aeayüz düzenlenöesi

app.py

@@ -12,344 +12,266 @@ 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(
-            densepose_path="./ckpts/densepose",
-            schp_path="./ckpts/schp",
-        )
-
-        self.densepose_predictor = DensePosePredictor(
-            config_path="./ckpts/densepose/densepose_rcnn_R_50_FPN_s1x.yaml",
-            weights_path="./ckpts/densepose/model_final_162be9.pkl",
-        )
-
-        self.parsing = Parsing(
-            atr_path="./ckpts/humanparsing/parsing_atr.onnx",
-            lip_path="./ckpts/humanparsing/parsing_lip.onnx",
-        )
-
-        self.openpose = OpenPose(
-            body_model_path="./ckpts/openpose/body_pose_model.pth",
-        )
-
-        vt_model_hd = LeffaModel(
-            pretrained_model_name_or_path="./ckpts/stable-diffusion-inpainting",
-            pretrained_model="./ckpts/virtual_tryon.pth",
-            dtype="float16",
-        )
-        self.vt_inference_hd = LeffaInference(model=vt_model_hd)
-
-        vt_model_dc = LeffaModel(
-            pretrained_model_name_or_path="./ckpts/stable-diffusion-inpainting",
-            pretrained_model="./ckpts/virtual_tryon_dc.pth",
-            dtype="float16",
-        )
-        self.vt_inference_dc = LeffaInference(model=vt_model_dc)
-
-        pt_model = LeffaModel(
-            pretrained_model_name_or_path="./ckpts/stable-diffusion-xl-1.0-inpainting-0.1",
-            pretrained_model="./ckpts/pose_transfer.pth",
-            dtype="float16",
-        )
-        self.pt_inference = LeffaInference(model=pt_model)
-
-    def leffa_predict(
-        self,
-        src_image_path,
-        ref_image_path,
-        control_type,
-        ref_acceleration=False,
-        step=50,
-        scale=2.5,
-        seed=42,
-        vt_model_type="viton_hd",
-        vt_garment_type="upper_body",
-        vt_repaint=False
-    ):
-        assert control_type in [
-            "virtual_tryon", "pose_transfer"], "Invalid control type: {}".format(control_type)
-        src_image = Image.open(src_image_path)
-        ref_image = Image.open(ref_image_path)
-        src_image = resize_and_center(src_image, 768, 1024)
-        ref_image = resize_and_center(ref_image, 768, 1024)
-
-        src_image_array = np.array(src_image)
-
-        # Mask
-        if control_type == "virtual_tryon":
-            src_image = src_image.convert("RGB")
-            model_parse, _ = self.parsing(src_image.resize((384, 512)))
-            keypoints = self.openpose(src_image.resize((384, 512)))
-            if vt_model_type == "viton_hd":
-                mask = get_agnostic_mask_hd(
-                    model_parse, keypoints, vt_garment_type)
-            elif vt_model_type == "dress_code":
-                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)
-
-        # DensePose
-        if control_type == "virtual_tryon":
-            if vt_model_type == "viton_hd":
-                src_image_seg_array = self.densepose_predictor.predict_seg(
-                    src_image_array)[:, :, ::-1]
-                src_image_seg = Image.fromarray(src_image_seg_array)
-                densepose = src_image_seg
-            elif vt_model_type == "dress_code":
-                src_image_iuv_array = self.densepose_predictor.predict_iuv(
-                    src_image_array)
-                src_image_seg_array = src_image_iuv_array[:, :, 0:1]
-                src_image_seg_array = np.concatenate(
-                    [src_image_seg_array] * 3, axis=-1)
-                src_image_seg = Image.fromarray(src_image_seg_array)
-                densepose = src_image_seg
-        elif control_type == "pose_transfer":
-            src_image_iuv_array = self.densepose_predictor.predict_iuv(
-                src_image_array)[:, :, ::-1]
-            src_image_iuv = Image.fromarray(src_image_iuv_array)
-            densepose = src_image_iuv
-
-        # Leffa
-        transform = LeffaTransform()
-
-        data = {
-            "src_image": [src_image],
-            "ref_image": [ref_image],
-            "mask": [mask],
-            "densepose": [densepose],
-        }
-        data = transform(data)
-        if control_type == "virtual_tryon":
-            if vt_model_type == "viton_hd":
-                inference = self.vt_inference_hd
-            elif vt_model_type == "dress_code":
-                inference = self.vt_inference_dc
-        elif control_type == "pose_transfer":
-            inference = self.pt_inference
-        output = inference(
-            data,
-            ref_acceleration=ref_acceleration,
-            num_inference_steps=step,
-            guidance_scale=scale,
-            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):
-        return self.leffa_predict(src_image_path, ref_image_path, "virtual_tryon", ref_acceleration, step, scale, seed, vt_model_type, vt_garment_type, vt_repaint)
-
-    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")
 
-    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",
+    # 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:
+        # Başlık ve Açıklama
+        gr.Markdown(title, elem_classes=["title"])
+        gr.Markdown(description, elem_classes=["description"])
+
+        # Sekmeler
+        with gr.Tabs(elem_classes=["tabs"]):
+            # Virtual Try-on Sekmesi
+            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"],
                         )
-
-                        vt_garment_type = gr.Radio(
-                            label="Garment Type",
-                            choices=[("Upper", "upper_body"),
-                                     ("Lower", "lower_body"),
-                                     ("Dress", "dresses")],
-                            value="upper_body",
+                        gr.Examples(
+                            examples=person1_images,
+                            inputs=vt_src_image,
+                            examples_per_page=5,
+                            elem_classes=["examples"],
                         )
 
-                        vt_ref_acceleration = gr.Radio(
-                            label="Accelerate Reference UNet (may slightly reduce performance)",
-                            choices=[("True", True), ("False", False)],
-                            value=False,
+                    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"],
                         )
-
-                        vt_repaint = gr.Radio(
-                            label="Repaint Mode",
-                            choices=[("True", True), ("False", False)],
-                            value=False,
+                        gr.Examples(
+                            examples=garment_images,
+                            inputs=vt_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("### Result", elem_classes=["section-title"])
+                        vt_gen_image = gr.Image(
+                            label="Generated Image",
+                            height=400,
+                            elem_classes=["image-output"],
                         )
-
-                        vt_densepose = gr.Image(
-                            label="Generated DensePose",
-                            width=256,
-                            height=256,
+                        vt_gen_button = gr.Button(
+                            "Generate Image",
+                            variant="primary",
+                            size="lg",
+                            elem_classes=["generate-btn"],
                         )
 
-                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.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); }"
+                )
+
+            # Pose Transfer Sekmesi
+            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"],
                         )
-
-                        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,
+                        gr.Examples(
+                            examples=person1_images,
+                            inputs=pt_ref_image,
+                            examples_per_page=5,
+                            elem_classes=["examples"],
                         )
 
-                        pt_densepose = gr.Image(
-                            label="Generated DensePose",
-                            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"],
+                        )
+                        gr.Examples(
+                            examples=person2_images,
+                            inputs=pt_src_image,
+                            examples_per_page=5,
+                            elem_classes=["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)
+                    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_gen_button = gr.Button(
+                            "Generate Image",
+                            variant="primary",
+                            size="lg",
+                            elem_classes=["generate-btn"],
+                        )
 
-        demo.launch(share=True, server_port=7860,
-                    allowed_paths=["./ckpts/examples"])
+                        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); }"
+                )
+
+        # Altbilgi
+        gr.Markdown(footer_note, elem_classes=["footer"])
+
+        # Özel CSS
+        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"])