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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers import UniPCMultistepScheduler
import cv2
import gradio as gr
import numpy as np
import torch
from PIL import Image
# Constants
low_threshold = 100
high_threshold = 200
# Models
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# This command loads the individual model components on GPU on-demand. So, we don't
# need to explicitly call pipe.to("cuda").
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()
# Generator seed,
generator = torch.manual_seed(0)
def get_canny_filter(image):
if not isinstance(image, np.ndarray):
image = np.array(image)
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
return canny_image
def generate_images(image, prompt):
canny_image = get_canny_filter(image)
output = pipe(
prompt,
canny_image,
generator=generator,
num_images_per_prompt=3,
num_inference_steps=20,
)
all_outputs = []
all_outputs.append(canny_image)
for image in output.images:
all_outputs.append(image)
return all_outputs
gr.Interface(
generate_images,
inputs=[
gr.Image(type="pil"),
gr.Textbox(
label="Enter your prompt",
max_lines=1,
placeholder="Sandra Oh, best quality, extremely detailed",
),
],
outputs=gr.Gallery().style(grid=[2], height="auto"),
title="Generate controlled outputs with ControlNet and Stable Diffusion. ",
description="This Space uses Canny edge maps as the additional conditioning.",
examples=[["input_image_vermeer.png", "Sandra Oh, best quality, extremely detailed"]],
allow_flagging=False,
).launch(enable_queue=True)
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