Update app.py

app.py

@@ -1,27 +1,21 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import DiffusionPipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id = "stabilityai/sdxl-turbo"  # Modelo de exemplo (ajuste conforme necessário)
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Usando torch.float16 para melhorar a performance com GPUs (se disponível)
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)
 
+# Definindo parâmetros máximos
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -33,11 +27,13 @@ def infer(
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
+    # Geração de seed aleatória (caso solicitado)
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device).manual_seed(seed)
 
+    # Definindo uma resolução menor para acelerar a execução (ajuste conforme necessário)
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -50,7 +46,6 @@ def infer(
 
     return image, seed
 
-
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
@@ -105,7 +100,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,  # Valor de default
                 )
 
                 height = gr.Slider(
@@ -113,7 +108,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,  # Valor de default
                 )
 
             with gr.Row():
@@ -122,18 +117,19 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=7.5,  # Valor de default mais alto para mais controle
                 )
 
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
+                    label="Inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=20,  # Aumenta para melhorar a qualidade se necessário
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,