fal / ideogram-v4-instant

Ideogram 4 Instant — by fal

8 steps. One transformer. No runtime CFG.

Ideogram 4 Instant is an eight-step text-to-image checkpoint developed and released by fal , based on ideogram-ai/ideogram-4-fp8 . This release contains the BF16 weights from immediately before the quantization-aware distillation (QAD) stage. It combines timestep distillation with a single conditional branch for generation in just eight denoising steps.

Key features

• ⚡ 8-step inference — the Instant schedule at 1024×1024.
• 🎯 No runtime CFG — one conditional transformer call per denoising step; no negative branch or CFG blend.
• 🧠 Pre-QAD BF16 weights — approximately 9.28 billion parameters, captured immediately before QAD.
• 🧩 Standard Diffusers components — no repository Python code and no trust_remote_code .
• 📦 Transformer-only release — shared components come from Ideogram AI's public, gated Diffusers repository.

Read how fal built the single-branch, few-step Ideogram 4 serving path in Serving sub-second Ideogram v4 without quality loss .

Hosted API

The production-optimized model is available on fal through ideogram/v4/instant .

The hosted endpoint uses the later QAD-trained, FP4-optimized production weights. This repository intentionally publishes the BF16 checkpoint from before QAD .

Usage

This model expects Ideogram 4's structured JSON caption format. The hosted fal endpoint expands natural-language prompts automatically; local Diffusers inference does not. Expand the prompt with an Ideogram-compatible magic-prompt model first, or provide a complete structured caption like the one below.

The component wiring below uses the official public, gated ideogram-ai/ideogram-4-nf4-diffusers repository. Only its tokenizer, text encoder, VAE, and scheduler are used; neither of its diffusion transformers is loaded. You must accept Ideogram's access gate before downloading the components.

This release requires a Diffusers build where Ideogram4Pipeline.unconditional_transformer is optional, the single-branch path directly uses the conditional prediction, the native nonzero terminal is preserved, and Ideogram's frequency tables follow the model compute dtype.

import json

import torch
from diffusers import Ideogram4Pipeline, Ideogram4Transformer2DModel

repo_id = "fal/ideogram-v4-instant"
components_repo_id = "ideogram-ai/ideogram-4-nf4-diffusers"
components_revision = "1874bc70267ba2c823a7239e1d70dd308c8d64dc"

transformer = Ideogram4Transformer2DModel.from_pretrained(
    repo_id,
    subfolder="transformer",
    torch_dtype=torch.bfloat16,
)
pipe = Ideogram4Pipeline.from_pretrained(
    components_repo_id,
    revision=components_revision,
    transformer=transformer,
    unconditional_transformer=None,
    torch_dtype=torch.bfloat16,
).to("cuda")

prompt = json.dumps(
    {
        "high_level_description": (
            "A bold typographic poster centered on the exact words INSTANT BY FAL, "
            "printed in black and electric orange on warm white paper."
        ),
        "compositional_deconstruction": {
            "background": (
                "Warm white textured paper with even studio lighting and generous negative space."
            ),
            "elements": [
                {
                    "type": "text",
                    "text": "INSTANT BY FAL",
                    "desc": (
                        "Large uppercase geometric sans-serif lettering with crisp print edges, "
                        "precisely centered."
                    ),
                }
            ],
        },
    },
    ensure_ascii=False,
    separators=(",", ":"),
)

generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
    prompt,
    height=1024,
    width=1024,
    num_inference_steps=8,
    mu=0.0,
    std=1.75,
    generator=generator,
).images[0]
image.save("ideogram4-instant.png")

Omitting guidance arguments is intentional. With unconditional_transformer=None , the pipeline runs only the conditional transformer and uses its output directly.

Repository layout

.
├── README.md
├── LICENSE.md
├── NOTICE
├── assets/
│   └── ideogram-v4-by-fal.mp4
└── transformer/
    ├── config.json
    ├── diffusion_pytorch_model-00001-of-00004.safetensors
    ├── diffusion_pytorch_model-00002-of-00004.safetensors
    ├── diffusion_pytorch_model-00003-of-00004.safetensors
    ├── diffusion_pytorch_model-00004-of-00004.safetensors
    └── diffusion_pytorch_model.safetensors.index.json

Weights and provenance

This is the dense BF16 Instant checkpoint captured immediately before QAD. It is not a statically quantized FP4 or NVFP4 export. During conversion, fused QKV tensors were split into the standard Diffusers to_q , to_k , to_v , and to_out layout without changing their values.

The transformer was derived from ideogram-ai/ideogram-4-fp8 . Shared inference components are loaded from ideogram-ai/ideogram-4-nf4-diffusers ; neither transformer in that repository is loaded or used.

Ideogram 4 was created by Ideogram AI. This derivative checkpoint was developed and released by fal and is not an official Ideogram product or endorsed by Ideogram AI.

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.