Update app_demo.py

app_demo.py

@@ -1,27 +1,19 @@
 #!/usr/bin/env python
 from __future__ import annotations
-
 import os
 import random
 import time
-
 import gradio as gr
 import numpy as np
 import PIL.Image
 import torch
-
 from diffusers import DiffusionPipeline
-import torch
-
-import os
-import torch
 from tqdm import tqdm
 from safetensors.torch import load_file
 import gradio_user_history as gr_user_history
-
 from concurrent.futures import ThreadPoolExecutor
 import uuid
-import cv2
+#import cv2
 
 DESCRIPTION = '''# Fast Stable Diffusion CPU with Latent Consistency Model
 Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). [Project page](https://latent-consistency-models.github.io)
@@ -35,9 +27,21 @@ MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
 USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
 DTYPE = torch.float32  # torch.float16 works as well, but pictures seem to be a bit worse
 
-pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
+#pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
+
+
+
+pipe = DiffusionPipeline.from_pretrained(    "SimianLuo/LCM_Dreamshaper_v7",    custom_pipeline="latent_consistency_txt2img",
+    custom_revision="main",
+    low_cpu_mem_usage=True
+    pipe.safety_checker = None
+)
 pipe.to(torch_device="cpu", torch_dtype=DTYPE)
-pipe.safety_checker = None
+pipe.to("cpu")
+
+
+
+
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed: