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1. Model introduction

Ideogram 4 is Ideogram’s text-to-image diffusion model. SGLang Diffusion supports the official NF4 checkpoint, the official FP8 checkpoint, and the Comfy-Org NVFP4 transformer checkpoint. Compared with previous open-source image models, Ideogram 4 provides a significant aesthetic lift, with stronger composition, more polished visual style, and better typography-aware generation.
VariantHugging Face model IDNotes
NF4ideogram-ai/ideogram-4-nf4Official bitsandbytes NF4 checkpoint. Use this path first for low-memory deployment.
FP8ideogram-ai/ideogram-4-fp8Official FP8 checkpoint.
NVFP4Comfy-Org/Ideogram-4Comfy-Org NVFP4 transformer weights. SGLang loads non-transformer components from ideogram-ai/ideogram-4-fp8.

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2. Prerequisites

  • NVIDIA CUDA GPU.
  • SGLang installed with diffusion dependencies.
  • bitsandbytes>=0.46.1 for the NF4 checkpoint.
  • HF_TOKEN with access to the Ideogram 4 gated repositories.

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3. Serve the model

NF4:
Command
HF_TOKEN=$HF_TOKEN sglang serve \
  --model-path ideogram-ai/ideogram-4-nf4 \
  --num-gpus 1 \
  --performance-mode auto \
  --port 30010
FP8:
Command
HF_TOKEN=$HF_TOKEN sglang serve \
  --model-path ideogram-ai/ideogram-4-fp8 \
  --num-gpus 1 \
  --performance-mode auto \
  --port 30010
Comfy-Org NVFP4:
Command
HF_TOKEN=$HF_TOKEN sglang serve \
  --model-path Comfy-Org/Ideogram-4 \
  --num-gpus 1 \
  --performance-mode auto \
  --port 30010
Use B200 or another Blackwell GPU for NVFP4.

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4. Generate an image

Example
import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:30010/v1")

response = client.images.generate(
    model="ideogram-ai/ideogram-4-nf4",
    prompt="A cinematic poster of a quiet bookstore at dusk with elegant hand-lettered signage",
    size="1024x1024",
    n=1,
    response_format="b64_json",
    extra_body={"preset": "V4_QUALITY_48", "seed": 0},
)

image_bytes = base64.b64decode(response.data[0].b64_json)
with open("ideogram4.png", "wb") as f:
    f.write(image_bytes)
Ideogram 4 presets are V4_DEFAULT_20, V4_QUALITY_48, and V4_TURBO_12. The preset controls both num_inference_steps and guidance, so do not set those fields directly.