Update app.py

app.py

@@ -10,98 +10,116 @@ from gradio_imageslider import ImageSlider
 MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
-
 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 = "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()
+    try:
+        instruction = ("With this idea, describe in English a detailed img2vid prompt in a single paragraph of up to 200 characters maximun, 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")
-
-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()
+    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()