Merge branch 'main' into install/force-torch-reinstall

.github/workflows/build-container.yml

@@ -47,7 +47,7 @@ jobs:
             type=semver,pattern={{version}}
             type=semver,pattern={{major}}.{{minor}}
             type=semver,pattern={{major}}
-            type=raw,value='sha'-{{sha}}-${{ matrix.flavor}}
+            type=sha,enable=true,prefix=sha-,suffix=${{ matrix.flavor}},format=short
             type=raw,value={{branch}}-${{ matrix.flavor }}
           flavor: |
             latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}

invokeai/frontend/README.md

@@ -1,28 +1,20 @@
-# Stable Diffusion Web UI
+# InvokeAI UI dev setup
 
-## Run
+The UI is in `invokeai/frontend`.
 
-- `python scripts/dream.py --web` serves both frontend and backend at
-  http://localhost:9090
+## Environment set up
 
-## Evironment
-
-Install [node](https://nodejs.org/en/download/) (includes npm) and optionally
+Install [node](https://nodejs.org/en/download/) (includes npm) and
 [yarn](https://yarnpkg.com/getting-started/install).
 
-From `frontend/` run `npm install` / `yarn install` to install the frontend
-packages.
+From `invokeai/frontend/` run `yarn install` to get everything set up.
 
 ## Dev
 
-1. From `frontend/`, run `npm dev` / `yarn dev` to start the dev server.
-2. Run `python scripts/dream.py --web`.
-3. Navigate to the dev server address e.g. `http://localhost:5173/`.
+1. Start the dev server: `yarn dev`
+2. Start the InvokeAI UI per usual: `invokeai --web`
+3. Point your browser to the dev server address e.g. `http://localhost:5173/`
 
-To build for dev: `npm build-dev` / `yarn build-dev`
+To build for dev: `yarn build-dev`
 
-To build for production: `npm build` / `yarn build`
-
-## TODO
-
-- Search repo for "TODO"
+To build for production: `yarn build`

ldm/invoke/generator/base.py

@@ -122,6 +122,10 @@ class Generator:
 
                 seed = self.new_seed()
 
+                # Free up memory from the last generation.
+                if self.model.device.type == 'cuda':
+                    torch.cuda.empty_cache()
+
         return results
 
     def sample_to_image(self,samples)->Image.Image:
@@ -240,7 +244,12 @@ class Generator:
 
     def get_perlin_noise(self,width,height):
         fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
-        noise = torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
+        # limit noise to only the diffusion image channels, not the mask channels
+        input_channels = min(self.latent_channels, 4)
+        noise = torch.stack([
+            rand_perlin_2d((height, width),
+                           (8, 8),
+                           device = self.model.device).to(fixdevice) for _ in range(input_channels)], dim=0).to(self.model.device)
         return noise
 
     def new_seed(self):
@@ -341,3 +350,27 @@ class Generator:
 
     def torch_dtype(self)->torch.dtype:
         return torch.float16 if self.precision == 'float16' else torch.float32
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height):
+        device         = self.model.device
+        # limit noise to only the diffusion image channels, not the mask channels
+        input_channels = min(self.latent_channels, 4)
+        if self.use_mps_noise or device.type == 'mps':
+            x = torch.randn([1,
+                             input_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device='cpu').to(device)
+        else:
+            x = torch.randn([1,
+                             input_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device=device)
+        if self.perlin > 0.0:
+            perlin_noise = self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
+            x = (1-self.perlin)*x + self.perlin*perlin_noise
+        return x

ldm/invoke/generator/img2img.py

@@ -63,22 +63,3 @@ class Img2Img(Generator):
             shape = like.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
         return x
-
-    def get_noise(self,width,height):
-        # copy of the Txt2Img.get_noise
-        device         = self.model.device
-        if self.use_mps_noise or device.type == 'mps':
-            x = torch.randn([1,
-                                self.latent_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device='cpu').to(device)
-        else:
-            x = torch.randn([1,
-                                self.latent_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device=device)
-        if self.perlin > 0.0:
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
-        return x

ldm/invoke/generator/txt2img.py

@@ -51,26 +51,4 @@ class Txt2Img(Generator):
         return make_image
 
 
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height):
-        device         = self.model.device
-        # limit noise to only the diffusion image channels, not the mask channels
-        input_channels = min(self.latent_channels, 4)
-        if self.use_mps_noise or device.type == 'mps':
-            x = torch.randn([1,
-                             input_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device='cpu').to(device)
-        else:
-            x = torch.randn([1,
-                             input_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device=device)
-        if self.perlin > 0.0:
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
-        return x
 

ldm/invoke/merge_diffusers.py

@@ -15,20 +15,18 @@ from pathlib import Path
 from typing import List, Union
 
 import npyscreen
-from diffusers import DiffusionPipeline, logging as dlogging
+from diffusers import DiffusionPipeline
+from diffusers import logging as dlogging
+from npyscreen import widget
 from omegaconf import OmegaConf
 
