Update app.py

app.py

@@ -1,154 +1,61 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-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
-
-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
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
+import spaces
+import gradio as gr
+from diffusers import FluxImg2ImgPipeline
+from PIL import Image
+
+# 1. Carga del modelo (Fuera de la función)
+# Usamos FLUX.1-schnell para velocidad en Spaces
+model_id = "black-forest-labs/FLUX.1-schnell"
+print("Cargando pipeline en GPU...")
+pipe = FluxImg2ImgPipeline.from_pretrained(
+    model_id, 
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+# 2. Carga del LoRA
+print("Inyectando LoRA...")
+pipe.load_lora_weights("joyfox/Kontext-Cosplay-Lora", weight_name="kontext_cosplay_lora_20000.safetensors")
+
+# 3. Función de generación decorada
+@spaces.GPU
+def generate_cosplay(image, prompt):
+    if image is None:
+        return None
+    
+    # Aseguramos que la imagen sea del tamaño correcto para FLUX
+    image = image.convert("RGB").resize((1024, 1024))
+    
+    # El prompt debe incluir el trigger 'r2cos'
+    full_prompt = f"{prompt}, r2cos"
+    
+    # Semilla fija para consistencia
+    generator = torch.Generator(device="cuda").manual_seed(42)
+    
+    # Ejecución Img2Img
+    # strength=0.6 significa que mantenemos 40% de la original y 60% es la transformación
+    result = pipe(
+        prompt=full_prompt,
+        image=image,
+        strength=0.6,
+        num_inference_steps=6,
+        guidance_scale=0.0,
+        generator=generator
     ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+    
+    return result
+
+# 4. Interfaz Gradio
+demo = gr.Interface(
+    fn=generate_cosplay, 
+    inputs=[
+        gr.Image(type="pil", label="Foto de Referencia"), 
+        gr.Textbox(label="Descripción del personaje o estilo")
+    ], 
+    outputs=gr.Image(label="Resultado Cosplay"),
+    title="Kontext-Cosplay AI: Transformación de Estilo",
+    description="Sube tu foto y describe el estilo que quieres aplicar."
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()