Winnougan/Ideogram_Instant_NSFW

Ideogram 4 Instant — NSFW

"10 steps. Fine-tuned for NSFW Adult Style".

Ideogram 4 Instant NSFW is a fine-tuned text-to-image checkpoint built on top of `ideogram-ai/ideogram-4-fp8` (via the `fal/ideogram-v4-fast` instant/fast release). [Describe what this finetune changes — e.g. the dataset it was trained on, the style/domain it targets, what makes it different from the base Instant checkpoint.]

Key features

• —⚡ [Number]-step inference — [describe schedule, e.g. optimized for the Instant/Fast pipeline].
• —🎯 [Describe any CFG/architecture behavior inherited from base model or changed by finetune]
• —🧠 [Describe finetuning method] — e.g. LoRA, full finetune, QLoRA, dataset size, training steps.
• —🧩 Standard Diffusers components — no repository Python code and no trust_remote_code (update this if your finetune requires custom code).
• —📦 [Describe release scope] — e.g. transformer-only, LoRA weights only, full pipeline.

Usage

This model expects Ideogram 4's structured JSON caption format. Local Diffusers inference does not auto-expand natural-language prompts — provide a complete structured caption, for example:

import json
import torch
from diffusers import Ideogram4Pipeline, Ideogram4Transformer2DModel

repo_id = "your-username/ideogram-4-instant-finetune"
components_repo_id = "ideogram-ai/ideogram-4-nf4-diffusers"  # or your preferred shared components repo

transformer = Ideogram4Transformer2DModel.from_pretrained(
    repo_id,
    subfolder="transformer",
    torch_dtype=torch.bfloat16,
)
pipe = Ideogram4Pipeline.from_pretrained(
    components_repo_id,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

prompt = json.dumps(
    {
        "high_level_description": "Describe your image here.",
        "compositional_deconstruction": {
            "background": "Describe the background.",
            "elements": [
                {"type": "text", "text": "YOUR TEXT", "desc": "Description of the element."}
            ],
        },
    },
    ensure_ascii=False,
    separators=(",", ":"),
)

generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
    prompt,
    height=1024,
    width=1024,
    num_inference_steps=20,
    guidance_scale=1.0,
    generator=generator,
).images[0]
image.save("output.png")

Note: Update this snippet to match your finetune's actual inference requirements (precision, number of steps, guidance settings, any custom pipeline code, LoRA loading, etc.).

Training details

• —Base model: `ideogram-ai/ideogram-4-fp8` / fal/ideogram-v4-fast
• —Method: [LoRA / DreamBooth / full finetune / QAD / etc.]
• —Dataset: [describe dataset, size, licensing of training data]
• —Steps / epochs: [fill in]
• —Hardware: [fill in]
• —Hyperparameters: [learning rate, batch size, rank if LoRA, etc.]

Repository layout

.
├── README.md
├── LICENSE.md
├── NOTICE
└── transformer/  (or lora/ depending on release type)
    ├── config.json
    └── diffusion_pytorch_model.safetensors

Weights and provenance

This model is a fine-tune derived from Ideogram 4 (via ideogram-ai/ideogram-4-fp8 and/or fal/ideogram-v4-fast). [Describe any conversion, quantization, or format changes made during training/export.]

Ideogram 4 was created by Ideogram AI. The Instant/Fast base checkpoint was developed and released by fal. This derivative fine-tune was developed and released by [Your Name/Org] and is not an official Ideogram or fal product, and is not endorsed by Ideogram AI or fal.

License

As a derivative of Ideogram 4, this model inherits the Ideogram 4 Non-Commercial Model Agreement. The complete inherited license is included in `LICENSE.md` and governs use and redistribution of this model. By downloading or using these weights, you acknowledge and agree to the terms of that license, including its non-commercial use restriction.