Merge branch 'feat_longer_prompts' of github.com:damian0815/InvokeAI into feat_longer_prompts

invokeai/app/cli_app.py

@@ -102,6 +102,29 @@ def generate_matching_edges(
     return edges
 
 
+class SessionError(Exception):
+    """Raised when a session error has occurred"""
+    pass
+
+
+def invoke_all(context: CliContext):
+    """Runs all invocations in the specified session"""
+    context.invoker.invoke(context.session, invoke_all=True)
+    while not context.session.is_complete():
+        # Wait some time
+        session = context.get_session()
+        time.sleep(0.1)
+
+    # Print any errors
+    if context.session.has_error():
+        for n in context.session.errors:
+            print(
+                f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {session.errors[n]}"
+            )
+        
+        raise SessionError()
+
+
 def invoke_cli():
     args = Args()
     config = args.parse_args()
@@ -134,7 +157,6 @@ def invoke_cli():
 
     invoker = Invoker(services)
     session: GraphExecutionState = invoker.create_execution_state()
-
     parser = get_command_parser()
 
     # Uncomment to print out previous sessions at startup
@@ -151,8 +173,7 @@ def invoke_cli():
 
         try:
             # Refresh the state of the session
-            session = invoker.services.graph_execution_manager.get(session.id)
+            history = list(get_graph_execution_history(context.session))
-            history = list(get_graph_execution_history(session))
 
             # Split the command for piping
             cmds = cmd_input.split("|")
@@ -164,7 +185,7 @@ def invoke_cli():
                     raise InvalidArgs("Empty command")
 
                 # Parse args to create invocation
-                args = vars(parser.parse_args(shlex.split(cmd.strip())))
+                args = vars(context.parser.parse_args(shlex.split(cmd.strip())))
 
                 # Override defaults
                 for field_name, field_default in context.defaults.items():
@@ -176,11 +197,11 @@ def invoke_cli():
                 command = CliCommand(command=args)
 
                 # Run any CLI commands immediately
-                # TODO: this won't behave as expected if piping and using e.g. history,
-                # since invocations are gathered and then run together at the end.
-                # This is more efficient if the CLI is running against a distributed
-                # backend, so it's preferable not to change that behavior.
                 if isinstance(command.command, BaseCommand):
+                    # Invoke all current nodes to preserve operation order
+                    invoke_all(context)
+
+                    # Run the command
                     command.command.run(context)
                     continue
 
@@ -193,7 +214,7 @@ def invoke_cli():
                     from_node = (
                         next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
                         if current_id != start_id
-                        else session.graph.get_node(from_id)
+                        else context.session.graph.get_node(from_id)
                     )
                     matching_edges = generate_matching_edges(
                         from_node, command.command
@@ -203,7 +224,7 @@ def invoke_cli():
                 # Parse provided links
                 if "link_node" in args and args["link_node"]:
                     for link in args["link_node"]:
-                        link_node = session.graph.get_node(link)
+                        link_node = context.session.graph.get_node(link)
                         matching_edges = generate_matching_edges(
                             link_node, command.command
                         )
@@ -227,37 +248,24 @@ def invoke_cli():
 
                 current_id = current_id + 1
 
-            # Command line was parsed successfully
+                # Add the node to the session
-            # Add the invocations to the session
+                context.session.add_node(command.command)
-            for invocation in new_invocations:
+                for edge in edges:
-                session.add_node(invocation[0])
-                for edge in invocation[1]:
                     print(edge)
-                    session.add_edge(edge)
+                    context.session.add_edge(edge)
 
-            # Execute all available invocations
+            # Execute all remaining nodes
-            invoker.invoke(session, invoke_all=True)
+            invoke_all(context)
-            while not session.is_complete():
-                # Wait some time
-                session = context.get_session()
-                time.sleep(0.1)
-
-            # Print any errors
-            if session.has_error():
-                for n in session.errors:
-                    print(
-                        f"Error in node {n} (source node {session.prepared_source_mapping[n]}): {session.errors[n]}"
-                    )
-
-                # Start a new session
-                print("Creating a new session")
-                session = invoker.create_execution_state()
-                context.session = session
 
         except InvalidArgs:
             print('Invalid command, use "help" to list commands')
             continue
 
