Update sdxl_text_to_image.py and clear_memory.py

api/routers/sdxl_text_to_image.py

@@ -1,27 +1,13 @@
-<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
-=======
-import sys
-sys.path.append("../scripts")  # Path of the scripts directory
->>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
 import config
 from fastapi import APIRouter, HTTPException
 from typing import List
 from diffusers import DiffusionPipeline
 import torch
 from functools import lru_cache
-<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
 from scripts.api_utils import accelerator
 from models.sdxl_input import InputFormat
 from async_batcher.batcher import AsyncBatcher
 from scripts.api_utils import pil_to_b64_json, pil_to_s3_json
-=======
-from s3_manager import S3ManagerService
-from PIL import Image
-import io
-
-
-
->>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
 torch._inductor.config.conv_1x1_as_mm = True
 torch._inductor.config.coordinate_descent_tuning = True
 torch._inductor.config.epilogue_fusion = False
@@ -34,38 +20,10 @@ device = accelerator()
 router = APIRouter()
 
 
-<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
-=======
-
-
-
-def pil_to_b64_json(image):
-    image_id = str(uuid.uuid4())
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    b64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return {"image_id": image_id, "b64_image": b64_image}
-
-
-def pil_to_s3_json(image: Image.Image,file_name) -> str:
-    image_id = str(uuid.uuid4())
-    s3_uploader = S3ManagerService()
-    image_bytes = io.BytesIO()
-    image.save(image_bytes, format="PNG")
-    image_bytes.seek(0)
-
-    unique_file_name = s3_uploader.generate_unique_file_name(file_name)
-    s3_uploader.upload_file(image_bytes, unique_file_name)
-    signed_url = s3_uploader.generate_signed_url(
-        unique_file_name, exp=43200
-    )  # 12 hours
-    return {"image_id": image_id, "url": signed_url}
->>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
 
 
 # Load the diffusion pipeline
 @lru_cache(maxsize=1)
-<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
 def load_pipeline(model_name, adapter_name,enable_compile:bool):
     """
     Load the diffusion pipeline with the specified model and adapter names.
@@ -85,19 +43,6 @@ def load_pipeline(model_name, adapter_name,enable_compile:bool):
     if enable_compile is True:
         pipe.unet = torch.compile(pipe.unet, mode="max-autotune")
         pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune")
-=======
-def load_pipeline(model_name, adapter_name,adapter_name_2):
-    pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype= torch.bfloat16 ).to(
-        "cuda"
-    )
-    pipe.load_lora_weights(adapter_name)
-    pipe.load_lora_weights(adapter_name_2)
-    pipe.set_adapters([adapter_name, adapter_name_2], adapter_weights=[0.7, 0.5])
-    pipe.fuse_lora()
-    pipe.unload_lora_weights()
-    pipe.unet.to(memory_format=torch.channels_last)
-    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead")
->>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
     pipe.fuse_qkv_projections()
     return pipe
 

scripts/clear_memory.py

@@ -1,18 +0,0 @@
-import gc
-import torch
-from logger import rich_logger as l
-
-def clear_memory():
-    """
-    Clears the memory by collecting garbage and emptying the CUDA cache.
-
-    This function is useful when dealing with memory-intensive operations in Python, especially when using libraries like PyTorch.
-
-    Note:
-        This function requires the `gc` and `torch` modules to be imported.
-
-    """
-    gc.collect()
-    torch.cuda.empty_cache()
-    l.info("Memory Cleared")
-