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from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler
from PIL import Image
import gradio as gr
import torch
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2",
"stabilityai/stable-diffusion-2-base",
"stabilityai/stable-diffusion-2-1",
"stabilityai/stable-diffusion-2-1-base"
]
stable_inpiant_model_list = [
"stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-inpainting"
]
stable_prompt_list = [
"a photo of a man.",
"a photo of a girl."
]
stable_negative_prompt_list = [
"bad, ugly",
"deformed"
]
def stable_diffusion_img2img(
model_path:str,
image_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')
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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