Update app.py

app.py

@@ -1,44 +1,143 @@
 import gradio as gr
 import numpy as np
-from PIL import Image, ImageDraw, ImageFont
-
-# Função para gerar a imagem
-def generate_image():
-    # Criar uma imagem em branco
-    img = Image.new("RGB", (512, 512), color=(255, 255, 255))
-    draw = ImageDraw.Draw(img)
-
-    # Definir gradiente de amarelo para rosa
-    gradient = np.zeros((512, 512, 3), dtype=np.uint8)
-    for i in range(512):
-        for j in range(512):
-            r = int((i / 512) * 255)  # Gradiente de vermelho
-            g = int((i / 512) * 255)  # Gradiente de verde
-            b = int(255 - (i / 512) * 255)  # Gradiente de azul
-            gradient[i, j] = [r, g, b]
-
-    # Converter o gradiente para uma imagem PIL
-    gradient_img = Image.fromarray(gradient)
-    img.paste(gradient_img, (0, 0))
-
-    # Adicionar o título na imagem
-    font = ImageFont.load_default()
-    draw.text((20, 20), "FLUX.1 [dev] 🖥️", font=font, fill=(0, 0, 0))
-
-    return img
-
-# Definir a interface Gradio
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Markdown("### FLUX.1 [dev] 🖥️")
-        gr.Markdown(
-            "This is a Gradio app using FLUX.1. The app generates an image with a yellow to pink gradient."
-        )
-    result = gr.Image(label="Generated Image")
+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
 
-    gr.Button("Generate Image").click(generate_image, outputs=[result])
+# Definindo variáveis e carregando modelos
+dtype = torch.float16  # Usando float16 para melhorar a performance
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-demo.launch()
+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
+@spaces.GPU(duration=75)
+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
+    
+    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.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        
+        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.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
+    )
+
+demo.launch()