Update app.py

app.py

@@ -1,4 +1,4 @@
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
 import torch
 from transformers import pipeline
 import gradio as gr
@@ -20,7 +20,7 @@ InferenceClient=InferenceClient()
 apol=[]
 
 pipe = accelerator.prepare(StableDiffusionXLPipeline.from_pretrained("kopyl/miniSDXL", torch_dtype=torch.float32, variant=None, use_safetensors=True, safety_checker=None))
-pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe.unet.to(memory_format=torch.channels_last)
 pipe = accelerator.prepare(pipe.to("cpu"))
 
@@ -42,6 +42,11 @@ def chdr(apol,prompt,modil,stips,fnamo,gaul):
                 json.dump(dobj, f)
             HfApi.upload_folder(repo_id="JoPmt/hf_community_images",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
         dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type,'haed':gaul,}
+        try:
+            for pxn in glob.glob('./tmpo/*.png'):
+                os.remove(pxn)
+        except:
+            print("lou")
         with open(tre, 'w') as f:
             json.dump(dobj, f)
         HfApi.upload_folder(repo_id="JoPmt/Tst_datast_imgs",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)