aeayüz düzenleme 3

app.py

@@ -9,11 +9,13 @@ from leffa_utils.densepose_predictor import DensePosePredictor
 from leffa_utils.utils import resize_and_center, list_dir, get_agnostic_mask_hd, get_agnostic_mask_dc
 from preprocess.humanparsing.run_parsing import Parsing
 from preprocess.openpose.run_openpose import OpenPose
+
 import gradio as gr
 
 # Download checkpoints
 snapshot_download(repo_id="franciszzj/Leffa", local_dir="./ckpts")
 
+
 class LeffaPredictor(object):
     def __init__(self):
         self.mask_predictor = AutoMasker(
@@ -90,6 +92,9 @@ class LeffaPredictor(object):
                 mask = get_agnostic_mask_dc(
                     model_parse, keypoints, vt_garment_type)
             mask = mask.resize((768, 1024))
+            # garment_type_hd = "upper" if vt_garment_type in [
+            #     "upper_body", "dresses"] else "lower"
+            # mask = self.mask_predictor(src_image, garment_type_hd)["mask"]
         elif control_type == "pose_transfer":
             mask = Image.fromarray(np.ones_like(src_image_array) * 255)
 
@@ -139,6 +144,7 @@ class LeffaPredictor(object):
             seed=seed,
             repaint=vt_repaint,)
         gen_image = output["generated_image"][0]
+        # gen_image.save("gen_image.png")
         return np.array(gen_image), np.array(mask), np.array(densepose)
 
     def dehasoft(self, src_image_path, ref_image_path, ref_acceleration, step, scale, seed, vt_model_type, vt_garment_type, vt_repaint):
@@ -147,260 +153,203 @@ class LeffaPredictor(object):
     def leffa_predict_pt(self, src_image_path, ref_image_path, ref_acceleration, step, scale, seed):
         return self.leffa_predict(src_image_path, ref_image_path, "pose_transfer", ref_acceleration, step, scale, seed)
 
+
 if __name__ == "__main__":
+
     leffa_predictor = LeffaPredictor()
     example_dir = "./ckpts/examples"
     person1_images = list_dir(f"{example_dir}/person1")
     person2_images = list_dir(f"{example_dir}/person2")
     garment_images = list_dir(f"{example_dir}/garment")
 
-    # Özelleştirilmiş Tema
-    theme = gr.themes.Soft(
-        primary_hue="indigo",
-        secondary_hue="purple",
-        neutral_hue="gray",
-        radius_size="lg",
-        text_size="lg",
-        spacing_size="md",
-    ).set(
-        body_background_fill="#f5f5f5",
-        background_fill_primary="#ffffff",
-        button_primary_background_fill="#4f46e5",
-        button_primary_background_fill_hover="#6b7280",
-        button_primary_text_color="#ffffff",
-        shadow="0 4px 6px rgba(0, 0, 0, 0.1)",
-    )
-
-    # Başlık ve Açıklama
-    title = "# Dehasoft AI Studio"
-    description = """
-    Welcome to **Dehasoft AI Studio**! Transform appearances with virtual try-on or adjust poses with pose transfer using cutting-edge AI models. 
-    Powered by VITON-HD, DressCode, and DeepFashion datasets.
-    """
-    footer_note = """
-    **Note:** Models are trained on academic datasets only. Virtual try-on leverages VITON-HD/DressCode, while pose transfer uses DeepFashion.
-    """
-
-    with gr.Blocks(theme=theme, title="Dehasoft AI Studio") as demo:
-        gr.Markdown(title, elem_classes=["title"])
-        gr.Markdown(description, elem_classes=["description"])
-
-        with gr.Tabs(elem_classes=["tabs"]):
-            with gr.TabItem("Virtual Try-On", elem_id="vt_tab"):
-                with gr.Row(equal_height=True):
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Upload Person Image", elem_classes=["section-title"])
-                        vt_src_image = gr.Image(
-                            sources=["upload"],
-                            type="filepath",
-                            label="Person Image",
-                            interactive=True,
-                            height=400,
-                            elem_classes=["image-upload"],
-                        )
-                        gr.Examples(
-                            examples=person1_images,
-                            inputs=vt_src_image,
-                            examples_per_page=5,
-                            elem_classes=["examples"],
+    title = "## Dehasoft"
+    link = """Dehasoft"""
+    news = """Dehasoft"""
+    description = "Dehasoft"
+    note = "Note: The models used in the demo are trained solely on academic datasets. Virtual try-on uses VITON-HD/DressCode, and pose transfer uses DeepFashion."
+
+    with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.pink, secondary_hue=gr.themes.colors.red)).queue() as demo:
+        gr.Markdown(title)
+        gr.Markdown(link)
+        gr.Markdown(news)
+        gr.Markdown(description)
+
+        with gr.Tab("Control Appearance (Virtual Try-on)"):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("#### Person Image")
+                    vt_src_image = gr.Image(
+                        sources=["upload"],
+                        type="filepath",
+                        label="Person Image",
+                        width=512,
+                        height=512,
+                    )
+
+                    gr.Examples(
+                        inputs=vt_src_image,
+                        examples_per_page=10,
+                        examples=person1_images,
+                    )
+
+                with gr.Column():
+                    gr.Markdown("#### Garment Image")
+                    vt_ref_image = gr.Image(
+                        sources=["upload"],
+                        type="filepath",
+                        label="Garment Image",
+                        width=512,
+                        height=512,
+                    )
+
+                    gr.Examples(
+                        inputs=vt_ref_image,
+                        examples_per_page=10,
+                        examples=garment_images,
+                    )
+
+                with gr.Column():
+                    gr.Markdown("#### Generated Image")
+                    vt_gen_image = gr.Image(
+                        label="Generated Image",
+                        width=512,
+                        height=512,
+                    )
+
+                    with gr.Row():
+                        vt_gen_button = gr.Button("Generate")
+
+                    with gr.Accordion("Advanced Options", open=False):
+                        vt_model_type = gr.Radio(
+                            label="Model Type",
+                            choices=[("VITON-HD (Recommended)", "viton_hd"),
+                                     ("DressCode (Experimental)", "dress_code")],
+                            value="viton_hd",
                         )
 
