Create app.py

app.py

@@ -0,0 +1,160 @@
+from __future__ import annotations
+
+import math
+import random
+
+import gradio as gr
+import torch
+from PIL import Image, ImageOps
+from diffusers import StableDiffusionInstructPix2PixPipeline
+
+# Path to the SafeTensor model in Colab
+model_path = "/content/uberRealisticPornMerge_urpmv12.instruct-pix2pix.safetensors"
+
+def main():
+    # Load the SafeTensor model from Colab
+    safe_pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_path, 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 = safe_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=f"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=f"Text CFG", interactive=True)
+            image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
+
+        gr.Markdown(help_text)
+        
+        # Define actions for buttons
+        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],
+        )
+
+    demo.queue(concurrency_count=1)
+    demo.launch(share=False)
+
+
+if __name__ == "__main__":
+    main()
+