Add support for AUTOMATIC1111's image upscaling (found in extras)

backend/src/nodes/impl/external_stable_diffusion.py

@@ -4,6 +4,7 @@
 import io
 import os
 from enum import Enum
+from hashlib import md5
 from typing import Dict, Optional, Union
 
 import cv2
@@ -27,6 +28,8 @@
 STABLE_DIFFUSION_IMG2IMG_PATH = f"/sdapi/v1/img2img"
 STABLE_DIFFUSION_INTERROGATE_PATH = f"/sdapi/v1/interrogate"
 STABLE_DIFFUSION_OPTIONS_PATH = f"/sdapi/v1/options"
+STABLE_DIFFUSION_EXTRA_SINGLE_IMAGE_PATH = f"/sdapi/v1/extra-single-image"
+STABLE_DIFFUSION_UPSCALERS_PATH = f"/sdapi/v1/upscalers"
 
 
 def _stable_diffusion_url(path):
@@ -142,6 +145,34 @@ def verify_api_connection():
     if not has_api_connection:
         raise ValueError("Cannot connect to stable diffusion API service.")
 
+def get_upscalers():
+    """
+    get_upscalers is intended to load the list of available upscalers during import time
+    """
+    global has_api_connection  # pylint: disable=global-statement
+    if not has_api_connection:
+        # Quiet failure - missing API makes it impossible to insantiate the node anyway.
+        return Enum('StableDiffusionUpscaler', {})
+
+    response = get(STABLE_DIFFUSION_UPSCALERS_PATH)
+    upscalers = {}
+    for u in response:
+        if u['name']=="None":
+            # Leave it out, we don't need no-op in chaiNNer.
+            continue
+
+        # Id requirements are strict, but webui can be customised with arbitrarily-named upscalers.
+        id = 'UPSCALER_' + md5(u['name'].encode('utf-8')).hexdigest().upper()
+        upscalers[id] = u['name']
+
+    upscalers_enum = Enum('StableDiffusionUpscaler', upscalers)
+
+    labels = {}
+    for e in upscalers_enum:
+        labels[e] = e.value
+
+    return upscalers_enum, labels
+
 
 def decode_base64_image(image_bytes: Union[bytes, str]) -> np.ndarray:
     image = Image.open(io.BytesIO(base64.b64decode(image_bytes)))

backend/src/packages/chaiNNer_external/external_stable_diffusion/automatic1111/upscaling.py

