mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 08:44:33 +02:00
Replace Original Merge Method Major Cleanup
This commit is contained in:
AI-Casanova
+4
-200
@@ -1,5 +1,4 @@
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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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@@ -13,8 +12,6 @@ from modules.merging.merge_utils import TRIPLE_METHODS
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from modules import shared, images, sd_models, sd_vae, sd_models_config, devices
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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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@@ -55,202 +52,7 @@ def to_half(tensor, enable):
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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,
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save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights,
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save_metadata): # pylint: disable=unused-argument
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shared.state.begin('merge')
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save_as_half = save_as_half == 0
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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"Model merge loading secondary model: {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"Model merge loading tertiary model: {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"Model merge loading primary model: {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("Model merge: running")
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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(
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"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(
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"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,
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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[
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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"Model merge: baking in VAE: {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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theta_0 = theta_0.to_dict()
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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"Model merge saved: {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)],
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"Checkpoint saved to " + output_modelname]
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def run_MEHmodelmerger(id_task, **kwargs): # pylint: disable=unused-argument
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def run_modelmerger(id_task, **kwargs): # pylint: disable=unused-argument
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shared.state.begin('merge')
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def fail(message):
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@@ -377,6 +179,7 @@ def run_MEHmodelmerger(id_task, **kwargs): # pylint: disable=unused-argument
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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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try:
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theta_0 = theta_0.to_dict()
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except:
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@@ -391,7 +194,8 @@ def run_MEHmodelmerger(id_task, **kwargs): # pylint: disable=unused-argument
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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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devices.torch_gc(force=True)
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if kwargs["device"].type != "cpu":
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devices.torch_gc(force=True)
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shared.log.info(f"Model merge saved: {output_modelname}.")
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shared.state.textinfo = "Checkpoint saved"
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shared.state.end()
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@@ -4,6 +4,7 @@ from contextlib import contextmanager
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from typing import Dict, Optional, Tuple
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import safetensors.torch
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import torch
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from tensordict import TensorDict
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from tqdm import tqdm
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import modules.memstats
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import modules.devices as devices
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@@ -37,7 +38,6 @@ KEY_POSITION_IDS = ".".join(
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)
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def fix_clip(model: Dict) -> Dict:
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if KEY_POSITION_IDS in model.keys():
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model[KEY_POSITION_IDS] = torch.tensor(
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@@ -80,9 +80,9 @@ def load_thetas(
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) -> Dict:
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log_vram("before loading models")
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if prune:
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thetas = {k: prune_sd_model(read_state_dict(m, "cpu")) for k, m in models.items()}
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thetas = {k: prune_sd_model(TensorDict.from_dict(read_state_dict(m, "cpu"))) for k, m in models.items()}
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else:
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thetas = {k: read_state_dict(m, device) for k, m in models.items()}
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thetas = {k: TensorDict.from_dict(read_state_dict(m, device)) for k, m in models.items()}
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for model_key, model in thetas.items():
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for key, block in model.items():
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@@ -152,7 +152,7 @@ def un_prune_model(
