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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Merge remote-tracking branch 'Melanpan/master'
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commit
326fe7d44b
@ -5,6 +5,7 @@ import os
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import sys
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import traceback
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import tqdm
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import csv
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import torch
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@ -262,6 +263,20 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
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hypernetwork.save(last_saved_file)
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if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
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write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
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with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
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csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"])
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if write_csv_header:
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csv_writer.writeheader()
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csv_writer.writerow({"step": hypernetwork.step,
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"loss": f"{losses.mean():.7f}",
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"learn_rate": scheduler.learn_rate})
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if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
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last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
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@ -6,6 +6,7 @@ import torch
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import tqdm
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import html
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import datetime
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import csv
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from PIL import Image, PngImagePlugin
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@ -256,6 +257,21 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
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last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
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embedding.save(last_saved_file)
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if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:
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write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True
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with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
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csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"])
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if write_csv_header:
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csv_writer.writeheader()
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csv_writer.writerow({"epoch": epoch_num + 1,
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"epoch_step": epoch_step - 1,
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"loss": f"{losses.mean():.7f}",
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"learn_rate": scheduler.learn_rate})
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if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
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last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
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@ -1172,6 +1172,7 @@ def create_ui(wrap_gradio_gpu_call):
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training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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steps = gr.Number(label='Max steps', value=100000, precision=0)
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create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
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write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0)
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save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
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save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
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preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
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@ -1250,6 +1251,7 @@ def create_ui(wrap_gradio_gpu_call):
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steps,
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create_image_every,
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save_embedding_every,
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write_csv_every,
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template_file,
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save_image_with_stored_embedding,
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preview_from_txt2img,
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@ -1272,6 +1274,7 @@ def create_ui(wrap_gradio_gpu_call):
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steps,
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create_image_every,
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save_embedding_every,
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write_csv_every,
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template_file,
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preview_from_txt2img,
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*txt2img_preview_params,
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