diff --git a/scripts/controlnet_legacy_txt2img_example.py b/scripts/controlnet_legacy_txt2img_example.py
deleted file mode 100644
index 8400cc0290..0000000000
--- a/scripts/controlnet_legacy_txt2img_example.py
+++ /dev/null
@@ -1,52 +0,0 @@
-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_weight=1.0,
-    seed=0,
-    num_steps=30,
-    precision="float16",
-)
-generate_output = next(outputs)
-out_image = generate_output.image
-out_image.show()
diff --git a/scripts/create_checkpoint_template.py b/scripts/create_checkpoint_template.py
deleted file mode 100755
index 4b9bfe0ea2..0000000000
--- a/scripts/create_checkpoint_template.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python
-"""
-Read a checkpoint/safetensors file and write out a template .json file containing
-its metadata for use in fast model probing.
-"""
-
-import argparse
-import json
-
-from pathlib import Path
-
-from invokeai.backend.model_management.models.base import read_checkpoint_meta
-
-parser = argparse.ArgumentParser(description="Create a .json template from checkpoint/safetensors model")
-parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file")
-parser.add_argument("--template", "--out", type=Path, help="Path to the output .json file")
-
-opt = parser.parse_args()
-ckpt = read_checkpoint_meta(opt.checkpoint)
-while "state_dict" in ckpt:
-    ckpt = ckpt["state_dict"]
-
-tmpl = {}
-
-for key, tensor in ckpt.items():
-    tmpl[key] = list(tensor.shape)
-
-try:
-    with open(opt.template, "w") as f:
-        json.dump(tmpl, f)
-    print(f"Template written out as {opt.template}")
-except Exception as e:
-    print(f"An exception occurred while writing template: {str(e)}")
diff --git a/scripts/invokeai-model-install.py b/scripts/invokeai-model-install.py
old mode 100644
new mode 100755
index b4b0dbc8ef..0cac857f8c
--- a/scripts/invokeai-model-install.py
+++ b/scripts/invokeai-model-install.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+
 from invokeai.frontend.install.model_install import main
 
 main()
diff --git a/scripts/verify_checkpoint_template.py b/scripts/verify_checkpoint_template.py
deleted file mode 100755
index 15194290f5..0000000000
--- a/scripts/verify_checkpoint_template.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env python
-"""
-Read a checkpoint/safetensors file and compare it to a template .json.
-Returns True if their metadata match.
-"""
-
-import sys
-import argparse
-import json
-
-from pathlib import Path
-
-from invokeai.backend.model_management.models.base import read_checkpoint_meta
-
-parser = argparse.ArgumentParser(description="Compare a checkpoint/safetensors file to a JSON metadata template.")
-parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file")
-parser.add_argument("--template", "--out", type=Path, help="Path to the template .json file to match against")
-
-opt = parser.parse_args()
-ckpt = read_checkpoint_meta(opt.checkpoint)
-while "state_dict" in ckpt:
-    ckpt = ckpt["state_dict"]
-
-checkpoint_metadata = {}
-
-for key, tensor in ckpt.items():
-    checkpoint_metadata[key] = list(tensor.shape)
-
-with open(opt.template, "r") as f:
-    template = json.load(f)
-
-if checkpoint_metadata == template:
-    print("True")
-    sys.exit(0)
-else:
-    print("False")
-    sys.exit(-1)