Update app.py

app.py

@@ -80,9 +80,9 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     
 def load_and_prepare_model():
-    vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False).to(device=device, dtype=torch.bfloat16)
-    vaeRV = AutoencoderKL.from_pretrained("stabilityai/RealVisXL_V5.0", safety_checker=None, use_safetensors=False).to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
-    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
+    #vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False).to(device=device, dtype=torch.bfloat16)
+    vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", safety_checker=None, use_safetensors=False).to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
         #torch_dtype=torch.bfloat16,
@@ -134,9 +134,9 @@ def save_image(img):
     return unique_name
 
 def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
-    filename= f'tst_A_{timestamp}.txt'
+    filename= f'rv_C_{timestamp}.txt'
     with open(filename, "w") as f:
-        f.write(f"Realvis 5.0 (Tester B) \n")
+        f.write(f"Realvis 5.0 (Tester C) \n")
         f.write(f"Date/time: {timestamp} \n")
         f.write(f"Prompt: {prompt} \n")
         f.write(f"Steps: {num_inference_steps} \n")
@@ -145,10 +145,6 @@ 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-bf16 before cuda then attn_proc / scale factor 8 \n")
-        f.write(f"Model UNET: sexy_beauty model \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")
     upload_to_ftp(filename) 
     
 @spaces.GPU(duration=30)
@@ -184,7 +180,7 @@ def generate_30(
     uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
-    sd_image_path = f"rv50_B_{timestamp}.png"
+    sd_image_path = f"rv_C_{timestamp}.png"
     rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)    
     unique_name = str(uuid.uuid4()) + ".png"  
@@ -224,7 +220,7 @@ def generate_60(
     uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
-    sd_image_path = f"rv50_B_{timestamp}.png"
+    sd_image_path = f"rv_C_{timestamp}.png"
     rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)    
     unique_name = str(uuid.uuid4()) + ".png"  
@@ -264,7 +260,7 @@ def generate_90(
     uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
-    sd_image_path = f"rv50_B_{timestamp}.png"
+    sd_image_path = f"rv_C_{timestamp}.png"
     rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)    
     unique_name = str(uuid.uuid4()) + ".png"