Update app.py

app.py

@@ -10,7 +10,7 @@ import numpy as np
 import PIL.Image
 import spaces
 import torch
-from diffusers import AutoencoderKL, DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers import AutoencoderKL, DiffusionPipeline
 
 DESCRIPTION = "# SDXL"
 if not torch.cuda.is_available():
@@ -26,7 +26,21 @@ ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1"
 ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-models = ["cagliostrolab/animagine-xl-3.0"]  # Substitua isso pelo valor real do modelo selecionado
+models = ["runwayml/stable-diffusion-v1-5",
+"stabilityai/stable-diffusion-xl-base-1.0",
+"stablediffusionapi/juggernaut-xl-v8",
+"emilianJR/epiCRealism",
+"SG161222/Realistic_Vision_V5.1_noVAE",
+"cagliostrolab/animagine-xl-3.0",
+"misri/cyberrealistic_v41BackToBasics",
+"malcolmrey/serenity",
+"SG161222/RealVisXL_V3.0",
+"stablediffusionapi/realistic-stock-photo-v2",
+"stablediffusionapi/pixel-art-diffusion-xl",
+"playgroundai/playground-v2-1024px-aesthetic",
+"dataautogpt3/ProteusV0.3",
+"stablediffusionapi/disney-pixar-cartoon",
+"RunDiffusion/Juggernaut-XL-Lightning"]  
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
@@ -62,12 +76,10 @@ def generate(
 
         if not use_vae:  
             pipe = DiffusionPipeline.from_pretrained(dropdown_model, torch_dtype=torch.float16)
-            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
             
         if use_vae:
             vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
             pipe = DiffusionPipeline.from_pretrained(dropdown_model, vae=vae, torch_dtype=torch.float16)
-            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
             
         if use_lora:
             pipe.load_lora_weights(lora)