Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
+import random
+
+# ── Device detection ───────────────────────────────────────────────────────────
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
+
+print(f"Running on: {DEVICE.upper()} | dtype: {DTYPE}")
+
+# ── Model ──────────────────────────────────────────────────────────────────────
+MODEL_REPO = "cyberdelia/CyberRealisticPony"
+MODEL_FILE = "CyberRealisticPony_V16.0_FP16.safetensors"
+MODEL_URL  = f"https://huggingface.co/{MODEL_REPO}/resolve/main/{MODEL_FILE}"
+
+# ── Pony quality tags (added automatically — do not type these in your prompt) ─
+PONY_POS_PREFIX = "score_9, score_8_up, score_7_up, "
+PONY_NEG_PREFIX = "score_6, score_5, score_4, "
+
+# ── Load model ─────────────────────────────────────────────────────────────────
+print("Loading model — this may take several minutes on first start...")
+
+pipe = StableDiffusionXLPipeline.from_single_file(
+    MODEL_URL,
+    torch_dtype=DTYPE,
+    use_safetensors=True,
+)
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+    pipe.scheduler.config,
+    use_karras_sigmas=True,
+)
+
+pipe.enable_attention_slicing()
+
+if DEVICE == "cuda":
+    pipe.enable_xformers_memory_efficient_attention()
+
+pipe = pipe.to(DEVICE)
+
+print("Model ready.")
+
+# ── Generation function ────────────────────────────────────────────────────────
+def generate_image(prompt, negative_prompt, width, height, steps, guidance, seed, randomize_seed):
+    if not prompt or prompt.strip() == "":
+        raise gr.Error("Please enter a prompt before generating.")
+
+    if randomize_seed:
+        seed = random.randint(0, 2**32 - 1)
+
+    seed = int(seed)
+    generator = torch.Generator(device=DEVICE).manual_seed(seed)
+
+    full_prompt   = PONY_POS_PREFIX + prompt.strip()
+    full_negative = PONY_NEG_PREFIX + negative_prompt.strip()
+
+    output = pipe(
+        prompt=full_prompt,
+        negative_prompt=full_negative,
+        width=int(width),
+        height=int(height),
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance),
+        generator=generator,
+        clip_skip=2,
+    )
+
+    return output.images[0], seed
+
+
+# ── UI ─────────────────────────────────────────────────────────────────────────
+with gr.Blocks(title="CyberRealistic Pony", theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("# 🐴 CyberRealistic Pony Generator")
+    gr.Markdown(
+        "> **Model:** CyberRealistic Pony v16.0 · SDXL / Pony architecture  \n"
+        "> ⚠️ **Running on CPU** — generation takes 30–90+ minutes per image. "
+        "Upgrade to ZeroGPU in Space Settings for practical speeds.\n\n"
+        "_Quality tags (`score_9, score_8_up, score_7_up`) are prepended to your prompt automatically._"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="a woman in a futuristic city, cinematic lighting, highly detailed, photorealistic",
+                lines=4,
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value=(
+                    "(worst quality:1.2), (low quality:1.2), (normal quality:1.2), "
+                    "lowres, bad anatomy, bad hands, signature, watermarks, "
+                    "ugly, imperfect eyes, skewed eyes, unnatural face, "
+                    "unnatural body, error, extra limb, missing limbs"
+                ),
+                lines=3,
+            )
+
+            gr.Markdown("### Image Size")
+            with gr.Row():
+                width  = gr.Slider(minimum=512, maximum=896,  value=768, step=64, label="Width")
+                height = gr.Slider(minimum=512, maximum=1152, value=896, step=64, label="Height")
+
+            gr.Markdown("### Sampling")
+            steps    = gr.Slider(minimum=10, maximum=30, value=20, step=1,   label="Steps — keep low on CPU")
+            guidance = gr.Slider(minimum=1.0, maximum=12.0, value=5.0, step=0.5, label="Guidance Scale (CFG) — recommended: 5")
+
+            gr.Markdown("### Seed")
+            with gr.Row():
+                seed           = gr.Number(label="Seed", value=42, precision=0, minimum=0, maximum=2**32 - 1)
+                randomize_seed = gr.Checkbox(label="Randomise Seed", value=True)
+
+            generate_btn = gr.Button("Generate Image 🖼️", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            output_image = gr.Image(label="Generated Image", type="pil", height=500)
+            used_seed    = gr.Number(label="Seed Used", interactive=False)
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, width, height, steps, guidance, seed, randomize_seed],
+        outputs=[output_image, used_seed],
+    )
+
+demo.launch()