Push

app.py

@@ -0,0 +1,206 @@
+import os
+import numpy as np
+from typing import cast
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import gradio as gr
+from gradio.components.image_editor import EditorValue
+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,
+    torch_dtype=torch.bfloat16,
+    custom_pipeline=SUB_MODEL_REPO_ID,
+).to(DEVICE)
+
+
+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(
+    image_and_mask: EditorValue | None,
+    seed: int = 0,
+    num_inference_steps: int = 28,
+    max_dimension: int = 1024,
+    condition_scale: float = 1.0,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
+) -> Image.Image | None:
+    if not image_and_mask:
+        gr.Info("Please upload an image and draw a mask")
+        return None
+    image_np = image_and_mask["background"]
+    image_np = cast(np.ndarray, image_np)
+
+    # If the image is empty, return None
+    if np.sum(image_np) == 0:
+        gr.Info("Please upload an image")
+        return None
+
+    alpha_channel = image_and_mask["layers"][0]
+    alpha_channel = cast(np.ndarray, alpha_channel)
+    mask_np = np.where(alpha_channel[:, :, 3] == 0, 0, 255).astype(np.uint8)
+
+    # if mask_np is empty, return None
+    if np.sum(mask_np) == 0:
+        gr.Info("Please mark the areas you want to remove")
+        return None
+
+    pipeline.load(
+        SUB_MODEL_REPO_ID,
+        subfolder=SUB_MODEL_SUBFOLDER,
+    )
+
+    image = Image.fromarray(image_np)
+    # Resize to max dimension
+    image.thumbnail((max_dimension, max_dimension))
+    # Ensure dimensions are multiple of 16 (for VAE)
+    image = crop_divisible_by_16(image)
+
+    mask = Image.fromarray(mask_np)
+    mask.thumbnail((max_dimension, max_dimension))
+    mask = crop_divisible_by_16(mask)
+
+    # Image masked is the image with the mask applied (black background)
+    image_masked = Image.new("RGBA", image.size, (0, 0, 0, 0))
+    image_masked.paste(image, (0, 0), mask)
+
+    prompt = "[VIRTUAL STAGING]. An empty room."
+
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+
+    final_image = pipeline(
+        condition_image=image_masked,
+        condition_scale=condition_scale,
+        prompt=prompt,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        max_sequence_length=512,
+    ).images[0]
+
+    return final_image
+
+
+intro_markdown = r"""
+# Inpainting 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() 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,
+            )
+            image_and_mask = gr.ImageMask(
+                label="Image and Mask",
+                layers=False,
+                show_fullscreen_button=False,
+                sources=["upload"],
+                show_download_button=False,
+                interactive=True,
+                brush=gr.Brush(default_size=75, colors=["#000000"], color_mode="fixed"),
+                transforms=[],
+            )
+        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):
+                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=[
+            image_and_mask,
+            seed,
+            num_inference_steps,
+            max_dimension,
+            condition_scale,
+        ],
+        outputs=[result],
+    )
+
+
+demo.launch()

pyproject.toml

@@ -0,0 +1,18 @@
+[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==0.31.0",
+    "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",
+]

requirements.txt

@@ -0,0 +1,10 @@
+diffusers==0.31.0
+transformers
+accelerate
+safetensors
+sentencepiece
+peft
+gradio
+spaces
+pillow
+gradio_imageslider