Update SDXL-LoRA inference pipeline and model weights

Former-commit-id: c803a77350d44492c1875b98c83808ed560b6cef

product_diffusion_api/endpoints.py

@@ -28,7 +28,8 @@ async def root():
         'author': 'Vikramjeet Singh',
         'contact': {
             'email': 'singh.vikram.1782000@gmail.com',
-            'github': 'https://github.com/vikramxD'
+            'github': 'https://github.com/vikramxD',
+            'website': 'https://vikramxd.github.io'
         },
         'license': 'MIT',
     }

product_diffusion_api/routers/sdxl_text_to_image.py

@@ -1,5 +1,5 @@
 import sys
-
+from torchao.quantization import apply_dynamic_quant
 sys.path.append("../scripts")  # Path of the scripts directory
 import config
 from fastapi import APIRouter, HTTPException
@@ -10,17 +10,48 @@ from typing import List
 import uuid
 from diffusers import DiffusionPipeline
 import torch
-import torch_tensorrt
 from functools import lru_cache
 
 torch._inductor.config.conv_1x1_as_mm = True
 torch._inductor.config.coordinate_descent_tuning = True
 torch._inductor.config.epilogue_fusion = False
 torch._inductor.config.coordinate_descent_check_all_directions = True
+torch._inductor.config.force_fuse_int_mm_with_mul = True
+torch._inductor.config.use_mixed_mm = True
 
 router = APIRouter()
 
 
+def dynamic_quant_filter_fn(mod, *args):
+    return (
+        isinstance(mod, torch.nn.Linear)
+        and mod.in_features > 16
+        and (mod.in_features, mod.out_features)
+        not in [
+            (1280, 640),
+            (1920, 1280),
+            (1920, 640),
+            (2048, 1280),
+            (2048, 2560),
+            (2560, 1280),
+            (256, 128),
+            (2816, 1280),
+            (320, 640),
+            (512, 1536),
+            (512, 256),
+            (512, 512),
+            (640, 1280),
+            (640, 1920),
+            (640, 320),
+            (640, 5120),
+            (640, 640),
+            (960, 320),
+            (960, 640),
+        ]
+    )
+
+
+
 # Utility function to convert PIL image to base64 encoded JSON
 def pil_to_b64_json(image):
     # Generate a UUID for the image
@@ -37,14 +68,14 @@ def load_pipeline(model_name, adapter_name):
         "cuda"
     )
     pipe.load_lora_weights(adapter_name)
-    pipe.unet = torch.compile(
-    pipe.unet,
-    mode = 'max-autotune'
-    )
+    pipe.unload_lora_weights()
     pipe.unet.to(memory_format=torch.channels_last)
-    pipe.vae.to(memory_format=torch.channels_last)
-    
+    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
     pipe.fuse_qkv_projections()
+    apply_dynamic_quant(pipe.unet, dynamic_quant_filter_fn)
+    apply_dynamic_quant(pipe.vae, dynamic_quant_filter_fn)
+    
+  
 
     return pipe