Merge branch 'AUTOMATIC1111:master' into master

1 files changed, 19 insertions, 6 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 9e5a2f38..262806a1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -13,7 +13,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional

 

 import modules.sd_hijack

-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx

+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts

 from modules.sd_hijack import model_hijack

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

@@ -172,7 +172,7 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)

         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)

 

-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_unet) if devices.unet_needs_upcast else source_image))

+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_vae) if devices.unet_needs_upcast else source_image))

         conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image

         conditioning = torch.nn.functional.interpolate(

             self.sd_model.depth_model(midas_in),

@@ -185,7 +185,12 @@ class StableDiffusionProcessing:
         conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.

         return conditioning

 

-    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):

+    def edit_image_conditioning(self, source_image):

+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))

+

+        return conditioning_image

+

+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):

         self.is_using_inpainting_conditioning = True

 

         # Handle the different mask inputs

@@ -212,7 +217,7 @@ class StableDiffusionProcessing:
         )

 

         # Encode the new masked image using first stage of network.

-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_unet) if devices.unet_needs_upcast else conditioning_image))

+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_vae) if devices.unet_needs_upcast else conditioning_image))

 

         # Create the concatenated conditioning tensor to be fed to `c_concat`

         conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])

@@ -228,6 +233,9 @@ class StableDiffusionProcessing:
         if isinstance(self.sd_model, LatentDepth2ImageDiffusion):

             return self.depth2img_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image)

 

+        if self.sd_model.cond_stage_key == "edit":

+            return self.edit_image_conditioning(source_image)

+

         if self.sampler.conditioning_key in {'hybrid', 'concat'}:

             return self.inpainting_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image, latent_image, image_mask=image_mask)

 

@@ -409,7 +417,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
 

 def decode_first_stage(model, x):

     with devices.autocast(disable=x.dtype == devices.dtype_vae):

-        x = model.decode_first_stage(x)

+        x = model.decode_first_stage(x.to(devices.dtype_vae) if devices.unet_needs_upcast else x)

 

     return x

 

@@ -650,6 +658,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

                 image = Image.fromarray(x_sample)

 

+                if p.scripts is not None:

+                    pp = scripts.PostprocessImageArgs(image)

+                    p.scripts.postprocess_image(p, pp)

+                    image = pp.image

+

                 if p.color_corrections is not None and i < len(p.color_corrections):

                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:

                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)

@@ -993,7 +1006,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 

         image = torch.from_numpy(batch_images)

         image = 2. * image - 1.

-        image = image.to(device=shared.device, dtype=devices.dtype_unet if devices.unet_needs_upcast else None)

+        image = image.to(device=shared.device, dtype=devices.dtype_vae if devices.unet_needs_upcast else None)

 

         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))