fix onnx i2i

lshqqytiger · Sep 2, 2023 · 39e6d26 · 39e6d26
1 parent 2dd9603
commit 39e6d26
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Showing 2 changed files with 6 additions and 6 deletions.
diff --git a/modules/sd_onnx.py b/modules/sd_onnx.py
@@ -501,20 +501,22 @@ def forward(self) -> processing.Processed:
         image_mask = self.image_mask
 
         if image_mask is not None:
-            image_mask = image_mask.convert('L')
+            # image_mask is passed in as RGBA by Gradio to support alpha masks,
+            # but we still want to support binary masks.
+            image_mask = processing.create_binary_mask(image_mask)
 
             if self.inpainting_mask_invert:
                 image_mask = ImageOps.invert(image_mask)
 
             if self.mask_blur_x > 0:
                 np_mask = np.array(image_mask)
-                kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+                kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
                 np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
                 image_mask = Image.fromarray(np_mask)
 
             if self.mask_blur_y > 0:
                 np_mask = np.array(image_mask)
-                kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+                kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
                 np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
                 image_mask = Image.fromarray(np_mask)
 
@@ -591,8 +593,7 @@ def forward(self) -> processing.Processed:
         else:
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
-        image = torch.from_numpy(batch_images)
-        image = 2. * image - 1.
+        image = 2. * torch.from_numpy(batch_images) - 0.7878375
 
         if type(self.prompt) == list:
             assert(len(self.prompt) > 0)

diff --git a/modules/sd_onnx_hijack.py b/modules/sd_onnx_hijack.py
@@ -243,7 +243,6 @@ def OnnxStableDiffusionImg2ImgPipeline__call__(
     offset = self.scheduler.config.get("steps_offset", 0)
     init_timestep = int(num_inference_steps * strength) + offset
     init_timestep = min(init_timestep, num_inference_steps)
-    init_timestep = num_inference_steps
 
     timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
     timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)