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from typing import Dict, List, Any |
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from transformers import pipeline |
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import torch |
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import base64 |
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from io import BytesIO |
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from PIL import Image |
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import numpy as np |
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from diffusers import AutoPipelineForImage2Image |
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from diffusers.utils import load_image |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") |
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self.pipe.to("cuda") |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str`) |
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date (:obj: `str`) |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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inputs = data.pop("inputs", data) |
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encoded_image = data.pop("image", None) |
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num_inference_steps = data.pop("num_inference_steps", 25) |
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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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strength = data.pop("strength", 0.7) |
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denoising_start = data.pop("denoising_start_step", 0) |
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denoising_end = data.pop("denoising_end_step", 1) |
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num_images_per_prompt = data.pop("num_images_per_prompt", 1) |
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aesthetic_score = data.pop("aesthetic_score", 0.6) |
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if encoded_image is not None: |
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image = self.decode_base64_image(encoded_image) |
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print("Image is getting loaded") |
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else: |
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print("Image is None") |
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image = None |
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print(f"Prompt: {inputs}, strength: {strength}, inf steps: {num_inference_steps}, denoise start: {denoising_start}, denoise_end: {denoising_end}") |
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print(f"Imgs per prompt: {num_images_per_prompt}, aesthetic_score: {aesthetic_score}, guidance_scale: {guidance_scale}, negative_prompt: {negative_prompt}") |
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out = self.pipe(inputs, |
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image=image, |
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strength=strength, |
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num_inference_steps=num_inference_steps, |
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denoising_start=denoising_start, |
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denoising_end=denoising_end, |
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num_images_per_prompt=num_images_per_prompt, |
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aesthetic_score=aesthetic_score, |
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guidance_scale=guidance_scale, |
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negative_prompt=negative_prompt |
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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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pil_image = Image.fromarray(np.array(image)) |
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return pil_image |
