mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
2b1aaf4ee7
- scripts and documentation updated to match - ran preflight checks on both web and CLI and seems to be working
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
import torch
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import warnings
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import numpy as np
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from PIL import Image
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class ESRGAN():
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def __init__(self, bg_tile_size=400) -> None:
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self.bg_tile_size = bg_tile_size
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if not torch.cuda.is_available(): # CPU or MPS on M1
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use_half_precision = False
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else:
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use_half_precision = True
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def load_esrgan_bg_upsampler(self):
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if not torch.cuda.is_available(): # CPU or MPS on M1
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use_half_precision = False
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else:
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use_half_precision = True
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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from realesrgan import RealESRGANer
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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model_path = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
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scale = 4
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bg_upsampler = RealESRGANer(
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scale=scale,
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model_path=model_path,
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model=model,
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tile=self.bg_tile_size,
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tile_pad=10,
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pre_pad=0,
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half=use_half_precision,
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)
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return bg_upsampler
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def process(self, image, strength: float, seed: str = None, upsampler_scale: int = 2):
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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try:
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upsampler = self.load_esrgan_bg_upsampler()
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except Exception:
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import traceback
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import sys
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print('>> Error loading Real-ESRGAN:', file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if upsampler_scale == 0:
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print('>> Real-ESRGAN: Invalid scaling option. Image not upscaled.')
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return image
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if seed is not None:
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print(
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f'>> Real-ESRGAN Upscaling seed:{seed} : scale:{upsampler_scale}x'
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)
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output, _ = upsampler.enhance(
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np.array(image, dtype=np.uint8),
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outscale=upsampler_scale,
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alpha_upsampler='realesrgan',
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)
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res = Image.fromarray(output)
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if strength < 1.0:
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# Resize the image to the new image if the sizes have changed
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if output.size != image.size:
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image = image.resize(res.size)
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res = Image.blend(image, res, strength)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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upsampler = None
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return res
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