README.md

---
base_model_relation: quantized
library_name: diffusers
tags:
- sdnq
- ideogram
- ideogram_4
- 8-bit
base_model:
- ideogram-ai/ideogram-4-fp8
---

This model is a direct conversion of [Ideogram-4 FP8](https://huggingface.co/ideogram-ai/ideogram-4-fp8) to [SDNQ](https://github.com/Disty0/sdnq) Diffusers format with identical weights from the original FP8 model.  
 
```
pip install sdnq
```

```py
import os
import json
import requests

import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
from sdnq.common import use_torch_compile as triton_is_available
from sdnq.loader import apply_sdnq_options_to_model

pipe = diffusers.Ideogram4Pipeline.from_pretrained("Disty0/Ideogram-4-SDNQ-FP8", torch_dtype=torch.bfloat16)

# Enable FP8 MatMul for AMD, Intel ARC and Nvidia GPUs:
if triton_is_available and (torch.cuda.is_available() or torch.xpu.is_available()):
    pipe.transformer = apply_sdnq_options_to_model(pipe.transformer, use_quantized_matmul=True)
    pipe.unconditional_transformer = apply_sdnq_options_to_model(pipe.unconditional_transformer, use_quantized_matmul=True)
    pipe.text_encoder = apply_sdnq_options_to_model(pipe.text_encoder, use_quantized_matmul=True)
    # pipe.transformer = torch.compile(pipe.transformer) # optional for faster speeds
    # pipe.unconditional_transformer = torch.compile(pipe.unconditional_transformer) # optional for faster speeds

pipe.enable_model_cpu_offload()

# Expand the prompt into a structured JSON caption with Ideogram's free hosted magic-prompt API.
# Get a key at https://developer.ideogram.ai/  (set IDEOGRAM_API_KEY).
resp = requests.post(
    "https://api.ideogram.ai/v1/ideogram-v4/magic-prompt",
    headers={"Api-Key": "your_ideogram_api_key"},
    json={"text_prompt": "a ginger cat wearing a tiny wizard hat reading a spellbook", "aspect_ratio": "1x1"},
).json()
caption = json.dumps(resp["json_prompt"])  # or: token="hf_xxxxxxxxx", token is needed as the repo is gated

# Pass the caption straight to the pipeline (no prompt_upsampling — it's already upsampled).
image = pipe(
    caption, 
    height=1024, # model supports up to 2048
    width=1024, # model supports up to 2048
    generator=torch.manual_seed(0),
).images[0]
image.save("ideogram4-sdnq-fp8.png")
```