Switch to ZeroGPU minimal Gradio app

README.md

@@ -1,12 +1,42 @@
 ---
-title: PixelForge
-emoji: 🖼
+title: PixelForge ZeroGPU
+emoji: 🎨
 colorFrom: purple
-colorTo: red
+colorTo: blue
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 5.17.0
+python_version: 3.10
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# PixelForge ZeroGPU
+
+Minimaler Gradio-Space für stabile ZeroGPU-Nutzung.
+
+## Was dieser Space macht
+
+- Text-zu-Bild mit SD-Turbo
+- Einfache Oberfläche (Prompt, Negative Prompt, Schritte, Guidance)
+- Keine unnötigen Projektdateien
+
+## Hinweis
+
+Wenn der Space neu startet, kann der erste Run länger dauern (Modell-Download).
+
+## In den neuen Space pushen
+
+1. Leeren Space mit ZeroGPU erstellen (SDK: Gradio).
+2. Nur den Inhalt dieses Ordners in das Space-Repo legen.
+3. Commit + Push.
+
+Beispiel (PowerShell):
+
+git clone https://huggingface.co/spaces/<USERNAME>/<SPACE_NAME>
+cd <SPACE_NAME>
+copy ..\hf_zerogpu_space\README.md .
+copy ..\hf_zerogpu_space\app.py .
+copy ..\hf_zerogpu_space\requirements.txt .
+git add .
+git commit -m "Init ZeroGPU Gradio Space"
+git push

app.py

@@ -1,154 +1,85 @@
-import gradio as gr
-import numpy as np
-import random
+import os
+from typing import Optional
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import gradio as gr
 import torch
+from diffusers import AutoPipelineForText2Image
+
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+MODEL_ID = os.getenv("MODEL_ID", "stabilityai/sd-turbo")
+MAX_STEPS = int(os.getenv("MAX_STEPS", "8"))
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+_pipe: Optional[AutoPipelineForText2Image] = None
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+def get_pipe() -> AutoPipelineForText2Image:
+    global _pipe
+    if _pipe is not None:
+        return _pipe
+
+    dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+    _pipe = AutoPipelineForText2Image.from_pretrained(
+        MODEL_ID,
+        torch_dtype=dtype,
+        safety_checker=None,
+    )
+    _pipe = _pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+    return _pipe
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+def generate_image(
+    prompt: str,
+    negative_prompt: str,
+    steps: int,
+    guidance: float,
+    seed: int,
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
+    if not prompt.strip():
+        raise gr.Error("Prompt darf nicht leer sein.")
+
+    pipe = get_pipe()
+    steps = max(1, min(int(steps), MAX_STEPS))
+
+    generator = torch.Generator(device=("cuda" if torch.cuda.is_available() else "cpu"))
+    generator.manual_seed(int(seed))
+
+    result = pipe(
+        prompt=prompt.strip(),
+        negative_prompt=negative_prompt.strip() or None,
+        num_inference_steps=steps,
+        guidance_scale=float(guidance),
         generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
     )
+    image = result.images[0]
+
+    device_info = "GPU" if torch.cuda.is_available() else "CPU"
+    meta = f"Model: {MODEL_ID} | Device: {device_info} | Steps: {steps}"
+    return image, meta
+
+
+with gr.Blocks(title="PixelForge ZeroGPU") as demo:
+    gr.Markdown("## PixelForge ZeroGPU")
+    gr.Markdown("Leichte ZeroGPU-App für Text-zu-Bild mit SD-Turbo.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt = gr.Textbox(label="Prompt", placeholder="z. B. cinematic cyberpunk city at night", lines=3)
+            negative_prompt = gr.Textbox(label="Negative Prompt", value="blurry, low quality", lines=2)
+            steps = gr.Slider(1, MAX_STEPS, value=min(4, MAX_STEPS), step=1, label="Steps")
+            guidance = gr.Slider(0.0, 8.0, value=0.0, step=0.1, label="Guidance")
+            seed = gr.Number(label="Seed", value=42, precision=0)
+            run_btn = gr.Button("Bild erzeugen", variant="primary")
+
+        with gr.Column(scale=1):
+            image_out = gr.Image(label="Ergebnis", type="pil")
+            info_out = gr.Textbox(label="Info")
+
+    run_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, steps, guidance, seed],
+        outputs=[image_out, info_out],
+    )
+
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))

requirements.txt

@@ -1,6 +1,7 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+gradio>=5.17.0
+diffusers>=0.32.0
+transformers>=4.48.0
+accelerate>=1.2.0
+safetensors>=0.5.0
+Pillow>=10.0.0
+torch