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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
from PIL import Image
import torch
import numpy as np
import cv2
controlnet_conditioning_scale = 0.5 # recommended for good generalization
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
)
pipe.enable_model_cpu_offload()
low_threshold = 100
high_threshold = 200
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 process(input_image, prompt)
canny_image = get_canny_filter(input_image)
images = pipe(
prompt,image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
).images
return [canny_image,images[0]]
block = gr.Blocks().queue()
with block:
gr.Markdown("## ControlNet SDXL Canny")
gr.HTML('''
<p style="margin-bottom: 10px; font-size: 94%">
This is a demo for ControlNet SDXL, which is a neural network structure to control Stable Diffusion XL model by adding extra condition such as canny edge detection.
</p>
''')
gr.HTML("<p>You can duplicate this Space to run it privately without a queue and load additional checkpoints. : <a style='display:inline-block' href='https://huggingface.co/spaces/RamAnanth1/controlnet-sdxl-canny?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a> </p>")
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="numpy")
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button(label="Run")
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
ips = [input_image, prompt]
run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
# examples_list = [
# # [
# # "bird.png",
# # "bird",
# # "Canny Edge Map"
# # ],
# # [
# # "turtle.png",
# # "turtle",
# # "Scribble",
# # "best quality, extremely detailed",
# # 'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
# # 1,
# # 512,
# # 20,
# # 9.0,
# # 123490213,
# # 0.0,
# # 100,
# # 200
# # ],
# [
# "pose1.png",
# "Chef in the Kitchen",
# "Pose",
# # "best quality, extremely detailed",
# # 'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
# # 1,
# # 512,
# # 20,
# # 9.0,
# # 123490213,
# # 0.0,
# # 100,
# # 200
# ]
# ]
# examples = gr.Examples(examples=examples_list,inputs = [input_image, prompt], outputs = [result_gallery], cache_examples = True, fn = process)
gr.Markdown("")
block.launch(debug = True) |