mirror of https://github.com/vladmandic/automatic
241 lines
11 KiB
Python
241 lines
11 KiB
Python
import os
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import re
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import html
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import json
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import shutil
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import torch
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import tqdm
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import gradio as gr
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import safetensors.torch
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from modules import shared, images, sd_models, sd_vae, sd_models_config
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checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
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def run_pnginfo(image):
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if image is None:
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return '', '', ''
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geninfo, items = images.read_info_from_image(image)
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items = {**{'parameters': geninfo}, **items}
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info = ''
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for key, text in items.items():
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if key != 'UserComment':
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info += f"<div><b>{html.escape(str(key))}</b>: {html.escape(str(text))}</div>"
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return '', geninfo, info
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def create_config(ckpt_result, config_source, a, b, c):
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def config(x):
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res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
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return res if res != shared.sd_default_config else None
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if config_source == 0:
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cfg = config(a) or config(b) or config(c)
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elif config_source == 1:
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cfg = config(b)
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elif config_source == 2:
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cfg = config(c)
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else:
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cfg = None
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if cfg is None:
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return
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filename, _ = os.path.splitext(ckpt_result)
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checkpoint_filename = filename + ".yaml"
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shared.log.info("Copying config: {cfg} -> {checkpoint_filename}")
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shutil.copyfile(cfg, checkpoint_filename)
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def to_half(tensor, enable):
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if enable and tensor.dtype == torch.float:
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return tensor.half()
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return tensor
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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, save_metadata): # pylint: disable=unused-argument
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shared.state.begin()
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shared.state.job = 'model-merge'
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def fail(message):
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shared.state.textinfo = message
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shared.state.end()
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return [*[gr.update() for _ in range(4)], message]
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def weighted_sum(theta0, theta1, alpha):
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return ((1 - alpha) * theta0) + (alpha * theta1)
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def get_difference(theta1, theta2):
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return theta1 - theta2
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def add_difference(theta0, theta1_2_diff, alpha):
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return theta0 + (alpha * theta1_2_diff)
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def filename_weighted_sum():
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a = primary_model_info.model_name
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b = secondary_model_info.model_name
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Ma = round(1 - multiplier, 2)
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Mb = round(multiplier, 2)
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return f"{Ma}({a}) + {Mb}({b})"
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def filename_add_difference():
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a = primary_model_info.model_name
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b = secondary_model_info.model_name
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c = tertiary_model_info.model_name
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M = round(multiplier, 2)
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return f"{a} + {M}({b} - {c})"
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def filename_nothing():
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return primary_model_info.model_name
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theta_funcs = {
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"Weighted sum": (filename_weighted_sum, None, weighted_sum),
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"Add difference": (filename_add_difference, get_difference, add_difference),
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"No interpolation": (filename_nothing, None, None),
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}
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filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method]
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shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0)
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if not primary_model_name or primary_model_name == 'None':
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return fail("Failed: Merging requires a primary model.")
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primary_model_info = sd_models.checkpoints_list[primary_model_name]
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if theta_func2 and (not secondary_model_name or secondary_model_name == 'None'):
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return fail("Failed: Merging requires a secondary model.")
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secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None
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if theta_func1 and (not tertiary_model_name or tertiary_model_name == 'None'):
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return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.")
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tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
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result_is_inpainting_model = False
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result_is_instruct_pix2pix_model = False
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if theta_func2:
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shared.state.textinfo = "Loading B"
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shared.log.info(f"Loading {secondary_model_info.filename}...")
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theta_1 = sd_models.read_state_dict(secondary_model_info.filename)
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else:
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theta_1 = None
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if theta_func1:
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shared.state.textinfo = "Loading C"
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shared.log.info(f"Loading {tertiary_model_info.filename}...")
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theta_2 = sd_models.read_state_dict(tertiary_model_info.filename)
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shared.state.textinfo = 'Merging B and C'
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shared.state.sampling_steps = len(theta_1.keys())
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for key in tqdm.tqdm(theta_1.keys()):
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if key in checkpoint_dict_skip_on_merge:
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continue
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if 'model' in key:
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if key in theta_2:
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t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
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theta_1[key] = theta_func1(theta_1[key], t2)
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else:
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theta_1[key] = torch.zeros_like(theta_1[key])
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shared.state.sampling_step += 1
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del theta_2
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shared.state.nextjob()
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shared.state.textinfo = f"Loading {primary_model_info.filename}..."
