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inference.py

@@ -490,8 +490,6 @@ def replace_bg(task: Task):
         width=task.get_width(),
         height=task.get_height(),
         steps=task.get_steps(),
-        extend_object=task.rbg_extend_object(),
-        product_scale_width=task.get_image_scale(),
         apply_high_res=task.get_high_res_fix(),
         conditioning_scale=task.rbg_controlnet_conditioning_scale(),
         model_type=task.get_modelType(),
@@ -525,7 +523,7 @@ def load_model_by_task(task: Task):
         inpainter.load()
         safety_checker.apply(inpainter)
     elif task.get_type() == TaskType.REPLACE_BG:
-        replace_background.load(inpainter=inpainter, high_res=high_res)
+        replace_background.load(base=text2img_pipe, high_res=high_res)
     else:
         if task.get_type() == TaskType.TILE_UPSCALE:
             controlnet.load_model("tile_upscaler")

inference2.py

@@ -177,8 +177,6 @@ def replace_bg(task: Task):
         width=task.get_width(),
         height=task.get_height(),
         steps=task.get_steps(),
-        extend_object=task.rbg_extend_object(),
-        product_scale_width=task.get_image_scale(),
         conditioning_scale=task.rbg_controlnet_conditioning_scale(),
         model_type=task.get_modelType(),
     )

internals/pipelines/controlnets.py

@@ -165,7 +165,7 @@ class ControlNet(AbstractPipeline):
         torch.manual_seed(seed)
 
         init_image = download_image(imageUrl).resize((width, height))
-        init_image = self.__canny_detect_edge(init_image)
+        init_image = ControlNet.canny_detect_edge(init_image)
 
         kwargs = {
             "prompt": prompt,
@@ -361,7 +361,8 @@ class ControlNet(AbstractPipeline):
         depth = Image.fromarray(depth)
         return depth
 
-    def __canny_detect_edge(self, image: Image.Image) -> Image.Image:
+    @staticmethod
+    def canny_detect_edge(image: Image.Image) -> Image.Image:
         image_array = np.array(image)
 
         low_threshold = 100

internals/pipelines/replace_background.py

@@ -7,6 +7,7 @@ from diffusers import (
     ControlNetModel,
     StableDiffusionControlNetInpaintPipeline,
     StableDiffusionInpaintPipeline,
+    StableDiffusionControlNetPipeline,
     UniPCMultistepScheduler,
 )
 from PIL import Image, ImageFilter, ImageOps
@@ -36,25 +37,24 @@ class ReplaceBackground(AbstractPipeline):
         self,
         upscaler: Optional[Upscaler] = None,
         remove_background: Optional[RemoveBackgroundV2] = None,
-        inpainter: Optional[InPainter] = None,
+        base: Optional[AbstractPipeline] = None,
         high_res: Optional[HighRes] = None,
     ):
         if self.__loaded:
             return
         controlnet_model = ControlNetModel.from_pretrained(
-            "lllyasviel/control_v11p_sd15_lineart",
+            "lllyasviel/control_v11p_sd15_canny",
             torch_dtype=torch.float16,
             cache_dir=get_hf_cache_dir(),
         ).to("cuda")
-        if inpainter:
-            inpainter.load()
-            pipe = StableDiffusionControlNetInpaintPipeline(
-                **inpainter.pipe.components,
+        if base:
+            pipe = StableDiffusionControlNetPipeline(
+                **base.pipe.components,
                 controlnet=controlnet_model,
             )
         else:
-            pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                "runwayml/stable-diffusion-inpainting",
+            pipe = StableDiffusionControlNetPipeline.from_pretrained(
+                get_model_dir(),
                 controlnet=controlnet_model,
                 torch_dtype=torch.float16,
                 cache_dir=get_hf_cache_dir(),
@@ -88,46 +88,32 @@ class ReplaceBackground(AbstractPipeline):
         image: Union[str, Image.Image],
         width: int,
         height: int,
-        product_scale_width: float,
         prompt: List[str],
         negative_prompt: List[str],
-        extend_object: bool,
         conditioning_scale: float,
         seed: int,
         steps: int,
         apply_high_res: bool = False,
         model_type: ModelType = ModelType.REAL,
     ):
-        # image = Image.open("original.png")
         if type(image) is str:
             image = download_image(image)
 
         torch.manual_seed(seed)
         torch.cuda.manual_seed(seed)
 
-        image = image.convert("RGB")
-        if max(image.size) > 1024:
-            image = ImageUtil.resize_image(image, dimension=1024)
-        image = self.remove_background.remove(image, model_type=model_type)
-
         width = int(width)
         height = int(height)
 
-        n_width = int(width * product_scale_width)
-        n_height = int(n_width * height // width)
-
-        print(width, height, n_width, n_height)
+        resolution = max(width, height)
 
-        resolution = min(n_width, n_height)
-        if extend_object:
-            condition_image = ControlNet.linearart_condition_image(image)
-            condition_image = ImageUtil.resize_image(condition_image, resolution)
-            condition_image = ImageUtil.padd_image(condition_image, width, height)
-            condition_image = condition_image.convert("RGB")
+        image = image.convert("RGB")
 
         image = ImageUtil.resize_image(image, resolution)
         image = ImageUtil.padd_image(image, width, height)
 
+        image = self.remove_background.remove(image, model_type=model_type)
+
         mask = image.copy()
         pixdata = mask.load()
 
@@ -140,19 +126,15 @@ class ReplaceBackground(AbstractPipeline):
                 else:
                     pixdata[x, y] = (0, 0, 0, 255)
 
-        if not extend_object:
-            condition_image = ControlNet.linearart_condition_image(image)
+        condition_image = ControlNet.canny_detect_edge(image)
         mask = mask.convert("RGB")
 
         result = self.pipe.__call__(
             prompt=prompt,
             negative_prompt=negative_prompt,
-            image=image,
-            mask_image=mask,
-            control_image=condition_image,
+            image=condition_image,
             controlnet_conditioning_scale=conditioning_scale,
             guidance_scale=9,
-            strength=1,
             height=height,
             num_inference_steps=steps,
             width=width,

internals/util/image.py

@@ -45,16 +45,6 @@ def from_bytes(data: bytes) -> Image.Image:
 
 def padd_image(image: Image.Image, to_width: int, to_height: int) -> Image.Image:
     iw, ih = image.size
-
-    value = min(to_width, to_height)
-    # resize Image
-    if iw > ih:
-        image = image.resize((value, int(value * ih / iw)))
-    elif ih > iw:
-        image = image.resize((int(value * iw / ih), value))
-
-    # padd Image
-    iw, ih = image.size
     img = Image.new("RGBA", (to_width, to_height), (0, 0, 0, 0))
     img.paste(image, ((to_width - iw) // 2, (to_height - ih) // 2))
     return img