app.py

import gc, random
import gradio as gr
import torch, spaces
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler

# ---- config ----
MODEL_ID = "runwayml/stable-diffusion-v1-5"
DTYPE = torch.float16  # ZeroGPU slice runs fp16 nicely

# lazy cache
_PIPE = None

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.launch()