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shared.log.info(f"Loading {primary_model_info.filename}...")
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theta_0 = sd_models.read_state_dict(primary_model_info.filename)
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shared.log.info("Merging...")
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shared.state.textinfo = 'Merging A and B'
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shared.state.sampling_steps = len(theta_0.keys())
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for key in tqdm.tqdm(theta_0.keys()):
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if theta_1 and 'model' in key and key in theta_1:
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if key in checkpoint_dict_skip_on_merge:
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continue
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a = theta_0[key]
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b = theta_1[key]
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# this enables merging an inpainting model (A) with another one (B);
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# where normal model would have 4 channels, for latenst space, inpainting model would
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# have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
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if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
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if a.shape[1] == 4 and b.shape[1] == 9:
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raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
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if a.shape[1] == 4 and b.shape[1] == 8:
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raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.")
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if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model...
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common. Otherwise we get an error due to dimension mismatch.
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result_is_instruct_pix2pix_model = True
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else:
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assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
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result_is_inpainting_model = True
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else:
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theta_0[key] = theta_func2(a, b, multiplier)
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theta_0[key] = to_half(theta_0[key], save_as_half)
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shared.state.sampling_step += 1
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del theta_1
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bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None)
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if bake_in_vae_filename is not None:
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shared.log.info(f"Baking in VAE from {bake_in_vae_filename}")
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shared.state.textinfo = 'Baking in VAE'
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vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename)
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for key in vae_dict.keys():
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theta_0_key = 'first_stage_model.' + key
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if theta_0_key in theta_0:
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theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half)
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del vae_dict
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if save_as_half and not theta_func2:
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for key in theta_0.keys():
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theta_0[key] = to_half(theta_0[key], save_as_half)
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if discard_weights:
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regex = re.compile(discard_weights)
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for key in list(theta_0):
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if re.search(regex, key):
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theta_0.pop(key, None)
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ckpt_dir = shared.opts.ckpt_dir or sd_models.model_path
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filename = filename_generator() if custom_name == '' else custom_name
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filename += ".inpainting" if result_is_inpainting_model else ""
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filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else ""
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filename += "." + checkpoint_format
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output_modelname = os.path.join(ckpt_dir, filename)
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shared.state.nextjob()
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shared.state.textinfo = "Saving"
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metadata = None
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if save_metadata:
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metadata = {"format": "pt", "sd_merge_models": {}}
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merge_recipe = {
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"type": "webui", # indicate this model was merged with webui's built-in merger
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"primary_model_hash": primary_model_info.sha256,
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"secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
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"tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
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"interp_method": interp_method,
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"multiplier": multiplier,
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"save_as_half": save_as_half,
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"custom_name": custom_name,
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"config_source": config_source,
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"bake_in_vae": bake_in_vae,
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"discard_weights": discard_weights,
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"is_inpainting": result_is_inpainting_model,
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"is_instruct_pix2pix": result_is_instruct_pix2pix_model
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}
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metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
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def add_model_metadata(checkpoint_info):
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checkpoint_info.calculate_shorthash()
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metadata["sd_merge_models"][checkpoint_info.sha256] = {
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"name": checkpoint_info.name,
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"legacy_hash": checkpoint_info.hash,
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"sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
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}
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metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
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add_model_metadata(primary_model_info)
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if secondary_model_info:
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add_model_metadata(secondary_model_info)
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if tertiary_model_info:
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add_model_metadata(tertiary_model_info)
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metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
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_, extension = os.path.splitext(output_modelname)
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if extension.lower() == ".safetensors":
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safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
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else:
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torch.save(theta_0, output_modelname)
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sd_models.list_models()
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created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
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if created_model:
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created_model.calculate_shorthash()
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create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
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shared.log.info(f"Checkpoint saved to {output_modelname}.")
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shared.state.textinfo = "Checkpoint saved"
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shared.state.end()
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return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]
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