app.py

######################
# Datos importados
######################

#Datos importados de: https://huggingface.co/runwayml/stable-diffusion-v1-5

######################
# Import libraries
######################

import gradio as gr

# ######################
# Variables globales
# ######################

model_id = "botp/stable-diffusion-v1-5" #"runwayml/stable-diffusion-v1-5"
#model_id = "runwayml/stable-diffusion-v1-5" -> original

# ######################
# 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("cpu")
            
            
            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=("inta", "inta"), share=True)



'''
FALTA:
-BOTON PARA DESCARGAR IMAGEN
-TIEMPO QUE TARDA
'''