Upload 3 files

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+import os
+
+# Modell-ID (Unzensiertes Modell basierend auf SD 1.5)
+model_id = "Kernel/sd-nsfw"
+
+# Lade die Pipeline
+# Wir nutzen float32 für CPU, da dies am stabilsten ist.
+# safety_checker=None deaktiviert den eingebauten Filter.
+print("Lade Modell...")
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float32,
+    safety_checker=None,
+    requires_safety_checker=False
+)
+
+# Nutze einen schnelleren Scheduler für CPU
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cpu")
+
+def generate_image(prompt, negative_prompt, steps, guidance_scale):
+    print(f"Generiere Bild für: {prompt}")
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=guidance_scale
+    ).images[0]
+    return image
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎨 Unzensierter KI-Bildgenerator (CPU-optimiert)")
+    gr.Markdown("Diese App nutzt das Modell `Kernel/sd-nsfw` auf Hugging Face Spaces (CPU).")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="Was möchtest du sehen?", lines=3)
+            negative_prompt = gr.Textbox(label="Negativer Prompt", placeholder="Was soll nicht im Bild sein?", value="low quality, blurry, distorted")
+            
+            with gr.Row():
+                steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Inferenz-Schritte (CPU: 20 empfohlen)")
+                guidance_scale = gr.Slider(minimum=1, maximum=20, value=7.5, step=0.5, label="Guidance Scale")
+            
+            generate_btn = gr.Button("Bild generieren", variant="primary")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Generiertes Bild")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, steps, guidance_scale],
+        outputs=output_image
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("⚠️ **Hinweis:** Da dies auf einer CPU läuft, kann die Generierung 1-2 Minuten dauern.")
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+diffusers
+transformers
+accelerate

🎨 Uncensored AI Image Generator (CPU-Optimized).md

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+---
+title: Uncensored AI Image Generator (CPU)
+emoji: 🎨
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+python_version: 3.9
+app_file: app.py
+---
+
+# 🎨 Uncensored AI Image Generator (CPU-Optimized)
+
+This Gradio app allows generating images from text prompts using an uncensored Stable Diffusion model, optimized to run on CPU.
+
+## 🚀 Model
+
+The app uses the `Kernel/sd-nsfw` model from Hugging Face. This is a Stable Diffusion v1-5 NSFW REALISM model designed for photorealistic image generation. To ensure compatibility with CPU hardware and optimize generation speed, the model is loaded with `torch_dtype=torch.float32` and moved to the CPU (`.to("cpu")`). The built-in safety checker has been disabled to allow for uncensored image generation.
+
+## ✨ Features
+
+*   **Text-to-Image Generation:** Create images from any text description.
+*   **Negative Prompt:** Specify what you do *not* want to see in the generated image to improve quality (e.g., "low quality, blurry, distorted").
+*   **Customizable Parameters:** Control the number of inference steps and the guidance scale for finer results.
+*   **CPU-Optimized:** Designed to work without a dedicated GPU, making it ideal for Hugging Face Spaces with CPU resources.
+
+## ⚙️ Local Installation & Usage
+
+To run this app locally, follow these steps:
+
+1.  **Clone the repository:**
+    ```bash
+    git clone https://github.com/YourUsername/YourRepoName
+    cd YourRepoName
+    ```
+    *(Note: This step is not necessary for Hugging Face Spaces, as you will upload the files directly.)*
+
+2.  **Create a virtual environment (optional but recommended):**
+    ```bash
+    python3 -m venv venv
+    source venv/bin/activate
+    ```
+
+3.  **Install dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4.  **Run the app:**
+    ```bash
+    python app.py
+    ```
+
+The app will then be available in your browser at `http://127.0.0.1:7860/` (or a similar port).
+
+## ☁️ Deployment on Hugging Face Spaces
+
+To deploy this app on Hugging Face Spaces, create a new Space and upload the `app.py`, `requirements.txt`, and `README.md` files. Make sure to select `Gradio` as the SDK and set the Python version to `3.9` or higher.
+
+## ⚠️ Important Performance Note
+
+Since this app runs on a CPU, image generation can take **1-2 minutes or longer**, especially with a higher number of inference steps. Please be patient. For faster generation, using GPU-accelerated models is recommended.
+
+## 📜 License
+
+The `Kernel/sd-nsfw` model is licensed under the [CreativeML OpenRAIL M License](https://huggingface.co/spaces/CompVis/stable-diffusion-license).
+
+---