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import cv2
import gradio as gr
import numpy as np
import torch
from diffusers import ControlNetModel, DiffusionPipeline, StableDiffusionControlNetPipeline
from PIL import Image
low_threshold = 100
high_threshold = 200
def generate(
prompt, negative_prompt, num_inference_steps, width, height, guidance_scale, seed, input_image
):
generator = torch.manual_seed(seed)
if input_image is None:
pipeline = DiffusionPipeline.from_pretrained("Lykon/DreamShaper")
return pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
width=width,
height=height,
guidance_scale=guidance_scale,
generator=generator,
).images[0]
image = cv2.Canny(input_image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipeline = StableDiffusionControlNetPipeline.from_pretrained("Lykon/DreamShaper", controlnet=controlnet)
return pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
width=width,
height=height,
guidance_scale=guidance_scale,
generator=generator,
image=canny_image,
).images[0]
iface = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(label="Prompt", value=""),
gr.Textbox(label="Negative Prompt", value=""),
gr.Slider(label="Sampling Steps", minimum=1, maximum=150, value=30, step=1),
gr.Slider(label="Width", minimum=64, maximum=2048, value=512, step=1),
gr.Slider(label="Height", minimum=64, maximum=2048, value=512, step=1),
gr.Slider(label="CFG Scale", minimum=1, maximum=30, value=9, step=0.5),
gr.Slider(
label="Seed",
info="Refresh the page to generate a new random seed.",
minimum=0,
maximum=4294967294,
step=1,
randomize=True,
),
gr.Image(label="Input Image", source='upload', type="numpy")
],
outputs="image",
)
iface.launch()
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