further improvements to ability to find location of data files

- implement the following pattern for finding data files under both
  regular and editable install conditions:

  import invokeai.foo.bar as bar
  path = bar.__path__[0]

- this *seems* to work reliably with Python 3.9. Testing on 3.10 needs
  to be performed.

ldm/invoke/ckpt_generator/base.py

@@ -17,12 +17,13 @@ import cv2 as cv
 from einops import rearrange, repeat
 from pathlib import Path
 from pytorch_lightning import seed_everything
-from invokeai import assets
+import invokeai.assets.web as web_assets
 from ldm.invoke.devices import choose_autocast
 from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
 from ldm.util import rand_perlin_2d
 
 downsampling = 8
+CAUTION_IMG = 'caution.png'
 
 class CkptGenerator():
     def __init__(self, model, precision):
@@ -315,16 +316,7 @@ class CkptGenerator():
         path = None
         if self.caution_img:
             return self.caution_img
-        path = None
+        path = Path(web_assets.__path__[0]) / CAUTION_IMG
-        for candidate in [
-                *assets.__path__,
-                Path(__file__).parent / '..' / '..' / '..' / 'invokeai' / 'assets'
-        ]:
-            if Path(candidate,CAUTION_IMG).exists():
-                path = Path(candidate,CAUTION_IMG)
-                break
-        if not path:
-            return
         caution = Image.open(path)
         self.caution_img = caution.resize((caution.width // 2, caution.height //2))
         return self.caution_img

ldm/invoke/config/configure_invokeai.py

@@ -22,7 +22,7 @@ from urllib import request
 import requests
 import transformers
 from diffusers import StableDiffusionPipeline, AutoencoderKL
-from invokeai import configs
+import invokeai.configs as configs
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from ldm.invoke.devices import choose_precision, choose_torch_device
 from getpass_asterisk import getpass_asterisk
@@ -52,7 +52,7 @@ Model_dir = 'models'
 Weights_dir = 'ldm/stable-diffusion-v1/'
 
 # the initial "configs" dir is now bundled in the `invokeai.configs` package
-Dataset_path = Path(configs.__path__[-1]) / 'INITIAL_MODELS.yaml'
+Dataset_path = Path(configs.__path__[0]) / 'INITIAL_MODELS.yaml'
 
 Default_config_file =  Path (global_config_dir()) / 'models.yaml'
 SD_Configs = Path (global_config_dir()) / 'stable-diffusion'

ldm/invoke/generator/base.py

@@ -13,6 +13,7 @@ from contextlib import nullcontext
 import cv2
 import numpy as np
 import torch
+
 from PIL import Image, ImageFilter, ImageChops
 from diffusers import DiffusionPipeline
 from einops import rearrange
@@ -20,12 +21,12 @@ from pathlib import Path
 from pytorch_lightning import seed_everything
 from tqdm import trange
 
-from invokeai import assets
+import invokeai.assets.web as web_assets
 from ldm.models.diffusion.ddpm import DiffusionWrapper
 from ldm.util import rand_perlin_2d
 
 downsampling = 8
-CAUTION_IMG = 'web/caution.png'
+CAUTION_IMG = 'caution.png'
 
 class Generator:
     downsampling_factor: int
@@ -321,16 +322,8 @@ class Generator:
         path = None
         if self.caution_img:
             return self.caution_img
-        path = None
+        path = Path(web_assets.__path__[0]) / CAUTION_IMG
-        for candidate in [
+        print(f'DEBUG: path to caution = {path}')
-                *assets.__path__,
-                Path(__file__).parent / '..' / '..' / '..' / 'invokeai' / 'assets'
-        ]:
-            if Path(candidate,CAUTION_IMG).exists():
-                path = Path(candidate,CAUTION_IMG)
-                break
-        if not path:
-            return
         caution = Image.open(path)
         self.caution_img = caution.resize((caution.width // 2, caution.height //2))
         return self.caution_img