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from diffsynth import ModelManager, download_models, FluxImagePipeline
import torch
# Download models (automatically)
# `models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin`: [link](https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter/blob/main/ip-adapter.bin)
# `models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder`: [link](https://huggingface.co/google/siglip-so400m-patch14-384)
download_models(["InstantX/FLUX.1-dev-IP-Adapter", "FLUX.1-dev"])
# Load models
model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda")
model_manager.load_models([
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
"models/FLUX/FLUX.1-dev/text_encoder_2",
"models/FLUX/FLUX.1-dev/ae.safetensors",
"models/FLUX/FLUX.1-dev/flux1-dev.safetensors",
])
seed = 42
pipe = FluxImagePipeline.from_model_manager(model_manager)
torch.manual_seed(seed)
origin_prompt = "a rabbit in a garden, colorful flowers"
image = pipe(
prompt=origin_prompt,
cfg_scale=1.0, embedded_guidance=3.5,
height=1280, width=960, num_inference_steps=30
)
image.save("style image.jpg")
torch.manual_seed(seed)
image = pipe(
prompt="A piggy",
cfg_scale=1.0, embedded_guidance=3.5,
height=1280, width=960, num_inference_steps=30,
ipadapter_images=[image], ipadapter_scale=0.7
)
image.save("A piggy.jpg")
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