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import gradio as gr
import torch
from diffusers import StableDiffusionImg2ImgPipeline
from PIL import Image
from diffusion_webui.utils.model_list import stable_model_list
from diffusion_webui.utils.scheduler_list import (
SCHEDULER_MAPPING,
get_scheduler,
)
class StableDiffusionImage2ImageGenerator:
def __init__(self):
self.pipe = None
def load_model(self, stable_model_path, scheduler):
if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
stable_model_path, safety_checker=None, torch_dtype=torch.float16
)
self.pipe.model_name = stable_model_path
self.pipe.scheduler_name = scheduler
self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
self.pipe.to("cuda")
self.pipe.enable_xformers_memory_efficient_attention()
return self.pipe
def generate_image(
self,
image_path: str,
stable_model_path: str,
prompt: str,
negative_prompt: str,
num_images_per_prompt: int,
scheduler: str,
guidance_scale: int,
num_inference_step: int,
seed_generator=0,
):
pipe = self.load_model(
stable_model_path=stable_model_path,
scheduler=scheduler,
)
if seed_generator == 0:
random_seed = torch.randint(0, 1000000, (1,))
generator = torch.manual_seed(random_seed)
else:
generator = torch.manual_seed(seed_generator)
image = Image.open(image_path)
images = pipe(
prompt,
image=image,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
generator=generator,
).images
return images
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image2image_image_file = gr.Image(
type="filepath", label="Image"
).style(height=260)
image2image_prompt = gr.Textbox(
lines=1,
placeholder="Prompt",
show_label=False,
)
image2image_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt",
show_label=False,
)
with gr.Row():
with gr.Column():
image2image_model_path = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label="Stable Model Id",
)
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",
)
with gr.Row():
with gr.Column():
image2image_scheduler = gr.Dropdown(
choices=list(SCHEDULER_MAPPING.keys()),
value=list(SCHEDULER_MAPPING.keys())[0],
label="Scheduler",
)
image2image_num_images_per_prompt = gr.Slider(
minimum=1,
maximum=4,
step=1,
value=1,
label="Number Of Images",
)
image2image_seed_generator = gr.Slider(
minimum=0,
maximum=1000000,
step=1,
value=0,
label="Seed(0 for random)",
)
image2image_predict_button = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
).style(grid=(1, 2))
image2image_predict_button.click(
fn=StableDiffusionImage2ImageGenerator().generate_image,
inputs=[
image2image_image_file,
image2image_model_path,
image2image_prompt,
image2image_negative_prompt,
image2image_num_images_per_prompt,
image2image_scheduler,
image2image_guidance_scale,
image2image_num_inference_step,
image2image_seed_generator,
],
outputs=[output_image],
)
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