-from ldm.invoke.globals import (
-    Globals,
-    global_cache_dir,
-    global_config_file,
-    global_models_dir,
-    global_set_root,
-)
+from ldm.invoke.globals import (Globals, global_cache_dir, global_config_file,
+                                global_models_dir, global_set_root)
 from ldm.invoke.model_manager import ModelManager
 
 DEST_MERGED_MODEL_DIR = "merged_models"
 
+
 def merge_diffusion_models(
     model_ids_or_paths: List[Union[str, Path]],
     alpha: float = 0.5,
@@ -48,10 +46,10 @@ def merge_diffusion_models(
          cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
     """
     with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
+        warnings.simplefilter("ignore")
         verbosity = dlogging.get_verbosity()
         dlogging.set_verbosity_error()
-        
+
         pipe = DiffusionPipeline.from_pretrained(
             model_ids_or_paths[0],
             cache_dir=kwargs.get("cache_dir", global_cache_dir()),
@@ -188,13 +186,12 @@ class FloatTitleSlider(npyscreen.TitleText):
 
 
 class mergeModelsForm(npyscreen.FormMultiPageAction):
-
     interpolations = ["weighted_sum", "sigmoid", "inv_sigmoid", "add_difference"]
 
     def __init__(self, parentApp, name):
         self.parentApp = parentApp
-        self.ALLOW_RESIZE=True
-        self.FIX_MINIMUM_SIZE_WHEN_CREATED=False
+        self.ALLOW_RESIZE = True
+        self.FIX_MINIMUM_SIZE_WHEN_CREATED = False
         super().__init__(parentApp, name)
 
     @property
@@ -205,29 +202,29 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         self.parentApp.setNextForm(None)
 
