Create app.py

app.py

@@ -0,0 +1,56 @@
+import spaces
+import torch
+import gradio as gr
+from diffusers import StableDiffusionXLPipeline
+
+MODEL_REPO = "BinaryLight1011/Imageflow"
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    MODEL_REPO,
+    torch_dtype=torch.float16,
+    variant="fp16",
+    use_safetensors=True,
+)
+pipe.to("cuda")
+
+
+@spaces.GPU(duration=60)
+def generate(prompt, negative_prompt, width, height, guidance_scale, steps, seed):
+    generator = torch.Generator(device="cuda").manual_seed(int(seed))
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt or None,
+        width=int(width),
+        height=int(height),
+        guidance_scale=float(guidance_scale),
+        num_inference_steps=int(steps),
+        generator=generator,
+    ).images[0]
+    return image
+
+
+with gr.Blocks(title="Imageflow") as demo:
+    gr.Markdown("# 🖼️ Imageflow — Text-to-Image (SDXL)")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", lines=4, placeholder="Descreva a imagem...")
+            negative_prompt = gr.Textbox(label="Negative prompt (opcional)", lines=2)
+            with gr.Row():
+                width = gr.Slider(512, 1536, value=1024, step=64, label="Largura")
+                height = gr.Slider(512, 1536, value=1024, step=64, label="Altura")
+            guidance_scale = gr.Slider(1.0, 15.0, value=7.0, step=0.5, label="Guidance scale")
+            steps = gr.Slider(10, 100, value=30, step=5, label="Inference steps")
+            seed = gr.Number(value=42, label="Seed")
+            btn = gr.Button("Gerar imagem", variant="primary")
+
+        with gr.Column():
+            output_image = gr.Image(label="Resultado")
+
+    btn.click(
+        fn=generate,
+        inputs=[prompt, negative_prompt, width, height, guidance_scale, steps, seed],
+        outputs=output_image,
+    )
+
+demo.launch()