Revert "Merge pull request #7931 from space-nuko/img2img-enhance"

This reverts commit 426875937048e21305ac24bea53df06523bdaa81, reversing
changes made to 1b63afbedc7789c0eb9a4742b780ab304d7a9caf.

1 files changed, 5 insertions, 32 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 509b80b9..6d9c6a8d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -946,7 +946,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

     sampler = None

 

-    def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs):

+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):

         super().__init__(**kwargs)

 

         self.init_images = init_images

@@ -966,37 +966,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.mask = None

         self.nmask = None

         self.image_conditioning = None

-        self.scale = scale

-        self.upscaler = upscaler

-

-    def get_final_size(self):

-        if self.scale > 1:

-            img = self.init_images[0]

-            width = int(img.width * self.scale)

-            height = int(img.height * self.scale)

-            return width, height

-        else:

-            return self.width, self.height

-

 

     def init(self, all_prompts, all_seeds, all_subseeds):

         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

         crop_region = None

 

-        if self.scale > 1:

-            self.extra_generation_params["Img2Img upscale"] = self.scale

-

-        # Non-latent upscalers are run before sampling

-        # Latent upscalers are run during sampling

-        init_upscaler = None

-        if self.upscaler is not None:

-            self.extra_generation_params["Img2Img upscaler"] = self.upscaler

-            if self.upscaler not in shared.latent_upscale_modes:

-                assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}"

-                init_upscaler = self.upscaler

-

-        self.width, self.height = self.get_final_size()

-

         image_mask = self.image_mask

 

         if image_mask is not None:

@@ -1019,7 +993,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image_mask = images.resize_image(2, mask, self.width, self.height)

                 self.paste_to = (x1, y1, x2-x1, y2-y1)

             else:

-                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler)

+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)

                 np_mask = np.array(image_mask)

                 np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)

                 self.mask_for_overlay = Image.fromarray(np_mask)

@@ -1036,7 +1010,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             image = images.flatten(img, opts.img2img_background_color)

 

             if crop_region is None and self.resize_mode != 3:

-                image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler)

+                image = images.resize_image(self.resize_mode, image, self.width, self.height)

 

             if image_mask is not None:

                 image_masked = Image.new('RGBa', (image.width, image.height))

@@ -1081,9 +1055,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 

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

 

-        latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")

-        if latent_scale_mode is not None:

-            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])

+        if self.resize_mode == 3:

+            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

 

         if image_mask is not None:

             init_mask = latent_mask