| 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]) |
|
|
