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import pickle
from typing import Union
import numpy as np
import opensr_test
import torch
from diffusers import LDMSuperResolutionPipeline
def create_stable_diffusion_model(
device: Union[str, torch.device] = "cuda"
) -> LDMSuperResolutionPipeline:
"""Create the stable diffusion model
Returns:
LDMSuperResolutionPipeline: The model to use for
super resolution.
"""
model_id = "CompVis/ldm-super-resolution-4x-openimages"
pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
pipeline = pipeline.to(device)
return pipeline
def run_diffuser(
model: LDMSuperResolutionPipeline,
lr: torch.Tensor,
hr: torch.Tensor,
device: Union[str, torch.device] = "cuda",
) -> dict:
"""Run the model on the low resolution image
Args:
model (LDMSuperResolutionPipeline): The model to use
lr (torch.Tensor): The low resolution image
hr (torch.Tensor): The high resolution image
device (Union[str, torch.device], optional): The device
to use. Defaults to "cuda".
Returns:
dict: The results of the model
"""
# move the images to the device
lr = (torch.from_numpy(lr[[3, 2, 1]]) / 2000).to(device).clamp(0, 1)
if lr.shape[1] == 121:
# add padding
lr = torch.nn.functional.pad(
lr[None], pad=(3, 4, 3, 4), mode="reflect"
).squeeze()
# run the model
with torch.no_grad():
sr = model(lr[None], num_inference_steps=100, eta=1)
sr = torch.from_numpy(np.array(sr.images[0]) / 255).permute(2, 0, 1).float()
# remove padding
sr = sr[:, 3 * 4 : -4 * 4, 3 * 4 : -4 * 4]
lr = lr[:, 3:-4, 3:-4]
else:
# run the model
with torch.no_grad():
sr = model(lr[None], num_inference_steps=100, eta=1)
sr = torch.from_numpy(np.array(sr.images[0]) / 255).permute(2, 0, 1).float()
lr = (lr.cpu().numpy() * 2000).astype(np.uint16)
hr = ((hr[0:3] / 2000).clip(0, 1) * 2000).astype(np.uint16)
sr = (sr.cpu().numpy() * 2000).astype(np.uint16)
results = {"lr": lr, "hr": hr, "sr": sr}
return results
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