Updating app.py and txt

app.py

@@ -1,142 +1,52 @@
-import gradio as gr
-import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+
+# Cargar el modelo Stable Diffusion
+model_id = "stabilityai/stable-diffusion-2-1"
+pipe = StableDiffusionPipeline.from_pretrained(model_id)
+pipe.to("cuda")  # Si estás usando GPU
+
+# Función para generar imágenes
+def generate_image(prompt, style, width, height, guidance_scale, steps, seed):
+    # Configurar el generador de semillas
+    generator = torch.manual_seed(seed) if seed is not None else None
+
+    # Ajustar el estilo en función del input del usuario
+    if style == "Realismo":
+        prompt += ", realistic, high detail, photorealistic"
+    elif style == "Arte digital":
+        prompt += ", digital art, sharp details, vibrant colors"
+    elif style == "Ciencia Ficción":
+        prompt += ", sci-fi, futuristic, high-tech"
+    elif style == "Surrealismo":
+        prompt += ", surreal, dream-like, abstract"
+
+    # Generar la imagen con Stable Diffusion
+    image = pipe(prompt, height=height, width=width, guidance_scale=guidance_scale, 
+                 num_inference_steps=steps, generator=generator).images[0]
+    return image
+
+# Configuración de la interfaz de Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("# Generador de Imágenes estilo MidJourney")
+    prompt = gr.Textbox(label="Descripción (Prompt)", placeholder="Describe la imagen...")
+    style = gr.Dropdown(choices=["Realismo", "Arte digital", "Ciencia Ficción", "Surrealismo"], label="Estilo", value="Realismo")
+    width = gr.Slider(512, 1024, step=64, label="Ancho", value=768)
+    height = gr.Slider(512, 1024, step=64, label="Altura", value=768)
+    guidance_scale = gr.Slider(5, 20, step=0.5, label="Nivel de guía del prompt", value=7.5)
+    steps = gr.Slider(10, 100, step=5, label="Número de pasos de inferencia", value=50)
+    seed = gr.Number(label="Semilla (dejar vacío para aleatorio)", value=None, optional=True)
     
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+    # Botón para generar la imagen
+    btn = gr.Button("Generar imagen")
     
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    # Salida de la imagen generada
+    output = gr.Image(label="Imagen generada")
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        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)
+    # Vincular los inputs y outputs
+    btn.click(generate_image, inputs=[prompt, style, width, height, guidance_scale, steps, seed], outputs=output)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=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, #Replace with defaults that work for your model
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2, #Replace with defaults that work for your model
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
+# Lanzar la aplicación Gradio
+demo.launch()
 
-demo.queue().launch()

requirements.txt

@@ -1,6 +1,4 @@
-accelerate
+torch>=1.9.0
 diffusers
-invisible_watermark
-torch
 transformers
-xformers
+gradio