import os import re import shutil import torch import tqdm from modules import shared, images, sd_models, sd_vae from modules.ui import plaintext_to_html import gradio as gr import safetensors.torch def run_pnginfo(image): if image is None: return '', '', '' geninfo, items = images.read_info_from_image(image) items = {**{'parameters': geninfo}, **items} info = '' for key, text in items.items(): info += f"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "Nothing found in the image." info = f"

{message}

" return '', geninfo, info def create_config(ckpt_result, config_source, a, b, c): def config(x): res = sd_models.find_checkpoint_config(x) if x else None return res if res != shared.sd_default_config else None if config_source == 0: cfg = config(a) or config(b) or config(c) elif config_source == 1: cfg = config(b) elif config_source == 2: cfg = config(c) else: cfg = None if cfg is None: return filename, _ = os.path.splitext(ckpt_result) checkpoint_filename = filename + ".yaml" print("Copying config:") print(" from:", cfg) print(" to:", checkpoint_filename) shutil.copyfile(cfg, checkpoint_filename) checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"] def to_half(tensor, enable): if enable and tensor.dtype == torch.float: return tensor.half() return tensor def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights): shared.state.begin() shared.state.job = 'model-merge' def fail(message): shared.state.textinfo = message shared.state.end() return [*[gr.update() for _ in range(4)], message] def weighted_sum(theta0, theta1, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) def get_difference(theta1, theta2): return theta1 - theta2 def add_difference(theta0, theta1_2_diff, alpha): return theta0 + (alpha * theta1_2_diff) def filename_weighted_sum(): a = primary_model_info.model_name b = secondary_model_info.model_name Ma = round(1 - multiplier, 2) Mb = round(multiplier, 2) return f"{Ma}({a}) + {Mb}({b})" def filename_add_difference(): a = primary_model_info.model_name b = secondary_model_info.model_name c = tertiary_model_info.model_name M = round(multiplier, 2) return f"{a} + {M}({b} - {c})" def filename_nothing(): return primary_model_info.model_name theta_funcs = { "Weighted sum": (filename_weighted_sum, None, weighted_sum), "Add difference": (filename_add_difference, get_difference, add_difference), "No interpolation": (filename_nothing, None, None), } filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method] shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0) if not primary_model_name: return fail("Failed: Merging requires a primary model.") primary_model_info = sd_models.checkpoints_list[primary_model_name] if theta_func2 and not secondary_model_name: return fail("Failed: Merging requires a secondary model.") secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None if theta_func1 and not tertiary_model_name: return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.") tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None result_is_inpainting_model = False if theta_func2: shared.state.textinfo = f"Loading B" print(f"Loading {secondary_model_info.filename}...") theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu') else: theta_1 = None if theta_func1: shared.state.textinfo = f"Loading C" print(f"Loading {tertiary_model_info.filename}...") theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu') shared.state.textinfo = 'Merging B and C' shared.state.sampling_steps = len(theta_1.keys()) for key in tqdm.tqdm(theta_1.keys()): if key in checkpoint_dict_skip_on_merge: continue if 'model' in key: if key in theta_2: t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) theta_1[key] = theta_func1(theta_1[key], t2) else: theta_1[key] = torch.zeros_like(theta_1[key]) shared.state.sampling_step += 1 del theta_2 shared.state.nextjob() shared.state.textinfo = f"Loading {primary_model_info.filename}..." print(f"Loading {primary_model_info.filename}...") theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu') print("Merging...") shared.state.textinfo = 'Merging A and B' shared.state.sampling_steps = len(theta_0.keys()) for key in tqdm.tqdm(theta_0.keys()): if theta_1 and 'model' in key and key in theta_1: if key in checkpoint_dict_skip_on_merge: continue a = theta_0[key] b = theta_1[key] # this enables merging an inpainting model (A) with another one (B); # where normal model would have 4 channels, for latenst space, inpainting model would # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9 if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]: if a.shape[1] == 4 and b.shape[1] == 9: raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.") assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}" theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier) result_is_inpainting_model = True else: theta_0[key] = theta_func2(a, b, multiplier) theta_0[key] = to_half(theta_0[key], save_as_half) shared.state.sampling_step += 1 del theta_1 bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None) if bake_in_vae_filename is not None: print(f"Baking in VAE from {bake_in_vae_filename}") shared.state.textinfo = 'Baking in VAE' vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename, map_location='cpu') for key in vae_dict.keys(): theta_0_key = 'first_stage_model.' + key if theta_0_key in theta_0: theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half) del vae_dict if save_as_half and not theta_func2: for key in theta_0.keys(): theta_0[key] = to_half(theta_0[key], save_as_half) if discard_weights: regex = re.compile(discard_weights) for key in list(theta_0): if re.search(regex, key): theta_0.pop(key, None) ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path filename = filename_generator() if custom_name == '' else custom_name filename += ".inpainting" if result_is_inpainting_model else "" filename += "." + checkpoint_format output_modelname = os.path.join(ckpt_dir, filename) shared.state.nextjob() shared.state.textinfo = "Saving" print(f"Saving to {output_modelname}...") _, extension = os.path.splitext(output_modelname) if extension.lower() == ".safetensors": safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"}) else: torch.save(theta_0, output_modelname) sd_models.list_models() create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info) print(f"Checkpoint saved to {output_modelname}.") shared.state.textinfo = "Checkpoint saved" shared.state.end() return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]