Update app.py

app.py

@@ -15,8 +15,6 @@ import torch
 from diffusers import AutoencoderKL, StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 from typing import Tuple
 import paramiko
-import gc
-import time
 import datetime
 from gradio import themes
 from image_gen_aux import UpscaleWithModel
@@ -41,7 +39,7 @@ FTP_PASS = "GoogleBez12!"
 FTP_DIR = "1ink.us/stable_diff/"  # Remote directory on FTP server
 
 DESCRIPTIONXX = """
-    ## ⚡⚡⚡⚡ REALVISXL V5.0 BF16 (Tester A) ⚡⚡⚡⚡
+    ## ⚡⚡⚡⚡ REALVISXL V5.0 BF16 IP Adapter ⚡⚡⚡⚡
 """
 
 examples = [
@@ -50,13 +48,8 @@ examples = [
     "A profile photo of a dog, brown background, shot on Leica M6 --ar 128:85 --v 6.0 --style raw",
 ]
 
-MODEL_OPTIONS = {
-    "REALVISXL V5.0 BF16": "ford442/RealVisXL_V5.0_BF16",
-}
-
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
-ENABLE_CPU_OFFLOAD = 0
+
 BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))
 
 device = torch.device("cuda:0")
@@ -152,8 +145,6 @@ def load_and_prepare_model():
     #pipe.vae = AutoencoderKL.from_pretrained('stabilityai/sdxl-vae-bf16',subfolder='vae')  
     #pipe.vae = AutoencoderKL.from_pretrained('stabilityai/sdxl-vae',subfolder='vae',force_upcast=False,scaling_factor= 0.182158767676)  
     #pipe.vae.to(torch.bfloat16)
-
-
     
     '''
     scaling_factor (`float`, *optional*, defaults to 0.18215):
@@ -187,8 +178,8 @@ def load_and_prepare_model():
     #pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
     #pipe.scheduler=EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
 
-    pipe.to(device=device, dtype=torch.bfloat16)
-    #pipe.to(torch.bfloat16)
+    pipe.to(device) #=device, dtype=torch.bfloat16)
+    pipe.to(torch.bfloat16)
     #apply_hidiffusion(pipe)
 
     #pipe.unet.set_default_attn_processor()
@@ -239,9 +230,9 @@ def save_image(img):
     return unique_name
 
 def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
-    filename= f'tst_A_{timestamp}.txt'
+    filename= f'IP_{timestamp}.txt'
     with open(filename, "w") as f:
-        f.write(f"Realvis 5.0 (Tester A) \n")
+        f.write(f"Realvis 5.0 IP Adapter \n")
         f.write(f"Date/time: {timestamp} \n")
         f.write(f"Prompt: {prompt} \n")
         f.write(f"Steps: {num_inference_steps} \n")
@@ -250,10 +241,7 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
         f.write(f"Model VAE: sdxl-vae to bfloat safetensor=false before cuda then attn_proc / scale factor 8 \n")
-        f.write(f"Model UNET: realistic_porn \n")
-        f.write(f"Model HiDiffusion OFF \n")
-        f.write(f"Model do_resize ON \n")
-        f.write(f"added torch to prereq and changed accellerate  \n")
+        f.write(f"Model UNET: ford442/RealVisXL_V5.0_BF16 \n")
     upload_to_ftp(filename) 
     
 @spaces.GPU(duration=40)
@@ -302,7 +290,7 @@ def generate_30(
         with torch.no_grad():
             upscale = upscaler(sd_image, tiling=True, tile_width=256, tile_height=256)
         downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
-        downscale_path = f"rv50_upscale_{timestamp}.png"
+        downscale_path = f"rvIP_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
         upload_to_ftp(downscale_path) 
         image_paths = [save_image(downscale1)]     
@@ -356,7 +344,7 @@ def generate_60(
         with torch.no_grad():
             upscale = upscaler(sd_image, tiling=True, tile_width=256, tile_height=256)
         downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
-        downscale_path = f"rv50_upscale_{timestamp}.png"
+        downscale_path = f"rvIP_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
         upload_to_ftp(downscale_path) 
         image_paths = [save_image(downscale1)]     
@@ -410,7 +398,7 @@ def generate_90(
         with torch.no_grad():
             upscale = upscaler(sd_image, tiling=True, tile_width=256, tile_height=256)
         downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
-        downscale_path = f"rv50_upscale_{timestamp}.png"
+        downscale_path = f"rvIP_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
         upload_to_ftp(downscale_path) 
         image_paths = [save_image(downscale1)]