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import os
import html
import json
import time
import shutil
import torch
import tqdm
import gradio as gr
import safetensors.torch
from modules.merging.merge import merge_models
from modules.merging.merge_utils import TRIPLE_METHODS
from modules import shared, images, sd_models, sd_vae, sd_models_config, devices
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():
if key != 'UserComment':
info += f"<div><b>{html.escape(str(key))}</b>: {html.escape(str(text))}</div>"
return '', geninfo, info
def create_config(ckpt_result, config_source, a, b, c):
def config(x):
res = sd_models_config.find_checkpoint_config_near_filename(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"
shared.log.info("Copying config: {cfg} -> {checkpoint_filename}")
shutil.copyfile(cfg, checkpoint_filename)
def to_half(tensor, enable):
if enable and tensor.dtype == torch.float:
return tensor.half()
return tensor
def run_modelmerger(id_task, **kwargs): # pylint: disable=unused-argument
shared.state.begin('merge')
t0 = time.time()
def fail(message):
shared.state.textinfo = message
shared.state.end()
return [*[gr.update() for _ in range(4)], message]
kwargs["models"] = {
"model_a": sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None)).filename,
"model_b": sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None)).filename,
}
if kwargs.get("primary_model_name", None) in [None, 'None']:
return fail("Failed: Merging requires a primary model.")
primary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None))
if kwargs.get("secondary_model_name", None) in [None, 'None']:
return fail("Failed: Merging requires a secondary model.")
secondary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None))
if kwargs.get("tertiary_model_name", None) in [None, 'None'] and kwargs.get("merge_mode", None) in TRIPLE_METHODS:
return fail(f"Failed: Interpolation method ({kwargs.get('merge_mode', None)}) requires a tertiary model.")
tertiary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)) if kwargs.get("merge_mode", None) in TRIPLE_METHODS else None
del kwargs["primary_model_name"]
del kwargs["secondary_model_name"]
if kwargs.get("tertiary_model_name", None) is not None:
kwargs["models"] |= {"model_c": sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)).filename}
del kwargs["tertiary_model_name"]
if kwargs.get("alpha_base", None) and kwargs.get("alpha_in_blocks", None) and kwargs.get("alpha_mid_block", None) and kwargs.get("alpha_out_blocks", None):
try:
alpha = [float(x) for x in
[kwargs["alpha_base"]] + kwargs["alpha_in_blocks"].split(",") + [kwargs["alpha_mid_block"]] + kwargs["alpha_out_blocks"].split(",")]
assert len(alpha) == 26 or len(alpha) == 20, "Alpha Block Weights are wrong length (26 or 20 for SDXL)"
kwargs["alpha"] = alpha
except KeyError as ke:
shared.log.warning(f"Merge: Malformed manual block weight: {ke}")
elif kwargs.get("alpha_preset", None) or kwargs.get("alpha", None):
kwargs["alpha"] = kwargs.get("alpha_preset", kwargs["alpha"])
kwargs.pop("alpha_base", None)
kwargs.pop("alpha_in_blocks", None)
kwargs.pop("alpha_mid_block", None)
kwargs.pop("alpha_out_blocks", None)
kwargs.pop("alpha_preset", None)
if kwargs.get("beta_base", None) and kwargs.get("beta_in_blocks", None) and kwargs.get("beta_mid_block", None) and kwargs.get("beta_out_blocks", None):
try:
beta = [float(x) for x in
[kwargs["beta_base"]] + kwargs["beta_in_blocks"].split(",") + [kwargs["beta_mid_block"]] + kwargs["beta_out_blocks"].split(",")]
assert len(beta) == 26 or len(beta) == 20, "Beta Block Weights are wrong length (26 or 20 for SDXL)"
kwargs["beta"] = beta
except KeyError as ke:
shared.log.warning(f"Merge: Malformed manual block weight: {ke}")
elif kwargs.get("beta_preset", None) or kwargs.get("beta", None):
kwargs["beta"] = kwargs.get("beta_preset", kwargs["beta"])
kwargs.pop("beta_base", None)
kwargs.pop("beta_in_blocks", None)
kwargs.pop("beta_mid_block", None)
kwargs.pop("beta_out_blocks", None)
kwargs.pop("beta_preset", None)
if kwargs["device"] == "gpu":
kwargs["device"] = devices.device
elif kwargs["device"] == "shuffle":
kwargs["device"] = torch.device("cpu")
kwargs["work_device"] = devices.device
else:
kwargs["device"] = torch.device("cpu")
if kwargs.pop("unload", False):
sd_models.unload_model_weights()
try:
theta_0 = merge_models(**kwargs)
except Exception as e:
return fail(f"{e}")
try:
theta_0 = theta_0.to_dict() #TensorDict -> Dict if necessary
except Exception:
pass
bake_in_vae_filename = sd_vae.vae_dict.get(kwargs.get("bake_in_vae", None), None)
if bake_in_vae_filename is not None:
shared.log.info(f"Merge VAE='{bake_in_vae_filename}'")
shared.state.textinfo = 'Merge VAE'
vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename)
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], kwargs.get("precision", "fp16") == "fp16")
del vae_dict
ckpt_dir = shared.opts.ckpt_dir or sd_models.model_path
filename = kwargs.get("custom_name", "Unnamed_Merge")
filename += "." + kwargs.get("checkpoint_format", None)
output_modelname = os.path.join(ckpt_dir, filename)
shared.state.textinfo = "merge saving"
metadata = None
if kwargs.get("save_metadata", False):
metadata = {"format": "pt", "sd_merge_models": {}}
merge_recipe = {
"type": "SDNext", # indicate this model was merged with webui's built-in merger
"primary_model_hash": primary_model_info.sha256,
"secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
"tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
"merge_mode": kwargs.get('merge_mode', None),
"alpha": kwargs.get('alpha', None),
"beta": kwargs.get('beta', None),
"precision": kwargs.get('precision', None),
"custom_name": kwargs.get("custom_name", "Unamed_Merge"),
}
metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
def add_model_metadata(checkpoint_info):
checkpoint_info.calculate_shorthash()
metadata["sd_merge_models"][checkpoint_info.sha256] = {
"name": checkpoint_info.name,
"legacy_hash": checkpoint_info.hash,
"sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
}
metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
add_model_metadata(primary_model_info)
if secondary_model_info:
add_model_metadata(secondary_model_info)
if tertiary_model_info:
add_model_metadata(tertiary_model_info)
metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
_, extension = os.path.splitext(output_modelname)
if os.path.exists(output_modelname) and not kwargs.get("overwrite", False):
return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model alredy exists: {output_modelname}"]
if extension.lower() == ".safetensors":
safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
else:
torch.save(theta_0, output_modelname)
t1 = time.time()
shared.log.info(f"Merge complete: saved='{output_modelname}' time={t1-t0:.2f}")
sd_models.list_models()
created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
if created_model:
created_model.calculate_shorthash()
devices.torch_gc(force=True)
shared.state.end()
return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model saved to {output_modelname}"]
def run_modelconvert(model, checkpoint_formats, precision, conv_type, custom_name, unet_conv, text_encoder_conv,
vae_conv, others_conv, fix_clip):
# position_ids in clip is int64. model_ema.num_updates is int32
dtypes_to_fp16 = {torch.float32, torch.float64, torch.bfloat16}
dtypes_to_bf16 = {torch.float32, torch.float64, torch.float16}
def conv_fp16(t: torch.Tensor):
return t.half() if t.dtype in dtypes_to_fp16 else t
def conv_bf16(t: torch.Tensor):
return t.bfloat16() if t.dtype in dtypes_to_bf16 else t
def conv_full(t):
return t
_g_precision_func = {
"full": conv_full,
"fp32": conv_full,
"fp16": conv_fp16,
"bf16": conv_bf16,
}
def check_weight_type(k: str) -> str:
if k.startswith("model.diffusion_model"):
return "unet"
elif k.startswith("first_stage_model"):
return "vae"
elif k.startswith("cond_stage_model"):
return "clip"
return "other"
def load_model(path):
if path.endswith(".safetensors"):
m = safetensors.torch.load_file(path, device="cpu")
else:
m = torch.load(path, map_location="cpu")
state_dict = m["state_dict"] if "state_dict" in m else m
return state_dict
def fix_model(model, fix_clip=False):
# code from model-toolkit
nai_keys = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.'
}
for k in list(model.keys()):
for r in nai_keys:
if type(k) == str and k.startswith(r):
new_key = k.replace(r, nai_keys[r])
model[new_key] = model[k]
del model[k]
shared.log.warning(f"Model convert: fixed NovelAI error key: {k}")
break
if fix_clip:
i = "cond_stage_model.transformer.text_model.embeddings.position_ids"
if i in model:
correct = torch.Tensor([list(range(77))]).to(torch.int64)
now = model[i].to(torch.int64)
broken = correct.ne(now)
broken = [i for i in range(77) if broken[0][i]]
model[i] = correct
if len(broken) != 0:
shared.log.warning(f"Model convert: fixed broken CLiP: {broken}")
return model
if model == "":
return "Error: you must choose a model"
if len(checkpoint_formats) == 0:
return "Error: at least choose one model save format"
extra_opt = {
"unet": unet_conv,
"clip": text_encoder_conv,
"vae": vae_conv,
"other": others_conv
}
shared.state.begin('convert')
model_info = sd_models.checkpoints_list[model]
shared.state.textinfo = f"Loading {model_info.filename}..."
shared.log.info(f"Model convert loading: {model_info.filename}")
state_dict = load_model(model_info.filename)
ok = {} # {"state_dict": {}}
conv_func = _g_precision_func[precision]
def _hf(wk: str, t: torch.Tensor):
if not isinstance(t, torch.Tensor):
return
w_t = check_weight_type(wk)
conv_t = extra_opt[w_t]
if conv_t == "convert":
ok[wk] = conv_func(t)
elif conv_t == "copy":
ok[wk] = t
elif conv_t == "delete":
return
shared.log.info("Model convert: running")
if conv_type == "ema-only":
for k in tqdm.tqdm(state_dict):
ema_k = "___"
try:
ema_k = "model_ema." + k[6:].replace(".", "")
except Exception:
pass
if ema_k in state_dict:
_hf(k, state_dict[ema_k])
elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
_hf(k, state_dict[k])
elif conv_type == "no-ema":
for k, v in tqdm.tqdm(state_dict.items()):
if "model_ema." not in k:
_hf(k, v)
else:
for k, v in tqdm.tqdm(state_dict.items()):
_hf(k, v)
ok = fix_model(ok, fix_clip=fix_clip)
output = ""
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
save_name = f"{model_info.model_name}-{precision}"
if conv_type != "disabled":
save_name += f"-{conv_type}"
if custom_name != "":
save_name = custom_name
for fmt in checkpoint_formats:
ext = ".safetensors" if fmt == "safetensors" else ".ckpt"
_save_name = save_name + ext
save_path = os.path.join(ckpt_dir, _save_name)
shared.log.info(f"Model convert saving: {save_path}")
if fmt == "safetensors":
safetensors.torch.save_file(ok, save_path)
else:
torch.save({"state_dict": ok}, save_path)
output += f"Checkpoint saved to {save_path}<br>"
shared.state.end()
return output
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