move all models into subdirectories of ./models

- this required an update to the invoke-ai fork of gfpgan
- simultaneously reverted consolidation of environment and
  requirements files, as their presence in a directory
  triggered setup.py to try to install a sub-package.

ldm/invoke/args.py

@@ -500,13 +500,13 @@ class Args(object):
         postprocessing_group.add_argument(
             '--gfpgan_model_path',
             type=str,
-            default='experiments/pretrained_models/GFPGANv1.4.pth',
+            default='./GFPGANv1.4.pth',
             help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
         )
         postprocessing_group.add_argument(
             '--gfpgan_dir',
             type=str,
-            default='./src/gfpgan',
+            default='./models/gfpgan',
             help='Indicates the directory containing the GFPGAN code.',
         )
         web_server_group.add_argument(

ldm/invoke/conditioning.py

@@ -47,7 +47,6 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
         parsed_prompt = pp.parse_conjunction(prompt_string_cleaned).prompts[0]
 
     parsed_negative_prompt: FlattenedPrompt = pp.parse_conjunction(unconditioned_words).prompts[0]
-    print(f">> Parsed prompt to {parsed_prompt}")
 
     conditioning = None
     cac_args:CrossAttentionControl.Arguments = None

ldm/invoke/restoration/gfpgan.py

@@ -10,8 +10,9 @@ from PIL import Image
 class GFPGAN():
     def __init__(
             self,
-            gfpgan_dir='src/gfpgan',
-            gfpgan_model_path='experiments/pretrained_models/GFPGANv1.4.pth') -> None:
+            gfpgan_dir='models/gfpgan',
+            gfpgan_model_path='GFPGANv1.4.pth'
+    ) -> None:
 
         self.model_path = os.path.join(gfpgan_dir, gfpgan_model_path)
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
@@ -74,6 +75,7 @@ class GFPGAN():
                 image = image.resize(res.size)
             res = Image.blend(image, res, strength)
 
+
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         self.gfpgan = None

ldm/invoke/txt2mask.py

@@ -29,14 +29,14 @@ work fine.
 
 import torch
 import numpy as  np
-from clipseg_models.clipseg import CLIPDensePredT
+from clipseg.clipseg import CLIPDensePredT
 from einops import rearrange, repeat
 from PIL import Image, ImageOps
 from torchvision import transforms
 
 CLIP_VERSION = 'ViT-B/16'
-CLIPSEG_WEIGHTS = 'src/clipseg/weights/rd64-uni.pth'
-CLIPSEG_WEIGHTS_REFINED = 'src/clipseg/weights/rd64-uni-refined.pth'
+CLIPSEG_WEIGHTS = 'models/clipseg/clipseg_weights/rd64-uni.pth'
+CLIPSEG_WEIGHTS_REFINED = 'models/clipseg/clipseg_weights/rd64-uni-refined.pth'
 CLIPSEG_SIZE = 352
 
 class SegmentedGrayscale(object):