Create app.py

app.py

@@ -0,0 +1,214 @@
+import argparse
+import random
+
+import gradio as gr
+import numpy as np
+
+# import spaces
+import torch
+from torchvision import transforms
+from transformers import AutoModelForImageSegmentation
+
+from .inference_i2mv_sdxl import prepare_pipeline, remove_bg, run_pipeline
+
+# Device and dtype
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Hyperparameters
+NUM_VIEWS = 6
+HEIGHT = 768
+WIDTH = 768
+MAX_SEED = np.iinfo(np.int32).max
+
+pipe = prepare_pipeline(
+    base_model="stabilityai/stable-diffusion-xl-base-1.0",
+    vae_model="madebyollin/sdxl-vae-fp16-fix",
+    unet_model=None,
+    lora_model=None,
+    adapter_path="huanngzh/mv-adapter",
+    scheduler=None,
+    num_views=NUM_VIEWS,
+    device=device,
+    dtype=dtype,
+)
+
+# remove bg
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+birefnet.to(device)
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+
+# @spaces.GPU()
+def infer(
+    prompt,
+    image,
+    do_rembg=True,
+    seed=42,
+    randomize_seed=False,
+    guidance_scale=3.0,
+    num_inference_steps=50,
+    reference_conditioning_scale=1.0,
+    negative_prompt="watermark, ugly, deformed, noisy, blurry, low contrast",
+    progress=gr.Progress(track_tqdm=True),
+):
+    if do_rembg:
+        remove_bg_fn = lambda x: remove_bg(x, birefnet, transform_image, device)
+    else:
+        remove_bg_fn = None
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    images, preprocessed_image = run_pipeline(
+        pipe,
+        num_views=NUM_VIEWS,
+        text=prompt,
+        image=image,
+        height=HEIGHT,
+        width=WIDTH,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        seed=seed,
+        remove_bg_fn=remove_bg_fn,
+        reference_conditioning_scale=reference_conditioning_scale,
+        negative_prompt=negative_prompt,
+        device=device,
+    )
+    return images, preprocessed_image, seed
+
+
+examples = [
+    [
+        "A decorative figurine of a young anime-style girl",
+        "assets/demo/i2mv/A_decorative_figurine_of_a_young_anime-style_girl.png",
+        True,
+        21,
+    ],
+    [
+        "A juvenile emperor penguin chick",
+        "assets/demo/i2mv/A_juvenile_emperor_penguin_chick.png",
+        True,
+        0,
+    ],
+    [
+        "A striped tabby cat with white fur sitting upright",
+        "assets/demo/i2mv/A_striped_tabby_cat_with_white_fur_sitting_upright.png",
+        True,
+        0,
+    ],
+]
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown(
+            f"""# MV-Adapter [Image-to-Multi-View]
+Generate 768x768 multi-view images from a single image using SDXL <br>
+[[page](https://huanngzh.github.io/MV-Adapter-Page/)] [[repo](https://github.com/huanngzh/MV-Adapter)]
+        """
+        )
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                input_image = gr.Image(
+                    label="Input Image",
+                    sources=["upload", "webcam", "clipboard"],
+                    type="pil",
+                )
+                preprocessed_image = gr.Image(label="Preprocessed Image", type="pil")
+
+            prompt = gr.Textbox(
+                label="Prompt", placeholder="Enter your prompt", value="high quality"
+            )
+            do_rembg = gr.Checkbox(label="Remove background", value=True)
+            run_button = gr.Button("Run")
+
+            with gr.Accordion("Advanced Settings", open=False):
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                with gr.Row():
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=50,
+                    )
+
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="CFG scale",
+                        minimum=0.0,
+                        maximum=10.0,
+                        step=0.1,
+                        value=3.0,
+                    )
+
+                with gr.Row():
+                    reference_conditioning_scale = gr.Slider(
+                        label="Image conditioning scale",
+                        minimum=0.0,
+                        maximum=2.0,
+                        step=0.1,
+                        value=1.0,
+                    )
+
+                with gr.Row():
+                    negative_prompt = gr.Textbox(
+                        label="Negative prompt",
+                        placeholder="Enter your negative prompt",
+                        value="watermark, ugly, deformed, noisy, blurry, low contrast",
+                    )
+
+        with gr.Column():
+            result = gr.Gallery(
+                label="Result",
+                show_label=False,
+                columns=[3],
+                rows=[2],
+                object_fit="contain",
+                height="auto",
+            )
+
+    with gr.Row():
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt, input_image, do_rembg, seed],
+            outputs=[result, preprocessed_image, seed],
+            cache_examples=True,
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            input_image,
+            do_rembg,
+            seed,
+            randomize_seed,
+            guidance_scale,
+            num_inference_steps,
+            reference_conditioning_scale,
+            negative_prompt,
+        ],
+        outputs=[result, preprocessed_image, seed],
+    )
+
+demo.launch()