Initial Commit

README.md

@@ -1,12 +1,30 @@
 ---
-title: VirtualStagingDemo
+title: VirtualStaging
 emoji: 🌖
 colorFrom: pink
 colorTo: red
 sdk: gradio
+python_version: 3.12
 sdk_version: 5.12.0
+suggested_hardware: a100-large
 app_file: app.py
-pinned: false
+# fullWidth: true
+# header: mini
+# models: blanchon/VirtualStaging
+# datasets: blanchon/VirtualStagingDataset
+tags:
+  - image-generation
+  - image-to-image
+  - furniture
+  - virtual-staging
+  - home-decor
+  - home-design
+pinned: true
+# preload_from_hub:
+#  - blanchon/VirtualStaging
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# VirtualStaging
+
+...

app.py

@@ -0,0 +1,190 @@
+import os
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import gradio as gr
+import spaces
+
+DEVICE = "cuda"
+
+MAIN_MODEL_REPO_ID = os.getenv("MAIN_MODEL_REPO_ID", None)
+SUB_MODEL_REPO_ID = os.getenv("SUB_MODEL_REPO_ID", None)
+SUB_MODEL_SUBFOLDER = os.getenv("SUB_MODEL_SUBFOLDER", None)
+
+if MAIN_MODEL_REPO_ID is None:
+    raise ValueError("MAIN_MODEL_REPO_ID is not set")
+if SUB_MODEL_REPO_ID is None:
+    raise ValueError("SUB_MODEL_REPO_ID is not set")
+if SUB_MODEL_SUBFOLDER is None:
+    raise ValueError("SUB_MODEL_SUBFOLDER is not set")
+
+pipeline = DiffusionPipeline.from_pretrained(
+    MAIN_MODEL_REPO_ID,
+    custom_pipeline=SUB_MODEL_REPO_ID,
+).to(DEVICE)
+
+pipeline.load(
+    SUB_MODEL_REPO_ID,
+    subfolder=SUB_MODEL_SUBFOLDER,
+)
+
+
+def crop_divisible_by_16(image: Image.Image) -> Image.Image:
+    w, h = image.size
+    w = w - w % 16
+    h = h - h % 16
+    return image.crop((0, 0, w, h))
+
+
+@spaces.GPU(duration=150)
+def predict(
+    room_image_input: Image.Image,
+    room_image_category: str,
+    custom_prompt: str | None = None,
+    num_inference_steps: int = 28,
+    max_dimension: int = 1024,
+    seed: int = 0,
+    condition_scale: float = 1.0,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
+) -> Image.Image:
+    # Resize to max dimension
+    room_image_input.thumbnail((max_dimension, max_dimension))
+    # Ensure dimensions are multiple of 16 (for VAE)
+    room_image_input = crop_divisible_by_16(room_image_input)
+
+    prompt = f"[VIRTUAL STAGING] {room_image_category}\n"
+    if custom_prompt:
+        prompt += f" {custom_prompt}"
+
+    generator = torch.Generator(device=DEVICE).manual_seed(seed)
+
+    final_images = pipeline(
+        condition_image=room_image_input,
+        condition_scale=condition_scale,
+        prompt=prompt,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    )
+
+    return final_images
+
+
+intro_markdown = r"""
+# Virtual Staging Demo
+"""
+
+css = r"""
+#col-left {
+    margin: 0 auto;
+    max-width: 650px;
+}
+#col-right {
+    margin: 0 auto;
+    max-width: 650px;
+}
+#col-showcase {
+    margin: 0 auto;
+    max-width: 1100px;
+}
+"""
+
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(intro_markdown)
+
+    with gr.Row(visible=False) as content:
+        with gr.Column(elem_id="col-left"):
+            gr.HTML(
+                """
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 1. Upload a room image ⬇️
+                </div>
+            </div>
+            """,
+                max_height=50,
+            )
+            room_image_input = gr.Image(
+                label="room",
+                type="pil",
+                sources=["upload"],
+                image_mode="RGB",
+            )
+            room_image_category = gr.Dropdown(
+                label="Room category",
+                choices=[
+                    "bedroom",
+                    "living room",
+                    "bathroom",
+                ],
+                info="Select the room category",
+                multiselect=False,
+            )
+        with gr.Column(elem_id="col-right"):
+            gr.HTML(
+                """
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 2. Press Run to launch
+                </div>
+            </div>
+            """,
+                max_height=50,
+            )
+            result = gr.Image(label="result")
+            run_button = gr.Button("Run")
+
+            with gr.Accordion("Advanced Settings", open=False):
+                custom_prompt = gr.Text(
+                    label="Prompt",
+                    max_lines=3,
+                    placeholder="Enter a custom prompt",
+                    container=False,
+                )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=100_000,
+                    step=1,
+                    value=0,
+                )
+                condition_scale = gr.Slider(
+                    label="Condition Scale",
+                    minimum=-10.0,
+                    maximum=10.0,
+                    step=0.10,
+                    value=1.0,
+                )
+                with gr.Column():
+                    max_dimension = gr.Slider(
+                        label="Max Dimension",
+                        minimum=512,
+                        maximum=2048,
+                        step=128,
+                        value=1024,
+                    )
+
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=28,
+                    )
+
+    run_button.click(
+        fn=predict,
+        inputs=[
+            room_image_input,
+            room_image_category,
+            custom_prompt,
+            seed,
+            num_inference_steps,
+            max_dimension,
+            seed,
+        ],
+        outputs=[result],
+    )
+
+
+demo.launch()

pyproject.toml

@@ -0,0 +1,21 @@
+[project]
+name = "VirtualStaging"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "accelerate>=1.2.1",
+    "diffusers",
+    "gradio>=5.12.0",
+    "gradio-imageslider>=0.0.20",
+    "peft>=0.14.0",
+    "pillow>=11.1.0",
+    "safetensors>=0.5.2",
+    "sentencepiece>=0.2.0",
+    "spaces>=0.32.0",
+    "transformers>=4.48.0",
+]
+
+[tool.uv.sources]
+diffusers = { git = "https://github.com/huggingface/diffusers.git" }

requirements.txt

@@ -0,0 +1,10 @@
+git+https://github.com/huggingface/diffusers.git
+transformers
+accelerate
+safetensors
+sentencepiece
+peft
+gradio
+spaces
+pillow
+gradio_imageslider