text-to-image (and optional image-to-image) generative model. It converts natural language prompts into images across multiple styles, including photorealistic, illustration, 3D render, anime, and concept art.
ed users: Developers, artists, researchers. • Status: [Beta/Stable/Research] Intended Use • Creative ideation and concept art • Storyboarding, mood boards, and visual exploration • Educational and research use in generative modeling Not intended for: • Medical, legal, or other safety‑critical decisions • Creating deceptive content without clear disclosure • Violating copyright, publicity, or privacy rights How It Works • A text encoder maps the prompt to an embedding. • A diffusion UNet iteratively denoises latent representations toward the prompt. • A decoder maps latents to pixel space. • Optional guidance (classifier‑free) controls prompt adherence. Inputs • prompt (string): Required description of the target image. • negative_prompt (string, optional): Concepts to avoid. • width, height (int): Output resolution in pixels (e.g., 512 × 512). • steps (int): Denoising steps (e.g., 20–40). • guidance_scale (float): Prompt adherence (typical 4–9). • seed (int, optional): Reproducibility; omit for randomness. • num_images (int, optional): Number of images to generate. • init_image (URL or bytes, optional): For image‑to‑image or inpainting. • strength (float, optional): How strongly to follow init_image (0–1). • mask (URL or bytes, optional): White = keep, black = regenerate. Outputs • images: Array of generated images (PNG/JPEG/WebP). • seed: The seed used for each image. • safety_tags: Optional flags indicating filtered or sensitive content. • metadata: Parameters used (steps, guidance, dimensions, etc.). Quick Start • Keep prompts concrete: subject, attributes, setting, lighting, style. • Example prompt: ▪ “A cozy cabin interior at dusk, warm volumetric lighting, wooden textures, steam from a mug, ultra-detailed, cinematic, 35mm.” • Example negative prompt: ▪ “low quality, blurry, extra fingers, watermark, text artifacts.” Example Parameters • width: 768 • height: 768 • steps: 28 • guidance_scale: 6.5 • seed: 123456789 • num_images: 1 Capabilities • Text-to-Image • Image-to-Image stylization • Inpainting/Outpainting • Control via condition maps (e.g., Canny, Depth, Pose) if enabled Limitations • Can struggle with: ▪ Complex text rendering within images ▪ Fine-grained spatial relationships or small text ▪ Hands and subtle facial details in some styles • May reflect biases present in training data. • Photorealistic people may resemble real individuals unintentionally; disclose AI generation and review outputs before publishing. Safety and Ethical Considerations • Content policy: Disallows illegal content, CSAM, explicit violence or sexual content involving minors, doxxing, and attempts to impersonate real people without consent. • Watermarking: [Enabled/Disabled]; removal is prohibited if present. • Filters: [Describe any NSFW or safety filters if applied]. • Responsible use: Disclose AI generation where relevant; obtain consent for depictions of identifiable people; respect IP and publicity rights. Dataset and Training • Data: Large-scale image–text pairs from publicly available sources and licensed datasets. • Preprocessing: Caption filtering, resolution normalization, deduplication. • Training: Diffusion objective with classifier‑free guidance; mixed precision; [epochs/steps if you wish]. • Known gaps: Limited coverage of niche domains or languages outside [list]. Evaluation • Qualitative: Human preference on style fidelity and prompt adherence. • Quantitative: Reported metrics where applicable (e.g., FID, CLIPScore, PickScore). • Benchmarks are indicative; real‑world quality varies by prompt and style. Deployment Notes • VRAM needs: ~[X] GB for [resolution]; use attention slicing or tiling for higher resolutions. • Throughput: ~[N] it/s on [GPU]; steps × it/s ≈ latency. • Reproducibility: Fix seeds and parameters; note nondeterminism across hardware/drivers. Versioning • Current: v[X.Y] • Changes: ▪ Improved prompt adherence ▪ Reduced artifacts in faces/hands ▪ Faster sampler defaults License • Code: [License name] — [License URL] • Weights: [License name] — [Weights URL] • Usage: Ensure compliance with local laws and platform policies. Citation If you use this model, please cite: • [Your team or organization], “[Model Name]: A Diffusion Model for Text-to-Image Generation,” [Year]. [Paper URL] • BibTeX: ▪ @misc{yourmodel[year], title={[Model Name]}, author={[Authors]}, year={[Year]}, url={[Paper URL]}} Contact and Support • Issues: [Issue tracker or email] • Security: Report vulnerabilities to [security contact] with subject “Model Security” • Community: [Discord/Forum/Discussion link] • Changelog: See Releases tab or [CHANGELOG link] Optional fields you can put in the other boxes on your page: • Weights URL: link to your weights (e.g., Hugging Face, Replicate, S3) • Paper URL: arXiv or docs link • License URL: full license text Want me to tailor this to your exact model and platform? Share your model name, sampler defaults, resolution limits, and any safety filters you’ve enabled, and I’ll customize the sections.