     def create(self):
-        window_height,window_width=curses.initscr().getmaxyx()
-        
+        window_height, window_width = curses.initscr().getmaxyx()
+
         self.model_names = self.get_model_names()
         max_width = max([len(x) for x in self.model_names])
         max_width += 6
-        horizontal_layout = max_width*3 < window_width
-        
+        horizontal_layout = max_width * 3 < window_width
+
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            color='CONTROL',
+            color="CONTROL",
             value=f"Select two models to merge and optionally a third.",
             editable=False,
         )
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            color='CONTROL',
+            color="CONTROL",
             value=f"Use up and down arrows to move, <space> to select an item, <tab> and <shift-tab> to move from one field to the next.",
             editable=False,
         )
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            value='MODEL 1',
-            color='GOOD',
+            value="MODEL 1",
+            color="GOOD",
             editable=False,
             rely=4 if horizontal_layout else None,
         )
@@ -242,57 +239,57 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         )
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            value='MODEL 2',
-            color='GOOD',
+            value="MODEL 2",
+            color="GOOD",
             editable=False,
-            relx=max_width+3 if horizontal_layout else None,
+            relx=max_width + 3 if horizontal_layout else None,
             rely=4 if horizontal_layout else None,
         )
         self.model2 = self.add_widget_intelligent(
             npyscreen.SelectOne,
-            name='(2)',
+            name="(2)",
             values=self.model_names,
             value=1,
             max_height=len(self.model_names),
             max_width=max_width,
-            relx=max_width+3 if horizontal_layout else None,
+            relx=max_width + 3 if horizontal_layout else None,
             rely=5 if horizontal_layout else None,
             scroll_exit=True,
         )
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            value='MODEL 3',
-            color='GOOD',
+            value="MODEL 3",
+            color="GOOD",
             editable=False,
-            relx=max_width*2+3 if horizontal_layout else None,
+            relx=max_width * 2 + 3 if horizontal_layout else None,
             rely=4 if horizontal_layout else None,
         )
         models_plus_none = self.model_names.copy()
-        models_plus_none.insert(0,'None')
+        models_plus_none.insert(0, "None")
         self.model3 = self.add_widget_intelligent(
             npyscreen.SelectOne,
-            name='(3)',
+            name="(3)",
             values=models_plus_none,
             value=0,
-            max_height=len(self.model_names)+1,
+            max_height=len(self.model_names) + 1,
             max_width=max_width,
             scroll_exit=True,
-            relx=max_width*2+3 if horizontal_layout else None,
+            relx=max_width * 2 + 3 if horizontal_layout else None,
             rely=5 if horizontal_layout else None,
         )
-        for m in [self.model1,self.model2,self.model3]:
+        for m in [self.model1, self.model2, self.model3]:
             m.when_value_edited = self.models_changed
         self.merged_model_name = self.add_widget_intelligent(
             npyscreen.TitleText,
             name="Name for merged model:",
-            labelColor='CONTROL',
+            labelColor="CONTROL",
             value="",
             scroll_exit=True,
         )
         self.force = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Force merge of incompatible models",
-            labelColor='CONTROL',
+            labelColor="CONTROL",
             value=False,
             scroll_exit=True,
         )
@@ -301,7 +298,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
             name="Merge Method:",
             values=self.interpolations,
             value=0,
-            labelColor='CONTROL',
+            labelColor="CONTROL",
             max_height=len(self.interpolations) + 1,
             scroll_exit=True,
         )
@@ -312,7 +309,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
             step=0.05,
             lowest=0,
             value=0.5,
-            labelColor='CONTROL',
+            labelColor="CONTROL",
             scroll_exit=True,
         )
         self.model1.editing = True
@@ -322,43 +319,43 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
         selected_model1 = self.model1.value[0]
         selected_model2 = self.model2.value[0]
         selected_model3 = self.model3.value[0]
-        merged_model_name = f'{models[selected_model1]}+{models[selected_model2]}'
+        merged_model_name = f"{models[selected_model1]}+{models[selected_model2]}"
         self.merged_model_name.value = merged_model_name
-        
+
         if selected_model3 > 0:
-            self.merge_method.values=['add_difference'],
-            self.merged_model_name.value += f'+{models[selected_model3]}'
+            self.merge_method.values = (["add_difference"],)
+            self.merged_model_name.value += f"+{models[selected_model3]}"
         else:
-            self.merge_method.values=self.interpolations
-        self.merge_method.value=0
+            self.merge_method.values = self.interpolations
+        self.merge_method.value = 0
 
     def on_ok(self):
         if self.validate_field_values() and self.check_for_overwrite():
             self.parentApp.setNextForm(None)
             self.editing = False
             self.parentApp.merge_arguments = self.marshall_arguments()
-            npyscreen.notify('Starting the merge...')
+            npyscreen.notify("Starting the merge...")
         else:
             self.editing = True
 
     def on_cancel(self):
         sys.exit(0)
 
-    def marshall_arguments(self)->dict:
+    def marshall_arguments(self) -> dict:
         model_names = self.model_names
         models = [
             model_names[self.model1.value[0]],
             model_names[self.model2.value[0]],
-            ]
+        ]
         if self.model3.value[0] > 0:
-            models.append(model_names[self.model3.value[0]-1])
+            models.append(model_names[self.model3.value[0] - 1])
 
         args = dict(
             models=models,
-            alpha = self.alpha.value,
-            interp = self.interpolations[self.merge_method.value[0]],
-            force = self.force.value,
-            merged_model_name = self.merged_model_name.value,
+            alpha=self.alpha.value,
+            interp=self.interpolations[self.merge_method.value[0]],
+            force=self.force.value,
+            merged_model_name=self.merged_model_name.value,
         )
         return args
 
@@ -371,18 +368,22 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
                 f"The chosen merged model destination, {model_out}, is already in use. Overwrite?"
             )
 