+        except SessionError:
+            # Start a new session
+            print("Session error: creating a new session")
+            context.session = context.invoker.create_execution_state()
+
         except ExitCli:
             break
 

invokeai/backend/generator/base.py

@@ -99,6 +99,7 @@ class Generator:
             h_symmetry_time_pct=h_symmetry_time_pct,
             v_symmetry_time_pct=v_symmetry_time_pct,
             attention_maps_callback=attention_maps_callback,
+            seed=seed,
             **kwargs,
         )
         results = []
@@ -289,9 +290,7 @@ class Generator:
             if self.variation_amount > 0:
                 random.seed()  # reset RNG to an actually random state, so we can get a random seed for variations
                 seed = random.randrange(0, np.iinfo(np.uint32).max)
-            return (seed, initial_noise)
+        return (seed, initial_noise)
-        else:
-            return (seed, None)
 
     # returns a tensor filled with random numbers from a normal distribution
     def get_noise(self, width, height):

invokeai/backend/generator/img2img.py

@@ -1,8 +1,10 @@
 """
 invokeai.backend.generator.img2img descends from .generator
 """
+from typing import Optional
 
 import torch
+from accelerate.utils import set_seed
 from diffusers import logging
 
 from ..stable_diffusion import (
@@ -35,6 +37,7 @@ class Img2Img(Generator):
         h_symmetry_time_pct=None,
         v_symmetry_time_pct=None,
         attention_maps_callback=None,
+        seed=None,
         **kwargs,
     ):
         """
@@ -65,6 +68,7 @@ class Img2Img(Generator):
             # FIXME: use x_T for initial seeded noise
             # We're not at the moment because the pipeline automatically resizes init_image if
             # necessary, which the x_T input might not match.
+            # In the meantime, reset the seed prior to generating pipeline output so we at least get the same result.
             logging.set_verbosity_error()  # quench safety check warnings
             pipeline_output = pipeline.img2img_from_embeddings(
                 init_image,
@@ -73,6 +77,7 @@ class Img2Img(Generator):
                 conditioning_data,
                 noise_func=self.get_noise_like,
                 callback=step_callback,
+                seed=seed
             )
             if (
                 pipeline_output.attention_map_saver is not None
@@ -83,7 +88,9 @@ class Img2Img(Generator):
 
         return make_image
 
-    def get_noise_like(self, like: torch.Tensor):
+    def get_noise_like(self, like: torch.Tensor, seed: Optional[int]):
+        if seed is not None:
+            set_seed(seed)
         device = like.device
         if device.type == "mps":
             x = torch.randn_like(like, device="cpu").to(device)

invokeai/backend/generator/inpaint.py

@@ -223,6 +223,7 @@ class Inpaint(Img2Img):
         inpaint_height=None,
         inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
         attention_maps_callback=None,
+        seed=None,
         **kwargs,
     ):
         """
@@ -319,6 +320,7 @@ class Inpaint(Img2Img):
                 conditioning_data=conditioning_data,
                 noise_func=self.get_noise_like,
                 callback=step_callback,
+                seed=seed
             )
 
             if (

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -690,6 +690,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         callback: Callable[[PipelineIntermediateState], None] = None,
         run_id=None,
         noise_func=None,
+        seed=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB"))
@@ -703,7 +704,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device=self._model_group.device_for(self.unet),
             dtype=self.unet.dtype,
         )
-        noise = noise_func(initial_latents)
+        noise = noise_func(initial_latents, seed)
 
