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.

docs/installation/INSTALL_DOCKER.md

@@ -70,7 +70,7 @@ Some Suggestions of variables you may want to change besides the Token:
 | `HUGGINGFACE_TOKEN="hg_aewirhghlawrgkjbarug2"`                      | This is the only required variable, without you can't get the checkpoint |
 | `ARCH=aarch64`                                                      | if you are using a ARM based CPU                                         |
 | `INVOKEAI_TAG=yourname/invokeai:latest`                             | the Container Repository / Tag which will be used                        |
-| `INVOKEAI_CONDA_ENV_FILE=environments/environment-linux-aarch64.yml`| since environment.yml wouldn't work with aarch                           |
+| `INVOKEAI_CONDA_ENV_FILE=environment-linux-aarch64.yml`             | since environment.yml wouldn't work with aarch                           |
 | `INVOKEAI_GIT="-b branchname https://github.com/username/reponame"` | if you want to use your own fork                                         |
 
 #### Build the Image

docs/installation/INSTALL_LINUX.md

@@ -48,7 +48,7 @@ title: Manual Installation, Linux
 
        ```bash
        (base) rm -rf src      # (this is a precaution in case there is already a src directory)
-       (base) ~/InvokeAI$ conda env create -f environments/environment-cuda.yml
+       (base) ~/InvokeAI$ conda env create -f environment-cuda.yml
        (base) ~/InvokeAI$ conda activate invokeai
        (invokeai) ~/InvokeAI$
        ```
@@ -57,7 +57,7 @@ title: Manual Installation, Linux
 
        ```bash
        (base) rm -rf src      # (this is a precaution in case there is already a src directory)
-       (base) ~/InvokeAI$ conda env create -f environments/environment-rocm.yml
+       (base) ~/InvokeAI$ conda env create -f environment-AMD.yml
        (base) ~/InvokeAI$ conda activate invokeai
        (invokeai) ~/InvokeAI$
        ```

docs/installation/INSTALL_WINDOWS.md

@@ -45,7 +45,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
 
        ```bash
        rmdir src      # (this is a precaution in case there is already a src directory)
-       conda env create -f environments/environment-cuda.yml
+       conda env create -f environment-cuda.yml
        conda activate invokeai
        (invokeai)>
        ```
@@ -54,7 +54,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
 
        ```bash
        rmdir src      # (this is a precaution in case there is already a src directory)
-       conda env create -f environments/environment-rocm.yml
+       conda env create -f environment-AMD.yml
        conda activate invokeai
        (invokeai)>
        ```

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_dir='models/gfpgan',
-            gfpgan_model_path='experiments/pretrained_models/GFPGANv1.4.pth') -> None:
+            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 = 'models/clipseg/clipseg_weights/rd64-uni.pth'
-CLIPSEG_WEIGHTS_REFINED = 'src/clipseg/weights/rd64-uni-refined.pth'
+CLIPSEG_WEIGHTS_REFINED = 'models/clipseg/clipseg_weights/rd64-uni-refined.pth'
 CLIPSEG_SIZE = 352
 
 class SegmentedGrayscale(object):

scripts/preload_models.py

@@ -415,7 +415,7 @@ def download_kornia():
 
 #---------------------------------------------
 def download_clip():
-    print('Loading CLIP model...',end='')
+    print('Loading CLIP model (ignore deprecation errors)...',end='')
     sys.stdout.flush()
     version = 'openai/clip-vit-large-patch14'
     tokenizer = CLIPTokenizer.from_pretrained(version)
@@ -424,7 +424,7 @@ def download_clip():
 
 #---------------------------------------------
 def download_gfpgan():
-    print('Installing models from RealESRGAN and facexlib...',end='')
+    print('Installing models from RealESRGAN and facexlib  (ignore deprecation errors)...',end='')
     try:
         from realesrgan import RealESRGANer
         from realesrgan.archs.srvgg_arch import SRVGGNetCompact
@@ -442,19 +442,19 @@ def download_gfpgan():
         print('Error loading ESRGAN:')
         print(traceback.format_exc())
 
-    print('Loading models from GFPGAN')
+    print('Loading models from GFPGAN...',end='')
     for model in (
             [
                 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
-                'src/gfpgan/experiments/pretrained_models/GFPGANv1.4.pth'
+                'models/gfpgan/GFPGANv1.4.pth'
             ],
             [
                 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth',
-                './gfpgan/weights/detection_Resnet50_Final.pth'
+                'models/gfpgan/weights/detection_Resnet50_Final.pth'
             ],
             [
                 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth',
-                './gfpgan/weights/parsing_parsenet.pth'
+                'models/gfpgan/weights/parsing_parsenet.pth'
             ],
     ):
         model_url,model_dest  = model
@@ -489,22 +489,23 @@ def download_clipseg():
     import zipfile
     try:
         model_url  = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
-        model_dest = 'src/clipseg/clipseg_weights.zip'
+        model_dest = 'models/clipseg/clipseg_weights'
-        weights_dir = 'src/clipseg/weights'
+        weights_zip = 'models/clipseg/weights.zip'
-        if not os.path.exists(weights_dir):
+        
+        if not os.path.exists(model_dest):
             os.makedirs(os.path.dirname(model_dest), exist_ok=True)
-        if not os.path.exists('src/clipseg/weights/rd64-uni-refined.pth'):
+        if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
-            request.urlretrieve(model_url,model_dest)
+            request.urlretrieve(model_url,weights_zip)
-            with zipfile.ZipFile(model_dest,'r') as zip:
+            with zipfile.ZipFile(weights_zip,'r') as zip:
-                zip.extractall('src/clipseg')
+                zip.extractall('models/clipseg')
-                os.rename('src/clipseg/clipseg_weights','src/clipseg/weights')
+            os.remove(weights_zip)
-            os.remove(model_dest)
+
-            from clipseg_models.clipseg import CLIPDensePredT
+            from clipseg.clipseg import CLIPDensePredT
             model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, )
             model.eval()
             model.load_state_dict(
                 torch.load(
-                    'src/clipseg/weights/rd64-uni-refined.pth',
+                    'models/clipseg/clipseg_weights/rd64-uni-refined.pth',
                     map_location=torch.device('cpu')
                     ),
                 strict=False,

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
     name='invoke-ai',
     version='2.1.3',
-    description='',
+    description='InvokeAI text to image generation toolkit',
     packages=find_packages(),
     install_requires=[
         'torch',
@@ -11,3 +11,4 @@ setup(
         'tqdm',
     ],
 )
+