Update app.py

app.py

@@ -1,4 +1,4 @@
-import os
+ import os
 import gradio as gr
 import numpy as np
 import random
@@ -15,95 +15,93 @@ client = AsyncInferenceClient()
 llm_client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
 def enable_lora(lora_add, basemodel):
-    return basemodel if not lora_add else lora_add
+return basemodel if not lora_add else lora_add
 
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
-    try:
-        if seed == -1:
-            seed = random.randint(0, MAX_SEED)
-        seed = int(seed)
-        text = prompt + "," + lora_word
-        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
-        return image, seed
-    except Exception as e:
-        return f"Error al generar imagen: {e}", None
+try:
+if seed == -1:
+seed = random.randint(0, MAX_SEED)
+seed = int(seed)
+text = prompt + "," + lora_word
+image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
+return image, seed
+except Exception as e:
+return f"Error al generar imagen: {e}", None
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
-    try:
-        client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
-        result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
-        return result[1]
-    except Exception as e:
-        return None
+try:
+client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
+result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
+return result[1]
+except Exception as e:
+return None
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
-    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
-    
-    improved_prompt = await improve_prompt(prompt)
-    combined_prompt = f"{prompt} {improved_prompt}"
-
-    image, seed = await generate_image(combined_prompt, model, "", width, height, scales, steps, seed)
-    
-    if isinstance(image, str) and image.startswith("Error"):
-        return [image, None]
-    
-    image_path = "temp_image.jpg"
-    image.save(image_path, format="JPEG")
-    
-    if process_upscale:
-        upscale_image_path = get_upscale_finegrain(combined_prompt, image_path, upscale_factor)
-        if upscale_image_path is not None:
-            upscale_image = Image.open(upscale_image_path)
-            upscale_image.save("upscale_image.jpg", format="JPEG")
-            return [image_path, "upscale_image.jpg"]
-        else:
-            return [image_path, image_path]
-    else:
-        return [image_path, image_path]
+model = enable_lora(lora_model, basemodel) if process_lora else basemodel
+
+improved_prompt = await improve_prompt(prompt)
+combined_prompt = f"{prompt} {improved_prompt}"
+
+image, seed = await generate_image(combined_prompt, model, "", width, height, scales, steps, seed)
+
+if isinstance(image, str) and image.startswith("Error"):
+return [image, None]
+
+image_path = "temp_image.jpg"
+image.save(image_path, format="JPEG")
+
+if process_upscale:
+upscale_image_path = get_upscale_finegrain(combined_prompt, image_path, upscale_factor)
+if upscale_image_path is not None:
+upscale_image = Image.open(upscale_image_path)
+upscale_image.save("upscale_image.jpg", format="JPEG")
+return [image_path, "upscale_image.jpg"]
+else:
+return [image_path, image_path]
+else:
+return [image_path, image_path]
 
 async def improve_prompt(prompt):
-    try:
-        instruction = "Improve and translate this prompt into English, adding detailed descriptions of style, cinematography, cameras, atmosphere, and lighting for the best quality, up to 200 words."
-        formatted_prompt = f"{instruction}: {prompt}"
-        response = llm_client.text_generation(formatted_prompt, max_new_tokens=300)  # Allowing more tokens for detailed description
-        improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip()
+try:
+instruction = "With this idea, describe in English a detailed img2vid prompt in a single paragraph of up to 300 characters, developing atmosphere, characters, lighting, and cameras."
+formatted_prompt = f"{prompt}: {instruction}"
+response = llm_client.text_generation(formatted_prompt, max_new_tokens=300, language="english")
+improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip()
 
-        return improved_text
-    except Exception as e:
-        return f"Error mejorando el prompt: {e}"
+return improved_text
+except Exception as e:
+return f"Error mejorando el prompt: {e}"
 
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
 
 with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
-    with gr.Column(elem_id="col-container"):
-        with gr.Row():
-            with gr.Column(scale=3):
-                output_res = ImageSlider(label="Flux / Upscaled")
-            with gr.Column(scale=2):
-                prompt = gr.Textbox(label="Descripción de imágen")
-                basemodel_choice = gr.Dropdown(label="Modelo", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
-                lora_model_choice = gr.Dropdown(label="LORA Realismo", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"], value="XLabs-AI/flux-RealismLora")
-                
-                with gr.Row():
-                    process_lora = gr.Checkbox(label="Procesar LORA")
-                    process_upscale = gr.Checkbox(label="Procesar Escalador")
-
-                upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
-
-                improved_prompt = gr.Textbox(label="Prompt Mejorado", interactive=False)
-
-                improve_btn = gr.Button("Mejora mi prompt")
-                improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt)
-                
-                with gr.Accordion(label="Opciones Avanzadas", open=False):
-                    width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
-                    height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768)
-                    scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=10)
-                    steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=20)
-                    seed = gr.Number(label="Semilla", value=-1)
-    
-                btn = gr.Button("Generar")
-                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res)
-    demo.launch()
+with gr.Column(elem_id="col-container"):
+with gr.Row():
+with gr.Column(scale=3):
+output_res = ImageSlider(label="Flux / Upscaled")
+with gr.Column(scale=2):
+prompt = gr.Textbox(label="Descripción de imágen")
+basemodel_choice = gr.Dropdown(label="Modelo", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
+lora_model_choice = gr.Dropdown(label="LORA Realismo", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"], value="XLabs-AI/flux-RealismLora")
+
+with gr.Row():
+process_lora = gr.Checkbox(label="Procesar LORA")
+process_upscale = gr.Checkbox(label="Procesar Escalador")
+
+improved_prompt = gr.Textbox(label="Prompt Mejorado", interactive=False)
+improve_btn = gr.Button("Mejora mi prompt")
+improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt)
+
+with gr.Accordion(label="Opciones Avanzadas", open=False):
+width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
+height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768)
+upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
+scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=10)
+steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=20)
+seed = gr.Number(label="Semilla", value=-1)
+
+btn = gr.Button("Generar")
+btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res)
+demo.launch()