Update app.py

app.py

@@ -1,3 +1,5 @@
+pip install onnxruntime
+
 import gradio as gr
 import numpy as np
 import random
@@ -5,10 +7,16 @@ import random
 # import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 from diffusers import StableDiffusionPipeline
+from diffusers import OnnxRuntimeModel
 import torch
 
+
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"  # Replace to the model you would like to use
+model_repo_id = "runwayml/stable-diffusion-v1-5"  # Replace to the model you would like to use
+
+pipe = OnnxRuntimeModel.from_pretrained("model_path", provider="CPUExecutionProvider")
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch.float16)
+pipe.enable_attention_slicing()  # Divide o cálculo de atenção para melhorar o desempenho em dispositivos com menos memória
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
@@ -21,6 +29,7 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 752
 
+
 from datasets import load_dataset, Dataset
 
 dataset = load_dataset("LEIDIA/Data_Womleimg")  # Exemplo do seu dataset no Hugging Face
@@ -49,6 +58,16 @@ descriptions = [
 ]
 
 
+def infer(prompt, num_inference_steps):
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=num_inference_steps,
+        height=MAX_IMAGE_SIZE,
+        width=MAX_IMAGE_SIZE,
+    ).images[0]
+    return image
+
+
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
@@ -90,18 +109,20 @@ css = """
 }
 """
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Wom_Test")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+# Interface Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Text-to-Image Optimized for CPU")
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt")
+        num_inference_steps = gr.Slider(
+            label="Inference Steps", minimum=1, maximum=50, step=1, value=15
+        )
+    with gr.Row():
+        generate_button = gr.Button("Generate")
+    result = gr.Image(label="Generated Image")
+    generate_button.click(infer, inputs=[prompt, num_inference_steps], outputs=result)
+
+demo.launch()
 
             run_button = gr.Button("Run", scale=0, variant="primary")
 
@@ -156,7 +177,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=10,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=15,  # Replace with defaults that work for your model
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])