Update app.py

app.py

@@ -1,3 +1,60 @@
 import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
 
-gr.load("models/Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration").launch()
+# Carregar o modelo base
+base_model = "stabilityai/stable-diffusion-2-1"
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
+
+# Carregar o LoRA a partir do repositório fornecido
+lora_repo = "Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration"
+pipe.load_lora_weights(lora_repo)
+
+pipe.to("cuda")
+
+MAX_SEED = 2**32 - 1
+
+def generate_image(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=steps,
+        guidance_scale=cfg_scale,
+        width=width,
+        height=height,
+        generator=generator,
+        joint_attention_kwargs={"scale": lora_scale},
+    ).images[0]
+    
+    return image, seed
+
+with gr.Blocks() as app:
+    gr.Markdown("# Flux RealismLora Image Generator")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt = gr.TextArea(label="Prompt", placeholder="Digite o prompt", lines=5)
+            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, máximo=20, passo=0.5, valor=7.5)
+            steps = gr.Slider(label="Steps", mínimo=1, máximo=100, passo=1, valor=50)
+            width = gr.Slider(label="Width", mínimo=256, máximo=1536, passo=64, valor=768)
+            height = gr.Slider(label="Height", mínimo=256, máximo=1536, passo=64, valor=768)
+            randomize_seed = gr.Checkbox(False, label="Randomize seed")
+            seed = gr.Slider(label="Seed", mínimo=0, máximo=MAX_SEED, passo=1, valor=42)
+            lora_scale = gr.Slider(label="LoRA Scale", mínimo=0, máximo=1, passo=0.01, valor=0.85)
+            generate_button = gr.Button("Generate")
+        
+        with gr.Column(scale=1):
+            result = gr.Image(label="Generated Image")
+            gr.Markdown("Gere imagens usando RealismLora com um prompt de texto.")
+    
+    generate_button.click(
+        generate_image,
+        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
+        outputs=[result, seed]
+    )
+
+app.queue()
+app.launch()