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| 1 | +# Copyright (C) 2024 Intel Corporation |
| 2 | +# SPDX-License-Identifier: Apache-2.0 |
| 3 | +import base64 |
| 4 | +import os |
| 5 | +import threading |
| 6 | + |
| 7 | +from comps import CustomLogger, OpeaComponent, SDImg2ImgInputs, ServiceType |
| 8 | + |
| 9 | +logger = CustomLogger("opea_imagetoimage") |
| 10 | +logflag = os.getenv("LOGFLAG", False) |
| 11 | + |
| 12 | +import torch |
| 13 | +from diffusers import AutoPipelineForImage2Image |
| 14 | +from diffusers.utils import load_image |
| 15 | + |
| 16 | +pipe = None |
| 17 | +args = None |
| 18 | +initialization_lock = threading.Lock() |
| 19 | +initialized = False |
| 20 | + |
| 21 | + |
| 22 | +def initialize( |
| 23 | + model_name_or_path="stabilityai/stable-diffusion-xl-refiner-1.0", |
| 24 | + device="cpu", |
| 25 | + token=None, |
| 26 | + bf16=True, |
| 27 | + use_hpu_graphs=False, |
| 28 | +): |
| 29 | + global pipe, args, initialized |
| 30 | + with initialization_lock: |
| 31 | + if not initialized: |
| 32 | + # initialize model and tokenizer |
| 33 | + if os.getenv("MODEL", None): |
| 34 | + model_name_or_path = os.getenv("MODEL") |
| 35 | + kwargs = {} |
| 36 | + if bf16: |
| 37 | + kwargs["torch_dtype"] = torch.bfloat16 |
| 38 | + if not token: |
| 39 | + token = os.getenv("HF_TOKEN") |
| 40 | + if device == "hpu": |
| 41 | + kwargs( |
| 42 | + { |
| 43 | + "use_habana": True, |
| 44 | + "use_hpu_graphs": use_hpu_graphs, |
| 45 | + "gaudi_config": "Habana/stable-diffusion", |
| 46 | + "token": token, |
| 47 | + } |
| 48 | + ) |
| 49 | + if "stable-diffusion-xl" in model_name_or_path: |
| 50 | + from optimum.habana.diffusers import GaudiStableDiffusionXLImg2ImgPipeline |
| 51 | + |
| 52 | + pipe = GaudiStableDiffusionXLImg2ImgPipeline.from_pretrained( |
| 53 | + model_name_or_path, |
| 54 | + **kwargs, |
| 55 | + ) |
| 56 | + else: |
| 57 | + raise NotImplementedError( |
| 58 | + "Only support stable-diffusion-xl now, " + f"model {model_name_or_path} not supported." |
| 59 | + ) |
| 60 | + elif device == "cpu": |
| 61 | + pipe = AutoPipelineForImage2Image.from_pretrained(model_name_or_path, token=token, **kwargs) |
| 62 | + else: |
| 63 | + raise NotImplementedError(f"Only support cpu and hpu device now, device {device} not supported.") |
| 64 | + logger.info("Stable Diffusion model initialized.") |
| 65 | + initialized = True |
| 66 | + |
| 67 | + |
| 68 | +class OpeaImageToImage(OpeaComponent): |
| 69 | + """A specialized ImageToImage component derived from OpeaComponent for Stable Diffusion model . |
| 70 | +
|
| 71 | + Attributes: |
| 72 | + model_name_or_path (str): The name of the Stable Diffusion model used. |
| 73 | + device (str): which device to use. |
| 74 | + token(str): Huggingface Token. |
| 75 | + bf16(bool): Is use bf16. |
| 76 | + use_hpu_graphs(bool): Is use hpu_graphs. |
| 77 | + """ |
| 78 | + |
| 79 | + def __init__( |
| 80 | + self, |
| 81 | + name: str, |
| 82 | + description: str, |
| 83 | + config: dict = None, |
| 84 | + seed=42, |
| 85 | + model_name_or_path="stabilityai/stable-diffusion-xl-refiner-1.0", |
| 86 | + device="cpu", |
| 87 | + token=None, |
| 88 | + bf16=True, |
| 89 | + use_hpu_graphs=False, |
| 90 | + ): |
| 91 | + super().__init__(name, ServiceType.IMAGE2IMAGE.name.lower(), description, config) |
| 92 | + initialize( |
| 93 | + model_name_or_path=model_name_or_path, device=device, token=token, bf16=bf16, use_hpu_graphs=use_hpu_graphs |
| 94 | + ) |
| 95 | + self.pipe = pipe |
| 96 | + self.seed = seed |
| 97 | + |
| 98 | + def invoke(self, input: SDImg2ImgInputs): |
| 99 | + """Invokes the ImageToImage service to generate Images for the provided input. |
| 100 | +
|
| 101 | + Args: |
| 102 | + input (SDImg2ImgInputs): The input in SD images format. |
| 103 | + """ |
| 104 | + image = load_image(input.image).convert("RGB") |
| 105 | + prompt = input.prompt |
| 106 | + num_images_per_prompt = input.num_images_per_prompt |
| 107 | + |
| 108 | + generator = torch.manual_seed(self.seed) |
| 109 | + images = pipe( |
| 110 | + image=image, prompt=prompt, generator=generator, num_images_per_prompt=num_images_per_prompt |
| 111 | + ).images |
| 112 | + image_path = os.path.join(os.getcwd(), prompt.strip().replace(" ", "_").replace("/", "")) |
| 113 | + os.makedirs(image_path, exist_ok=True) |
| 114 | + results = [] |
| 115 | + for i, image in enumerate(images): |
| 116 | + save_path = os.path.join(image_path, f"image_{i + 1}.png") |
| 117 | + image.save(save_path) |
| 118 | + with open(save_path, "rb") as f: |
| 119 | + bytes = f.read() |
| 120 | + b64_str = base64.b64encode(bytes).decode() |
| 121 | + results.append(b64_str) |
| 122 | + |
| 123 | + return results |
| 124 | + |
| 125 | + def check_health(self) -> bool: |
| 126 | + """Checks the health of the ImageToImage service. |
| 127 | +
|
| 128 | + Returns: |
| 129 | + bool: True if the service is reachable and healthy, False otherwise. |
| 130 | + """ |
| 131 | + try: |
| 132 | + if self.pipe: |
| 133 | + return True |
| 134 | + else: |
| 135 | + return False |
| 136 | + except Exception as e: |
| 137 | + # Handle connection errors, timeouts, etc. |
| 138 | + logger.error(f"Health check failed: {e}") |
| 139 | + return False |
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