import gradio as gr
from diffusers import DiffusionPipeline,StableDiffusionInpaintPipeline
import torch
from .utils.prompt2prompt import generate
from .utils.device import get_device
from .utils.schedulers import SCHEDULER_LIST, get_scheduler_list
from .download import get_share_js, CSS, get_community_loading_icon
INPAINT_MODEL_LIST = {
"Stable Diffusion 2" : "stabilityai/stable-diffusion-2-inpainting",
"Stable Diffusion 1" : "runwayml/stable-diffusion-inpainting",
}
class StableDiffusionInpaintGenerator:
def __init__(self):
self.pipe = None
def load_model(self, model_path, scheduler):
model_path = INPAINT_MODEL_LIST[model_path]
if self.pipe is None:
self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_path, torch_dtype=torch.float32
)
device = get_device()
self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
self.pipe.to(device)
self.pipe.enable_attention_slicing()
return self.pipe
def generate_image(
self,
pil_image: str,
model_path: str,
prompt: str,
negative_prompt: str,
scheduler: str,
guidance_scale: int,
num_inference_step: int,
height: int,
width: int,
seed_generator=0,
):
image = pil_image["image"].convert("RGB").resize((width, height))
mask_image = pil_image["mask"].convert("RGB").resize((width, height))
pipe = self.load_model(model_path,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)
output = pipe(
prompt=prompt,
image=image,
mask_image=mask_image,
negative_prompt=negative_prompt,
num_images_per_prompt=1,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
generator=generator,
).images
return output
def app():
demo = gr.Blocks(css=CSS)
with demo:
with gr.Row():
with gr.Column():
stable_diffusion_inpaint_image_file = gr.Image(
source="upload",
tool="sketch",
elem_id="image-upload-inpainting",
type="pil",
label="Upload",
).style(height=260)
stable_diffusion_inpaint_prompt = gr.Textbox(
lines=1,
placeholder="Prompt, keywords that explains how you want to modify the image.",
show_label=False,
elem_id="prompt-text-input-inpainting",
value=''
)
stable_diffusion_inpaint_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt, keywords that describe what you don't want in your image",
show_label=False,
elem_id = "negative-prompt-text-input-inpainting",
value=''
)
# add button for generating a prompt from the prompt
stable_diffusion_inpaint_generate = gr.Button(
label="Generate Prompt",
type="primary",
align="center",
value = "Generate Prompt"
)
# show a text box with the generated prompt
stable_diffusion_inpaint_generated_prompt = gr.Textbox(
lines=1,
placeholder="Generated Prompt",
show_label=False,
info="Auto generated prompts for inspiration.",
)
stable_diffusion_inpaint_model_id = gr.Dropdown(
choices=list(INPAINT_MODEL_LIST.keys()),
value=list(INPAINT_MODEL_LIST.keys())[0],
label="Inpaint Model Selection",
elem_id="model-dropdown-inpainting",
info="Select the model you want to use for inpainting."
)
stable_diffusion_inpaint_scheduler = gr.Dropdown(
choices=SCHEDULER_LIST,
value=SCHEDULER_LIST[0],
label="Scheduler",
elem_id="scheduler-dropdown-inpainting",
info="Scheduler list for models. Different schdulers result in different outputs."
)
stable_diffusion_inpaint_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
elem_id = "guidance-scale-slider-inpainting",
info = "Guidance scale determines how much the prompt will affect the image. Higher the value, more the effect."
)
stable_diffusion_inpaint_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
elem_id = "num-inference-step-slider-inpainting",
info = "Number of inference step determines the quality of the image. Higher the number, better the quality."
)
stable_diffusion_inpaint_size = gr.Slider(
minimum=128,
maximum=1280,
step=32,
value=512,
label="Image Size",
elem_id="image-size-slider-inpainting",
info = "Image size determines the height and width of the generated image. Higher the value, better the quality however slower the computation."
)
stable_diffusion_inpaint_seed_generator = gr.Slider(
label="Seed(0 for random)",
minimum=0,
maximum=1000000,
value=0,
elem_id="seed-slider-inpainting",
info="Set the seed to a specific value to reproduce the results."
)
stable_diffusion_inpaint_predict = gr.Button(
value="Generate image"
)
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery-inpainting",
).style(grid=(1, 2))
with gr.Group(elem_id="container-advanced-btns"):
with gr.Group(elem_id="share-btn-container"):
community_icon_html, loading_icon_html = get_community_loading_icon("inpainting")
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Save artwork", elem_id="share-btn-inpainting")
gr.HTML(
"""
<div id="model-description-img2img">
<h3>Inpainting Models</h3>
<p>Inpainting models will take a masked image and modify the masked image with the given prompt.</p>
<p>Prompt should describe how you want to modify the image. For example, if you want to modify the image to have a blue sky, you can use the prompt "sky is blue".</p>
<p>Negative prompt should describe what you don't want in your image. For example, if you don't want the image to have a red sky, you can use the negative prompt "sky is red".</p>
<hr>
<p>Stable Diffusion 1 & 2: Default model for many tasks. </p>
</div>
"""
)
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_inpaint_scheduler,
stable_diffusion_inpaint_guidance_scale,
stable_diffusion_inpaint_num_inference_step,
stable_diffusion_inpaint_size,
stable_diffusion_inpaint_size,
stable_diffusion_inpaint_seed_generator,
],
outputs=[output_image],
)
stable_diffusion_inpaint_generate.click(
fn=generate,
inputs=[stable_diffusion_inpaint_prompt],
outputs=[stable_diffusion_inpaint_generated_prompt],
)
return demo