Merge branch 'develop'

README.md

@@ -81,6 +81,15 @@ Each component of the output image is scaled in the range of 0.0 to 1.0.
 Depth estimation using Marigold.
 
 
+### Generator/Stereo Image Generator
+
+Generates stereo image; 
+This custom node calls the image transformation algorithm contained in the following extension for A1111
+(automatically cloned).
+https://github.com/thygate/stable-diffusion-webui-depthmap-script
+
+
+
 ### utils/OpenAI Translate to English
 
 Translates text written in any language into English using GPT-4.

__init__.py

@@ -8,6 +8,7 @@ from .negi.noise_image_generator import NoiseImageGenerator
 from .negi.open_pose_to_point_list import OpenPoseToPointList
 from .negi.point_list_to_mask import PointListToMask
 from .negi.depth_estimation_by_marigold import DepthEstimationByMarigold
+from .negi.stereo_image_generator import StereoImageGenerator
 
 NODE_CLASS_MAPPINGS = {
     "NegiTools_OpenAiDalle3": OpenAiDalle3,
@@ -21,6 +22,7 @@ NODE_CLASS_MAPPINGS = {
     "NegiTools_OpenPoseToPointList": OpenPoseToPointList,
     "NegiTools_PointListToMask": PointListToMask,
     "NegiTools_DepthEstimationByMarigold": DepthEstimationByMarigold,
+    "NegiTools_StereoImageGenerator": StereoImageGenerator,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -35,4 +37,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "NegiTools_OpenPoseToPointList": "OpenPose to Point List 🧅",
     "NegiTools_PointListToMask": "Point List to Mask 🧅",
     "NegiTools_DepthEstimationByMarigold": "Depth Estimation by Marigold (experimental) 🧅",
+    "NegiTools_StereoImageGenerator": "Stereo Image Generator 🧅",
 }

negi/stereo_image_generator.py

@@ -0,0 +1,102 @@
+import os
+import subprocess
+import importlib
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+
+
+_dependency_dir = "dependencies"
+_repository_name = "stable-diffusion-webui-depthmap-script"
+
+
+class StereoImageGenerator:
+    def __check_environment(self):
+        if not os.path.isdir(os.path.join(self.dep_dir, _repository_name)):
+            r0 = subprocess.run([
+                "git", "clone", "https://github.com/thygate/stable-diffusion-webui-depthmap-script.git"
+            ], cwd=self.dep_dir)
+            if r0.returncode != 0:
+                subprocess.run(["rm", "-rf", _repository_name], cwd=self.dep_dir)
+                raise RuntimeError("Marigold repository not found or connection error")
+
+    def __init__(self):
+        self.dep_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), _dependency_dir)
+        os.makedirs(self.dep_dir, exist_ok=True)
+        self.rep_dir = os.path.join(self.dep_dir, _repository_name)
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "depth_image": ("IMAGE",),
+                "divergence": ("FLOAT", {
+                    "default": 5.0, "min": 0.05, "max": 10.0, "step": 0.01, "round": 0.001, "display": "slider"
+                }),
+                "stereo_offset_exponent": ("FLOAT", {
+                    "default": 1.0, "min": 0.1, "max": 3.0, "step": 0.1, "round": 0.01, "display": "slider"
+                }),
+                "fill_technique": ([
+                    "polylines_sharp", "polylines_soft", "naive", "naive_interpolating", "none"
+                ],),
+                "output_mode": ([
+                    "L-R", "R-L", "L-R-L",
+                ],),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
+    RETURN_NAMES = ("STEREO_IMAGE", "IMAGE_L", "IMAGE_R")
+    FUNCTION = "doit"
+    OUTPUT_NODE = False
+    CATEGORY = "Generator"
+
+    from .noise_image_generator import NoiseImageGenerator
+
+    def doit(self, image, depth_image, divergence, stereo_offset_exponent, fill_technique, output_mode):
+        self.__check_environment()
+        m = importlib.import_module(
+            "." + ".".join([_dependency_dir, _repository_name, "src", "stereoimage_generation"]),
+            ".".join(__name__.split(".")[:-2]))
+
+        xw = image.shape[2]
+        yw = image.shape[1]
+        image = torchvision.transforms.functional.to_pil_image(torch.permute(image[0], (2, 0, 1)))
+
+        depth_map = depth_image.to('cpu').detach().numpy()[0, :, :, 0]
+        depth_min = np.min(depth_map)
+        depth_max = np.max(depth_map)
+        if depth_max == depth_min:
+            depth_max = depth_min + 1.0
+        depth_map = (depth_map - depth_min) * (1.0 / (depth_max - depth_min))
+        depth_map = 1.0 - depth_map
+
+        modes = ["left-only", "only-right"]
+        if output_mode == "L-R":
+            modes.append("left-right")
+        elif output_mode == "R-L":
+            modes.append("right-left")
+        elif output_mode == "L-R-L":
+            pass
+        else:
+            raise ValueError()
+
+        images = m.create_stereoimages(
+            image, depth_map, divergence, modes=modes,
+            stereo_offset_exponent=stereo_offset_exponent, fill_technique=fill_technique)
+
+        if output_mode == "L-R-L":
+            out_image = Image.new("RGB", (xw * 3, yw))
+            out_image.paste(images[0], (0, 0))
+            out_image.paste(images[1], (xw, 0))
+            out_image.paste(images[0], (xw * 2, 0))
+            images.append(out_image)
+
+        return (
+            torch.from_numpy(np.expand_dims(np.array(images[2]) * (1.0 / 255), axis=0)),
+            torch.from_numpy(np.expand_dims(np.array(images[0]) * (1.0 / 255), axis=0)),
+            torch.from_numpy(np.expand_dims(np.array(images[1]) * (1.0 / 255), axis=0)),
+        )

requirements.txt

@@ -1,2 +1,3 @@
 openai >= 1.3.0
 controlnet-aux >= 0.0.7
+numba >= 0.58.1