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from typing import Dict, List, Any |
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import torch |
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from torch import autocast |
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from huggingface_hub import hf_hub_download |
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from diffusers import DiffusionPipeline |
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import base64 |
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from io import BytesIO |
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from safetensors.torch import load_file |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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print("device ~>", device) |
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class EndpointHandler: |
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def __init__(self, path=""): |
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print("path ~>", path) |
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self.pipe = DiffusionPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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torch_dtype=torch.float16 if device.type == "cuda" else None, |
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variant="fp16", |
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).to(device) |
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self.pipe.load_lora_weights("SvenN/sdxl-emoji", weight_name="lora.safetensors") |
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self.pipe.fuse_lora() |
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embedding_path = hf_hub_download( |
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repo_id="SvenN/sdxl-emoji", filename="embeddings.pti", repo_type="model" |
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) |
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state_dict = load_file(embedding_path) |
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self.pipe.load_textual_inversion(state_dict["text_encoders_0"], token=["<s0>", "<s1>"], text_encoder=self.pipe.text_encoder, tokenizer=self.pipe.tokenizer) |
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self.pipe.load_textual_inversion(state_dict["text_encoders_1"], token=["<s0>", "<s1>"], text_encoder=self.pipe.text_encoder_2, tokenizer=self.pipe.tokenizer_2) |
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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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inputs = data.pop("inputs", data) |
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images = self.pipe( |
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inputs, |
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cross_attention_kwargs={"scale": 0.8}, |
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num_inference_steps=25 |
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).images |
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image = images[0] |
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return image |
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if __name__ == "__main__": |
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handler = EndpointHandler() |
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print(handler) |
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output = handler({"inputs": "emoji of a tiger face, white background"}) |
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print(output) |
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