Text-to-Image
Diffusers
Safetensors
Ideogram4Pipeline
image-generation
diffusion
flow-matching
dit
ideogram
Instructions to use ideogram-ai/ideogram-4-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ideogram-ai/ideogram-4-fp8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ideogram-ai/ideogram-4-fp8", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Switch transformer & unconditional_transformer to split Q/K/V
#10
by multimodalart HF Staff - opened
Updates transformer/ and unconditional_transformer/ from fused attention.qkv/attention.o to split attention.to_q/to_k/to_v/to_out.0, matching the diffusers loader change in huggingface/diffusers#13859 (commit fbe4750).
Also deletes the per-folder diffusion_pytorch_model.safetensors.index.json files: their weight_map still pointed at the old fused qkv/o keys (and only ever referenced the single diffusion_pytorch_model.safetensors), so they would break index-based loading. This matches diffusers-internal-dev/ideogram-4-fp8-diffusers, which carries no index.json.
The q/k/v weights are contiguous row-slices of the old fused weight, so the conversion is lossless. Configs are unchanged.
multimodalart changed pull request title from Switch transformer & unconditional_transformer to split Q/K/V to Match diffusers keys
multimodalart changed pull request title from Match diffusers keys to Switch transformer & unconditional_transformer to split Q/K/V