Test ZeroGPU

app.py

@@ -1,84 +1,14 @@
-import gc, random
 import gradio as gr
-import torch, spaces
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+import spaces
+import torch
 
-# ---- config ----
-MODEL_ID = "runwayml/stable-diffusion-v1-5"
-DTYPE = torch.float16  # ZeroGPU slice runs fp16 nicely
+zero = torch.Tensor([0]).cuda()
+print(zero.device) # <-- 'cpu' 🤔
 
-# lazy cache
-_PIPE = None
+@spaces.GPU
+def greet(n):
+    print(zero.device) # <-- 'cuda:0' 🤗
+    return f"Hello {zero + n} Tensor"
 
-def get_pipe():
-    global _PIPE
-    if _PIPE is None:
-        pipe = StableDiffusionPipeline.from_pretrained(
-            MODEL_ID,
-            torch_dtype=DTYPE,
-            safety_checker=None,
-            use_safetensors=True,
-            low_cpu_mem_usage=True,
-        )
-        # fast, stable scheduler
-        pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-            pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="dpmsolver++"
-        )
-        # memory savers (great for Spaces/ZeroGPU)
-        pipe.enable_attention_slicing()
-        pipe.enable_vae_slicing()
-        pipe.enable_model_cpu_offload()
-        _PIPE = pipe
-    return _PIPE
-
-def snap8(x: int) -> int:
-    x = max(256, min(1024, int(x)))
-    return x - (x % 8)
-
-@spaces.GPU(duration=120)
-def generate(prompt: str, negative: str, steps: int, cfg: float, width: int, height: int, seed: int):
-    pipe = get_pipe()
-    w, h = snap8(width), snap8(height)
-
-    # seed
-    if int(seed) < 0:
-        seed = random.randint(0, 2**31 - 1)
-    gen = torch.Generator(device="cuda").manual_seed(int(seed))
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-    gc.collect()
-
-    with torch.autocast(device_type="cuda", dtype=DTYPE):
-        out = pipe(
-            prompt=str(prompt),
-            negative_prompt=str(negative or ""),
-            num_inference_steps=int(steps),
-            guidance_scale=float(cfg),
-            width=w, height=h,
-            generator=gen,
-        )
-    return out.images[0]
-
-# -------- UI --------
-with gr.Blocks() as demo:
-    gr.Markdown("# 🎨 Stable Diffusion 1.5 — ZeroGPU (public, minimal)")
-
-    with gr.Row():
-        with gr.Column():
-            prompt  = gr.Textbox(label="Prompt", value="a cozy reading nook, warm sunlight, cinematic lighting, highly detailed")
-            negative = gr.Textbox(label="Negative (optional)", value="lowres, blurry, watermark, text")
-            steps   = gr.Slider(8, 40, value=28, step=1, label="Steps")
-            cfg     = gr.Slider(1.0, 12.0, value=7.0, step=0.5, label="CFG")
-            width   = gr.Slider(256, 1024, value=640, step=16, label="Width")
-            height  = gr.Slider(256, 1024, value=640, step=16, label="Height")
-            seed    = gr.Number(value=-1, precision=0, label="Seed (-1 random)")
-            btn     = gr.Button("Generate", variant="primary")
-        with gr.Column():
-            out = gr.Image(label="Result", interactive=False)
-
-    btn.click(generate, [prompt, negative, steps, cfg, width, height, seed], out)
-
-if __name__ == "__main__":
-    # Keep it plain so the Space builds cleanly
-    demo.queue().launch(show_api=True, show_error=True, ssr_mode=False)
+demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
+demo.launch()