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Upload Garment Image", elem_classes=["section-title"])
-                        vt_ref_image = gr.Image(
-                            sources=["upload"],
-                            type="filepath",
-                            label="Garment Image",
-                            interactive=True,
-                            height=400,
-                            elem_classes=["image-upload"],
-                        )
-                        gr.Examples(
-                            examples=garment_images,
-                            inputs=vt_ref_image,
-                            examples_per_page=5,
-                            elem_classes=["examples"],
+                        vt_garment_type = gr.Radio(
+                            label="Garment Type",
+                            choices=[("Upper", "upper_body"),
+                                     ("Lower", "lower_body"),
+                                     ("Dress", "dresses")],
+                            value="upper_body",
                         )
 
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Result", elem_classes=["section-title"])
-                        vt_gen_image = gr.Image(
-                            label="Generated Image",
-                            height=400,
-                            elem_classes=["image-output"],
-                        )
-                        vt_gen_button = gr.Button(
-                            "Generate Image",
-                            variant="primary",
-                            size="lg",
-                            elem_classes=["generate-btn"],
+                        vt_ref_acceleration = gr.Radio(
+                            label="Accelerate Reference UNet (may slightly reduce performance)",
+                            choices=[("True", True), ("False", False)],
+                            value=False,
                         )
 
