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from __future__ import annotations |
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import math |
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import gradio as gr |
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import torch |
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from PIL import Image, ImageOps |
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from diffusers import StableDiffusionInstructPix2PixPipeline |
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def main(): |
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pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("McGill-NLP/AURORA", safety_checker=None).to("cuda") |
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example_image = Image.open("example.jpg").convert("RGB") |
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def generate( |
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input_image: Image.Image, |
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instruction: str, |
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steps: int, |
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seed: int, |
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text_cfg_scale: float, |
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image_cfg_scale: float, |
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): |
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width, height = input_image.size |
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factor = 512 / max(width, height) |
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factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) |
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width = int((width * factor) // 64) * 64 |
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height = int((height * factor) // 64) * 64 |
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input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) |
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if instruction == "": |
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return [input_image, seed] |
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generator = torch.manual_seed(seed) |
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edited_image = pipe( |
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instruction, image=input_image, |
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guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale, |
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num_inference_steps=steps, generator=generator, |
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).images[0] |
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return [seed, text_cfg_scale, image_cfg_scale, edited_image] |
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def reset(): |
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return ["", 50, 42, 7.5, 1.5, None, None] |
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with gr.Blocks() as demo: |
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gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 10px;"> |
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AURORA: Learning Action and Reasoning-Centric Image Editing from Videos and Simulations |
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</h1> |
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<p> |
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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. |
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</p>""") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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instruction = gr.Textbox(value="move the lemon to the right of the table", lines=1, label="Edit instruction", interactive=True) |
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with gr.Column(scale=1, min_width=100): |
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generate_button = gr.Button("Generate", variant="primary") |
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with gr.Column(scale=1, min_width=100): |
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reset_button = gr.Button("Reset", variant="stop") |
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with gr.Row(): |
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input_image = gr.Image(value=example_image, label="Input image", type="pil", interactive=True) |
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edited_image = gr.Image(label=f"Edited image", type="pil", interactive=False) |
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with gr.Row(): |
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steps = gr.Number(value=50, precision=0, label="Steps", interactive=True) |
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seed = gr.Number(value=42, precision=0, label="Seed", interactive=True) |
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text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True) |
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image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True) |
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generate_button.click( |
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fn=generate, |
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inputs=[ |
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input_image, |
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instruction, |
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steps, |
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seed, |
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text_cfg_scale, |
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image_cfg_scale, |
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], |
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outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image], |
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) |
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reset_button.click( |
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fn=reset, |
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inputs=[], |
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outputs=[instruction, steps, seed, text_cfg_scale, image_cfg_scale, edited_image, input_image], |
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) |
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demo.queue() |
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demo.launch() |
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if __name__ == "__main__": |
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main() |
