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import io
import cv2
import base64
import requests
from PIL import Image
"""
To use this example make sure you've done the following steps before executing:
1. Ensure automatic1111 is running in api mode with the controlnet extension.
Use the following command in your terminal to activate:
./webui.sh --no-half --api
2. Validate python environment meet package dependencies.
If running in a local repo you'll likely need to pip install cv2, requests and PIL
"""
def generate(url: str, payload: dict, file_suffix: str = ""):
response = requests.post(url=url, json=payload).json()
if "images" not in response:
print(response)
else:
for i, base64image in enumerate(response["images"]):
Image.open(io.BytesIO(base64.b64decode(base64image.split(",", 1)[0]))).save(
f"{url.split('/')[-1]}-{i}{file_suffix}.png"
)
def read_image(img_path: str) -> str:
img = cv2.imread(img_path)
_, bytes = cv2.imencode(".png", img)
encoded_image = base64.b64encode(bytes).decode("utf-8")
return encoded_image
input_image = read_image("stock_mountain.png")
txt2img_payload = {
"alwayson_scripts": {
"ControlNet": {
"args": [
{
"batch_images": "",
"control_mode": "Balanced",
"enabled": True,
"guidance_end": 1,
"guidance_start": 0,
"image": input_image,
"low_vram": False,
"model": "control_v11p_sd15_canny [d14c016b]",
"module": "canny",
"pixel_perfect": False,
"processor_res": -1,
"resize_mode": "Crop and Resize",
"save_detected_map": True,
"threshold_a": -1,
"threshold_b": -1,
"weight": 1,
}
]
}
},
"batch_size": 1,
"cfg_scale": 7,
"comments": {},
"disable_extra_networks": False,
"do_not_save_grid": False,
"do_not_save_samples": False,
"enable_hr": False,
"height": 512,
"width": 768,
"hr_negative_prompt": "",
"hr_prompt": "",
"hr_resize_x": 0,
"hr_resize_y": 0,
"hr_scale": 2,
"hr_second_pass_steps": 0,
"hr_upscaler": "Latent",
"n_iter": 1,
"negative_prompt": "",
"override_settings": {},
"override_settings_restore_afterwards": True,
"prompt": "(masterpiece: 1.3), (highres: 1.3), best quality, a large avalanche",
"restore_faces": False,
"s_churn": 0.0,
"s_min_uncond": 0,
"s_noise": 1.0,
"s_tmax": None,
"s_tmin": 0.0,
"sampler_name": "DPM++ 2M Karras",
"script_args": [],
"script_name": None,
"seed": 42,
"seed_enable_extras": True,
"seed_resize_from_h": -1,
"seed_resize_from_w": -1,
"steps": 30,
"styles": [],
"subseed": -1,
"subseed_strength": 0,
"tiling": False,
}
if __name__ == "__main__":
url = "http://localhost:7860/sdapi/v1/"
for weight_factor in (0.3, 0.5, 0.8):
advanced_weighting = [weight_factor ** float(12 - i) for i in range(13)]
txt2img_payload["alwayson_scripts"]["ControlNet"]["args"][0][
"advanced_weighting"
] = advanced_weighting
generate(url + "txt2img", txt2img_payload, file_suffix=f"fac{weight_factor}")
for linear_start in (0.3, 0.5, 0.8):
step = (1.0 - linear_start) / 12
advanced_weighting = [linear_start + i * step for i in range(13)]
txt2img_payload["alwayson_scripts"]["ControlNet"]["args"][0][
"advanced_weighting"
] = advanced_weighting
generate(url + "txt2img", txt2img_payload, file_suffix=f"linear{linear_start}")
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