text stringlengths 1 93.6k |
|---|
img1 = Image.open(self.list_A[index]).convert('RGB')
|
img2 = Image.open(self.list_B[index]).convert('RGB')
|
# labl = Image.open(self.list_L[index]).convert('P')
|
if self.model == "SC":
|
labl = self.list_L[index]
|
return self.transform(img1), self.transform(img2), labl, name
|
else:
|
labl = Image.open(self.list_L[index]).convert('P')
|
return self.transform(img1), self.transform(img2), self.transforml(labl), name
|
def __len__(self):
|
return len(self.list_L) # len(self.files1)
|
# Configure dataloaders
|
def Get_dataloader(path,batch, reshape_size, model=None):
|
#Image.BICUBIC
|
transforms_ = [transforms.Resize(reshape_size),
|
transforms.ToTensor(),
|
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]
|
transforms_L = [transforms.Resize(reshape_size),
|
transforms.ToTensor(),
|
]
|
train_dataloader = DataLoader(
|
ImageDataset(path, transforms_=transforms_, transforms_L=transforms_L, model=model),
|
batch_size=batch, shuffle=True, num_workers=2, drop_last=True)
|
return train_dataloader
|
def Get_dataloader_test(path,batch, reshape_size=None, model=None):
|
transforms_ = [transforms.Resize(reshape_size),
|
transforms.ToTensor(),
|
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5)) ]
|
transforms_L = [transforms.Resize(reshape_size),
|
transforms.ToTensor(),
|
]
|
test_dataloader = DataLoader(
|
ImageDataset_test(path, transforms_=transforms_, transforms_L=transforms_L, model=model),
|
batch_size=batch, shuffle=False, num_workers=2, drop_last=False)
|
return test_dataloader
|
# <FILESEP>
|
import inspect
|
from typing import List, Optional, Union
|
import torch
|
from tqdm.auto import tqdm
|
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
from torch import autocast
|
from diffusers import (
|
AutoencoderKL,
|
DDIMScheduler,
|
DiffusionPipeline,
|
PNDMScheduler,
|
UNet2DConditionModel,
|
LMSDiscreteScheduler,
|
)
|
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
|
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
class StableDiffusionPipeline(DiffusionPipeline):
|
def __init__(
|
self,
|
vae: AutoencoderKL,
|
text_encoder: CLIPTextModel,
|
tokenizer: CLIPTokenizer,
|
unet: UNet2DConditionModel,
|
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
safety_checker: StableDiffusionSafetyChecker,
|
feature_extractor: CLIPFeatureExtractor,
|
):
|
super().__init__()
|
scheduler = scheduler.set_format("pt")
|
self.register_modules(
|
vae=vae,
|
text_encoder=text_encoder,
|
tokenizer=tokenizer,
|
unet=unet,
|
scheduler=scheduler,
|
safety_checker=safety_checker,
|
feature_extractor=feature_extractor,
|
)
|
@torch.no_grad()
|
def __call__(
|
self,
|
prompt: Union[str, List[str]],
|
height: Optional[int] = 512,
|
width: Optional[int] = 512,
|
num_inference_steps: Optional[int] = 50,
|
guidance_scale: Optional[float] = 7.5,
|
eta: Optional[float] = 0.0,
|
generator: Optional[torch.Generator] = None,
|
torch_device: Optional[Union[str, torch.device]] = None,
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.