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21 lines
1.3 KiB
Python
21 lines
1.3 KiB
Python
import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
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ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-ValueController", origin_file_pattern="single/prefer_embed/value.ckpt", offload_device="cpu", offload_dtype=torch.float8_e4m3fn)
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],
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)
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pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-ValueController", origin_file_pattern="single/dit_lora/dit_value.ckpt"))
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pipe.enable_vram_management()
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for i in range(10):
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image = pipe(prompt="a cat", seed=0, value_controller_inputs=[i/10])
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image.save(f"value_control_{i}.jpg") |