Create app.py

app.py

@@ -0,0 +1,34 @@
+import torch
+import gradio as gr
+from diffusers import DiffusionPipeline
+
+MODEL_ID = "black-forest-labs/FLUX.1-schnell"
+
+# Pipeline laden (CPU)
+pipe = DiffusionPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32
+)
+pipe.to("cpu")
+
+def generate(prompt):
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=20,
+        guidance_scale=3.5
+    ).images[0]
+    return image
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.Textbox(
+        label="Prompt",
+        placeholder="anime girl with fennec ears sitting on a log in the woods"
+    ),
+    outputs=gr.Image(type="pil"),
+    title="FLUX Text-to-Image (CPU Space)",
+    description="Runs on CPU using diffusers. Slow but works."
+)
+
+if __name__ == "__main__":
+    demo.launch()