Merge branch 'main' into main-text-fixup-PR

invokeai/app/invocations/generate.py

@@ -4,6 +4,8 @@ from datetime import datetime, timezone
 from typing import Any, Literal, Optional, Union
 
 import numpy as np
+
+from torch import Tensor
 from PIL import Image
 from pydantic import Field
 from skimage.exposure.histogram_matching import match_histograms
@@ -12,7 +14,9 @@ from ..services.image_storage import ImageType
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput
-from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
+from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator, Generator
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.util.util import image_to_dataURL
 
 SAMPLER_NAME_VALUES = Literal[
     tuple(InvokeAIGenerator.schedulers())
@@ -41,18 +45,32 @@ class TextToImageInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, sample: Any = None, step: int = 0
+        self, context: InvocationContext, sample: Tensor, step: int
     ) -> None:  
+        # TODO: only output a preview image when requested
+        image = Generator.sample_to_lowres_estimated_image(sample)
+
+        (width, height) = image.size
+        width *= 8
+        height *= 8
+
+        dataURL = image_to_dataURL(image, image_format="JPEG")
+
         context.services.events.emit_generator_progress(
             context.graph_execution_state_id,
             self.id,
+            {
+                "width": width,
+                "height": height,
+                "dataURL": dataURL
+            },
             step,
-            float(step) / float(self.steps),
+            self.steps,
         )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        def step_callback(sample, step=0):
+        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, sample, step)
+            self.dispatch_progress(context, state.latents, state.step)
 
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache

invokeai/app/services/events.py

@@ -1,7 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any, Dict
+from typing import Any, Dict, TypedDict
 
+ProgressImage = TypedDict(
+    "ProgressImage", {"dataURL": str, "width": int, "height": int}
+)
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -23,8 +26,9 @@ class EventServiceBase:
         self,
         graph_execution_state_id: str,
         invocation_id: str,
+        progress_image: ProgressImage | None,
         step: int,
-        percent: float,
+        total_steps: int,
     ) -> None:
         """Emitted when there is generation progress"""
         self.__emit_session_event(
@@ -32,8 +36,9 @@ class EventServiceBase:
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
                 invocation_id=invocation_id,
+                progress_image=progress_image,
                 step=step,
-                percent=percent,
+                total_steps=total_steps,
             ),
         )
 

invokeai/app/services/graph.py

@@ -773,6 +773,24 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
+    # Declare all fields as required; necessary for OpenAPI schema generation build.
+    # Technically only fields without a `default_factory` need to be listed here.
+    # See: https://github.com/pydantic/pydantic/discussions/4577
+    class Config:
+        schema_extra = {
+            'required': [
+                'id',
+                'graph',
+                'execution_graph',
+                'executed',
+                'executed_history',
+                'results',
+                'errors',
+                'prepared_source_mapping',
+                'source_prepared_mapping',
+            ]
+        }
+
     def next(self) -> BaseInvocation | None:
         """Gets the next node ready to execute."""
 

invokeai/backend/generator/base.py

@@ -497,7 +497,8 @@ class Generator:
         matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
         return matched_result
 
-    def sample_to_lowres_estimated_image(self, samples):
+    @staticmethod
+    def sample_to_lowres_estimated_image(samples):
         # origingally adapted from code by @erucipe and @keturn here:
         # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
 

invokeai/backend/util/util.py

@@ -3,6 +3,9 @@ import math
 import multiprocessing as mp
 import os
 import re
+import io
+import base64
+
 from collections import abc
 from inspect import isfunction
 from pathlib import Path
@@ -364,3 +367,16 @@ def url_attachment_name(url: str) -> dict:
 def download_with_progress_bar(url: str, dest: Path) -> bool:
     result = download_with_resume(url, dest, access_token=None)
     return result is not None
+
+
+def image_to_dataURL(image: Image.Image, image_format: str = "PNG") -> str:
+    """
+    Converts an image into a base64 image dataURL.
+    """
+    buffered = io.BytesIO()
+    image.save(buffered, format=image_format)
+    mime_type = Image.MIME.get(image_format.upper(), "image/" + image_format.lower())
+    image_base64 = f"data:{mime_type};base64," + base64.b64encode(
+        buffered.getvalue()
+    ).decode("UTF-8")
+    return image_base64

pyproject.toml

@@ -38,7 +38,7 @@ dependencies = [
   "albumentations",
   "click",
   "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
-  "compel==0.1.10",
+  "compel==1.0.1",
   "datasets",
   "diffusers[torch]~=0.14",
   "dnspython==2.2.1",