Delete handler.py

handler.py

@@ -1,36 +0,0 @@
-import base64
-import io
-from PIL import Image
-import torch
-from diffusers import StableDiffusionImg2ImgPipeline
-
-pipe = None
-
-class EndpointHandler:
-    def __init__(self,model_dir):
-        self.model_dir = model_dir
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    def init(self):
-        global pipe
-        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "InstantX/InstantID",
-            torch_dtype=torch.float16,
-            safety_checker=None
-        ).to(self.device)
-        pipe.enable_attention_slicing()
-
-    def inference(self, model_inputs: dict) -> dict:
-        img_bytes = base64.b64decode(model_inputs.get("image_base64"))
-        init_image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
-        output = pipe(
-            prompt=model_inputs.get("prompt", ""),
-            image=init_image,
-            strength=float(model_inputs.get("strength", 0.75)),
-            guidance_scale=float(model_inputs.get("guidance_scale", 7.5)),
-            num_inference_steps=int(model_inputs.get("num_inference_steps", 50)),
-        )
-        sticker = output.images[0]
-        buf = io.BytesIO()
-        sticker.save(buf, format="PNG")
-        return {"generated_image_base64": base64.b64encode(buf.getvalue()).decode("utf-8")}