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Model Quant (model_handler), working Model Viewer, Fusion, and Debug information. Model Selection and reloading. (#8)
215bdba
verified
import torch | |
import numpy as np | |
import huggingface_hub | |
import zipfile | |
import os | |
from collections import OrderedDict | |
def model_info(model_path): | |
model = torch.load(model_path, map_location=torch.device('cpu')) | |
info = { | |
'config': model['config'], | |
'info': model['info'], | |
'epochs': model['info'].split('epoch')[0], | |
'sr': model['sr'], | |
'f0': model['f0'], | |
'size': model['size'] if 'size' in model['weight'] else 'fp32', | |
} | |
return info | |
def merge(path1, path2, alpha1, sr, f0, info, name, version): | |
try: | |
def extract(ckpt): | |
a = ckpt["model"] | |
opt = OrderedDict() | |
opt["weight"] = {} | |
for key in a.keys(): | |
if "enc_q" in key: | |
continue | |
opt["weight"][key] = a[key] | |
return opt | |
ckpt1 = torch.load(path1, map_location="cpu") | |
ckpt2 = torch.load(path2, map_location="cpu") | |
cfg = ckpt1["config"] | |
if "model" in ckpt1: | |
ckpt1 = extract(ckpt1) | |
else: | |
ckpt1 = ckpt1["weight"] | |
if "model" in ckpt2: | |
ckpt2 = extract(ckpt2) | |
else: | |
ckpt2 = ckpt2["weight"] | |
if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): | |
return "Fail to merge the models. The model architectures are not the same." | |
opt = OrderedDict() | |
opt["weight"] = {} | |
for key in ckpt1.keys(): | |
# try: | |
if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: | |
min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) | |
opt["weight"][key] = ( | |
alpha1 * (ckpt1[key][:min_shape0].float()) | |
+ (1 - alpha1) * (ckpt2[key][:min_shape0].float()) | |
).half() | |
else: | |
opt["weight"][key] = ( | |
alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float()) | |
).half() | |
# except: | |
# pdb.set_trace() | |
opt["config"] = cfg | |
""" | |
if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000] | |
elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000] | |
elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000] | |
""" | |
opt["sr"] = sr | |
opt["f0"] = 1 if f0 == "Yes" else 0 | |
opt["version"] = version | |
opt["info"] = info | |
torch.save(opt, "models/" + name + ".pth") | |
return "models/" + name + ".pth" | |
except: | |
return "Fail to merge the models. The model architectures are not the same." # <- L if u see this u suck | |
def model_quant(model_path, size): | |
""" | |
Quantize the model to a lower precision. - this is the floating point version | |
Args: | |
model_path: str, path to the model file | |
size: str, one of ["fp2", "fp4", "fp8", "fp16"] | |
Returns: | |
str, message indicating the success of the operation | |
""" | |
size_options = ["fp2", "fp4", "fp8", "fp16"] | |
if size not in size_options: | |
raise ValueError(f"Size must be one of {size_options}") | |
model_base = torch.load(model_path, map_location=torch.device('cpu')) | |
model = model_base['weight'] | |
#model = json.loads(json.dumps(model)) | |
if size == "fp16": | |
for key in model.keys(): | |
model[key] = model[key].half() # 16-bit floating point | |
elif size == "fp8": | |
for key in model.keys(): | |
model[key] = model[key].half().half() # 8-bit floating point <- this is the most common one | |
elif size == "fp4": | |
for key in model.keys(): | |
model[key] = model[key].half().half().half() # 4-bit floating point <- ok maybe you're mentally ill if you choose this (very low precision) | |
elif size == "fp2": | |
for key in model.keys(): | |
model[key] = model[key].half().half().half().half() # 2-bit floating point <- if you choose this you're a fucking dickhead coming | |
print(model_path) | |
output_path = model_path.split('.pth')[0] + f'_{size}.pth' | |
output_style = { | |
'weight': model, | |
'config': model_base['config'], | |
'info': model_base['info'], | |
'sr': model_base['sr'], | |
'f0': model_base['f0'], | |
'credits': f"Quantized to {size} precision, using Ilaria RVC, (Mikus's script)", | |
"size": size | |
} | |
torch.save(output_style, output_path) | |
#AmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithrax | |
# our data isnt safe anymore currently typing this and there is a 100% chance that it'll be stolen and used for training another fucking dogshit language model by a horrible company like openai | |
# i say this as a person who communicates with microsoft and i will stop mentioning this as they're so closely tied together nowadays | |
# as fred durst has said - "That's your best friend and your worst enemy - your own brain." - keep your shit local and never trust scumbag companies even if they make the models oss - they're stealing data | |
# this is probably the only rant i'll have in this entire space and i put it in a notable spot | |
return "Model quantized successfully" # <- enjoy this fucking hot shit that looks like a steaming turd paired with skibidi toilet and the unibomber | |
def upload_model(repo, pth, index, token): | |
""" | |
Upload a model to the Hugging Face Hub | |
Args: | |
repo: str, the name of the repository | |
pth: str, path to the model file | |
index: str, the index of the model in the repository | |
token: str, the API token | |
Returns: | |
str, message indicating the success of the operation | |
""" | |
readme = f""" | |
# {repo} | |
This is a model uploaded by Ilaria RVC, using Mikus's script. | |
""" | |
repo_name = repo.split('/')[1] | |
with zipfile.ZipFile(f'{repo_name}.zip', 'w') as zipf: | |
zipf.write(pth, os.path.basename(pth)) | |
zipf.write(index, os.path.basename(index)) | |
zipf.writestr('README.md', readme) | |
huggingface_hub.HfApi().create_repo(token=token, name=repo, exist_ok=True) | |
huggingface_hub.HfApi().upload_file(token=token, path=f'{repo.split("/")[1]}.zip', repo_id=repo) | |
os.remove(f'{repo.split("/")[1]}.zip') | |
return "Model uploaded successfully" |