Update app.py

app.py

@@ -31,14 +31,42 @@ async def generate_image(prompt, model, lora_word, width, height, scales, steps,
     image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
     return image, seed
 
-async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
+def get_clarity_upscale(prompt, img_path, upscale_factor):
+    client = Client("jbilcke-hf/clarity-upscaler")
+    result = client.predict(
+        img_path,
+        prompt,
+        "",
+        upscale_factor,
+        1,
+        3,
+        3,
+        "16",
+        "16",
+        "epicrealism_naturalSinRC1VAE.safetensors [84d76a0328]",
+        "DPM++ 2M Karras",
+        1,
+        3,
+        True,
+        3,
+        "Hello!!",
+        "Hello!!",
+        api_name="/predict"
+    )
+    print(result)
+    return result
+
+async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora, upscaler_choice):
     model = lora_model
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
     image_path = "temp_image.png"
     image.save(image_path)
     
     if process_upscale:
-        upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
+        if upscaler_choice == "FineGrain":
+            upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
+        elif upscaler_choice == "Upscaler Clarity":
+            upscale_image = get_clarity_upscale(prompt, image_path, upscale_factor)
     else:
         upscale_image = image_path
     
@@ -69,6 +97,7 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
                 process_lora = gr.Checkbox(label="Process LORA", value=True)
                 upscale_factor = gr.Radio(label="UpScale Factor", choices=[2, 4, 8], value=2, scale=2)
                 process_upscale = gr.Checkbox(label="Process Upscale", value=False)
+                upscaler_choice = gr.Radio(label="Upscaler", choices=["FineGrain", "Upscaler Clarity"], value="FineGrain")
                 
                 with gr.Accordion(label="Advanced Options", open=False):
                     width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=512)
@@ -85,7 +114,7 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
                     queue=False
                 ).then(
                     fn=gen,
-                    inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora],
+                    inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora, upscaler_choice],
                     outputs=[output_res]
                 )
 demo.launch()