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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()
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