| | import os |
| | import os.path |
| | from modules import shared |
| | import modules.scripts as scripts |
| | from scripts import model_util, util |
| | from scripts.model_util import MAX_MODEL_COUNT |
| |
|
| |
|
| | LORA_TRAIN_METADATA_NAMES = { |
| | "ss_session_id": "Session ID", |
| | "ss_training_started_at": "Training started at", |
| | "ss_output_name": "Output name", |
| | "ss_learning_rate": "Learning rate", |
| | "ss_text_encoder_lr": "Text encoder LR", |
| | "ss_unet_lr": "UNet LR", |
| | "ss_num_train_images": "# of training images", |
| | "ss_num_reg_images": "# of reg images", |
| | "ss_num_batches_per_epoch": "Batches per epoch", |
| | "ss_num_epochs": "Total epochs", |
| | "ss_epoch": "Epoch", |
| | "ss_batch_size_per_device": "Batch size/device", |
| | "ss_total_batch_size": "Total batch size", |
| | "ss_gradient_checkpointing": "Gradient checkpointing", |
| | "ss_gradient_accumulation_steps": "Gradient accum. steps", |
| | "ss_max_train_steps": "Max train steps", |
| | "ss_lr_warmup_steps": "LR warmup steps", |
| | "ss_lr_scheduler": "LR scheduler", |
| | "ss_network_module": "Network module", |
| | "ss_network_dim": "Network dim", |
| | "ss_network_alpha": "Network alpha", |
| | "ss_mixed_precision": "Mixed precision", |
| | "ss_full_fp16": "Full FP16", |
| | "ss_v2": "V2", |
| | "ss_resolution": "Resolution", |
| | "ss_clip_skip": "Clip skip", |
| | "ss_max_token_length": "Max token length", |
| | "ss_color_aug": "Color aug", |
| | "ss_flip_aug": "Flip aug", |
| | "ss_random_crop": "Random crop", |
| | "ss_shuffle_caption": "Shuffle caption", |
| | "ss_cache_latents": "Cache latents", |
| | "ss_enable_bucket": "Enable bucket", |
| | "ss_min_bucket_reso": "Min bucket reso.", |
| | "ss_max_bucket_reso": "Max bucket reso.", |
| | "ss_seed": "Seed", |
| | "ss_keep_tokens": "Keep tokens", |
| | "ss_dataset_dirs": "Dataset dirs.", |
| | "ss_reg_dataset_dirs": "Reg dataset dirs.", |
| | "ss_sd_model_name": "SD model name", |
| | "ss_vae_name": "VAE name", |
| | "ss_training_comment": "Comment" |
| | } |
| |
|
| |
|
| | xy_grid = None |
| | script_class = None |
| | axis_params = [{}] * MAX_MODEL_COUNT |
| |
|
| |
|
| | def update_axis_params(i, module, model): |
| | axis_params[i] = {"module": module, "model": model} |
| |
|
| |
|
| | def get_axis_model_choices(i): |
| | module = axis_params[i].get("module", "None") |
| | model = axis_params[i].get("model", "None") |
| |
|
| | if module == "LoRA": |
| | if model != "None": |
| | sort_by = shared.opts.data.get("additional_networks_sort_models_by", "name") |
| | return model_util.get_model_list(module, model, "", sort_by) |
| |
|
| | return [f"select `Model {i+1}` in `Additional Networks`. models in same folder for selected one will be shown here."] |
| |
|
| |
|
| | def update_script_args(p, value, arg_idx): |
| | global script_class |
| | for s in scripts.scripts_txt2img.alwayson_scripts: |
| | if isinstance(s, script_class): |
| | args = list(p.script_args) |
| | |
| | args[s.args_from + arg_idx] = value |
| | p.script_args = tuple(args) |
| | break |
| |
|
| |
|
| | def confirm_models(p, xs): |
| | for x in xs: |
| | if x in ["", "None"]: |
| | continue |
| | if not model_util.find_closest_lora_model_name(x): |
| | raise RuntimeError(f"Unknown LoRA model: {x}") |
| |
|
| |
|
| | def apply_module(p, x, xs, i): |
| | update_script_args(p, True, 0) |
| | update_script_args(p, x, 2 + 4 * i) |
| |
|
| |
|
| | def apply_model(p, x, xs, i): |
| | name = model_util.find_closest_lora_model_name(x) |
| | update_script_args(p, True, 0) |
| | update_script_args(p, name, 3 + 4 * i) |
| |
|
| |
|
| | def apply_weight(p, x, xs, i): |
| | update_script_args(p, True, 0) |
| | update_script_args(p, x, 4 + 4 * i ) |
| | update_script_args(p, x, 5 + 4 * i) |
| |
|
| |
|
| | def apply_weight_unet(p, x, xs, i): |
| | update_script_args(p, True, 0) |
| | update_script_args(p, x, 4 + 4 * i) |
| |
|
| |
|
| | def apply_weight_tenc(p, x, xs, i): |
| | update_script_args(p, True, 0) |
| | update_script_args(p, x, 5 + 4 * i) |
| |
|
| |
|
| | def format_lora_model(p, opt, x): |
| | global xy_grid |
| | model = model_util.find_closest_lora_model_name(x) |
| | if model is None or model.lower() in ["", "none"]: |
| | return "None" |
| |
|
| | value = xy_grid.format_value(p, opt, model) |
| |
|
| | model_path = model_util.lora_models.get(model) |
| | metadata = model_util.read_model_metadata(model_path, "LoRA") |
| | if not metadata: |
| | return value |
| |
|
| | metadata_names = util.split_path_list(shared.opts.data.get("additional_networks_xy_grid_model_metadata", "")) |
| | if not metadata_names: |
| | return value |
| |
|
| | for name in metadata_names: |
| | name = name.strip() |
| | if name in metadata: |
| | formatted_name = LORA_TRAIN_METADATA_NAMES.get(name, name) |
| | value += f"\n{formatted_name}: {metadata[name]}, " |
| |
|
| | return value.strip(" ").strip(",") |
| |
|
| |
|
| | def initialize(script): |
| | global xy_grid, script_class |
| | xy_grid = None |
| | script_class = script |
| | for scriptDataTuple in scripts.scripts_data: |
| | if os.path.basename(scriptDataTuple.path) == "xy_grid.py" or os.path.basename(scriptDataTuple.path) == "xyz_grid.py": |
| | xy_grid = scriptDataTuple.module |
| | for i in range(MAX_MODEL_COUNT): |
| | model = xy_grid.AxisOption(f"AddNet Model {i+1}", str, lambda p, x, xs, i=i: apply_model(p, x, xs, i), format_lora_model, confirm_models, cost=0.5, choices=lambda i=i: get_axis_model_choices(i)) |
| | weight = xy_grid.AxisOption(f"AddNet Weight {i+1}", float, lambda p, x, xs, i=i: apply_weight(p, x, xs, i), xy_grid.format_value_add_label, None, cost=0.5) |
| | weight_unet = xy_grid.AxisOption(f"AddNet UNet Weight {i+1}", float, lambda p, x, xs, i=i: apply_weight_unet(p, x, xs, i), xy_grid.format_value_add_label, None, cost=0.5) |
| | weight_tenc = xy_grid.AxisOption(f"AddNet TEnc Weight {i+1}", float, lambda p, x, xs, i=i: apply_weight_tenc(p, x, xs, i), xy_grid.format_value_add_label, None, cost=0.5) |
| | xy_grid.axis_options.extend([model, weight, weight_unet, weight_tenc]) |
| |
|
