Update app.py

app.py

@@ -4,6 +4,7 @@
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
+
 import spaces
 import os
 import random
@@ -12,15 +13,14 @@ import gradio as gr
 import numpy as np
 from PIL import Image
 import torch
-#import diffusers
-from diffusers import AutoencoderKL, StableDiffusionXLPipeline
-from diffusers import EulerAncestralDiscreteScheduler
 from typing import Tuple
 import paramiko
 import datetime
-#from diffusers import DPMSolverSDEScheduler
+#import diffusers
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, EulerAncestralDiscreteScheduler
 from diffusers.models.attention_processor import AttnProcessor2_0
 from transformers import CLIPTextModelWithProjection, CLIPTextModel, CLIPTokenizer
+
 torch.backends.cuda.matmul.allow_tf32 = False
 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
 torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
@@ -37,7 +37,7 @@ FTP_PASS = os.getenv("FTP_PASS")
 FTP_DIR = "1ink.us/stable_diff/"  # Remote directory on FTP server
 
 DESCRIPTIONXX = """
-    ## ⚡⚡⚡⚡ REALVISXL V5.0 BF16 (Tester B) ⚡⚡⚡⚡
+    ## ⚡⚡⚡⚡ REALVISXL V5.0 BF16 (Tester G) ⚡⚡⚡⚡
 """
 
 examples = [
@@ -89,7 +89,7 @@ tokenizer_1=CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu
 tokenizer_2=CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='tokenizer_2',token=True)
 scheduler=EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='scheduler',token=True)
 vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", low_cpu_mem_usage=False, safety_checker=None, use_safetensors=False, torch_dtype=torch.float32, token=True) #.to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
-
+UNet2DConditionModel
 def load_and_prepare_model():
     #vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='vae', safety_checker=None, use_safetensors=True, token=True)
     #vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False, low_cpu_mem_usage=False, torch_dtype=torch.float32, token=True) #.to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
@@ -163,16 +163,14 @@ def save_image(img):
     return unique_name
 
 def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
-    filename= f'tst_B_{timestamp}.txt'
+    filename= f'tst_G_{timestamp}.txt'
     with open(filename, "w") as f:
-        f.write(f"Realvis 5.0 (Tester B) \n")
+        f.write(f"Realvis 5.0 (Tester G) \n")
         f.write(f"Date/time: {timestamp} \n")
         f.write(f"Prompt: {prompt} \n")
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
         f.write(f"SPACE SETUP: \n")
-        f.write(f"Use Model Dtype: no \n")
-        f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
         f.write(f"To cuda and bfloat \n")
     upload_to_ftp(filename) 
     
@@ -470,8 +468,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
     gr.Markdown("### REALVISXL V5.0")
     predefined_gallery = gr.Gallery(label="REALVISXL V5.0", columns=3, show_label=False, value=load_predefined_images1())
 
-    #gr.Markdown("### LIGHTNING V5.0")
-    #predefined_gallery = gr.Gallery(label="LIGHTNING V5.0", columns=3, show_label=False, value=load_predefined_images())
 
     gr.Markdown(
     """