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https://github.com/invoke-ai/InvokeAI
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135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
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from pathlib import Path
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from typing import Literal
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import cv2 as cv
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import numpy as np
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from PIL import Image
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from pydantic import ConfigDict
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from realesrgan import RealESRGANer
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from invokeai.app.invocations.primitives import ImageField, ImageOutput
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from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
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from invokeai.backend.util.devices import choose_torch_device
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from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
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# TODO: Populate this from disk?
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# TODO: Use model manager to load?
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ESRGAN_MODELS = Literal[
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"RealESRGAN_x4plus.pth",
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"RealESRGAN_x4plus_anime_6B.pth",
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"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
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"RealESRGAN_x2plus.pth",
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]
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if choose_torch_device() == torch.device("mps"):
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from torch import mps
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@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.2.0")
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class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
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"""Upscales an image using RealESRGAN."""
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image: ImageField = InputField(description="The input image")
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model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
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tile_size: int = InputField(
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default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)"
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)
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model_config = ConfigDict(protected_namespaces=())
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get_pil_image(self.image.image_name)
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models_path = context.services.configuration.models_path
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rrdbnet_model = None
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netscale = None
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esrgan_model_path = None
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if self.model_name in [
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"RealESRGAN_x4plus.pth",
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"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
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]:
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# x4 RRDBNet model
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rrdbnet_model = RRDBNet(
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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_block=23,
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num_grow_ch=32,
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scale=4,
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)
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netscale = 4
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elif self.model_name in ["RealESRGAN_x4plus_anime_6B.pth"]:
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# x4 RRDBNet model, 6 blocks
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rrdbnet_model = RRDBNet(
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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_block=6, # 6 blocks
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num_grow_ch=32,
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scale=4,
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)
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netscale = 4
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elif self.model_name in ["RealESRGAN_x2plus.pth"]:
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# x2 RRDBNet model
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rrdbnet_model = RRDBNet(
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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_block=23,
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num_grow_ch=32,
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scale=2,
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)
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netscale = 2
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else:
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msg = f"Invalid RealESRGAN model: {self.model_name}"
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context.services.logger.error(msg)
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raise ValueError(msg)
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esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=str(models_path / esrgan_model_path),
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model=rrdbnet_model,
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half=False,
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tile=self.tile_size,
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)
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# prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
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# TODO: This strips the alpha... is that okay?
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cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
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# We can pass an `outscale` value here, but it just resizes the image by that factor after
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# upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
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# upscaling, you'll need to add a resize node after this one.
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upscaled_image, img_mode = upsampler.enhance(cv_image)
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# back to PIL
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pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
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torch.cuda.empty_cache()
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if choose_torch_device() == torch.device("mps"):
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mps.empty_cache()
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image_dto = context.services.images.create(
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image=pil_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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is_intermediate=self.is_intermediate,
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metadata=self.metadata,
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workflow=self.workflow,
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)
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return ImageOutput(
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image=ImageField(image_name=image_dto.image_name),
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width=image_dto.width,
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height=image_dto.height,
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)
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