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import torch
import os
import numpy as np
from hashlib import md5
def hash_img(img):
return md5(np.array(img).tobytes()).hexdigest()
def hash_any(obj):
return md5(str(obj).encode()).hexdigest()
def refine_lr_with_sd(pil_image_list, concept_img_list, control_image_list, prompt_list, pipe=None, strength=0.35, neg_prompt_list="", output_size=(512, 512), controlnet_conditioning_scale=1.):
with torch.no_grad():
images = pipe(
image=pil_image_list,
ip_adapter_image=concept_img_list,
prompt=prompt_list,
neg_prompt=neg_prompt_list,
num_inference_steps=50,
strength=strength,
height=output_size[0],
width=output_size[1],
control_image=control_image_list,
guidance_scale=5.0,
controlnet_conditioning_scale=controlnet_conditioning_scale,
generator=torch.manual_seed(233),
).images
return images
SR_cache = None
def run_sr_fast(source_pils, scale=4):
from PIL import Image
from scripts.upsampler import RealESRGANer
import numpy as np
global SR_cache
if SR_cache is not None:
upsampler = SR_cache
else:
upsampler = RealESRGANer(
scale=4,
onnx_path="ckpt/realesrgan-x4.onnx",
tile=0,
tile_pad=10,
pre_pad=0,
half=True,
gpu_id=0,
)
ret_pils = []
for idx, img_pils in enumerate(source_pils):
np_in = isinstance(img_pils, np.ndarray)
assert isinstance(img_pils, (Image.Image, np.ndarray))
img = np.array(img_pils)
output, _ = upsampler.enhance(img, outscale=scale)
if np_in:
ret_pils.append(output)
else:
ret_pils.append(Image.fromarray(output))
if SR_cache is None:
SR_cache = upsampler
return ret_pils
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