Solve unexpected keyword argument 'predict_epsilon' #37

app.py

@@ -1,4 +1,5 @@
 from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 import gradio as gr
 import torch
 from PIL import Image
@@ -6,6 +7,7 @@ import utils
 import datetime
 import time
 import psutil
+from transformers import CLIPFeatureExtractor
 
 start_time = time.time()
 is_colab = utils.is_google_colab()
@@ -35,7 +37,6 @@ scheduler = DPMSolverMultistepScheduler(
     beta_schedule="scaled_linear",
     num_train_timesteps=1000,
     trained_betas=None,
-    predict_epsilon=True,
     thresholding=False,
     algorithm_type="dpmsolver++",
     solver_type="midpoint",
@@ -52,7 +53,7 @@ current_model = models[1] if is_colab else models[0]
 current_model_path = current_model.path
 
 if is_colab:
-  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
+  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=torch.float16),feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"))
 
 else: # download all models
   print(f"{datetime.datetime.now()} Downloading vae...")