Update app.py

app.py

@@ -15,8 +15,8 @@ from PIL import Image
 import torch
 from diffusers import AutoencoderKL, StableDiffusionXLPipeline
 from diffusers import EulerAncestralDiscreteScheduler
-#from diffusers import DPMSolverMultistepScheduler
-#from diffusers import DDIMScheduler
+from diffusers import DPMSolverMultistepScheduler
+
 from typing import Tuple
 import paramiko
 import gc
@@ -112,7 +112,7 @@ def load_and_prepare_model(model_id):
     vae = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae')
   #  vae = AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",safety_checker=None).to(torch.bfloat16)
     #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
-    sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler', steps_offset=1,timestep_spacing="trailing")
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler', steps_offset=1,timestep_spacing="trailing")
     #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)
       #  sched = EulerAncestralDiscreteScheduler.from_config('ford442/RealVisXL_V5.0_BF16', beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
     pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0")
@@ -139,9 +139,9 @@ def load_and_prepare_model(model_id):
     
     #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear",use_karras_sigmas=True, algorithm_type="dpmsolver++")
     #pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
-      # for set timestep  pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, algorithm_type="sde-dpmsolver++")
+    pipeline.scheduler = DPMSolverMultistepScheduler.from_pretrained('SG161222/RealVisXL_V5.0', algorithm_type='sde-dpmsolver++')
 
-    pipe.scheduler = sched
+    #pipe.scheduler = sched
     pipe.vae=vae.to(torch.bfloat16)
     pipe.unet=pipeX.unet.to(torch.bfloat16)
     #pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
@@ -229,7 +229,7 @@ def generate_30(
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
-    #    "timesteps": sampling_schedule,
+        "timesteps": sampling_schedule,
         "output_type": "pil",
     }
     if use_resolution_binning:
@@ -296,7 +296,7 @@ def generate_60(
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
-     #   "timesteps": sampling_schedule,
+        "timesteps": sampling_schedule,
         "output_type": "pil",
     }
     if use_resolution_binning:
@@ -363,7 +363,7 @@ def generate_90(
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
-    #    "timesteps": sampling_schedule,
+        "timesteps": sampling_schedule,
         "output_type": "pil",
     }
     if use_resolution_binning: