Merge branch 'master' into master

modules/processing.py

@@ -13,10 +13,11 @@ 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
+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
+import modules.paths as paths
 import modules.face_restoration
 import modules.images as images
 import modules.styles
@@ -184,7 +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
@@ -203,7 +209,7 @@ class StableDiffusionProcessing:
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
+        conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -222,11 +228,16 @@ class StableDiffusionProcessing:
         return image_conditioning
 
     def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        source_image = devices.cond_cast_float(source_image)
+
         # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
         # identify itself with a field common to all models. The conditioning_key is also hybrid.
         if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
             return self.depth2img_image_conditioning(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, latent_image, image_mask=image_mask)
 
@@ -439,8 +450,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Batch size": (None if p.batch_size < 2 else p.batch_size),
-        "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -580,10 +589,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         with devices.autocast():
             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
 
+            # for OSX, loading the model during sampling changes the generated picture, so it is loaded here
+            if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
+                sd_vae_approx.model()
+
             if not p.disable_extra_networks:
                 extra_networks.activate(p, extra_network_data)
 
-        with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+        with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
             processed = Processed(p, [], p.seed, "")
             file.write(processed.infotext(p, 0))
 
@@ -634,7 +647,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
-            with devices.autocast():
+
+            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 if type(p) == StableDiffusionProcessingTxt2Img:
                     if p.enable_hr:
                         if p.hr_prompt != '':
@@ -684,6 +698,11 @@ 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)