Add application file

imagenesGradio.py

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+######################
+# Datos importados
+######################
+
+#Datos importados de: https://huggingface.co/runwayml/stable-diffusion-v1-5
+
+######################
+# Import libraries
+######################
+
+import gradio as gr
+
+# ######################
+# Variables globales
+# ######################
+
+model_id = r"..\7. Generador_imagenes\3. Stable_diffusion web\stable-diffusion-v1-5"
+
+# ######################
+# Funciones auxiliares
+# ######################
+
+if False: 
+    def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):
+            
+            with st.container():
+
+                st.write(f'Vamos por la iteración {step}')
+                st.progress(step*2) #bar_progress debe empezar con 0 y terminar en 100
+                st.write(f'Quedan {timestep/100:.0f} segundos')
+
+# ######################
+# Modelo
+# ######################
+
+def ia_imagenes(modelo, prompt, prompt_negativo, uploaded_file, my_strength, my_guidance_scale):
+
+    if modelo == "Texto":                
+            
+            from diffusers import StableDiffusionPipeline
+            import torch
+
+            pipe = StableDiffusionPipeline.from_pretrained(
+                model_id,
+                revision="fp16" if torch.cuda.is_available() else "fp32",
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, 
+                requires_safety_checker = False
+                ).to("cuda")
+            
+            
+            image_pipe = pipe(prompt, negative_prompt=prompt_negativo, width=728, height=728) #otras variables: guidance_scale=guidance_scale, num_inference_steps=steps, callback = pipe_callback
+                            
+            imagen = image_pipe.images[0]
+
+            return imagen
+
+
+    elif modelo == "Imagen":
+
+        from diffusers import StableDiffusionImg2ImgPipeline
+        from PIL import Image
+        import torch
+
+        uploaded_file = Image.fromarray(uploaded_file)
+
+        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+                model_id,
+                revision="fp16" if torch.cuda.is_available() else "fp32",
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, 
+                requires_safety_checker = False
+                ).to("cuda")
+        
+        my_strength = 0.8 if my_strength == 0 or my_strength == None else my_strength
+        my_guidance_scale = 7.5 if my_guidance_scale == 0 or my_guidance_scale == None else my_guidance_scale
+        
+        imagen = pipe(prompt, image=uploaded_file, negative_prompt=prompt_negativo, strength = my_strength, guidance_scale = my_guidance_scale).images[0] #, strength=0.8, guidance_scale=7.5
+
+        return imagen
+    
+    else:
+        raise gr.Error("Te has olvidado de marcar una opción")
+
+
+demo = gr.Interface(
+    fn = ia_imagenes,
+    inputs = [
+        gr.Radio(["Texto", "Imagen"],value = "Texto"),
+        "text",
+        "text",
+        "image",
+        "number",
+        "number"
+
+    ],
+    outputs = "image",
+    title="Creación de Imagenes 🍆",
+)
+
+
+demo.launch(show_error = True, auth=("cesar", "mascara"), share=True)
+
+
+
+'''
+FALTA:
+-BOTON PARA DESCARGAR IMAGEN
+-TIEMPO QUE TARDA
+'''
+