-                        with gr.Accordion("Advanced Settings", open=False, elem_classes=["accordion"]):
-                            vt_model_type = gr.Radio(
-                                label="Model Type",
-                                choices=[("VITON-HD (Recommended)", "viton_hd"), ("DressCode (Experimental)", "dress_code")],
-                                value="viton_hd",
-                                elem_classes=["radio"],
-                            )
-                            vt_garment_type = gr.Radio(
-                                label="Garment Type",
-                                choices=[("Upper", "upper_body"), ("Lower", "lower_body"), ("Dress", "dresses")],
-                                value="upper_body",
-                                elem_classes=["radio"],
-                            )
-                            vt_ref_acceleration = gr.Checkbox(
-                                label="Accelerate Reference UNet",
-                                value=False,
-                                elem_classes=["checkbox"],
-                            )
-                            vt_repaint = gr.Checkbox(
-                                label="Repaint Mode",
-                                value=False,
-                                elem_classes=["checkbox"],
-                            )
-                            vt_step = gr.Slider(
-                                label="Inference Steps",
-                                minimum=30,
-                                maximum=100,
-                                step=1,
-                                value=30,
-                                elem_classes=["slider"],
-                            )
-                            vt_scale = gr.Slider(
-                                label="Guidance Scale",
-                                minimum=0.1,
-                                maximum=5.0,
-                                step=0.1,
-                                value=2.5,
-                                elem_classes=["slider"],
-                            )
-                            vt_seed = gr.Number(
-                                label="Random Seed",
-                                minimum=-1,
-                                maximum=2147483647,
-                                step=1,
-                                value=42,
-                                elem_classes=["number"],
-                            )
-
-                        with gr.Accordion("Debug Info", open=False, elem_classes=["accordion"]):
-                            vt_mask = gr.Image(label="Generated Mask", height=200)
-                            vt_densepose = gr.Image(label="Generated DensePose", height=200)
-
-                vt_gen_button.click(
-                    fn=leffa_predictor.dehasoft,
-                    inputs=[vt_src_image, vt_ref_image, vt_ref_acceleration, vt_step, vt_scale, vt_seed, vt_model_type, vt_garment_type, vt_repaint],
-                    outputs=[vt_gen_image, vt_mask, vt_densepose],
-                    _js="() => { document.querySelector('.generate-btn').classList.add('loading'); setTimeout(() => document.querySelector('.generate-btn').classList.remove('loading'), 5000); }"
-                )
-
-            with gr.TabItem("Pose Transfer", elem_id="pt_tab"):
-                with gr.Row(equal_height=True):
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Source Person Image", elem_classes=["section-title"])
-                        pt_ref_image = gr.Image(
-                            sources=["upload"],
-                            type="filepath",
-                            label="Person Image",
-                            interactive=True,
-                            height=400,
-                            elem_classes=["image-upload"],
+                        vt_repaint = gr.Radio(
+                            label="Repaint Mode",
+                            choices=[("True", True), ("False", False)],
+                            value=False,
                         )
-                        gr.Examples(
-                            examples=person1_images,
-                            inputs=pt_ref_image,
-                            examples_per_page=5,
-                            elem_classes=["examples"],
+
+                        vt_step = gr.Number(
+                            label="Inference Steps", minimum=30, maximum=100, step=1, value=30)
+
+                        vt_scale = gr.Number(
+                            label="Guidance Scale", minimum=0.1, maximum=5.0, step=0.1, value=2.5)
+
+                        vt_seed = gr.Number(
+                            label="Random Seed", minimum=-1, maximum=2147483647, step=1, value=42)
+
+                    with gr.Accordion("Debug", open=False):
+                        vt_mask = gr.Image(
+                            label="Generated Mask",
+                            width=256,
+                            height=256,
                         )
 
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Target Pose Image", elem_classes=["section-title"])
-                        pt_src_image = gr.Image(
-                            sources=["upload"],
-                            type="filepath",
-                            label="Target Pose Person Image",
-                            interactive=True,
-                            height=400,
-                            elem_classes=["image-upload"],
+                        vt_densepose = gr.Image(
+                            label="Generated DensePose",
+                            width=256,
+                            height=256,
                         )
-                        gr.Examples(
-                            examples=person2_images,
-                            inputs=pt_src_image,
-                            examples_per_page=5,
-                            elem_classes=["examples"],
+
+                vt_gen_button.click(fn=leffa_predictor.dehasoft, inputs=[
+                    vt_src_image, vt_ref_image, vt_ref_acceleration, vt_step, vt_scale, vt_seed, vt_model_type, vt_garment_type, vt_repaint], outputs=[vt_gen_image, vt_mask, vt_densepose])
+
+        with gr.Tab("Control Pose (Pose Transfer)"):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("#### Person Image")
+                    pt_ref_image = gr.Image(
+                        sources=["upload"],
+                        type="filepath",
+                        label="Person Image",
+                        width=512,
+                        height=512,
+                    )
+
+                    gr.Examples(
+                        inputs=pt_ref_image,
+                        examples_per_page=10,
+                        examples=person1_images,
+                    )
+
+                with gr.Column():
+                    gr.Markdown("#### Target Pose Person Image")
+                    pt_src_image = gr.Image(
+                        sources=["upload"],
+                        type="filepath",
+                        label="Target Pose Person Image",
+                        width=512,
+                        height=512,
+                    )
+
+                    gr.Examples(
+                        inputs=pt_src_image,
+                        examples_per_page=10,
+                        examples=person2_images,
+                    )
+
+                with gr.Column():
+                    gr.Markdown("#### Generated Image")
+                    pt_gen_image = gr.Image(
+                        label="Generated Image",
+                        width=512,
+                        height=512,
+                    )
+
+                    with gr.Row():
+                        pose_transfer_gen_button = gr.Button("Generate")
+
+                    with gr.Accordion("Advanced Options", open=False):
+                        pt_ref_acceleration = gr.Radio(
+                            label="Accelerate Reference UNet",
+                            choices=[("True", True), ("False", False)],
+                            value=False,
                         )
 
