| from __future__ import annotations |
| import math |
| import gradio as gr |
| import torch |
| from PIL import Image, ImageOps |
| from diffusers import StableDiffusionInstructPix2PixPipeline |
|
|
| def main(): |
| pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("McGill-NLP/AURORA", safety_checker=None).to("cuda") |
| example_image = Image.open("example.jpg").convert("RGB") |
|
|
| def generate( |
| input_image: Image.Image, |
| instruction: str, |
| steps: int, |
| seed: int, |
| text_cfg_scale: float, |
| image_cfg_scale: float, |
| ): |
| 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 ["", 50, 42, 7.5, 1.5, None, None] |
|
|
| with gr.Blocks() as demo: |
| gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 10px;"> |
| AURORA: Learning Action and Reasoning-Centric Image Editing from Videos and Simulations |
| </h1> |
| <p> |
| AURORA (Action Reasoning Object Attribute) enables training an instruction-guided image editing model that can perform action and reasoning-centric edits, in addition to "simpler" established object, attribute or global edits. |
| </p>""") |
| |
| with gr.Row(): |
| with gr.Column(scale=3): |
| instruction = gr.Textbox(value="move the lemon to the right of the table", lines=1, label="Edit instruction", interactive=True) |
| with gr.Column(scale=1, min_width=100): |
| generate_button = gr.Button("Generate", variant="primary") |
| with gr.Column(scale=1, min_width=100): |
| reset_button = gr.Button("Reset", variant="stop") |
|
|
| with gr.Row(): |
| input_image = gr.Image(value=example_image, label="Input image", type="pil", interactive=True) |
| edited_image = gr.Image(label=f"Edited image", type="pil", interactive=False) |
|
|
| with gr.Row(): |
| steps = gr.Number(value=50, precision=0, label="Steps", interactive=True) |
| seed = gr.Number(value=42, precision=0, label="Seed", 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) |
| |
| generate_button.click( |
| fn=generate, |
| inputs=[ |
| input_image, |
| instruction, |
| steps, |
| seed, |
| text_cfg_scale, |
| image_cfg_scale, |
| ], |
| outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image], |
| ) |
| reset_button.click( |
| fn=reset, |
| inputs=[], |
| outputs=[instruction, steps, seed, text_cfg_scale, image_cfg_scale, edited_image, input_image], |
| ) |
|
|
| demo.queue() |
| demo.launch() |
| |
|
|
| if __name__ == "__main__": |
| main() |
