259 lines
9.3 KiB
Python
259 lines
9.3 KiB
Python
import os
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import re
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import shutil
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import torch
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import tqdm
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from modules import shared, images, sd_models, sd_vae
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from modules.ui_common import plaintext_to_html
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import gradio as gr
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import safetensors.torch
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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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info += f"""
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<div>
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<p><b>{plaintext_to_html(str(key))}</b></p>
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<p>{plaintext_to_html(str(text))}</p>
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</div>
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""".strip()+"\n"
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if len(info) == 0:
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message = "Nothing found in the image."
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info = f"<div><p>{message}<p></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.find_checkpoint_config(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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print("Copying config:")
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print(" from:", cfg)
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print(" to:", checkpoint_filename)
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shutil.copyfile(cfg, checkpoint_filename)
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checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
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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):
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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:
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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:
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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:
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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 = f"Loading B"
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print(f"Loading {secondary_model_info.filename}...")
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theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
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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 = f"Loading C"
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print(f"Loading {tertiary_model_info.filename}...")
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theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
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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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print(f"Loading {primary_model_info.filename}...")
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theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
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print("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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print(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, map_location='cpu')
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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.cmd_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 += ".instrpix2pix" 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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print(f"Saving to {output_modelname}...")
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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={"format": "pt"})
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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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create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
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print(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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