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import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
from diffsynth import load_state_dict
from PIL import Image
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"),
],
)
state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Union-alpha_full/epoch-0.safetensors")
pipe.controlnet.models[0].load_state_dict(state_dict)
image = pipe(
prompt="a dog",
controlnet_inputs=[ControlNetInput(
image=Image.open("data/example_image_dataset/canny/image_1.jpg"),
scale=0.9,
processor_id="canny",
)],
height=768, width=768,
seed=0, rand_device="cuda",
)
image.save("image_FLUX.1-dev-Controlnet-Union-alpha_full.jpg")
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