add anyline params

app.py

@@ -2,6 +2,7 @@ import spaces
 import gradio as gr
 from gradio_imageslider import ImageSlider
 import torch
+
 torch.jit.script = lambda f: f
 from hidiffusion import apply_hidiffusion
 from diffusers import (
@@ -100,6 +101,8 @@ def predict(
     strength=1.0,
     controlnet_start=0.0,
     controlnet_end=1.0,
+    guassian_sigma=2.0,
+    intensity_threshold=3,
     progress=gr.Progress(track_tqdm=True),
 ):
     if IS_SPACES_ZERO:
@@ -110,7 +113,13 @@ def predict(
     conditioning, pooled = compel([prompt, negative_prompt])
     generator = torch.manual_seed(seed)
     last_time = time.time()
-    anyline_image = anyline(padded_image, detect_resolution=1024)
+    anyline_image = anyline(
+        padded_image,
+        detect_resolution=1280,
+        guassian_sigma=max(0.01, guassian_sigma),
+        intensity_threshold=intensity_threshold,
+    )
+
     images = pipe(
         image=padded_image,
         control_image=anyline_image,
@@ -222,6 +231,20 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                     value=1.0,
                     label="ControlNet End",
                 )
+                guassian_sigma = gr.Slider(
+                    minimum=0.01,
+                    maximum=10.0,
+                    step=0.1,
+                    value=2.0,
+                    label="(Anyline) Guassian Sigma",
+                )
+                intensity_threshold = gr.Slider(
+                    minimum=0,
+                    maximum=255,
+                    step=1,
+                    value=3,
+                    label="(Anyline) Intensity Threshold",
+                )
 
             btn = gr.Button()
         with gr.Column(scale=2):
@@ -241,6 +264,8 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
         strength,
         controlnet_start,
         controlnet_end,
+        guassian_sigma,
+        intensity_threshold,
     ]
     outputs = [image_slider, padded_image, anyline_image]
     btn.click(lambda x: None, inputs=None, outputs=image_slider).then(
@@ -261,7 +286,9 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                 0.8,
                 1.0,
                 0.0,
-                1.0,
+                0.9,
+                2,
+                3,
             ],
             [
                 "./examples/cybetruck.jpeg",
@@ -273,7 +300,9 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                 0.8,
                 0.8,
                 0.0,
-                1.0,
+                0.9,
+                2,
+                3,
             ],
             [
                 "./examples/jesus.png",
@@ -285,7 +314,9 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                 0.8,
                 0.8,
                 0.0,
-                1.0,
+                0.9,
+                2,
+                3,
             ],
             [
                 "./examples/anna-sullivan-DioLM8ViiO8-unsplash.jpg",
@@ -297,7 +328,9 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                 0.8,
                 0.8,
                 0.0,
-                1.0,
+                0.9,
+                2,
+                3,
             ],
             [
                 "./examples/img_aef651cb-2919-499d-aa49-6d4e2e21a56e_1024.jpg",
@@ -309,7 +342,9 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                 0.8,
                 0.8,
                 0.0,
-                1.0,
+                0.9,
+                2,
+                3,
             ],
             [
                 "./examples/huggingface.jpg",
@@ -321,7 +356,9 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
                 0.364,
                 0.8,
                 0.0,
-                1.0,
+                0.9,
+                2,
+                3,
             ],
         ],
         cache_examples="lazy",