update

README.md

@@ -4,7 +4,7 @@ emoji: ✏️
 colorFrom: yellow
 colorTo: gray
 sdk: gradio
-sdk_version: 4.0.2
+sdk_version: 3.48.0
 app_file: app.py
 pinned: true
 license: apache-2.0

app.py

@@ -11,174 +11,172 @@ import cv2
 import numpy as np
 import os
 import torch
+print(f"Is CUDA available: {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
 
-if __name__ == "__main__":
-    
-    print(f"Is CUDA available: {torch.cuda.is_available()}")
-    if torch.cuda.is_available():
-        device = "cuda"
-    else:
-        device = "cpu"
-
-    automatic_coloring_pipeline = StableDiffusionReferenceOnlyPipeline.from_pretrained(
-        "AisingioroHao0/stable-diffusion-reference-only-automatic-coloring-0.1.2"
-    ).to(device)
-    automatic_coloring_pipeline.scheduler = UniPCMultistepScheduler.from_config(
-        automatic_coloring_pipeline.scheduler.config
-    )
+automatic_coloring_pipeline = StableDiffusionReferenceOnlyPipeline.from_pretrained(
+    "AisingioroHao0/stable-diffusion-reference-only-automatic-coloring-0.1.2"
+).to(device)
+automatic_coloring_pipeline.scheduler = UniPCMultistepScheduler.from_config(
+    automatic_coloring_pipeline.scheduler.config
+)
 
-    segment_model = get_anime_segmentation_model(
-        model_path=huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.ckpt")
-    ).to(device)
+segment_model = get_anime_segmentation_model(
+    model_path=huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.ckpt")
+).to(device)
 
-    def character_segment(img):
-        if img is None:
-            return None
-        img = anime_character_segment(segment_model, img)
-        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
-        return img
+def character_segment(img):
+    if img is None:
+        return None
+    img = anime_character_segment(segment_model, img)
+    img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+    return img
 
-    def color_inversion(img):
-        if img is None:
-            return None
-        return 255 - img
+def color_inversion(img):
+    if img is None:
+        return None
+    return 255 - img
 
 
-    def get_line_art(img):
-        if img is None:
-            return None
-        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-        img = cv2.adaptiveThreshold(
-            img,
-            255,
-            cv2.ADAPTIVE_THRESH_MEAN_C,
-            cv2.THRESH_BINARY,
-            blockSize=5,
-            C=7,
-        )
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-        return img
+def get_line_art(img):
+    if img is None:
+        return None
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    img = cv2.adaptiveThreshold(
+        img,
+        255,
+        cv2.ADAPTIVE_THRESH_MEAN_C,
+        cv2.THRESH_BINARY,
+        blockSize=5,
+        C=7,
+    )
+    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+    return img
 
 
-    def inference(prompt, blueprint, num_inference_steps):
-        if prompt is None or blueprint is None:
-            return None
-        return np.array(
-            automatic_coloring_pipeline(
-                prompt=Image.fromarray(prompt),
-                blueprint=Image.fromarray(blueprint),
-                num_inference_steps=num_inference_steps,
-            ).images[0]
-        )
+def inference(prompt, blueprint, num_inference_steps):
+    if prompt is None or blueprint is None:
+        return None
+    return np.array(
+        automatic_coloring_pipeline(
+            prompt=Image.fromarray(prompt),
+            blueprint=Image.fromarray(blueprint),
+            num_inference_steps=num_inference_steps,
+        ).images[0]
+    )
 
 
-    def automatic_coloring(prompt, blueprint, num_inference_steps):
-        if prompt is None or blueprint is None:
-            return None
-        blueprint = color_inversion(blueprint)
-        return inference(prompt, blueprint, num_inference_steps)
+def automatic_coloring(prompt, blueprint, num_inference_steps):
+    if prompt is None or blueprint is None:
+        return None
+    blueprint = color_inversion(blueprint)
+    return inference(prompt, blueprint, num_inference_steps)
 
