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from typing import Dict, List, Any
import torch
from torch import autocast
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
import base64
from io import BytesIO
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device ~>", device)
class EndpointHandler:
def __init__(self, path=""):
print("path ~>", path)
self.pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16 if device.type == "cuda" else None,
variant="fp16",
).to(device)
self.pipe.load_lora_weights("SvenN/sdxl-emoji", weight_name="lora.safetensors")
text_encoders = [self.pipe.text_encoder, self.pipe.text_encoder_2]
tokenizers = [self.pipe.tokenizer, self.pipe.tokenizer_2]
embedding_path = hf_hub_download(
repo_id="SvenN/sdxl-emoji", filename="embeddings.pti", repo_type="model"
)
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. base64 encoded image
"""
inputs = data.pop("inputs", data)
# Automatically add trigger tokens to the beginning of the prompt
full_prompt = f"A <s0><s1> {inputs}"
images = self.pipe(
full_prompt,
cross_attention_kwargs={"scale": 0.8},
).images
image = images[0]
# encode image as base 64
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
# postprocess the prediction
return {"image": img_str.decode()}
if __name__ == "__main__":
handler = EndpointHandler()
print(handler)
output = handler({"inputs": "emoji of a tiger face, white background"})
print(output)
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