Added example of using ControlNet with legacy Txt2Img generator

scripts/controlnet_legacy_txt2img_example.py

@@ -0,0 +1,54 @@
+import os
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+from diffusers.utils import load_image
+from diffusers.models.controlnet import ControlNetModel
+from invokeai.backend.generator import Txt2Img
+from invokeai.backend.model_management import ModelManager
+
+
+print("loading 'Girl with a Pearl Earring' image")
+image = load_image(
+    "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
+)
+image.show()
+
+print("preprocessing image with Canny edge detection")
+image_np = np.array(image)
+low_threshold = 100
+high_threshold = 200
+canny_np = cv2.Canny(image_np, low_threshold, high_threshold)
+canny_image = Image.fromarray(canny_np)
+canny_image.show()
+
+# using invokeai model management for base model
+print("loading base model stable-diffusion-1.5")
+model_config_path = os.getcwd() + "/../configs/models.yaml"
+model_manager = ModelManager(model_config_path)
+model = model_manager.get_model('stable-diffusion-1.5')
+
+print("loading control model lllyasviel/sd-controlnet-canny")
+canny_controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny",
+                                                   torch_dtype=torch.float16).to("cuda")
+
+print("testing Txt2Img() constructor with control_model arg")
+txt2img_canny = Txt2Img(model, control_model=canny_controlnet)
+
+print("testing Txt2Img.generate() with control_image arg")
+outputs = txt2img_canny.generate(
+    prompt="old man",
+    control_image=canny_image,
+    control_scale=1.0,
+    seed=0,
+    num_steps=30,
+    precision="float16",
+)
+generate_output = next(outputs)
+out_image = generate_output.image
+out_image.show()
+
+
+