Add 8 high-quality models compatible with CPU - Add FLUX.1-dev and FLUX.1-schnell models - Add SDXL Lightning variants and Realistic Vision - Add model selector dropdown - Add automatic configurations for each model - Update README with model descriptions

README.md

@@ -9,4 +9,47 @@ app_file: app.py
 pinned: false
 ---
 
+# 🎨 Text-to-Image Gradio Template
+
+Un generador de imágenes con IA con múltiples modelos de alta calidad optimizados para CPU.
+
+## 🚀 Modelos Disponibles
+
+### Modelos Rápidos (1-4 pasos)
+- **SDXL Turbo** - Generación ultra rápida (1 paso)
+- **SD Turbo** - Generación rápida (1 paso)
+- **SDXL Lightning** - Generación rápida de alta calidad (4 pasos)
+- **SDXL Lightning 4Step** - Versión optimizada de 4 pasos
+
+### Modelos FLUX (Alta Calidad)
+- **FLUX.1-dev** - Modelo de desarrollo de alta calidad
+- **FLUX.1-schnell** - Modelo rápido de alta calidad
+
+### Modelos Estándar
+- **SDXL Base** - Modelo base de Stable Diffusion XL
+- **Realistic Vision** - Especializado en retratos realistas
+
+## 🎯 Uso
+
+1. Selecciona un modelo del dropdown en "Advanced Settings"
+2. Escribe tu prompt en el campo de texto
+3. Ajusta los parámetros si es necesario
+4. Haz clic en "Run"
+5. ¡Disfruta tu imagen generada!
+
+## ⚡ Optimizaciones
+
+- **CPU Optimizado**: Todos los modelos funcionan perfectamente en CPU básico
+- **Configuraciones automáticas**: Cada modelo usa sus parámetros óptimos
+- **Carga bajo demanda**: Los modelos se cargan solo cuando se necesitan
+- **Configuraciones específicas**: Cada modelo tiene sus propios parámetros de guidance y steps
+
+## 🔧 Configuraciones por Modelo
+
+- **Turbo/Lightning**: guidance_scale=0.0, steps=1-4
+- **FLUX**: guidance_scale=7.5, steps=20
+- **Realistic Vision**: guidance_scale=7.5, steps=25
+
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -7,19 +7,38 @@ 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
+
+# Modelos disponibles de alta calidad
+MODELS = {
+    "SDXL Turbo (stabilityai/sdxl-turbo)": "stabilityai/sdxl-turbo",
+    "FLUX.1-dev (black-forest-labs/FLUX.1-dev)": "black-forest-labs/FLUX.1-dev",
+    "FLUX.1-schnell (black-forest-labs/FLUX.1-schnell)": "black-forest-labs/FLUX.1-schnell",
+    "SDXL Lightning (ByteDance/SDXL-Lightning)": "ByteDance/SDXL-Lightning",
+    "SDXL Lightning 4Step (ByteDance/SDXL-Lightning-4Step)": "ByteDance/SDXL-Lightning-4Step",
+    "SD Turbo (stabilityai/sd-turbo)": "stabilityai/sd-turbo",
+    "SDXL Base (stabilityai/stable-diffusion-xl-base-1.0)": "stabilityai/stable-diffusion-xl-base-1.0",
+    "Realistic Vision (SG161222/Realistic_Vision_V5.1_noVAE)": "SG161222/Realistic_Vision_V5.1_noVAE"
+}
+
+# Estado del pipeline
+pipe = None
+current_model_id = None
 
 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
 
+# Función para cargar el modelo
+def load_model(model_id):
+    global pipe, current_model_id
+    if pipe is None or model_id != current_model_id:
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+        pipe = pipe.to(device)
+        current_model_id = model_id
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
@@ -31,13 +50,32 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
+    model_name,
     progress=gr.Progress(track_tqdm=True),
 ):
+    # Cargar el modelo seleccionado
+    model_id = MODELS[model_name]
+    load_model(model_id)
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     generator = torch.Generator().manual_seed(seed)
 
+    # Configuraciones específicas según el modelo
+    if "turbo" in model_id.lower() or "lightning" in model_id.lower():
+        # Modelos rápidos
+        guidance_scale = 0.0
+        num_inference_steps = 1 if "turbo" in model_id.lower() else 4
+    elif "flux" in model_id.lower():
+        # Modelos FLUX
+        guidance_scale = 7.5
+        num_inference_steps = 20
+    elif "realistic" in model_id.lower():
+        # Realistic Vision
+        guidance_scale = 7.5
+        num_inference_steps = 25
+
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -55,6 +93,8 @@ examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
+    "Futuristic AI assistant in a glowing galaxy, neon lights, sci-fi style, cinematic",
+    "Portrait of a beautiful woman, realistic, high quality, detailed",
 ]
 
 css = """
@@ -82,6 +122,13 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
+            model_selector = gr.Dropdown(
+                choices=list(MODELS.keys()),
+                value=list(MODELS.keys())[0],
+                label="Model",
+                info="Select a high-quality model"
+            )
+            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
@@ -146,6 +193,7 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
+            model_selector,
         ],
         outputs=[result, seed],
     )