-    def validate_field_values(self)->bool:
+    def validate_field_values(self) -> bool:
         bad_fields = []
         model_names = self.model_names
-        selected_models = set((model_names[self.model1.value[0]],model_names[self.model2.value[0]]))
+        selected_models = set(
+            (model_names[self.model1.value[0]], model_names[self.model2.value[0]])
+        )
         if self.model3.value[0] > 0:
-            selected_models.add(model_names[self.model3.value[0]-1])
+            selected_models.add(model_names[self.model3.value[0] - 1])
         if len(selected_models) < 2:
-            bad_fields.append(f'Please select two or three DIFFERENT models to compare. You selected {selected_models}')
+            bad_fields.append(
+                f"Please select two or three DIFFERENT models to compare. You selected {selected_models}"
+            )
         if len(bad_fields) > 0:
-            message = 'The following problems were detected and must be corrected:'
+            message = "The following problems were detected and must be corrected:"
             for problem in bad_fields:
-                message += f'\n* {problem}'
+                message += f"\n* {problem}"
             npyscreen.notify_confirm(message)
             return False
         else:
@@ -410,6 +411,7 @@ class Mergeapp(npyscreen.NPSAppManaged):
         npyscreen.setTheme(npyscreen.Themes.ElegantTheme)
         self.main = self.addForm("MAIN", mergeModelsForm, name="Merge Models Settings")
 
+
 def run_gui(args: Namespace):
     mergeapp = Mergeapp()
     mergeapp.run()
@@ -450,5 +452,27 @@ def main():
     ] = cache_dir  # because not clear the merge pipeline is honoring cache_dir
     args.cache_dir = cache_dir
 
+    try:
+        if args.front_end:
+            run_gui(args)
+        else:
+            run_cli(args)
+        print(f">> Conversion successful. New model is named {args.merged_model_name}")
+    except widget.NotEnoughSpaceForWidget as e:
+        if str(e).startswith("Height of 1 allocated"):
+            print(
+                "** You need to have at least two diffusers models defined in models.yaml in order to merge"
+            )
+        else:
+            print(f"** A layout error has occurred: {str(e)}")
+        sys.exit(-1)
+    except Exception as e:
+        print(">> An error occurred:")
+        traceback.print_exc()
+        sys.exit(-1)
+    except KeyboardInterrupt:
+        sys.exit(-1)
+
+
 if __name__ == "__main__":
     main()

ldm/invoke/training/textual_inversion.py

@@ -17,6 +17,7 @@ from pathlib import Path
 from typing import List, Tuple
 
 import npyscreen
+from npyscreen import widget
 from omegaconf import OmegaConf
 
 from ldm.invoke.globals import Globals, global_set_root
@@ -295,7 +296,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             for idx in range(len(model_names))
             if "default" in conf[model_names[idx]]
         ]
-        default = defaults[0] if len(defaults)>0 else 0
+        default = defaults[0] if len(defaults) > 0 else 0
         return (model_names, default)
 
     def marshall_arguments(self) -> dict:
@@ -438,11 +439,20 @@ def main():
             do_front_end(args)
         else:
             do_textual_inversion_training(**vars(args))
+    except widget.NotEnoughSpaceForWidget as e:
+        if str(e).startswith("Height of 1 allocated"):
+            print(
+                "** You need to have at least one diffusers models defined in models.yaml in order to train"
+            )
+        else:
+            print(f"** A layout error has occurred: {str(e)}")
+        sys.exit(-1)
     except AssertionError as e:
         print(str(e))
         sys.exit(-1)
     except KeyboardInterrupt:
         pass
 
+
 if __name__ == "__main__":
     main()

scripts/pypi_helper.py

@@ -1,26 +1,26 @@
-import requests as request
+import requests
 
-import ldm.invoke._version as version
+from ldm.invoke import __app_name__, __version__
 
-local_version = str(version.__version__)
+local_version = str(__version__).replace("-", "")
+package_name = str(__app_name__)
 
 
-def get_pypi_versions(package_name="InvokeAI") -> list[str]:
+def get_pypi_versions(package_name=package_name) -> list[str]:
     """Get the versions of the package from PyPI"""
     url = f"https://pypi.org/pypi/{package_name}/json"
-    response = request.get(url).json()
+    response = requests.get(url).json()
     versions: list[str] = list(response["releases"].keys())
     return versions
 
 
-def local_on_pypi(package_name="InvokeAI", local_version=local_version) -> bool:
+def local_on_pypi(package_name=package_name, local_version=local_version) -> bool:
     """Compare the versions of the package from PyPI and the local package"""
     pypi_versions = get_pypi_versions(package_name)
     return local_version in pypi_versions
 
 
 if __name__ == "__main__":
-    package_name = "InvokeAI"
     if local_on_pypi():
         print(f"Package {package_name} is up to date")
     else: