wehub-resource-sync
caf324b09d
Build documentation / build (push) Failing after 0s
Deploy "method_comparison" Gradio to Spaces / deploy (push) Has been cancelled
Deploy "PEFT shop" Gradio app to Spaces / deploy (push) Has been cancelled
tests on transformers main / tests (push) Has been cancelled
tests / check_code_quality (push) Has been cancelled
tests / tests (ubuntu-latest, 3.10) (push) Has been cancelled
tests / tests (ubuntu-latest, 3.11) (push) Has been cancelled
tests / tests (ubuntu-latest, 3.12) (push) Has been cancelled
tests / tests (ubuntu-latest, 3.13) (push) Has been cancelled
tests / tests (windows-latest, 3.10) (push) Has been cancelled
tests / tests (windows-latest, 3.11) (push) Has been cancelled
tests / tests (windows-latest, 3.12) (push) Has been cancelled
tests / tests (windows-latest, 3.13) (push) Has been cancelled
Secret Leaks / trufflehog (push) Has been cancelled
CI security linting / zizmor latest via Cargo (push) Has been cancelled
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
"""
|
|
This example demonstrates loading of LoRA adapter (via PEFT) into an FP8 INC-quantized FLUX model.
|
|
|
|
More info on Intel Neural Compressor (INC) FP8 quantization is available at:
|
|
https://github.com/intel/neural-compressor/tree/master/examples/helloworld/fp8_example
|
|
|
|
Requirements:
|
|
pip install optimum-habana sentencepiece neural-compressor[pt] peft
|
|
"""
|
|
|
|
import importlib
|
|
|
|
import torch
|
|
from neural_compressor.torch.quantization import FP8Config, convert, finalize_calibration, prepare
|
|
|
|
|
|
# Checks if HPU device is available
|
|
# Adapted from https://github.com/huggingface/accelerate/blob/b451956fd69a135efc283aadaa478f0d33fcbe6a/src/accelerate/utils/imports.py#L435
|
|
def is_hpu_available():
|
|
if (
|
|
importlib.util.find_spec("habana_frameworks") is None
|
|
or importlib.util.find_spec("habana_frameworks.torch") is None
|
|
):
|
|
return False
|
|
|
|
import habana_frameworks.torch # noqa: F401
|
|
|
|
return hasattr(torch, "hpu") and torch.hpu.is_available()
|
|
|
|
|
|
# Ensure HPU device is available before proceeding
|
|
if is_hpu_available():
|
|
from optimum.habana.diffusers import GaudiFluxPipeline
|
|
else:
|
|
raise RuntimeError("HPU device not found. This code requires Intel Gaudi device to run.")
|
|
|
|
# Example: FLUX model inference on HPU via optimum-habana pipeline
|
|
hpu_configs = {
|
|
"use_habana": True,
|
|
"use_hpu_graphs": True,
|
|
"sdp_on_bf16": True,
|
|
"gaudi_config": "Habana/stable-diffusion",
|
|
}
|
|
pipe = GaudiFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, **hpu_configs)
|
|
prompt = "A picture of sks dog in a bucket"
|
|
|
|
# Quantize FLUX transformer to FP8 using INC (Intel Neural Compressor)
|
|
quant_configs = {
|
|
"mode": "AUTO",
|
|
"observer": "maxabs",
|
|
"scale_method": "maxabs_hw",
|
|
"allowlist": {"types": [], "names": []},
|
|
"blocklist": {"types": [], "names": []},
|
|
"dump_stats_path": "/tmp/hqt_output/measure",
|
|
}
|
|
config = FP8Config(**quant_configs)
|
|
pipe.transformer = prepare(pipe.transformer, config)
|
|
pipe(prompt)
|
|
finalize_calibration(pipe.transformer)
|
|
pipe.transformer = convert(pipe.transformer)
|
|
|
|
# Load LoRA weights with PEFT
|
|
pipe.load_lora_weights("dsocek/lora-flux-dog", adapter_name="user_lora")
|
|
|
|
# Run inference
|
|
image = pipe(prompt).images[0]
|
|
image.save("dog.png")
|