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Result", elem_classes=["section-title"])
-                        pt_gen_image = gr.Image(
-                            label="Generated Image",
-                            height=400,
-                            elem_classes=["image-output"],
+                        pt_step = gr.Number(
+                            label="Inference Steps", minimum=30, maximum=100, step=1, value=30)
+
+                        pt_scale = gr.Number(
+                            label="Guidance Scale", minimum=0.1, maximum=5.0, step=0.1, value=2.5)
+
+                        pt_seed = gr.Number(
+                            label="Random Seed", minimum=-1, maximum=2147483647, step=1, value=42)
+
+                    with gr.Accordion("Debug", open=False):
+                        pt_mask = gr.Image(
+                            label="Generated Mask",
+                            width=256,
+                            height=256,
                         )
-                        pt_gen_button = gr.Button(
-                            "Generate Image",
-                            variant="primary",
-                            size="lg",
-                            elem_classes=["generate-btn"],
+
+                        pt_densepose = gr.Image(
+                            label="Generated DensePose",
+                            width=256,
+                            height=256,
                         )
 
-                        with gr.Accordion("Advanced Settings", open=False, elem_classes=["accordion"]):
-                            pt_ref_acceleration = gr.Checkbox(
-                                label="Accelerate Reference UNet",
-                                value=False,
-                                elem_classes=["checkbox"],
-                            )
-                            pt_step = gr.Slider(
-                                label="Inference Steps",
-                                minimum=30,
-                                maximum=100,
-                                step=1,
-                                value=30,
-                                elem_classes=["slider"],
-                            )
-                            pt_scale = gr.Slider(
-                                label="Guidance Scale",
-                                minimum=0.1,
-                                maximum=5.0,
-                                step=0.1,
-                                value=2.5,
-                                elem_classes=["slider"],
-                            )
-                            pt_seed = gr.Number(
-                                label="Random Seed",
-                                minimum=-1,
-                                maximum=2147483647,
-                                step=1,
-                                value=42,
-                                elem_classes=["number"],
-                            )
-
-                        with gr.Accordion("Debug Info", open=False, elem_classes=["accordion"]):
-                            pt_mask = gr.Image(label="Generated Mask", height=200)
-                            pt_densepose = gr.Image(label="Generated DensePose", height=200)
-
-                pt_gen_button.click(
-                    fn=leffa_predictor.leffa_predict_pt,
-                    inputs=[pt_src_image, pt_ref_image, pt_ref_acceleration, pt_step, pt_scale, pt_seed],
-                    outputs=[pt_gen_image, pt_mask, pt_densepose],
-                    _js="() => { document.querySelector('.generate-btn').classList.add('loading'); setTimeout(() => document.querySelector('.generate-btn').classList.remove('loading'), 5000); }"
-                )
-
-        gr.Markdown(footer_note, elem_classes=["footer"])
-
-        demo.css = """
-        .title { text-align: center; font-size: 2.5em; margin-bottom: 10px; color: #4f46e5; }
-        .description { text-align: center; font-size: 1.2em; margin-bottom: 20px; color: #374151; }
-        .section-title { font-size: 1.5em; color: #6b7280; margin-bottom: 10px; }
-        .image-upload, .image-output { border-radius: 10px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); }
-        .generate-btn { transition: all 0.3s ease; }
-        .generate-btn:hover { transform: scale(1.05); }
-        .generate-btn.loading { opacity: 0.7; cursor: not-allowed; }
-        .accordion { background-color: #f9fafb; border-radius: 8px; }
-        .radio, .checkbox, .slider, .number { margin: 5px 0; }
-        .examples { margin-top: 10px; }
-        .footer { text-align: center; margin-top: 20px; font-size: 0.9em; color: #6b7280; }
-        """
-
-    demo.launch(share=True, server_port=7860, allowed_paths=["./ckpts/examples"])
+                pose_transfer_gen_button.click(fn=leffa_predictor.leffa_predict_pt, inputs=[
+                    pt_src_image, pt_ref_image, pt_ref_acceleration, pt_step, pt_scale, pt_seed], outputs=[pt_gen_image, pt_mask, pt_densepose])
+
+        gr.Markdown(note)
+
+        demo.launch(share=True, server_port=7860,
+                    allowed_paths=["./ckpts/examples"])