update outpainting

app.py

@@ -5,8 +5,7 @@ from loadimg import load_img
 from torchvision import transforms
 from transformers import AutoModelForImageSegmentation
 from diffusers import FluxFillPipeline
-from PIL import Image, ImageDraw
-from diffusers.utils import load_image
+from PIL import Image, ImageOps
 
 torch.set_float32_matmul_precision(["high", "highest"][0])
 
@@ -38,169 +37,52 @@ def can_expand(source_width, source_height, target_width, target_height, alignme
 
 def prepare_image_and_mask(
     image,
-    width,
-    height,
-    overlap_percentage,
-    resize_percentage,
-    alignment,
-    overlap_left,
-    overlap_right,
-    overlap_top,
-    overlap_bottom,
+    padding_top=0,
+    padding_bottom=0,
+    padding_left=0,
+    padding_right=0,
 ):
-    target_size = (width, height)
-
-    scale_factor = min(target_size[0] / image.width, target_size[1] / image.height)
-    new_width = int(image.width * scale_factor)
-    new_height = int(image.height * scale_factor)
-
-    source = image.resize((new_width, new_height), Image.LANCZOS)
-
-    resize_percentage = 50
-
-    # Calculate new dimensions based on percentage
-    resize_factor = resize_percentage / 100
-    new_width = int(source.width * resize_factor)
-    new_height = int(source.height * resize_factor)
-
-    # Ensure minimum size of 64 pixels
-    new_width = max(new_width, 64)
-    new_height = max(new_height, 64)
-
-    # Resize the image
-    source = source.resize((new_width, new_height), Image.LANCZOS)
-
-    # Calculate the overlap in pixels based on the percentage
-    overlap_x = int(new_width * (overlap_percentage / 100))
-    overlap_y = int(new_height * (overlap_percentage / 100))
-
-    # Ensure minimum overlap of 1 pixel
-    overlap_x = max(overlap_x, 1)
-    overlap_y = max(overlap_y, 1)
-
-    # Calculate margins based on alignment
-    if alignment == "Middle":
-        margin_x = (target_size[0] - new_width) // 2
-        margin_y = (target_size[1] - new_height) // 2
-    elif alignment == "Left":
-        margin_x = 0
-        margin_y = (target_size[1] - new_height) // 2
-    elif alignment == "Right":
-        margin_x = target_size[0] - new_width
-        margin_y = (target_size[1] - new_height) // 2
-    elif alignment == "Top":
-        margin_x = (target_size[0] - new_width) // 2
-        margin_y = 0
-    elif alignment == "Bottom":
-        margin_x = (target_size[0] - new_width) // 2
-        margin_y = target_size[1] - new_height
-
-    # Adjust margins to eliminate gaps
-    margin_x = max(0, min(margin_x, target_size[0] - new_width))
-    margin_y = max(0, min(margin_y, target_size[1] - new_height))
-
-    # Create a new background image and paste the resized source image
-    background = Image.new("RGB", target_size, (255, 255, 255))
-    background.paste(source, (margin_x, margin_y))
-
-    # Create the mask
-    mask = Image.new("L", target_size, 255)
-    mask_draw = ImageDraw.Draw(mask)
-
-    # Calculate overlap areas
-    white_gaps_patch = 2
-
-    left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch
-    right_overlap = (
-        margin_x + new_width - overlap_x
-        if overlap_right
-        else margin_x + new_width - white_gaps_patch
-    )
-    top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch
-    bottom_overlap = (
-        margin_y + new_height - overlap_y
-        if overlap_bottom
-        else margin_y + new_height - white_gaps_patch
-    )
-
-    if alignment == "Left":
-        left_overlap = margin_x + overlap_x if overlap_left else margin_x
-    elif alignment == "Right":
-        right_overlap = (
-            margin_x + new_width - overlap_x if overlap_right else margin_x + new_width
-        )
-    elif alignment == "Top":
-        top_overlap = margin_y + overlap_y if overlap_top else margin_y
-    elif alignment == "Bottom":
-        bottom_overlap = (
-            margin_y + new_height - overlap_y
-            if overlap_bottom
-            else margin_y + new_height
-        )
-
-    # Draw the mask
-    mask_draw.rectangle(
-        [(left_overlap, top_overlap), (right_overlap, bottom_overlap)], fill=0
+    image = load_img(image).convert("RGB")
+    # expand image (left,top,right,bottom)
+    background = ImageOps.expand(
+        image,
+        border=(padding_left, padding_top, padding_right, padding_bottom),
+        fill="white",
     )
-
+    mask = Image.new("RGB", image.size, "black")
+    mask = ImageOps.expand(mask, border=(0, 20, 0, 0), fill="white")
     return background, mask
 
 
 def inpaint(
     image,
-    width,
-    height,
-    overlap_percentage,
-    num_inference_steps,
-    custom_resize_percentage,
-    prompt_input,
-    alignment,
-    overlap_left,
-    overlap_right,
-    overlap_top,
-    overlap_bottom,
+    padding_top=0,
+    padding_bottom=0,
+    padding_left=0,
+    padding_right=0,
+    prompt="",
     progress=gr.Progress(track_tqdm=True),
 ):
     background, mask = prepare_image_and_mask(
-        image,
-        width,
-        height,
-        overlap_percentage,
-        custom_resize_percentage,
-        alignment,
-        overlap_left,
-        overlap_right,
-        overlap_top,
-        overlap_bottom,
+        image, padding_top, padding_bottom, padding_left, padding_right
     )
 
-    if not can_expand(background.width, background.height, width, height, alignment):
-        alignment = "Middle"
-
-    cnet_image = background.copy()
-    cnet_image.paste(0, (0, 0), mask)
-
-    final_prompt = prompt_input
-
     # generator = torch.Generator(device="cuda").manual_seed(42)
 
     result = pipe(
-        prompt=final_prompt,
-        height=height,
-        width=width,
-        image=cnet_image,
+        prompt=prompt,
+        height=background.height,
+        width=background.width,
+        image=background,
         mask_image=mask,
-        num_inference_steps=num_inference_steps,
+        num_inference_steps=28,
         guidance_scale=30,
     ).images[0]
 
     result = result.convert("RGBA")
-    cnet_image.paste(result, (0, 0), mask)
+    return result
 
-    return cnet_image
 
-
-@spaces.GPU
 def rmbg(image, url):
     if image is None:
         image = url
@@ -217,16 +99,28 @@ def rmbg(image, url):
     return image
 
 
-def placeholder(img,*args):
-    return img
+@spaces.GPU
+def main(*args, **kwargs):
+    print(args, kwargs)
+    return None
 
 
 rmbg_tab = gr.Interface(
-    fn=rmbg, inputs=["image", "text"], outputs=["image"], api_name="rmbg"
+    fn=main, inputs=["image", "text"], outputs=["image"], api_name="rmbg"
 )
 
 outpaint_tab = gr.Interface(
-    fn=placeholder, inputs=["image","text",gr.Dropdown(["down","up","left","right","middle"])], outputs=["image"], api_name="outpainting"
+    fn=main,
+    inputs=[
+        "image",
+        gr.Slider(label="padding top"),
+        gr.Slider(label="padding bottom"),
+        gr.Slider(label="padding left"),
+        gr.Slider(label="padding right"),
+        gr.Text(label="prompt"),
+    ],
+    outputs=["image"],
+    api_name="outpainting",
 )
 
 demo = gr.TabbedInterface(