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Running
on
Zero
File size: 3,222 Bytes
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import gradio as gr
import os
import sys
import subprocess
import torch
from diffusers import StableDiffusion3Pipeline
from diffusers.models.controlnet_sd3 import ControlNetSD3Model
from diffusers.utils.torch_utils import randn_tensor
# Clone the specific branch
subprocess.run(["git", "clone", "-b", "sd3_control", "https://github.com/instantX-research/diffusers_sd3_control.git"])
# Change directory to the cloned repository and install it
os.chdir('diffusers_sd3_control')
subprocess.run(["pip", "install", "-e", "."])
# Add the path to the examples directory
sys.path.append(os.path.abspath('./examples/community'))
# Import the required pipeline
from pipeline_stable_diffusion_3_controlnet import StableDiffusion3CommonPipeline
# load pipeline
base_model = 'stabilityai/stable-diffusion-3-medium-diffusers'
pipe = StableDiffusion3CommonPipeline.from_pretrained(
base_model,
controlnet_list=['InstantX/SD3-Controlnet-Canny'],
)
pipe.to('cuda:0', torch.float16)
def resize_image(input_path, output_path, target_height):
# Open the input image
img = Image.open(input_path)
# Calculate the aspect ratio of the original image
original_width, original_height = img.size
original_aspect_ratio = original_width / original_height
# Calculate the new width while maintaining the aspect ratio and the target height
new_width = int(target_height * original_aspect_ratio)
# Resize the image while maintaining the aspect ratio and fixing the height
img = img.resize((new_width, target_height), Image.LANCZOS)
# Save the resized image
img.save(output_path)
return output_path
def infer(image_in, prompt):
prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
n_prompt = 'NSFW, nude, naked, porn, ugly'
image_to_canny = load_image(image_in)
image_to_canny = np.array(image_to_canny)
image_to_canny = cv2.Canny(image_to_canny, 100, 200)
image_to_canny = image_to_canny[:, :, None]
image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
image_to_canny = Image.fromarray(image_to_canny)
# controlnet config
controlnet_conditioning = [
dict(
control_index=0,
control_image=image_to_canny,
control_weight=0.7,
control_pooled_projections='zeros'
)
]
# infer
image = pipe(
prompt=prompt,
negative_prompt=n_prompt,
controlnet_conditioning=controlnet_conditioning,
num_inference_steps=28,
guidance_scale=7.0,
height=1024,
width=1024,
).images[0]
return image
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown("""
# SD3 ControlNet
""")
image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
prompt = gr.Textbox(label="Prompt")
submit_btn = gr.Button("Submit")
result = gr.Image(label="Result")
submit_btn.click(
fn = infer,
inputs = [image_in, prompt],
outputs = [result],
show_api=False
)
demo.queue().launch() |