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from typing import Dict, List, Any |
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import base64 |
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from PIL import Image |
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from io import BytesIO |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel |
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import torch |
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import numpy as np |
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import cv2 |
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import controlnet_hinter |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if device.type != 'cuda': |
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raise ValueError("need to run on GPU") |
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dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
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CONTROLNET_MAPPING = { |
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"canny_edge": { |
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"model_id": "lllyasviel/sd-controlnet-canny", |
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"hinter": controlnet_hinter.hint_canny |
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}, |
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"pose": { |
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"model_id": "lllyasviel/sd-controlnet-openpose", |
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"hinter": controlnet_hinter.hint_openpose |
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}, |
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"depth": { |
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"model_id": "lllyasviel/sd-controlnet-depth", |
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"hinter": controlnet_hinter.hint_depth |
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}, |
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"scribble": { |
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"model_id": "lllyasviel/sd-controlnet-scribble", |
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"hinter": controlnet_hinter.hint_scribble, |
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}, |
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"segmentation": { |
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"model_id": "lllyasviel/sd-controlnet-seg", |
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"hinter": controlnet_hinter.hint_segmentation, |
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}, |
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"normal": { |
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"model_id": "lllyasviel/sd-controlnet-normal", |
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"hinter": controlnet_hinter.hint_normal, |
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}, |
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"hed": { |
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"model_id": "lllyasviel/sd-controlnet-hed", |
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"hinter": controlnet_hinter.hint_hed, |
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}, |
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"hough": { |
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"model_id": "lllyasviel/sd-controlnet-mlsd", |
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"hinter": controlnet_hinter.hint_hough, |
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} |
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} |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.control_type = "normal" |
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self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],torch_dtype=dtype).to(device) |
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self.stable_diffusion_id = "runwayml/stable-diffusion-v1-5" |
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self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id, |
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controlnet=self.controlnet, |
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torch_dtype=dtype, |
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safety_checker=None).to(device) |
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self.generator = torch.Generator(device="cpu").manual_seed(3) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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""" |
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:param data: A dictionary contains `inputs` and optional `image` field. |
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:return: A dictionary with `image` field contains image in base64. |
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""" |
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prompt = data.pop("inputs", None) |
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image = data.pop("image", None) |
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controlnet_type = data.pop("controlnet_type", None) |
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if prompt is None and image is None: |
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return {"error": "Please provide a prompt and base64 encoded image."} |
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if controlnet_type is not None and controlnet_type != self.control_type: |
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print(f"changing controlnet from {self.control_type} to {controlnet_type} using {CONTROLNET_MAPPING[controlnet_type]['model_id']} model") |
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self.control_type = controlnet_type |
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self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"], |
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torch_dtype=dtype).to(device) |
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self.pipe.controlnet = self.controlnet |
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num_inference_steps = data.pop("num_inference_steps", 30) |
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guidance_scale = data.pop("guidance_scale", 7.5) |
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negative_prompt = data.pop("negative_prompt", None) |
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height = data.pop("height", None) |
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width = data.pop("width", None) |
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controlnet_conditioning_scale = data.pop("controlnet_conditioning_scale", 1.0) |
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image = self.decode_base64_image(image) |
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control_image = CONTROLNET_MAPPING[self.control_type]["hinter"](image) |
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out = self.pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=control_image, |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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num_images_per_prompt=1, |
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height=height, |
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width=width, |
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controlnet_conditioning_scale=controlnet_conditioning_scale, |
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generator=self.generator |
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) |
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return out.images[0] |
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def decode_base64_image(self, image_string): |
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base64_image = base64.b64decode(image_string) |
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buffer = BytesIO(base64_image) |
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image = Image.open(buffer) |
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return image |
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