url): | |
| if not os.path.exists(model_path): | |
| os.makedirs(os.path.dirname(model_path), exist_ok=True) | |
| print(f'Downloading VAEApprox model to: {model_path}') | |
| torch.hub.download_url_to_file(model_url, model_path) | |
| def model(): | |
| if not shared.sd_model.is_webui_legacy_model(): | |
| return None | |
| if shared.sd_model.is_sd3: | |
| model_name = "vaeapprox-sd3.pt" | |
| elif shared.sd_model.is_sdxl: | |
| model_name = "vaeapprox-sdxl.pt" | |
| else: | |
| model_name = "model.pt" | |
| loaded_model = sd_vae_approx_models.get(model_name) | |
| if loaded_model is None: | |
| model_path = os.path.join(paths.models_path, "VAE-approx", model_name) | |
| if not os.path.exists(model_path): | |
| model_path = os.path.join(paths.script_path, "models", "VAE-approx", model_name) | |
| if not os.path.exists(model_path): | |
| model_path = os.path.join(paths.models_path, "VAE-approx", model_name) | |
| download_model(model_path, 'https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/download/v1.0.0-pre/' + model_name) | |
| loaded_model = VAEApprox(latent_channels=shared.sd_model.forge_objects.vae.latent_channels) | |
| loaded_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None)) | |
| loaded_model.eval() | |
| loaded_model.to(devices.device, devices.dtype) | |
| sd_vae_approx_models[model_name] = loaded_model | |
| return loaded_model | |
| def cheap_approximation(sample): | |
| return torch.einsum("...lxy,lr -> ...rxy", sample, torch.tensor(shared.sd_model.model_config.latent_format.latent_rgb_factors).to(sample.device)) | |