InvokeAI/invokeai/app/invocations/upscale.py
psychedelicious 044d4c107a feat(nodes): move all invocation metadata (type, title, tags, category) to decorator
All invocation metadata (type, title, tags and category) are now defined in decorators.

The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.

Category is a new invocation metadata, but it is not used by the frontend just yet.

- `@invocation()` decorator for invocations

```py
@invocation(
    "sdxl_compel_prompt",
    title="SDXL Prompt",
    tags=["sdxl", "compel", "prompt"],
    category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
    ...
```

- `@invocation_output()` decorator for invocation outputs

```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
    ...
```

- update invocation docs
- add category to decorator
- regen frontend types
2023-08-30 18:35:12 +10:00

117 lines
4.1 KiB
Python

# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
from pathlib import Path
from typing import Literal
import cv2 as cv
import numpy as np
from basicsr.archs.rrdbnet_arch import RRDBNet
from PIL import Image
from realesrgan import RealESRGANer
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
# TODO: Populate this from disk?
# TODO: Use model manager to load?
ESRGAN_MODELS = Literal[
"RealESRGAN_x4plus.pth",
"RealESRGAN_x4plus_anime_6B.pth",
"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
"RealESRGAN_x2plus.pth",
]
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan")
class ESRGANInvocation(BaseInvocation):
"""Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image")
model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
models_path = context.services.configuration.models_path
rrdbnet_model = None
netscale = None
esrgan_model_path = None
if self.model_name in [
"RealESRGAN_x4plus.pth",
"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
]:
# x4 RRDBNet model
rrdbnet_model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
)
netscale = 4
elif self.model_name in ["RealESRGAN_x4plus_anime_6B.pth"]:
# x4 RRDBNet model, 6 blocks
rrdbnet_model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=6, # 6 blocks
num_grow_ch=32,
scale=4,
)
netscale = 4
elif self.model_name in ["RealESRGAN_x2plus.pth"]:
# x2 RRDBNet model
rrdbnet_model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=2,
)
netscale = 2
else:
msg = f"Invalid RealESRGAN model: {self.model_name}"
context.services.logger.error(msg)
raise ValueError(msg)
esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
upsampler = RealESRGANer(
scale=netscale,
model_path=str(models_path / esrgan_model_path),
model=rrdbnet_model,
half=False,
)
# prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
# We can pass an `outscale` value here, but it just resizes the image by that factor after
# upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
# upscaling, you'll need to add a resize node after this one.
upscaled_image, img_mode = upsampler.enhance(cv_image)
# back to PIL
pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=self.workflow,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)