148 lines
4.5 KiB
Python
148 lines
4.5 KiB
Python
from basicsr.archs.rrdbnet_arch import RRDBNet
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from gfpgan import GFPGANer
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from os import path
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from PIL import Image
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from realesrgan import RealESRGANer
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from typing import Literal, Union
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import numpy as np
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from .onnx import (
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ONNXNet,
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OnnxStableDiffusionUpscalePipeline,
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)
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from .utils import (
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ServerContext,
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Size,
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)
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# TODO: these should all be params or config
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pre_pad = 0
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tile_pad = 10
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class UpscaleParams():
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def __init__(
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self,
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upscale_model: str,
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provider: str,
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correction_model: Union[str, None] = None,
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denoise: float = 0.5,
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faces=True,
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face_strength: float = 0.5,
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format: Literal['onnx', 'pth'] = 'onnx',
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half=False,
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outscale: int = 1,
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scale: int = 4,
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) -> None:
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self.upscale_model = upscale_model
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self.provider = provider
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self.correction_model = correction_model
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self.denoise = denoise
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self.faces = faces
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self.face_strength = face_strength
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self.format = format
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self.half = half
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self.outscale = outscale
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self.scale = scale
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def resize(self, size: Size) -> Size:
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return Size(size.width * self.outscale, size.height * self.outscale)
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def make_resrgan(ctx: ServerContext, params: UpscaleParams, tile=0):
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model_file = '%s.%s' % (params.upscale_model, params.format)
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model_path = path.join(ctx.model_path, model_file)
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if not path.isfile(model_path):
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raise Exception('Real ESRGAN model not found at %s' % model_path)
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# use ONNX acceleration, if available
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if params.format == 'onnx':
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model = ONNXNet(ctx, model_file, provider=params.provider)
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elif params.format == 'pth':
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
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num_block=23, num_grow_ch=32, scale=params.scale)
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else:
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raise Exception('unknown platform %s' % params.format)
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dni_weight = None
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if params.upscale_model == 'realesr-general-x4v3' and params.denoise != 1:
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wdn_model_path = model_path.replace(
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'realesr-general-x4v3', 'realesr-general-wdn-x4v3')
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model_path = [model_path, wdn_model_path]
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dni_weight = [params.denoise, 1 - params.denoise]
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# TODO: shouldn't need the PTH file
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upsampler = RealESRGANer(
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scale=params.scale,
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model_path=path.join(ctx.model_path, '%s.pth' % params.upscale_model),
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dni_weight=dni_weight,
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model=model,
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tile=tile,
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tile_pad=tile_pad,
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pre_pad=pre_pad,
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half=params.half)
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return upsampler
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def upscale_resrgan(ctx: ServerContext, params: UpscaleParams, source_image: Image) -> Image:
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print('upscaling image with Real ESRGAN', params.scale)
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output = np.array(source_image)
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upsampler = make_resrgan(ctx, params, tile=512)
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output, _ = upsampler.enhance(output, outscale=params.outscale)
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output = Image.fromarray(output, 'RGB')
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print('final output image size', output.size)
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return output
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def upscale_gfpgan(ctx: ServerContext, params: UpscaleParams, image, upsampler=None) -> Image:
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print('correcting faces with GFPGAN model: %s' % params.correction_model)
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if params.correction_model is None:
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print('no face model given, skipping')
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return image
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if upsampler is None:
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upsampler = make_resrgan(ctx, params)
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face_path = path.join(ctx.model_path, '%s.pth' % (params.correction_model))
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# TODO: doesn't have a model param, not sure how to pass ONNX model
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face_enhancer = GFPGANer(
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model_path=face_path,
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upscale=params.outscale,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=upsampler)
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_, _, output = face_enhancer.enhance(
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image, has_aligned=False, only_center_face=False, paste_back=True, weight=params.face_strength)
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return output
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def upscale_stable_diffusion(ctx: ServerContext, params: UpscaleParams, image: Image) -> Image:
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print('upscaling with Stable Diffusion')
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pipeline = OnnxStableDiffusionUpscalePipeline.from_pretrained(params.upscale_model)
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result = pipeline('', image=image)
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return result.images[0]
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def run_upscale_pipeline(ctx: ServerContext, params: UpscaleParams, image: Image) -> Image:
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print('running upscale pipeline')
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if params.scale > 1:
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if 'esrgan' in params.upscale_model:
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image = upscale_resrgan(ctx, params, image)
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elif 'stable-diffusion' in params.upscale_model:
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image = upscale_stable_diffusion(ctx, params, image)
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if params.faces:
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image = upscale_gfpgan(ctx, params, image)
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return image
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