 
-    def style_transfer(prompt, blueprint, num_inference_steps):
-        if prompt is None or blueprint is None:
-            return None
-        prompt = character_segment(prompt)
-        blueprint = character_segment(blueprint)
-        blueprint = get_line_art(blueprint)
-        blueprint = color_inversion(blueprint)
-        return inference(prompt, blueprint, num_inference_steps)
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-        # Stable Diffusion Reference Only Automatic Coloring 0.1.2\n\n
-        demo for [https://github.com/aihao2000/stable-diffusion-reference-only](https://github.com/aihao2000/stable-diffusion-reference-only)
+def style_transfer(prompt, blueprint, num_inference_steps):
+    if prompt is None or blueprint is None:
+        return None
+    prompt = character_segment(prompt)
+    blueprint = character_segment(blueprint)
+    blueprint = get_line_art(blueprint)
+    blueprint = color_inversion(blueprint)
+    return inference(prompt, blueprint, num_inference_steps)
+with gr.Blocks() as demo:
+    gr.Markdown(
         """
-        )
-        with gr.Row():
-            with gr.Column():
-                prompt_input_compoent = gr.Image(shape=(512, 512), label="prompt")
-                prompt_character_segment_button = gr.Button(
-                    "character segment",
-                )
-                prompt_character_segment_button.click(
-                    character_segment,
-                    inputs=prompt_input_compoent,
-                    outputs=prompt_input_compoent,
-                )
-            with gr.Column():
-                blueprint_input_compoent = gr.Image(shape=(512, 512), label="blueprint")
-                blueprint_character_segment_button = gr.Button("character segment")
-                blueprint_character_segment_button.click(
-                    character_segment,
-                    inputs=blueprint_input_compoent,
-                    outputs=blueprint_input_compoent,
-                )
-                get_line_art_button = gr.Button(
-                    "get line art",
-                )
-                get_line_art_button.click(
-                    get_line_art,
-                    inputs=blueprint_input_compoent,
-                    outputs=blueprint_input_compoent,
-                )
-                color_inversion_button = gr.Button(
-                    "color inversion",
-                )
-                color_inversion_button.click(
-                    color_inversion,
-                    inputs=blueprint_input_compoent,
-                    outputs=blueprint_input_compoent,
-                )
-            with gr.Column():
-                result_output_component = gr.Image(shape=(512, 512), label="result")
-                num_inference_steps_input_component = gr.Number(
-                    20, label="num inference steps", minimum=1, maximum=1000, step=1
-                )
-                inference_button = gr.Button("inference")
-                inference_button.click(
-                    inference,
-                    inputs=[
-                        prompt_input_compoent,
-                        blueprint_input_compoent,
-                        num_inference_steps_input_component,
-                    ],
-                    outputs=result_output_component,
-                )
-                automatic_coloring_button = gr.Button("automatic coloring")
-                automatic_coloring_button.click(
-                    automatic_coloring,
-                    inputs=[
-                        prompt_input_compoent,
-                        blueprint_input_compoent,
-                        num_inference_steps_input_component,
-                    ],
-                    outputs=result_output_component,
-                )
-                style_transfer_button = gr.Button("style transfer")
-                style_transfer_button.click(
-                    style_transfer,
-                    inputs=[
-                        prompt_input_compoent,
-                        blueprint_input_compoent,
-                        num_inference_steps_input_component,
-                    ],
-                    outputs=result_output_component,
-                )
-        with gr.Row():
-            gr.Examples(
-                examples=[
-                    [
-                        os.path.join(
-                            os.path.dirname(__file__), "README.assets", "3x9_prompt.png"
-                        ),
-                        os.path.join(
-                            os.path.dirname(__file__), "README.assets", "3x9_blueprint.png"
-                        ),
-                    ],
+    # Stable Diffusion Reference Only Automatic Coloring 0.1.2\n\n
+    demo for [https://github.com/aihao2000/stable-diffusion-reference-only](https://github.com/aihao2000/stable-diffusion-reference-only)
+    """
+    )
+    with gr.Row():
+        with gr.Column():
+            prompt_input_compoent = gr.Image(shape=(512, 512), label="prompt")
+            prompt_character_segment_button = gr.Button(
+                "character segment",
+            )
+            prompt_character_segment_button.click(
+                character_segment,
+                inputs=prompt_input_compoent,
+                outputs=prompt_input_compoent,
+            )
+        with gr.Column():
+            blueprint_input_compoent = gr.Image(shape=(512, 512), label="blueprint")
+            blueprint_character_segment_button = gr.Button("character segment")
+            blueprint_character_segment_button.click(
+                character_segment,
+                inputs=blueprint_input_compoent,
+                outputs=blueprint_input_compoent,
+            )
+            get_line_art_button = gr.Button(
+                "get line art",
+            )
+            get_line_art_button.click(
+                get_line_art,
+                inputs=blueprint_input_compoent,
+                outputs=blueprint_input_compoent,
+            )
+            color_inversion_button = gr.Button(
+                "color inversion",
+            )
+            color_inversion_button.click(
+                color_inversion,
+                inputs=blueprint_input_compoent,
+                outputs=blueprint_input_compoent,
+            )
+        with gr.Column():
+            result_output_component = gr.Image(shape=(512, 512), label="result")
+            num_inference_steps_input_component = gr.Number(
+                20, label="num inference steps", minimum=1, maximum=1000, step=1
+            )
+            inference_button = gr.Button("inference")
+            inference_button.click(
+                inference,
+                inputs=[
+                    prompt_input_compoent,
+                    blueprint_input_compoent,
+                    num_inference_steps_input_component,
+                ],
+                outputs=result_output_component,
+            )
+            automatic_coloring_button = gr.Button("automatic coloring")
+            automatic_coloring_button.click(
+                automatic_coloring,
+                inputs=[
+                    prompt_input_compoent,
+                    blueprint_input_compoent,
+                    num_inference_steps_input_component,
                 ],
-                inputs=[prompt_input_compoent, blueprint_input_compoent],
                 outputs=result_output_component,
-                fn=lambda x, y: None,
-                cache_examples=True,
             )
+            style_transfer_button = gr.Button("style transfer")
+            style_transfer_button.click(
+                style_transfer,
+                inputs=[
+                    prompt_input_compoent,
+                    blueprint_input_compoent,
+                    num_inference_steps_input_component,
+                ],
+                outputs=result_output_component,
+            )
+    with gr.Row():
+        gr.Examples(
+            examples=[
+                [
+                    os.path.join(
+                        os.path.dirname(__file__), "README.assets", "3x9_prompt.png"
+                    ),
+                    os.path.join(
+                        os.path.dirname(__file__), "README.assets", "3x9_blueprint.png"
+                    ),
+                ],
+            ],
+            inputs=[prompt_input_compoent, blueprint_input_compoent],
+            outputs=result_output_component,
+            fn=lambda x, y: None,
+            cache_examples=True,
+        )
+if __name__ == "__main__":
     demo.queue(max_size=10).launch()