Update handler.py

handler.py

@@ -1,66 +1,18 @@
-# https://github.com/sayakpaul/diffusers-torchao
-#8s
-import os
-from typing import Any, Dict
-from PIL import Image
-import torch
-from diffusers import FluxPipeline
-from huggingface_inference_toolkit.logging import logger
-from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
-from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight, float8_weight_only
-import time
 
-class EndpointHandler:
-    def __init__(self,path=""):
-
-        self.pipe = FluxPipeline.from_pretrained(
-            "NoMoreCopyrightOrg/flux-dev",
-            torch_dtype=torch.bfloat16,
-        ).to("cuda")
-        apply_cache_on_pipe(self.pipe, residual_diff_threshold=0.12)
-        # quantize_(self.pipe.text_encoder, float8_weight_only())
-        # quantize_(self.pipe.transformer, float8_dynamic_activation_float8_weight())
-        self.pipe.transformer = torch.compile(
-            self.pipe.transformer, mode="max-autotune-no-cudagraphs",
-        )
-        self.pipe.vae = torch.compile(
-            self.pipe.vae, mode="max-autotune-no-cudagraphs",
-        )
-
-    def __call__(self, data: Dict[str, Any]) -> Image.Image:
-        logger.info(f"Received incoming request with {data=}")
-
-        if "inputs" in data and isinstance(data["inputs"], str):
-            prompt = data.pop("inputs")
-        elif "prompt" in data and isinstance(data["prompt"], str):
-            prompt = data.pop("prompt")
-        else:
-            raise ValueError(
-                "Provided input body must contain either the key `inputs` or `prompt` with the"
-                " prompt to use for the image generation, and it needs to be a non-empty string."
-            )
-
-        parameters = data.pop("parameters", {})
-
-        num_inference_steps = parameters.get("num_inference_steps", 28)
-        width = parameters.get("width", 1024)
-        height = parameters.get("height", 1024)
-        guidance_scale = parameters.get("guidance_scale", 3.5)
-
-        # seed generator (seed cannot be provided as is but via a generator)
-        seed = parameters.get("seed", 0)
-        generator = torch.manual_seed(seed)
-        start_time = time.time()
-        result = self.pipe(  # type: ignore
-            prompt,
-            height=height,
-            width=width,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            # output_type="pil" if dist.get_rank() == 0 else "pt",
-        ).images[0]
-        end_time = time.time()
-        time_taken = end_time - start_time
-        print(f"Time taken: {time_taken:.2f} seconds")
-        return result
+  builtins
+@overload def print(*values: object,
+          sep: str | None = " ",
+          end: str | None = "\n",
+          file: SupportsWrite[str] | None = None,
+          flush: Literal[False] = False) -> None
+Prints the values to a stream, or to sys. stdout by default.
+ 
+sep
+string inserted between values, default a space.
+end
+string appended after the last value, default a newline.
+file
+a file-like object (stream); defaults to the current sys. stdout.
+flush
+whether to forcibly flush the stream.
+docs. python. org 的 `print(*values, sep=" ", end="\n", file=None, flush=False)`