         return self.img2img_from_latents_and_embeddings(
             initial_latents,
@@ -731,9 +732,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device=self._model_group.device_for(self.unet),
         )
         result_latents, result_attention_maps = self.latents_from_embeddings(
-            initial_latents,
+            latents=initial_latents if strength < 1.0 else torch.zeros_like(
-            num_inference_steps,
+                initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
-            conditioning_data,
+            ),
+            num_inference_steps=num_inference_steps,
+            conditioning_data=conditioning_data,
             timesteps=timesteps,
             noise=noise,
             run_id=run_id,
@@ -779,6 +782,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         callback: Callable[[PipelineIntermediateState], None] = None,
         run_id=None,
         noise_func=None,
+        seed=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
         device = self._model_group.device_for(self.unet)
         latents_dtype = self.unet.dtype
@@ -802,7 +806,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         init_image_latents = self.non_noised_latents_from_image(
             init_image, device=device, dtype=latents_dtype
         )
-        noise = noise_func(init_image_latents)
+        noise = noise_func(init_image_latents, seed)
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
@@ -831,9 +835,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(
-                init_image_latents,
+                latents=init_image_latents if strength < 1.0 else torch.zeros_like(
-                num_inference_steps,
+                    init_image_latents, device=init_image_latents.device, dtype=init_image_latents.dtype
-                conditioning_data,
+                ),
+                num_inference_steps=num_inference_steps,
+                conditioning_data=conditioning_data,
                 noise=noise,
                 timesteps=timesteps,
                 additional_guidance=guidance,

invokeai/frontend/web/src/component.tsx

@@ -0,0 +1,38 @@
+import React, { lazy } from 'react';
+import { Provider } from 'react-redux';
+import { PersistGate } from 'redux-persist/integration/react';
+import { store } from './app/store';
+import { persistor } from './persistor';
+import '@fontsource/inter/100.css';
+import '@fontsource/inter/200.css';
+import '@fontsource/inter/300.css';
+import '@fontsource/inter/400.css';
+import '@fontsource/inter/500.css';
+import '@fontsource/inter/600.css';
+import '@fontsource/inter/700.css';
+import '@fontsource/inter/800.css';
+import '@fontsource/inter/900.css';
+
+import Loading from './Loading';
+
+// Localization
+import './i18n';
+
+const App = lazy(() => import('./app/App'));
+const ThemeLocaleProvider = lazy(() => import('./app/ThemeLocaleProvider'));
+
+export default function Component() {
+  return (
+    <React.StrictMode>
+      <Provider store={store}>
+        <PersistGate loading={<Loading />} persistor={persistor}>
+          <React.Suspense fallback={<Loading showText />}>
+            <ThemeLocaleProvider>
+              <App />
+            </ThemeLocaleProvider>
+          </React.Suspense>
+        </PersistGate>
+      </Provider>
+    </React.StrictMode>
+  );
+}

invokeai/frontend/web/src/main.tsx

@@ -1,37 +1,7 @@
-import React, { lazy } from 'react';
 import ReactDOM from 'react-dom/client';
-import { Provider } from 'react-redux';
-import { PersistGate } from 'redux-persist/integration/react';
-import { store } from './app/store';
-import { persistor } from './persistor';
-import '@fontsource/inter/100.css';
-import '@fontsource/inter/200.css';
-import '@fontsource/inter/300.css';
-import '@fontsource/inter/400.css';
-import '@fontsource/inter/500.css';
-import '@fontsource/inter/600.css';
-import '@fontsource/inter/700.css';
-import '@fontsource/inter/800.css';
-import '@fontsource/inter/900.css';
 
-import Loading from './Loading';
+import Component from './component';
-
-// Localization
-import './i18n';
-
-const App = lazy(() => import('./app/App'));
-const ThemeLocaleProvider = lazy(() => import('./app/ThemeLocaleProvider'));
 
 ReactDOM.createRoot(document.getElementById('root') as HTMLElement).render(
-  <React.StrictMode>
+  <Component />
-    <Provider store={store}>
-      <PersistGate loading={<Loading />} persistor={persistor}>
-        <React.Suspense fallback={<Loading showText />}>
-          <ThemeLocaleProvider>
-            <App />
-          </ThemeLocaleProvider>
-        </React.Suspense>
-      </PersistGate>
-    </Provider>
-  </React.StrictMode>
 );

pyproject.toml

@@ -63,6 +63,7 @@ dependencies = [
   "prompt-toolkit",
   "pypatchmatch",
   "pyreadline3",
+  "pytorch-lightning==1.7.7",
   "realesrgan",
   "requests==2.28.2",
   "rich~=13.3",