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del thetas
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devices.torch_gc(force=True)
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log_vram("remove thetas")
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original_a = read_state_dict(models["model_a"], device)
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original_a = TensorDict.from_dict(read_state_dict(models["model_a"], device))
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for key in tqdm(original_a.keys(), desc="un-prune model a"):
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if KEY_POSITION_IDS in key:
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continue
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@@ -162,8 +162,7 @@ def un_prune_model(
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merged.update({key: merged[key].half()})
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del original_a
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devices.torch_gc(force=True)
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# log_vram("remove original_a")
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original_b = read_state_dict(models["model_b"], device)
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original_b = TensorDict.from_dict(read_state_dict(models["model_b"], device))
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for key in tqdm(original_b.keys(), desc="un-prune model b"):
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if KEY_POSITION_IDS in key:
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continue
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+37
-118
@@ -4,7 +4,7 @@ import inspect
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from datetime import datetime
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import gradio as gr
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from modules import sd_models, sd_vae, extras
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from modules.ui_components import FormRow, ToolButton, InputAccordion
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from modules.ui_components import FormRow, ToolButton
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from modules.ui_common import create_refresh_button
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from modules.call_queue import wrap_gradio_gpu_call
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from modules.shared import opts, log, req
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@@ -75,88 +75,6 @@ def create_ui():
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)
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with gr.Tab(label="Merge"):
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with gr.Row(equal_height=False):
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with gr.Column(variant='compact'):
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with FormRow():
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custom_name = gr.Textbox(label="New model name")
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with FormRow():
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def sd_model_choices():
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return ['None'] + sd_models.checkpoint_tiles()
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primary_model_name = gr.Dropdown(sd_model_choices(), label="Primary model", value="None")
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create_refresh_button(primary_model_name, sd_models.list_models,
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lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_A")
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secondary_model_name = gr.Dropdown(sd_model_choices(), label="Secondary model",
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value="None")
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create_refresh_button(secondary_model_name, sd_models.list_models,
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lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_B")
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tertiary_model_name = gr.Dropdown(sd_model_choices(), label="Tertiary model", value="None")
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create_refresh_button(tertiary_model_name, sd_models.list_models,
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lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_C")
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with FormRow():
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interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"],
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value="Weighted sum", label="Interpolation Method")
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interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05,
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label='Interpolation ratio from Primary to Secondary', value=0.5)
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with FormRow():
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checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors",
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label="Model format")
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with gr.Box():
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save_as_half = gr.Radio(choices=["fp16", "fp32"], value="fp16", label="Model precision",
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type="index")
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with FormRow():
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config_source = gr.Radio(choices=["Primary", "Secondary", "Tertiary", "None"],
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value="Primary", label="Model configuration", type="index")
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with FormRow():
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bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None",
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label="Bake in VAE")
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create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list,
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lambda: {"choices": ["None"] + list(sd_vae.vae_dict)},
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"modelmerger_refresh_bake_in_vae")
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with FormRow():
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discard_weights = gr.Textbox(value="", label="Discard weights with matching name")
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with FormRow():
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save_metadata = gr.Checkbox(value=True, label="Save metadata")
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with gr.Row():
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modelmerger_merge = gr.Button(value="Merge", variant='primary')
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def modelmerger(*args):
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try:
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results = extras.run_modelmerger(*args)
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except Exception as e:
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modules.errors.display(e, 'model merge')
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sd_models.list_models() # to remove the potentially missing models from the list
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return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)],
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f"Error merging checkpoints: {e}"]
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return results
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modelmerger_merge.click(
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fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
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_js='modelmerger',
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inputs=[
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dummy_component,
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primary_model_name,
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secondary_model_name,
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tertiary_model_name,
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interp_method,
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interp_amount,
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save_as_half,
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custom_name,
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checkpoint_format,
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config_source,
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bake_in_vae,
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discard_weights,
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save_metadata,
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],
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outputs=[
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primary_model_name,
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secondary_model_name,
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tertiary_model_name,
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dummy_component,
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models_outcome,
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]
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)
|
||||
with gr.Tab(label="Advanced Merge"):
|
||||
def sd_model_choices():
|
||||
return ['None'] + sd_models.checkpoint_tiles()
|
||||
|
||||
@@ -167,7 +85,8 @@ def create_ui():
|
||||
with FormRow():
|
||||
merge_mode = gr.Dropdown(choices=merge_methods.__all__, value="weighted_sum",
|
||||
label="Interpolation Method")
|
||||
merge_mode_docs = gr.HTML(value=getattr(merge_methods, "weighted_sum").__doc__.replace("\n", "<br>"))
|
||||
merge_mode_docs = gr.HTML(
|
||||
value=getattr(merge_methods, "weighted_sum").__doc__.replace("\n", "<br>"))
|
||||
with FormRow():
|
||||
primary_model_name = gr.Dropdown(sd_model_choices(), label="Primary model", value="None")
|
||||
create_refresh_button(primary_model_name, sd_models.list_models,
|
||||
@@ -253,45 +172,45 @@ def create_ui():
|
||||
with FormRow():
|
||||
save_metadata = gr.Checkbox(value=True, label="Save metadata")
|
||||
with gr.Row():
|
||||
MEHmodelmerger_merge = gr.Button(value="Merge", variant='primary')
|
||||
modelmerger_merge = gr.Button(value="Merge", variant='primary')
|
||||
|
||||
def MEHmodelmerger(dummy_component,
|
||||
primary_model_name,
|
||||
secondary_model_name,
|
||||
tertiary_model_name,
|
||||
merge_mode,
|
||||
alpha,
|
||||
beta,
|
||||
alpha_preset,
|
||||
alpha_preset_lambda,
|
||||
alpha_base,
|
||||
alpha_in_blocks,
|
||||
alpha_mid_block,
|
||||
alpha_out_blocks,
|
||||
beta_preset,
|
||||
beta_preset_lambda,
|
||||
beta_base,
|
||||
beta_in_blocks,
|
||||
beta_mid_block,
|
||||
beta_out_blocks,
|
||||
precision,
|
||||
custom_name,
|
||||
checkpoint_format,
|
||||
save_metadata,
|
||||
weights_clip,
|
||||
prune,
|
||||
re_basin,
|
||||
re_basin_iterations,
|
||||
device,
|
||||
bake_in_vae):
|
||||
def modelmerger(dummy_component,
|
||||
primary_model_name,
|
||||
secondary_model_name,
|
||||
tertiary_model_name,
|
||||
merge_mode,
|
||||
alpha,
|
||||
beta,
|
||||
alpha_preset,
|
||||
alpha_preset_lambda,
|
||||
alpha_base,
|
||||
alpha_in_blocks,
|
||||
alpha_mid_block,
|
||||
alpha_out_blocks,
|
||||
beta_preset,
|
||||
beta_preset_lambda,
|
||||
beta_base,
|
||||
beta_in_blocks,
|
||||
beta_mid_block,
|
||||
beta_out_blocks,
|
||||
precision,
|
||||
custom_name,
|
||||
checkpoint_format,
|
||||
save_metadata,
|
||||
weights_clip,
|
||||
prune,
|
||||
re_basin,
|
||||
re_basin_iterations,
|
||||
device,
|
||||
bake_in_vae):
|
||||
kwargs = {}
|
||||
for x in inspect.getfullargspec(MEHmodelmerger)[0]:
|
||||
for x in inspect.getfullargspec(modelmerger)[0]:
|
||||
kwargs[x] = locals()[x]
|
||||
for key in list(kwargs.keys()):
|
||||
if kwargs[key] in [None, "None", "", 0, []]:
|
||||
del kwargs[key]
|
||||
try:
|
||||
results = extras.run_MEHmodelmerger(dummy_component, **kwargs)
|
||||
results = extras.run_modelmerger(dummy_component, **kwargs)
|
||||
except Exception as e:
|
||||
modules.errors.display(e, 'model merge')
|
||||
sd_models.list_models() # to remove the potentially missing models from the list
|
||||
@@ -358,8 +277,8 @@ def create_ui():
|
||||
beta_apply_preset.click(fn=load_presets, inputs=[beta_preset, beta_preset_lambda],
|
||||
outputs=[beta_base, beta_in_blocks, beta_mid_block, beta_out_blocks, tabs])
|
||||
|
||||
MEHmodelmerger_merge.click(
|
||||
fn=wrap_gradio_gpu_call(MEHmodelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
|
||||
modelmerger_merge.click(
|
||||
fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
|
||||
_js='modelmerger',
|
||||
inputs=[
|
||||
dummy_component,
|
||||
|
||||
Reference in New Issue
Block a user