Update requirements typo, update app.py

app.py

@@ -12,14 +12,17 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 device_name = torch.cuda.get_device_name(0)
 
 # setup pipeline
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=TOKEN_KEY)
+if device == "cuda":
+    pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=TOKEN_KEY)
+else:
+    pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=TOKEN_KEY)
 pipe = pipe.to(device)
 
 # define gradio function
-def generate(prompt:str, seed:int, guidance:float):
+def generate(prompt:str, seed:int, guidance:float, steps:int):
     generator = torch.Generator(device).manual_seed(int(seed))
     with autocast(device):
-        image = pipe(prompt=prompt, generator=generator, guidance_scale=guidance, steps=50).images[0]
+        image = pipe(prompt=prompt, generator=generator, guidance_scale=guidance, num_inference_steps=steps).images[0]
     return image
 
 if device == "cuda":
@@ -27,7 +30,7 @@ if device == "cuda":
     # create the gradio UI
     demo = gr.Interface(
         fn=generate,
-        inputs=[gr.Textbox(placeholder="castle on a mountain"), gr.Number(value=123456), gr.Slider(0,10)],
+        inputs=[gr.Textbox(placeholder="castle on a mountain"), gr.Number(value=123456), gr.Slider(0,10), gr.Number(value=50)],
         outputs="image",
         allow_flagging="never",
     )

requirements.txt

@@ -3,5 +3,5 @@ scipy
 ftfy
 torch>=1.10.0
 transformers
-diffuser>=0.3.0
+diffusers>=0.4.0
 gradio>=3.4