DiffSynth-Studio/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py at main · modelscope/DiffSynth-Studio · GitHub

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import torch
from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig, ControlNetInput
from diffsynth.utils.controlnet import Annotator
from modelscope import snapshot_download


vram_config = {
    "offload_dtype": torch.float8_e4m3fn,
    "offload_device": "cpu",
    "onload_dtype": torch.float8_e4m3fn,
    "onload_device": "cpu",
    "preparing_dtype": torch.float8_e4m3fn,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}
snapshot_download("sd_lora/Annotators", allow_file_pattern="dpt_hybrid-midas-501f0c75.pt", local_dir="models/Annotators")
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", **vram_config),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors", **vram_config),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", **vram_config),
        ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors", **vram_config),
    ],
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)

image_1 = pipe(
    prompt="a beautiful Asian girl, full body, red dress, summer",
    height=1024, width=1024,
    seed=6, rand_device="cuda",
)
image_1.save("image_1.jpg")

image_canny = Annotator("canny")(image_1)
image_depth = Annotator("depth")(image_1)

image_2 = pipe(
    prompt="a beautiful Asian girl, full body, red dress, winter",
    controlnet_inputs=[
        ControlNetInput(image=image_canny, scale=0.3, processor_id="canny"),
        ControlNetInput(image=image_depth, scale=0.3, processor_id="depth"),
    ],
    height=1024, width=1024,
    seed=7, rand_device="cuda",
)
image_2.save("image_2.jpg")