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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import numpy as np
import gradio as gr
import torch
import cv2
import random
def launch(image_input, prompt, negative_prompt, sd_models_dropdown):
return predict(image_input, prompt, negative_prompt, sd_models_dropdown)
def predict(image_input, prompt, negative_prompt, sd_models_dropdown):
image = np.array(image_input)
# Get canny image
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
selected_model = f"""{sd_models_dropdown}"""
pipe = StableDiffusionPipeline.from_pretrained(
selected_model,
torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
negative_promt=""
generator = torch.manual_seed(random.randint(0, 1000))
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=65,
generator=generator,
image=canny_image,
strength=0.15,
guidance_scale=15.5
).images[0]
return image
# SYSTEM INPUTS
image_input = gr.Image(label="Upload iamge candidate", type="filepath")
prompt = gr.Textbox(type="text", promt="Prompt")
negative_prompt = gr.Textbox(type="text", promt="Negative prompt")
sd_models_dropdown = gr.Dropdown(
[
"runwayml/stable-diffusion-v1-5",
],
label="Stable Diffusion Models")
# SYSTEM OUTPUTS
output_image = gr.outputs.Image()
demo = gr.Interface(
launch,
inputs=[image_input, prompt, negative_prompt, sd_models_dropdown],
outputs=output_image,
title="Stable Diffusion with ControlNet",
)
if __name__ == "__main__":
demo.launch() |