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import gradio as gr
import torch
from diffusers import DDIMScheduler, StableDiffusionImg2ImgPipeline
from PIL import Image
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1",
]
stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
stable_negative_prompt_list = ["bad, ugly", "deformed"]
data_list = [
"data/test.png",
]
def stable_diffusion_img2img(
image_path: str,
model_path: str,
prompt: str,
negative_prompt: str,
guidance_scale: int,
num_inference_step: int,
):
image = Image.open(image_path)
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_path, safety_checker=None, torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
output = pipe(
prompt=prompt,
image=image,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
).images
return output[0]
def stable_diffusion_img2img_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image2image2_image_file = gr.Image(
type="filepath", label="Image"
)
image2image_model_path = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label="Image-Image Model Id",
)
image2image_prompt = gr.Textbox(
lines=1, value=stable_prompt_list[0], label="Prompt"
)
image2image_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label="Negative Prompt",
)
with gr.Accordion("Advanced Options", open=False):
image2image_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
)
image2image_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
)
image2image_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Image(label="Output")
gr.Examples(
fn=stable_diffusion_img2img,
examples=[
[
data_list[0],
stable_model_list[0],
stable_prompt_list[0],
stable_negative_prompt_list[0],
7.5,
50,
],
],
inputs=[
image2image2_image_file,
image2image_model_path,
image2image_prompt,
image2image_negative_prompt,
image2image_guidance_scale,
image2image_num_inference_step,
],
outputs=[output_image],
cache_examples=False,
label="Image-Image Generator",
)
image2image_predict.click(
fn=stable_diffusion_img2img,
inputs=[
image2image2_image_file,
image2image_model_path,
image2image_prompt,
image2image_negative_prompt,
image2image_guidance_scale,
image2image_num_inference_step,
],
outputs=[output_image],
)
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