@@ -0,0 +1,186 @@
+from __future__ import annotations
+
+from enum import Enum
+
+import numpy as np
+from nodes.groups import Condition, if_enum_group, if_group
+from nodes.impl.external_stable_diffusion import (
+    STABLE_DIFFUSION_EXTRA_SINGLE_IMAGE_PATH, decode_base64_image,
+    encode_base64_image, get_upscalers, post, verify_api_connection)
+from nodes.node_cache import cached
+from nodes.properties.inputs import (BoolInput, EnumInput, ImageInput,
+                                     NumberInput, SliderInput)
+from nodes.properties.outputs import ImageOutput
+from nodes.utils.utils import get_h_w_c
+
+from .. import auto1111_group
+
+verify_api_connection()
+upscaler_enum, upscaler_labels = get_upscalers()
+
+class UpscalerMode(Enum):
+    SCALE_BY = "ScaleBy"
+    SCALE_TO = "ScaleTo"
+
+UPSCALER_MODE_LABELS = {
+    UpscalerMode.SCALE_BY: "Scale by",
+    UpscalerMode.SCALE_TO: "Scale to"
+}
+
+@auto1111_group.register(
+    schema_id="chainner:external_stable_diffusion:upscaling",
+    name="Upscale",
+    description="Upscale an image using Automatic1111",
+    icon="MdChangeCircle",
+    inputs=[
+        ImageInput(channels=3),
+        EnumInput(UpscalerMode, default_value=UpscalerMode.SCALE_BY, option_labels=UPSCALER_MODE_LABELS).with_id(1),
+        if_enum_group(1, UpscalerMode.SCALE_BY)(
+            SliderInput(
+                "Resize multiplier",
+                minimum=1.0,
+                default=4.0,
+                maximum=8.0,
+                slider_step=0.1,
+                controls_step=0.1,
+                precision=1,
+            ).with_id(2),
+        ),
+        if_enum_group(1, UpscalerMode.SCALE_TO)(
+            NumberInput("Width", controls_step=1, default=512).with_id(3),
+            NumberInput("Height", controls_step=1, default=512).with_id(4),
+            BoolInput("Crop to fit", default=True).with_id(5),
+        ),
+        EnumInput(
+            upscaler_enum,
+            label="Upscaler 1",
+            option_labels=upscaler_labels,
+        ),
+        BoolInput("Use second upscaler", default=False).with_id(7),
+        if_group(Condition.bool(7, True)) (
+            EnumInput(
+                upscaler_enum,
+                label="Upscaler 2",
+                option_labels=upscaler_labels,
+            ),
+            SliderInput(
+                "Upscaler 2 visibility",
+                minimum=0.0,
+                default=0.0,
+                maximum=1.0,
+                slider_step=0.001,
+                controls_step=0.001,
+                precision=3,
+            ),
+        )
+    ],
+    outputs=[
+        ImageOutput(
+            image_type="""
+                def nearest_valid(n: number) = int & floor(n);
+
+                let in_w = Input0.width;
+                let in_h = Input0.height;
+                let ratio_w = width/in_w;
+                let ratio_h = height/in_h;
+                let larger_ratio = if ratio_w>ratio_h { ratio_w } else { ratio_h };
+
+                let mode = Input1;
+                let factor = Input2;
+
+                let crop = Input5;
+                let width = Input3;
+                let height = Input4;
+
+                match mode==UpscalerMode::ScaleTo {
+                    true => match crop==true {
+                        true => Image {
+                            width: width,
+                            height: height
+                        },
+                        false => Image {
+                            width: nearest_valid(in_w*larger_ratio),
+                            height: nearest_valid(in_h*larger_ratio)
+                        }
+                    },
+                    false => Image{
+                        width: nearest_valid(in_w*factor),
+                        height: nearest_valid(in_h*factor)
+                    }
+                }
+            """,
+            channels=3,
+        )
+    ]
+)
+
+@cached
+def upscale_node(
+    image: np.ndarray,
+    mode: UpscalerMode,
+    upscaling_resize: float,
+    width: int,
+    height: int,
+    crop: bool,
+    upscaler_1: Enum,
+    use_second_upscaler: bool,
+    upscaler_2: Enum,
+    upscaler_2_visibility: float,
+) -> np.ndarray:
+    if mode==UpscalerMode.SCALE_BY:
+        resize_mode = 0
+    else:
+        resize_mode = 1
+
+    if use_second_upscaler:
+        u2 = upscaler_2.value
+    else:
+        u2 = "None"
+
+    request_data = {
+        "resize_mode": resize_mode,
+        "show_extras_results": False,
+        "gfpgan_visibility": 0.0,
+        "codeformer_visibility": 0.0,
+        "codeformer_weight": 0.0,
+        "upscaling_resize": upscaling_resize,
+        "upscaling_resize_w": width,
+        "upscaling_resize_h": height,
+        "upscaling_crop": crop,
+        "upscaler_1": upscaler_1.value,
+        "upscaler_2": u2,
+        "extras_upscaler_2_visibility": upscaler_2_visibility,
+        "upscale_first": False,
+        "image": encode_base64_image(image),
+
+    }
+    response = post(path=STABLE_DIFFUSION_EXTRA_SINGLE_IMAGE_PATH, json_data=request_data)
+    result = decode_base64_image(response["image"])
+
+    ih, iw, _ = get_h_w_c(image)
+    rh, rw, _ = get_h_w_c(result)
+    ratio_w = width/iw
+    ratio_h = height/ih
+    if ratio_w>ratio_h:
+        larger_ratio = ratio_w
+    else:
+        larger_ratio = ratio_h
+
+    if mode==UpscalerMode.SCALE_TO:
+        if crop:
+            assert (rw, rh) == (
+                width,
+                height,
+            ), f"Expected the returned image to be {width}x{height}px but found {w}x{h}px instead "
+        else:
+            assert (rw, rh) == (
+                int(iw * larger_ratio),
+                int(ih * larger_ratio),
+            ), f"Expected the returned image to be {width}x{height}px but found {w}x{h}px instead "
+    else:
+        assert (rw, rh) == (
+            int(iw * upscaling_resize),
+            int(ih * upscaling_resize),
+        ), f"Expected the returned image to be {width}x{height}px but found {w}x{h}px instead "
+
+    return result