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from typing import List, Dict, Any
import base64
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
import controlnet_hinter
# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
raise ValueError("Need to run on GPU")
# set mixed precision dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
# controlnet mapping for depth controlnet
CONTROLNET_MAPPING = {
"depth": {
"model_id": "lllyasviel/sd-controlnet-depth",
"hinter": controlnet_hinter.hint_depth
}
}
class EndpointHandler():
def __init__(self, path=""):
# define default controlnet id and load controlnet
self.control_type = "depth"
self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"], torch_dtype=dtype).to(device)
# Load StableDiffusionControlNetPipeline
self.stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id,
controlnet=self.controlnet,
torch_dtype=dtype,
safety_checker=None).to(device)
# Define Generator with seed
self.generator = torch.Generator(device="cpu").manual_seed(3)
def __call__(self, data: Any) -> Dict[str, str]:
# Extract parameters from the payload
prompt = data.get("prompt", None)
negative_prompt = data.get("negative_prompt", None)
width = data.get("width", None)
height = data.get("height", None)
num_inference_steps = data.get("steps", 30)
guidance_scale = data.get("cfg_scale", 7)
sampler_index = data.get("sampler_index", "DPM++ 2M Karras") # Default to "DPM++ 2M Karras" if not provided
# Extract controlnet configuration from payload
controlnet_config = data.get("alwayson_scripts", {}).get("controlnet", {}).get("args", [{}])[0]
# Run stable diffusion process
out = self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
height=height,
width=width,
controlnet_conditioning_scale=1.0,
generator=self.generator,
sampler_index=sampler_index # Pass the sampler_index to the stable diffusion process
)
# Get the generated image
generated_image = out.images[0]
# Process with controlnet if enabled
if controlnet_config.get("enabled", False):
input_image_base64 = controlnet_config.get("input_image", "")
input_image = self.decode_base64_image(input_image_base64)
controlnet_model = controlnet_config.get("model", "")
controlnet_control_mode = controlnet_config.get("control_mode", "")
processed_image = self.process_with_controlnet(generated_image, input_image, controlnet_model, controlnet_control_mode)
else:
processed_image = generated_image
# Return the final processed image as base64
return {"image": self.encode_base64_image(processed_image)}
def process_with_controlnet(self, generated_image, input_image, model, control_mode):
# Simulated controlnet processing (replace with actual implementation)
# Here, we're just using the input_image as-is. Replace this with your controlnet logic.
return input_image
def encode_base64_image(self, image):
# Encode the PIL Image to base64
buffer = BytesIO()
image.save(buffer, format="PNG")
return base64.b64encode(buffer.getvalue()).decode("utf-8")
def decode_base64_image(self, image_string):
base64_image = base64.b64decode(image_string)
buffer = BytesIO(base64_image)
image = Image.open(buffer)
return image
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