Loading modules/hypernetworks/hypernetwork.py +2 −2 Original line number Diff line number Diff line Loading @@ -309,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log with torch.autocast("cuda"): c = stack_conds([entry.cond for entry in entries]).to(devices.device) c = torch.vstack([entry.cond for entry in entries]).to(devices.device) # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) x = torch.stack([entry.latent for entry in entries]).to(devices.device) loss = shared.sd_model(x, c)[0] del x Loading @@ -331,7 +331,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{mean_loss:.7f}", "learn_rate": f"{scheduler.learn_rate:.7f}" "learn_rate": scheduler.learn_rate }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: Loading Loading
modules/hypernetworks/hypernetwork.py +2 −2 Original line number Diff line number Diff line Loading @@ -309,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log with torch.autocast("cuda"): c = stack_conds([entry.cond for entry in entries]).to(devices.device) c = torch.vstack([entry.cond for entry in entries]).to(devices.device) # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) x = torch.stack([entry.latent for entry in entries]).to(devices.device) loss = shared.sd_model(x, c)[0] del x Loading @@ -331,7 +331,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{mean_loss:.7f}", "learn_rate": f"{scheduler.learn_rate:.7f}" "learn_rate": scheduler.learn_rate }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: Loading
