mirror of
https://github.com/invoke-ai/InvokeAI
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105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
import warnings
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import numpy as np
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import torch
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from PIL import Image
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from PIL.Image import Image as ImageType
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import invokeai.backend.util.logging as logger
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from invokeai.app.services.config import InvokeAIAppConfig
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config = InvokeAIAppConfig.get_config()
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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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def load_esrgan_bg_upsampler(self, denoise_str):
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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 import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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model = SRVGGNetCompact(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_conv=32,
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upscale=4,
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act_type="prelu",
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)
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model_path = config.root_dir / "models/realesrgan/realesr-general-x4v3.pth"
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wdn_model_path = config.root_dir / "models/realesrgan/realesr-general-wdn-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, wdn_model_path],
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model=model,
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tile=self.bg_tile_size,
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dni_weight=[denoise_str, 1 - denoise_str],
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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(
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self,
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image: ImageType,
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strength: float,
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seed: str = None,
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upsampler_scale: int = 2,
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denoise_str: float = 0.75,
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):
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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(denoise_str)
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except Exception:
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import sys
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import traceback
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logger.error("Error loading Real-ESRGAN:")
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print(traceback.format_exc(), file=sys.stderr)
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if upsampler_scale == 0:
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logger.warning("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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logger.info(
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f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
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)
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# ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
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image = image.convert("RGB")
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# REALSRGAN expects a BGR np array; make array and flip channels
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bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
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output, _ = upsampler.enhance(
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bgr_image_array,
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outscale=upsampler_scale,
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alpha_upsampler="realesrgan",
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)
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# Flip the channels back to RGB
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res = Image.fromarray(output[..., ::-1])
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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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