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import gradio as gr
import torch
from diffusers import DiffusionPipeline
from diffusion_webui.utils.model_list import stable_inpiant_model_list
class StableDiffusionInpaintGenerator:
def __init__(self):
self.pipe = None
def load_model(self, stable_model_path):
if self.pipe is None or self.pipe.model_name != stable_model_path:
self.pipe = DiffusionPipeline.from_pretrained(
stable_model_path, revision="fp16", torch_dtype=torch.float16
)
self.pipe.to("cuda")
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe.model_name = stable_model_path
return self.pipe
def generate_image(
self,
pil_image: str,
stable_model_path: str,
prompt: str,
negative_prompt: str,
num_images_per_prompt: int,
guidance_scale: int,
num_inference_step: int,
seed_generator=0,
):
image = pil_image["image"].convert("RGB").resize((512, 512))
mask_image = pil_image["mask"].convert("RGB").resize((512, 512))
pipe = self.load_model(stable_model_path)
if seed_generator == 0:
random_seed = torch.randint(0, 1000000, (1,))
generator = torch.manual_seed(random_seed)
else:
generator = torch.manual_seed(seed_generator)
output = pipe(
prompt=prompt,
image=image,
mask_image=mask_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 output
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
stable_diffusion_inpaint_image_file = gr.Image(
source="upload",
tool="sketch",
elem_id="image_upload",
type="pil",
label="Upload",
).style(height=260)
stable_diffusion_inpaint_prompt = gr.Textbox(
lines=1,
placeholder="Prompt",
show_label=False,
)
stable_diffusion_inpaint_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt",
show_label=False,
)
stable_diffusion_inpaint_model_id = gr.Dropdown(
choices=stable_inpiant_model_list,
value=stable_inpiant_model_list[0],
label="Inpaint Model Id",
)
with gr.Row():
with gr.Column():
stable_diffusion_inpaint_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
)
stable_diffusion_inpaint_num_inference_step = (
gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
)
)
with gr.Row():
with gr.Column():
stable_diffusion_inpiant_num_images_per_prompt = gr.Slider(
minimum=1,
maximum=4,
step=1,
value=1,
label="Number Of Images",
)
stable_diffusion_inpaint_seed_generator = (
gr.Slider(
minimum=0,
maximum=1000000,
step=1,
value=0,
label="Seed(0 for random)",
)
)
stable_diffusion_inpaint_predict = gr.Button(
value="Generator"
)
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
).style(grid=(1, 2))
stable_diffusion_inpaint_predict.click(
fn=StableDiffusionInpaintGenerator().generate_image,
inputs=[
stable_diffusion_inpaint_image_file,
stable_diffusion_inpaint_model_id,
stable_diffusion_inpaint_prompt,
stable_diffusion_inpaint_negative_prompt,
stable_diffusion_inpiant_num_images_per_prompt,
stable_diffusion_inpaint_guidance_scale,
stable_diffusion_inpaint_num_inference_step,
stable_diffusion_inpaint_seed_generator,
],
outputs=[output_image],
)
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