Update app.py

app.py

@@ -33,6 +33,8 @@ def enable_lora(lora_add):
     else:
         return lora_add
 
+client = AsyncInferenceClient()
+
 async def generate_image(
     prompt:str,
     model:str,
@@ -50,7 +52,6 @@ async def generate_image(
     
     text = str(translator.translate(prompt, 'English')) + "," + lora_word
 
-    client = AsyncInferenceClient()
     try:
         image = await client.text_to_image(
             prompt=text,
@@ -66,23 +67,13 @@ async def generate_image(
     return image, seed
 
 async def upscale_image(image, upscale_factor):
-    client = AsyncInferenceClient()
     try:
-        result = await client.predict(
-            input_image=image,
+        result = await client.text_to_image(
             prompt="",
-            negative_prompt="",
-            seed=42,
-            upscale_factor=upscale_factor,
-            controlnet_scale=0.6,
-            controlnet_decay=1,
-            condition_scale=6,
-            tile_width=112,
-            tile_height=144,
-            denoise_strength=0.35,
+            height=image.height * upscale_factor,
+            width=image.width * upscale_factor,
+            guidance_scale=3.5,
             num_inference_steps=18,
-            solver="DDIM",
-            api_name="/process",
             model="finegrain/finegrain-image-enhancer"
         )
     except Exception as e:
@@ -99,15 +90,12 @@ async def gen(
     scales:float=3.5,
     steps:int=24,
     seed:int=-1,
-    upscale_factor:int=2,
-    progress=gr.Progress(track_tqdm=True)
+    upscale_factor:int=2
 ):
     model = enable_lora(lora_add)
     image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,seed)
-    image_path = "image.png"
-    image.save(image_path)
     
-    upscaled_image = await upscale_image(image_path, upscale_factor)
+    upscaled_image = await upscale_image(image, upscale_factor)
     return upscaled_image, seed
      
 with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo: