Text-to-Image
Diffusers
Safetensors
English
Chinese
LLaDAImagePipeline
image-generation
image-editing
image-to-image
Instructions to use inclusionAI/LLaDA-Image-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use inclusionAI/LLaDA-Image-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("inclusionAI/LLaDA-Image-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
File size: 4,049 Bytes
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"source": "/home/admin/LLaDA-Image-Multi-step/text_encoder",
"output": "/home/admin/LLaDA-Image-Multi-step-text-encoder-fp8-block128-dynamic",
"format": "float8_e4m3fn",
"weight_quantization": "symmetric absmax RTN per expert 2-D block",
"weight_block_size": [
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"weight_scale_shape": "[experts, ceil(N/block_n), ceil(K/block_k)]",
"activation_quantization": "dynamic symmetric absmax RTN per token",
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"elapsed_seconds": 43.155,
"runtime_note": "Real FP8 torch._scaled_mm reference path on CUDA; custom grouped FP8 MoE kernel needed for production throughput."
}
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