migration v2

html/locale_en.json

@@ -1248,6 +1248,11 @@
     {"id":"","label":"zero","localized":"","hint":"zero"},
     {"id":"","label":"zoe depth","localized":"","hint":"zoe depth"},
     {"id":"","label":"➠ control","localized":"","hint":"➠ control"},
-    {"id":"","label":"💾","localized":"","hint":"💾"}
+    {"id":"","label":"💾","localized":"","hint":"💾"},
+    {"id":"","label":"Color to Mask","localized":"","hint":"Pick the color you want to mask and inpaint. Click on the color in the image to automatically select it.\n Advised to use images like green screens to get precise results."},
+    {"id":"","label":"Color Tolerance","localized":"","hint":"Adjust the tolerance to include similar colors in the mask. Lower values = mask only very similar colors. Higher = values mask a wider range of similar colors."},
+    {"id":"","label":"Mask Padding","localized":"","hint":"Adjust padding to apply a inside offset to the mask. (Recommended value = 2 to remove leftovers at edges)"},
+    {"id":"","label":"Mask Blur","localized":"","hint":"Adjust blur to apply a smooth transition between image and inpainted area. (Recommended value = 0 for sharpness)"},
+    {"id":"","label":"Denoising Strength","localized":"","hint":"Change Denoising Strength to achieve desired inpaint amount."}
 ]
 }

scripts/automatic_color_inpaint.py

@@ -0,0 +1,131 @@
+import gradio as gr
+from diffusers.pipelines import StableDiffusionPipeline, StableDiffusionXLPipeline  # pylint: disable=unused-import
+from PIL import Image
+import numpy as np
+from modules import shared, scripts, processing
+
+"""
+Automatic Color Inpaint Script for SD.NEXT - SD & SDXL Support
+
+Author: Artheriax
+Credits: SD.NEXT team for script template
+Version: v1
+
+Contributions: A new script to automatically inpaint colors in images using Stable Diffusion, Stable Diffusion XL or Flux.
+"""
+
+## Config
+
+# script title
+supported_models = ['sd','sdxl', 'flux']
+
+title = 'Automatic Color Inpaint'
+
+# is script available in txt2img tab
+txt2img = False
+
+# is script available in img2img tab
+img2img = True
+
+# is pipeline ok to run in pure latent mode without implicit conversions
+latent = True
+
+# pipeline args values are defined in ui method below
+params = ['color_to_mask', 'mask_tolerance', 'mask_padding', 'mask_blur', 'inpaint_denoising_strength']
+
+
+### Script definition
+
+class Script(scripts.Script):
+    def title(self):
+        return title
+
+    def show(self, is_img2img):
+        if shared.native:
+            return img2img if is_img2img else txt2img
+        return False
+
+    # Define UI for pipeline
+    def ui(self, _is_img2img):
+        with gr.Row():
+            gr.HTML("&nbsp ACI: Automatic Color Inpaint<br>")
+        with gr.Row():
+            color_picker = gr.ColorPicker(
+                label="Color to Mask",
+                value="#04F404",  # Default to green screen green
+                info="Pick the color you want to mask and inpaint."
+            )
+            tolerance_slider = gr.Slider(
+                minimum=0,
+                maximum=100,
+                step=1,
+                value=25,
+                label="Color Tolerance",
+            )
+            padding_slider = gr.Slider(
+                minimum=0,
+                maximum=256,
+                step=1,
+                value=2,
+                label="Mask Padding",
+                info="(Recommended value = 2 to remove leftovers at edges)"
+            )
+            blur_slider = gr.Slider(
+                minimum=0,
+                maximum=64,
+                step=1,
+                value=0,
+                label="Mask Blur",
+                info="(Recommended value = 0 for sharpness)"
+            )
+            denoising_slider = gr.Slider(
+                minimum=0.01,
+                maximum=1,
+                step=0.01,
+                value=1,
+                label="Denoising Strength",
+            )
+        return [color_picker, tolerance_slider, padding_slider, blur_slider, denoising_slider]
+
+    # Run pipeline
+    def run(self, p: processing.StableDiffusionProcessing, *args):  # pylint: disable=arguments-differ
+        if shared.sd_model_type not in supported_models:
+            shared.log.warning(f'MoD: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_models}')
+            return None
+        color_to_mask_hex, mask_tolerance, mask_padding, mask_blur, inpaint_denoising_strength = args
+
+        # Convert hex color to RGB tuple (0-255)
+        color_to_mask_rgb = tuple(int(color_to_mask_hex[i:i+2], 16) for i in (1, 3, 5))
+
+        shared.log.debug(f'{title}: Color to Mask={color_to_mask_rgb}, Tolerance={mask_tolerance}, Padding={mask_padding}, Blur={mask_blur}, Denoising Strength={inpaint_denoising_strength}')
+
+        # Create Color Mask using vectorized operations
+        init_image = p.init_images[0].convert("RGB")
+        image_np = np.array(init_image)
+
+        # Calculate Euclidean distance for all pixels at once
+        diff = np.linalg.norm(image_np.astype(np.int16) - np.array(color_to_mask_rgb, dtype=np.int16), axis=2)
+        mask_np = (diff <= mask_tolerance).astype(np.uint8) * 255
+
+        mask_image = Image.fromarray(mask_np).convert("L")
+
+        # If an inpaint mask is already provided from the UI, combine it with the color mask
+        if p.image_mask:
+            combined_mask = Image.composite(
+                Image.new("L", mask_image.size, "white"),
+                p.image_mask.convert("L"),
+                mask_image
+            )
+            p.image_mask = combined_mask
+        else:
+            p.image_mask = mask_image
+
+        # override inpaint parameters
+        p.inpaint_full_res = True
+        p.inpaint_full_res_padding = mask_padding
+        p.mask_blur = mask_blur
+        p.denoising_strength = inpaint_denoising_strength
+
+        # Process the image using SD.Next’s inpainting
+        processed: processing.Processed = processing.process_images(p)
+        return processed