Update app.py

app.py

@@ -1,141 +1,34 @@
 import gradio as gr
-import numpy as np
-import random
-import torch
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
-from transformers import CLIPTextModel, CLIPTokenizer
-from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
-
-# Definindo variáveis e carregando modelos
-dtype = torch.float16  # Usando float16 para melhorar a performance
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-
-torch.cuda.empty_cache()
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-# Função de inferência otimizada
-@torch.inference_mode()  # Desabilitando cálculo de gradientes para acelerar a inferência
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    generator = torch.Generator(device).manual_seed(seed)
-    
-    # Usando autograd em precisão reduzida (float16) para acelerar a inferência
-    with torch.autocast("cuda", dtype=torch.float16):
-        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-                prompt=prompt,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                width=width,
-                height=height,
-                generator=generator,
-                output_type="pil",
-                good_vae=good_vae,
-            ):
-            yield img, seed
+from transformers import pipeline
+
+# Carregar o pipeline de geração de imagens
+pipe = pipeline("image-generation", model="stabilityai/stable-diffusion-2-1")
+
+def generate_image(prompt):
+    """
+    Gera uma imagem com base em um prompt textual usando o modelo.
+    """
+    try:
+        # Gera a imagem
+        image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5)[0]["image"]
+        return image
+    except Exception as e:
+        return f"Erro ao gerar imagem: {str(e)}"
+
+# Configurar a interface do Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Gerador de Imagens com Texto - Stable Diffusion")
     
-    torch.cuda.empty_cache()  # Limpar a memória após a inferência para liberar recursos
-
-# Exemplos
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-# Interface Gradio
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
-        
-        with gr.Row():
-            prompt = gr.Textbox(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Texto (Prompt)", placeholder="Descreva a imagem que deseja gerar...")
+            submit_button = gr.Button("Gerar Imagem")
         
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,
-                )
-        
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples="lazy"
-        )
-
-    gr.Button.click(
-        fn=infer,
-        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, seed],
-    )
+        with gr.Column():
+            output_image = gr.Image(label="Imagem Gerada")
 
-demo.launch()
+    submit_button.click(fn=generate_image, inputs=prompt, outputs=output_image)
 
+# Executar o app
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)