Create app_base.py

app_base.py

@@ -0,0 +1,276 @@
+#!/usr/bin/env python
+
+import os
+
+import gradio as gr
+import PIL.Image
+from diffusers.utils import load_image
+
+from model import ADAPTER_NAMES, Model
+from utils import (
+    DEFAULT_STYLE_NAME,
+    MAX_SEED,
+    STYLE_NAMES,
+    apply_style,
+    randomize_seed_fn,
+)
+
+CACHE_EXAMPLES = os.environ.get("CACHE_EXAMPLES") == "1"
+
+
+def create_demo(model: Model) -> gr.Blocks:
+    def run(
+        image: PIL.Image.Image,
+        prompt: str,
+        negative_prompt: str,
+        adapter_name: str,
+        style_name: str = DEFAULT_STYLE_NAME,
+        num_inference_steps: int = 30,
+        guidance_scale: float = 5.0,
+        adapter_conditioning_scale: float = 1.0,
+        adapter_conditioning_factor: float = 1.0,
+        seed: int = 0,
+        apply_preprocess: bool = True,
+        progress=gr.Progress(track_tqdm=True),
+    ) -> list[PIL.Image.Image]:
+        prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+
+        return model.run(
+            image=image,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            adapter_name=adapter_name,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            adapter_conditioning_scale=adapter_conditioning_scale,
+            adapter_conditioning_factor=adapter_conditioning_factor,
+            seed=seed,
+            apply_preprocess=apply_preprocess,
+        )
+
+    def process_example(
+        image_url: str,
+        prompt: str,
+        adapter_name: str,
+        guidance_scale: float,
+        adapter_conditioning_scale: float,
+        seed: int,
+        apply_preprocess: bool,
+    ) -> list[PIL.Image.Image]:
+        image = load_image(image_url)
+        return run(
+            image=image,
+            prompt=prompt,
+            negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured",
+            adapter_name=adapter_name,
+            style_name="(No style)",
+            guidance_scale=guidance_scale,
+            adapter_conditioning_scale=adapter_conditioning_scale,
+            seed=seed,
+            apply_preprocess=apply_preprocess,
+        )
+
+    examples = [
+        [
+            "assets/org_canny.jpg",
+            "Mystical fairy in real, magic, 4k picture, high quality",
+            "canny",
+            7.5,
+            0.75,
+            42,
+            True,
+        ],
+        [
+            "assets/org_sketch.png",
+            "a robot, mount fuji in the background, 4k photo, highly detailed",
+            "sketch",
+            7.5,
+            1.0,
+            42,
+            True,
+        ],
+        [
+            "assets/org_lin.jpg",
+            "Ice dragon roar, 4k photo",
+            "lineart",
+            7.5,
+            0.8,
+            42,
+            True,
+        ],
+        [
+            "assets/org_mid.jpg",
+            "A photo of a room, 4k photo, highly detailed",
+            "depth-midas",
+            7.5,
+            1.0,
+            42,
+            True,
+        ],
+        [
+            "assets/org_zoe.jpg",
+            "A photo of a orchid, 4k photo, highly detailed",
+            "depth-zoe",
+            5.0,
+            1.0,
+            42,
+            True,
+        ],
+        [
+            "assets/people.jpg",
+            "A couple, 4k photo, highly detailed",
+            "openpose",
+            5.0,
+            1.0,
+            42,
+            True,
+        ],
+        [
+            "assets/depth-midas-image.png",
+            "stormtrooper lecture, 4k photo, highly detailed",
+            "depth-midas",
+            7.5,
+            1.0,
+            42,
+            False,
+        ],
+        [
+            "assets/openpose-image.png",
+            "spiderman, 4k photo, highly detailed",
+            "openpose",
+            5.0,
+            1.0,
+            42,
+            False,
+        ],
+    ]
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    image = gr.Image(label="Input image", type="pil", height=600)
+                    prompt = gr.Textbox(label="Prompt")
+                    with gr.Row():
+                        adapter_name = gr.Dropdown(label="Adapter name", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0])
+                        style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
+                    run_button = gr.Button("Run")
+                with gr.Accordion("Advanced options", open=False):
+                    apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True)
+                    negative_prompt = gr.Textbox(
+                        label="Negative prompt",
+                        value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured",
+                    )
+                    num_inference_steps = gr.Slider(
+                        label="Number of steps",
+                        minimum=1,
+                        maximum=Model.MAX_NUM_INFERENCE_STEPS,
+                        step=1,
+                        value=25,
+                    )
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.1,
+                        maximum=30.0,
+                        step=0.1,
+                        value=5.0,
+                    )
+                    adapter_conditioning_scale = gr.Slider(
+                        label="Adapter conditioning scale",
+                        minimum=0.5,
+                        maximum=1,
+                        step=0.1,
+                        value=1.0,
+                    )
+                    adapter_conditioning_factor = gr.Slider(
+                        label="Adapter conditioning factor",
+                        info="Fraction of timesteps for which adapter should be applied",
+                        minimum=0.5,
+                        maximum=1.0,
+                        step=0.1,
+                        value=1.0,
+                    )
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=MAX_SEED,
+                        step=1,
+                        value=42,
+                    )
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+            with gr.Column():
+                result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False)
+
+        gr.Examples(
+            examples=examples,
+            inputs=[
+                image,
+                prompt,
+                adapter_name,
+                guidance_scale,
+                adapter_conditioning_scale,
+                seed,
+                apply_preprocess,
+            ],
+            outputs=result,
+            fn=process_example,
+            cache_examples=CACHE_EXAMPLES,
+        )
+
+        inputs = [
+            image,
+            prompt,
+            negative_prompt,
+            adapter_name,
+            style,
+            num_inference_steps,
+            guidance_scale,
+            adapter_conditioning_scale,
+            adapter_conditioning_factor,
+            seed,
+            apply_preprocess,
+        ]
+        prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        negative_prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        run_button.click(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name="run",
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    model = Model(ADAPTER_NAMES[0])
+    demo = create_demo(model)
+    demo.queue(max_size=20).launch()