build_nunchaku/src/examples/flux.1-dev-IP-adapter.py

import torch
from diffusers import FluxPipeline
from diffusers.utils import load_image

from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.caching.diffusers_adapters import apply_cache_on_pipe
from nunchaku.models.ip_adapter.diffusers_adapters import apply_IPA_on_pipe
from nunchaku.utils import get_precision

precision = get_precision()
transformer = NunchakuFluxTransformer2dModel.from_pretrained(
    f"nunchaku-tech/nunchaku-flux.1-dev/svdq-{precision}_r32-flux.1-dev.safetensors"
)
pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")

pipeline.load_ip_adapter(
    pretrained_model_name_or_path_or_dict="XLabs-AI/flux-ip-adapter-v2",
    weight_name="ip_adapter.safetensors",
    image_encoder_pretrained_model_name_or_path="openai/clip-vit-large-patch14",
)

apply_IPA_on_pipe(pipeline, ip_adapter_scale=1.1, repo_id="XLabs-AI/flux-ip-adapter-v2")

apply_cache_on_pipe(
    pipeline,
    use_double_fb_cache=True,
    residual_diff_threshold_multi=0.09,
    residual_diff_threshold_single=0.12,
)

IP_image = load_image(
    "https://huggingface.co/datasets/nunchaku-tech/test-data/resolve/main/ComfyUI-nunchaku/inputs/monalisa.jpg"
)

image = pipeline(
    prompt="holding an sign saying 'SVDQuant is fast!'",
    ip_adapter_image=IP_image.convert("RGB"),
    num_inference_steps=50,
).images[0]

image.save(f"flux.1-dev-IP-adapter-{precision}.png")