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import torch
import os
import requests
from tqdm import tqdm
from diffusers import DDPMScheduler, EulerDiscreteScheduler
from typing import Any, Optional, Union
# def make_1step_sched(pretrained_path, step=4):
# noise_scheduler_1step = EulerDiscreteScheduler.from_pretrained(pretrained_path, subfolder="scheduler")
# noise_scheduler_1step.set_timesteps(step, device="cuda")
# noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
# return noise_scheduler_1step
def make_1step_sched(pretrained_path):
noise_scheduler_1step = DDPMScheduler.from_pretrained(pretrained_path, subfolder="scheduler")
noise_scheduler_1step.set_timesteps(1, device="cuda")
noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
return noise_scheduler_1step
def my_lora_fwd(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
self._check_forward_args(x, *args, **kwargs)
adapter_names = kwargs.pop("adapter_names", None)
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif adapter_names is not None:
result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
torch_result_dtype = result.dtype
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
x = x.to(lora_A.weight.dtype)
if not self.use_dora[active_adapter]:
_tmp = lora_A(dropout(x))
if isinstance(lora_A, torch.nn.Conv2d):
_tmp = torch.einsum('...khw,...kr->...rhw', _tmp, self.de_mod)
elif isinstance(lora_A, torch.nn.Linear):
_tmp = torch.einsum('...lk,...kr->...lr', _tmp, self.de_mod)
else:
raise NotImplementedError('only conv and linear are supported yet.')
result = result + lora_B(_tmp) * scaling
else:
x = dropout(x)
result = result + self._apply_dora(x, lora_A, lora_B, scaling, active_adapter)
result = result.to(torch_result_dtype)
return result
def download_url(url, outf):
if not os.path.exists(outf):
print(f"Downloading checkpoint to {outf}")
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(outf, 'wb') as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
print("ERROR, something went wrong")
print(f"Downloaded successfully to {outf}")
else:
print(f"Skipping download, {outf} already exists")
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