Update

edit_app.py

@@ -46,154 +46,154 @@ example_instructions = [
 model_id = "timbrooks/instruct-pix2pix"
 
 
-def main():
-    pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
-        model_id, torch_dtype=torch.float16, safety_checker=None
-    ).to("cuda")
-    example_image = Image.open("imgs/example.jpg").convert("RGB")
-
-    def load_example(
-        steps: int,
-        randomize_seed: bool,
-        seed: int,
-        randomize_cfg: bool,
-        text_cfg_scale: float,
-        image_cfg_scale: float,
-    ):
-        example_instruction = random.choice(example_instructions)
-        return [example_image, example_instruction] + generate(
-            example_image,
-            example_instruction,
-            steps,
-            randomize_seed,
-            seed,
-            randomize_cfg,
-            text_cfg_scale,
-            image_cfg_scale,
-        )
-
-    def generate(
-        input_image: Image.Image,
-        instruction: str,
-        steps: int,
-        randomize_seed: bool,
-        seed: int,
-        randomize_cfg: bool,
-        text_cfg_scale: float,
-        image_cfg_scale: float,
-    ):
-        seed = random.randint(0, 100000) if randomize_seed else seed
-        text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
-        image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
-        width, height = input_image.size
-        factor = 512 / max(width, height)
-        factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
-        width = int((width * factor) // 64) * 64
-        height = int((height * factor) // 64) * 64
-        input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
-        if instruction == "":
-            return [input_image, seed]
-
-        generator = torch.manual_seed(seed)
-        edited_image = pipe(
-            instruction,
-            image=input_image,
-            guidance_scale=text_cfg_scale,
-            image_guidance_scale=image_cfg_scale,
-            num_inference_steps=steps,
-            generator=generator,
-        ).images[0]
-        return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
-    def reset():
-        return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
-    with gr.Blocks() as demo:
-        gr.HTML(
-            """<h1 style="font-weight: 900; margin-bottom: 7px;">
-   InstructPix2Pix: Learning to Follow Image Editing Instructions
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+    model_id, torch_dtype=torch.float16, safety_checker=None
+).to("cuda")
+example_image = Image.open("imgs/example.jpg").convert("RGB")
+
+
+def load_example(
+    steps: int,
+    randomize_seed: bool,
+    seed: int,
+    randomize_cfg: bool,
+    text_cfg_scale: float,
+    image_cfg_scale: float,
+):
+    example_instruction = random.choice(example_instructions)
+    return [example_image, example_instruction] + generate(
+        example_image,
+        example_instruction,
+        steps,
+        randomize_seed,
+        seed,
+        randomize_cfg,
+        text_cfg_scale,
+        image_cfg_scale,
+    )
+
+
+def generate(
+    input_image: Image.Image,
+    instruction: str,
+    steps: int,
+    randomize_seed: bool,
+    seed: int,
+    randomize_cfg: bool,
+    text_cfg_scale: float,
+    image_cfg_scale: float,
+):
+    seed = random.randint(0, 100000) if randomize_seed else seed
+    text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
+    image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
+
+    width, height = input_image.size
+    factor = 512 / max(width, height)
+    factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
+    width = int((width * factor) // 64) * 64
+    height = int((height * factor) // 64) * 64
+    input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
+
+    if instruction == "":
+        return [input_image, seed]
+
+    generator = torch.manual_seed(seed)
+    edited_image = pipe(
+        instruction,
+        image=input_image,
+        guidance_scale=text_cfg_scale,
+        image_guidance_scale=image_cfg_scale,
+        num_inference_steps=steps,
+        generator=generator,
+    ).images[0]
+    return [seed, text_cfg_scale, image_cfg_scale, edited_image]
+
+
+def reset():
+    return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
+
+
+with gr.Blocks() as demo:
+    gr.HTML(
+        """<h1 style="font-weight: 900; margin-bottom: 7px;">
+InstructPix2Pix: Learning to Follow Image Editing Instructions
 </h1>
 <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
 <br/>
 <a href="https://huggingface.co/spaces/timbrooks/instruct-pix2pix?duplicate=true">
 <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
 <p/>"""
+    )
+    with gr.Row():
+        with gr.Column(scale=1, min_width=100):
+            generate_button = gr.Button("Generate")
+        with gr.Column(scale=1, min_width=100):
+            load_button = gr.Button("Load Example")
+        with gr.Column(scale=1, min_width=100):
+            reset_button = gr.Button("Reset")
+        with gr.Column(scale=3):
+            instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
+
+    with gr.Row():
+        input_image = gr.Image(label="Input Image", type="pil", interactive=True)
+        edited_image = gr.Image(label="Edited Image", type="pil", interactive=False)
+        input_image.style(height=512, width=512)
+        edited_image.style(height=512, width=512)
+
+    with gr.Row():
+        steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
+        randomize_seed = gr.Radio(
+            ["Fix Seed", "Randomize Seed"],
+            value="Randomize Seed",
+            type="index",
+            show_label=False,
+            interactive=True,
         )
-        with gr.Row():
-            with gr.Column(scale=1, min_width=100):
-                generate_button = gr.Button("Generate")
-            with gr.Column(scale=1, min_width=100):
-                load_button = gr.Button("Load Example")
-            with gr.Column(scale=1, min_width=100):
-                reset_button = gr.Button("Reset")
-            with gr.Column(scale=3):
-                instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
-        with gr.Row():
-            input_image = gr.Image(label="Input Image", type="pil", interactive=True)
-            edited_image = gr.Image(label="Edited Image", type="pil", interactive=False)
-            input_image.style(height=512, width=512)
-            edited_image.style(height=512, width=512)
-
-        with gr.Row():
-            steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
-            randomize_seed = gr.Radio(
-                ["Fix Seed", "Randomize Seed"],
-                value="Randomize Seed",
-                type="index",
-                show_label=False,
-                interactive=True,
-            )
-            seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
-            randomize_cfg = gr.Radio(
-                ["Fix CFG", "Randomize CFG"],
-                value="Fix CFG",
-                type="index",
-                show_label=False,
-                interactive=True,
-            )
-            text_cfg_scale = gr.Number(value=7.5, label="Text CFG", interactive=True)
-            image_cfg_scale = gr.Number(value=1.5, label="Image CFG", interactive=True)
-
-        gr.Markdown(help_text)
-
-        load_button.click(
-            fn=load_example,
-            inputs=[
-                steps,
-                randomize_seed,
-                seed,
-                randomize_cfg,
-                text_cfg_scale,
-                image_cfg_scale,
-            ],
-            outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
-        )
-        generate_button.click(
-            fn=generate,
-            inputs=[
-                input_image,
-                instruction,
-                steps,
-                randomize_seed,
-                seed,
-                randomize_cfg,
-                text_cfg_scale,
-                image_cfg_scale,
-            ],
-            outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
-        )
-        reset_button.click(
-            fn=reset,
-            inputs=[],
-            outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
+        seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
+        randomize_cfg = gr.Radio(
+            ["Fix CFG", "Randomize CFG"],
+            value="Fix CFG",
+            type="index",
+            show_label=False,
+            interactive=True,
         )
+        text_cfg_scale = gr.Number(value=7.5, label="Text CFG", interactive=True)
+        image_cfg_scale = gr.Number(value=1.5, label="Image CFG", interactive=True)
+
+    gr.Markdown(help_text)
 
-    demo.queue(concurrency_count=1)
-    demo.launch(share=False)
+    load_button.click(
+        fn=load_example,
+        inputs=[
+            steps,
+            randomize_seed,
+            seed,
+            randomize_cfg,
+            text_cfg_scale,
+            image_cfg_scale,
+        ],
+        outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
+    )
+    generate_button.click(
+        fn=generate,
+        inputs=[
+            input_image,
+            instruction,
+            steps,
+            randomize_seed,
+            seed,
+            randomize_cfg,
+            text_cfg_scale,
+            image_cfg_scale,
+        ],
+        outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
+    )
+    reset_button.click(
+        fn=reset,
+        inputs=[],
+        outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
+    )
 
 
 if __name__ == "__main__":
-    main()
+    demo.queue(max_size=20).launch()