201 lines
5.9 KiB
Python
201 lines
5.9 KiB
Python
from basicsr.archs.rrdbnet_arch import RRDBNet
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from diffusers import (
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AutoencoderKL,
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DDPMScheduler,
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StableDiffusionUpscalePipeline,
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)
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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 Optional
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import numpy as np
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import torch
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from .chain import (
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ChainPipeline,
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StageParams,
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)
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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 .params import (
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ImageParams,
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Size,
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UpscaleParams,
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)
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from .utils import (
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ServerContext,
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)
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def load_resrgan(ctx: ServerContext, params: UpscaleParams, tile=0):
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'''
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TODO: cache this instance
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'''
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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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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=params.tile_pad,
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pre_pad=params.pre_pad,
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half=params.half)
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return upsampler
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def load_stable_diffusion(ctx: ServerContext, upscale: UpscaleParams):
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'''
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TODO: cache this instance
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'''
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if upscale.format == 'onnx':
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model_path = path.join(ctx.model_path, upscale.upscale_model)
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# ValueError: Pipeline <class 'onnx_web.onnx.pipeline_onnx_stable_diffusion_upscale.OnnxStableDiffusionUpscalePipeline'>
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# expected {'vae', 'unet', 'text_encoder', 'tokenizer', 'scheduler', 'low_res_scheduler'},
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# but only {'scheduler', 'tokenizer', 'text_encoder', 'unet'} were passed.
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pipeline = OnnxStableDiffusionUpscalePipeline.from_pretrained(
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model_path,
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vae=AutoencoderKL.from_pretrained(
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model_path, subfolder='vae_encoder'),
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low_res_scheduler=DDPMScheduler.from_pretrained(
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model_path, subfolder='scheduler'),
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)
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else:
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pipeline = StableDiffusionUpscalePipeline.from_pretrained(
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'stabilityai/stable-diffusion-x4-upscaler')
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return pipeline
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def upscale_resrgan(
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ctx: ServerContext,
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stage: StageParams,
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_params: ImageParams,
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source_image: Image.Image,
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*,
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upscale: UpscaleParams,
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) -> Image:
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print('upscaling image with Real ESRGAN', upscale.scale)
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output = np.array(source_image)
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upsampler = load_resrgan(ctx, upscale, tile=stage.tile_size)
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output, _ = upsampler.enhance(output, outscale=upscale.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 correct_gfpgan(
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ctx: ServerContext,
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_stage: StageParams,
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_params: ImageParams,
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image: Image.Image,
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*,
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upscale: UpscaleParams,
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upsampler: Optional[RealESRGANer] = None,
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) -> Image:
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if upscale.correction_model is None:
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print('no face model given, skipping')
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return image
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print('correcting faces with GFPGAN model: %s' % upscale.correction_model)
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if upsampler is None:
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upsampler = load_resrgan(ctx, upscale)
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face_path = path.join(ctx.model_path, '%s.pth' %
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(upscale.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=upscale.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=upscale.face_strength)
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return output
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def upscale_stable_diffusion(
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ctx: ServerContext,
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_stage: StageParams,
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params: ImageParams,
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source: Image.Image,
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*,
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upscale: UpscaleParams,
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) -> Image:
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print('upscaling with Stable Diffusion')
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pipeline = load_stable_diffusion(ctx, upscale)
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generator = torch.manual_seed(params.seed)
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seed = generator.initial_seed()
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return pipeline(
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params.prompt,
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source,
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generator=torch.manual_seed(seed),
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num_inference_steps=params.steps,
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).images[0]
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def run_upscale_correction(
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ctx: ServerContext,
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stage: StageParams,
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params: ImageParams,
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image: Image.Image,
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*,
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upscale: UpscaleParams,
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) -> Image.Image:
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print('running upscale pipeline')
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chain = ChainPipeline()
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kwargs = {'upscale': upscale}
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if upscale.scale > 1:
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if 'esrgan' in upscale.upscale_model:
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stage = StageParams(tile_size=stage.tile_size,
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outscale=upscale.outscale)
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chain.append((upscale_resrgan, stage, kwargs))
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elif 'stable-diffusion' in upscale.upscale_model:
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mini_tile = min(128, stage.tile_size)
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stage = StageParams(tile_size=mini_tile, outscale=upscale.outscale)
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chain.append((upscale_stable_diffusion, stage, kwargs))
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if upscale.faces:
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stage = StageParams(tile_size=stage.tile_size,
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outscale=upscale.outscale)
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chain.append((correct_gfpgan, stage, kwargs))
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return chain(ctx, params, image)
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