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171f557 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import torch
from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig
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",
}
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="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors", **vram_config),
],
vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)
lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors")
pipe.load_lora(pipe.dit, lora) # Use `pipe.clear_lora()` to drop the loaded LoRA.
# Empty prompt can automatically activate LoRA capabilities.
image = pipe(prompt="", seed=0, lora_encoder_inputs=lora)
image.save("image_1.jpg")
image = pipe(prompt="", seed=0)
image.save("image_1_origin.jpg")
# Prompt without trigger words can also activate LoRA capabilities.
image = pipe(prompt="a car", seed=0, lora_encoder_inputs=lora)
image.save("image_2.jpg")
image = pipe(prompt="a car", seed=0,)
image.save("image_2_origin.jpg")
# Adjust the activation intensity through the scale parameter.
image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=1.0)
image.save("image_3.jpg")
image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=0.5)
image.save("image_3_scale.jpg")
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