Create app.py

app.py

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+import torch
+import spaces
+import gradio as gr
+from diffusers import DiffusionPipeline
+import diffusers
+import numpy as np
+
+# =========================================================
+# MODEL CONFIGURATION
+# =========================================================
+MAX_SEED = np.iinfo(np.int32).max
+
+# =========================================================
+# LOAD PIPELINE
+# =========================================================
+print("Loading Z-Image-Turbo pipeline...")
+diffusers.utils.logging.set_verbosity_info()
+
+pipe = DiffusionPipeline.from_pretrained(
+    "Tongyi-MAI/Z-Image-Turbo",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=False,
+    attn_implementation="kernels-community/vllm-flash-attn3",
+)
+pipe.to("cuda")
+
+# =========================================================
+# IMAGE GENERATOR
+# =========================================================
+@spaces.GPU
+def generate_image(prompt, height, width, num_inference_steps, seed, randomize_seed, num_images):
+    if not prompt:
+        raise gr.Error("Please enter a prompt.")
+
+    if randomize_seed:
+        seed = torch.randint(0, 2**32 - 1, (1,)).item()
+
+    num_images = min(max(1, int(num_images)), 4)
+
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+
+    result = pipe(
+        prompt=prompt,
+        height=int(height),
+        width=int(width),
+        num_inference_steps=int(num_inference_steps),
+        guidance_scale=0.0,
+        generator=generator,
+        max_sequence_length=1024,
+        num_images_per_prompt=num_images,
+        output_type="pil",
+    )
+
+    return result.images, seed
+
+# =========================================================
+# GRADIO UI
+# =========================================================
+with gr.Blocks() as demo:
+    
+    gr.HTML("""
+    <style>
+        .gradio-container {
+            background: linear-gradient(135deg, #fef9f3 0%, #f0e6fa 50%, #e6f0fa 100%) !important;
+        }
+        footer {display: none !important;}
+    </style>
+    <div style="text-align: center; margin-bottom: 20px;">
+        <h1 style="color: #6b5b7a; font-size: 2.2rem; font-weight: 700; margin-bottom: 0.3rem;">
+            🖼️ NSFW Uncensored "Text to Image"
+        </h1>
+        <p style="color: #8b7b9b; font-size: 1rem;">Powered by Z-Image-Turbo Model</p>
+    </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt_input = gr.Textbox(
+                label="✏️ Prompt",
+                placeholder="Describe the image you want to create...",
+                lines=3
+            )
+            
+            with gr.Row():
+                height_input = gr.Slider(512, 2048, 1024, step=64, label="Height")
+                width_input = gr.Slider(512, 2048, 1024, step=64, label="Width")
+            
+            num_images_input = gr.Slider(1, 4, 2, step=1, label="🖼️ Number of Images")
+
+            with gr.Accordion("⚙️ Options", open=False):
+                steps_slider = gr.Slider(
+                    minimum=1, 
+                    maximum=30, 
+                    step=1, 
+                    value=18, 
+                    label="Inference Steps"
+                )
+                seed_input = gr.Slider(
+                    label="Seed", 
+                    minimum=0, 
+                    maximum=MAX_SEED, 
+                    step=1, 
+                    value=42
+                )
+                randomize_seed_checkbox = gr.Checkbox(
+                    label="Randomize Seed", 
+                    value=True
+                )
+
+            generate_button = gr.Button(
+                "✨ Generate Image", 
+                variant="primary"
+            )
+
+        with gr.Column(scale=1):
+            output_gallery = gr.Gallery(
+                label="🎨 Generated Images",
+                height=450,
+                columns=2
+            )
+            used_seed_output = gr.Number(label="Seed Used", interactive=False)
+
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt_input, height_input, width_input, steps_slider, seed_input, randomize_seed_checkbox, num_images_input],
+        outputs=[output_gallery, used_seed_output],
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch()