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use python run.py config/whatever_you_want.yml works,but use The following code loads lora weight error,be killed
from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("/ai/FLUX.1-dev", torch_dtype=torch.bfloat16) pipeline.enable_model_cpu_offload() pipeline.load_lora_weights('/ai/ai-toolkit/output/my_first_flux_lora_v1', weight_name='my_first_flux_lora_v1_000001000.safetensors') image = pipeline('a Yarn art style tarot card').images[0]
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@lonngxiang I think even with CPU offloading 24GB VRAM wouldn't be enough to get you there for inference without CUDA OOM.
16.5GB total - 4.5 for text encoder = 12 * 2bits = 24. OOM happens at around 95% (22.8GB) I think.
Just a guess here.
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maybe you can try smaller weight and height param (such as 512) in pipline.
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use python run.py config/whatever_you_want.yml works,but use The following code loads lora weight error,be killed
The text was updated successfully, but these errors were encountered: