Update app.py

app.py

@@ -20,9 +20,9 @@ generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4876364)
 def plex(cook, one, two):
     ###goof = load_image(img).resize((512, 512))
     negative_prompt = "lowres,text,bad quality,low quality,jpeg artifacts,ugly,bad hands,bad face,blurry,bad eyes,watermark,signature"
-    warp_out = warp_prior(prompt=cook, height=512,width=512,negative_prompt=negative_prompt,guidance_scale=two,timestep=DEFAULT_STAGE_C_TIMESTEPS, num_inference_steps=one,generator=generator,)
+    warp_out = warp_prior(prompt=cook, height=512,width=512,negative_prompt=negative_prompt,guidance_scale=two,timestep=DEFAULT_STAGE_C_TIMESTEPS,num_inference_steps=one)
     primpt = ""
-    imas = warp(image_embeddings=**warp_out, height=512, width=512, num_inference_steps=one, timestep=DEFAULT_STAGE_C_TIMESTEPS,prompt=cook,negative_prompt=primpt,guidance_scale=0.0,output_type="pil",generator=generator).images[0]
+    imas = warp(image_embeddings=**warp_out, height=512, width=512, num_inference_steps=one, timestep=DEFAULT_STAGE_C_TIMESTEPS,prompt=cook,negative_prompt=primpt,guidance_scale=0.0,output_type="pil").images
     return imas
 
 iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="prompt"), gr.Slider(label="Inference steps",minimum=1,step=1,maximum=10,value=5), gr.Slider(label="Prior guidance scale",minimum=4.1,step=0.1,maximum=19.9,value=4.1)],outputs=gr.Image(label="Generated Output Image"), title="Txt2Img Wrstchn SD", description="Txt2Img Wrstchn SD")