Update handler.py

handler.py

@@ -4,28 +4,20 @@ import os
 from typing import Any, Dict
 from PIL import Image
 import torch
-from diffusers import FluxPipeline
+import torch.distributed as dist
 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
+
+dist.init_process_group()
+torch.cuda.set_device(dist.get_rank())
+
+from para_attn.context_parallel import init_context_parallel_mesh
+from para_attn.context_parallel.diffusers_adapters import parallelize_pipe
+from para_attn.parallel_vae.diffusers_adapters import parallelize_vae
 
 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=}")
@@ -40,27 +32,4 @@ class EndpointHandler:
                 " 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
+        return "1"