feat[web]: use the predicted denoised image for previews (#2915)

Some schedulers report not only the noisy latents at the current
timestep, but also their estimate so far of what the de-noised latents
will be.

It makes for a more legible preview than the noisy latents do.

I think this is a huge improvement, but there are a few considerations:
- Need to not spook @JPPhoto by changing how previews look.
- Some schedulers (most notably **DPM Solver++**) don't provide this
data, and it falls back to the current behavior there. That's not
terrible, but seeing such a big difference in how _previews_ look from
one scheduler to the next might mislead people into thinking there's a
bigger difference in their overall effectiveness than there really is.

My fear of configuration-option-overwhelm leaves me inclined to _not_
add a configuration option for this, but we could.

invokeai/app/invocations/generate.py

@@ -1,17 +1,13 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from datetime import datetime, timezone
-from typing import Any, Literal, Optional, Union
+from functools import partial
+from typing import 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
 
 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, Generator
@@ -45,24 +41,27 @@ class TextToImageInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, sample: Tensor, step: int
-    ) -> None:  
-        # TODO: only output a preview image when requested
-        image = Generator.sample_to_lowres_estimated_image(sample)
-
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+    ) -> None:
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+        
+            sample = intermediate_state.predicted_original
+        else:
+             sample = intermediate_state.latents
+             
+        image = Generator(context.services.model_manager.get_model()).sample_to_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,
+                "width" : width,
                 "height": height,
-                "dataURL": dataURL
+                "dataURL": dataURL,
             },
             step,
             self.steps,
@@ -79,7 +78,7 @@ class TextToImageInvocation(BaseInvocation):
         model= context.services.model_manager.get_model()
         outputs = Txt2Img(model).generate(
             prompt=self.prompt,
-            step_callback=step_callback,
+            step_callback=partial(self.dispatch_progress, context),
             **self.dict(
                 exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
@@ -126,9 +125,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         )
         mask = None
 
-        def step_callback(sample, step=0):
-            self.dispatch_progress(context, sample, step)
-
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
@@ -138,7 +134,7 @@ class ImageToImageInvocation(TextToImageInvocation):
                 prompt=self.prompt,
                 init_image=image,
                 init_mask=mask,
-                step_callback=step_callback,
+            step_callback=partial(self.dispatch_progress, context),
                 **self.dict(
                     exclude={"prompt", "image", "mask"}
                 ),  # Shorthand for passing all of the parameters above manually
@@ -187,19 +183,16 @@ class InpaintInvocation(ImageToImageInvocation):
             else context.services.images.get(self.mask.image_type, self.mask.image_name)
         )
 
-        def step_callback(sample, step=0):
-            self.dispatch_progress(context, sample, step)
-
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        manager = context.services.model_manager.get_model()
+        model = context.services.model_manager.get_model()
         generator_output = next(
             Inpaint(model).generate(
                 prompt=self.prompt,
-                init_image=image,
-                mask_image=mask,
-                step_callback=step_callback,
+                init_img=image,
+                init_mask=mask,
+                step_callback=partial(self.dispatch_progress, context),
                 **self.dict(
                     exclude={"prompt", "image", "mask"}
                 ),  # Shorthand for passing all of the parameters above manually

invokeai/backend/web/invoke_ai_web_server.py

@@ -1022,7 +1022,7 @@ class InvokeAIWebServer:
                     "RGB"
                 )
 
-            def image_progress(sample, step):
+            def image_progress(intermediate_state: PipelineIntermediateState):
                 if self.canceled.is_set():
                     raise CanceledException
 
@@ -1030,6 +1030,14 @@ class InvokeAIWebServer:
                 nonlocal generation_parameters
                 nonlocal progress
 
+                step = intermediate_state.step
+                if intermediate_state.predicted_original is not None:
+                    # Some schedulers report not only the noisy latents at the current timestep,
+                    # but also their estimate so far of what the de-noised latents will be.
+                    sample = intermediate_state.predicted_original
+                else:
+                    sample = intermediate_state.latents
+
                 generation_messages = {
                     "txt2img": "common.statusGeneratingTextToImage",
                     "img2img": "common.statusGeneratingImageToImage",
@@ -1302,16 +1310,9 @@ class InvokeAIWebServer:
 
                 progress.set_current_iteration(progress.current_iteration + 1)
 
-            def diffusers_step_callback_adapter(*cb_args, **kwargs):
-                if isinstance(cb_args[0], PipelineIntermediateState):
-                    progress_state: PipelineIntermediateState = cb_args[0]
-                    return image_progress(progress_state.latents, progress_state.step)
-                else:
-                    return image_progress(*cb_args, **kwargs)
-
             self.generate.prompt2image(
                 **generation_parameters,
-                step_callback=diffusers_step_callback_adapter,
+                step_callback=image_progress,
                 image_callback=image_done,
             )