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from typing import Dict, List, Any |
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import torch |
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from PIL import Image |
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from io import BytesIO |
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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DDIMScheduler |
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from transformers.utils import logging |
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import base64 |
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import requests |
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from io import BytesIO |
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from PIL import Image |
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logging.set_verbosity_info() |
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logger = logging.get_logger("transformers") |
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def load_image(image_url): |
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if image_url.startswith('data:'): |
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image_data = base64.b64decode(image_url.split(',')[1]) |
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image = Image.open(BytesIO(image_data)) |
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else: |
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response = requests.get(image_url) |
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image = Image.open(BytesIO(response.content)) |
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return image |
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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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model_id = "stabilityai/stable-diffusion-2-1-base" |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.textPipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
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self.textPipe.scheduler = DDIMScheduler.from_config(self.textPipe.scheduler.config) |
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self.textPipe = self.textPipe.to(device) |
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self.imgPipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
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self.imgPipe.scheduler = DDIMScheduler.from_config(self.imgPipe.scheduler.config) |
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self.imgPipe = self.imgPipe.to(device) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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""" |
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Args: |
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data (:obj:): |
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includes the input data and the parameters for the inference. |
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Return: |
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A :obj:`dict`:. base64 encoded image |
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""" |
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prompt = data.pop("inputs", data) |
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url = data.pop("url", data) |
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init_image = load_image(url).convert("RGB") |
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init_image.thumbnail((512, 512)) |
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params = data.pop("parameters", data) |
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num_inference_steps = params.pop("num_inference_steps", 25) |
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guidance_scale = params.pop("guidance_scale", 7.5) |
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negative_prompt = params.pop("negative_prompt", None) |
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height = params.pop("height", None) |
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strength = params.pop("strength", 0.8) |
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width = params.pop("width", None) |
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manual_seed = params.pop("manual_seed", -1) |
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logger.info(f"strength: {strength}, manual_seed: {manual_seed}, inference_steps: {num_inference_steps}, guidance_scale: {guidance_scale}") |
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out = None |
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generator = torch.Generator(device='cuda') |
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generator.manual_seed(manual_seed) |
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out = self.imgPipe(prompt, |
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image=init_image, |
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strength=strength, |
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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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negative_prompt=negative_prompt, |
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) |
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return out.images[0] |
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