device_map = infer_auto_device_map(pipeline, max_memory={"cuda:0":"20GiB","cuda:1": "20GiB","cpu":"20GiB"}) # Move the pipeline modules to respective devices pipeline.to(device_map) 报错了,没有识别出cuda:0,错误是:ValueError: Device cuda:0 is not recognized, available devices are...
stabilityai/stable-diffusion-3-medium-diffusers"text_encoder = T5EncoderModel.from_pretrained( model_id, subfolder="text_encoder_3", quantization_config=quantization_config,)pipe = StableDiffusion3Pipeline.from_pretrained( model_id, text_encoder_3=text_encoder, device_map="balanced...
使用这个库,你也可以加载 8 比特量化版的 T5-XXL 模型,进一步减少显存需求。 importtorchfromdiffusersimportStableDiffusion3PipelinefromtransformersimportT5EncoderModel,BitsAndBytesConfig# Make sure you have `bitsandbytes` installed.quantization_config=BitsAndBytesConfig(load_in_8bit=True)model_id="stabilityai...
device_map="balanced", torch_dtype=torch.float16 ) 完整代码在这里。 显存优化小结 所有的基准测试都用了 2B 参数量的 SD3 模型,测试在一个 A100-80G 上进行,使用fp16精度推理,PyTorch 版本为 2.3。 我们对每个推理调用跑十次,记录平均峰值显存用量和 20 步采样的平均时长。 SD3 性能优化 为加速推理,我们...
(load_in_8bit=True)model_id="stabilityai/stable-diffusion-3-medium-diffusers"text_encoder=T5EncoderModel.from_pretrained(model_id,subfolder="text_encoder_3",quantization_config=quantization_config,)pipe=StableDiffusion3Pipeline.from_pretrained(model_id,text_encoder_3=text_encoder,device_map="...
device_map="balanced", torch_dtype=torch.float16 ) 完整代码在这里。 显存优化小结 所有的基准测试都用了 2B 参数量的 SD3 模型,测试在一个 A100-80G 上进行,使用fp16精度推理,PyTorch 版本为 2.3。 我们对每个推理调用跑十次,记录平均峰值显存用量和 20 步采样的平均时长。 SD3 性能优化 为加速推理,我们...
什么都不做ifnothasattr(self,"_all_hooks")orlen(self._all_hooks) ==0:# `enable_model_cpu_offload` 尚未被调用,因此静默返回return# 确保模型的状态与调用之前一致self.enable_model_cpu_offload(device=getattr(self,"_offload_device","cuda"))# 定义一个重置设备映射的函数defreset_device_map(self):...
Merge branch 'main' into lora-device-map Verified 1ed0eb0 Merge branch 'main' into lora-device-map Verified d2d59c3 Merge branch 'main' into lora-device-map Verified 5f3cae2 Merge branch 'main' into lora-device-map Verified 8f670e2 Merge branch 'main' into lora-device-map Verifi...
add_argument("--do_device_map", action="store_true") args = parser.parse_args() run_pipeline(args) Tested on the DGX (with accelerate installed from the source). CUDA_VISIBLE_DEVICES=1,2 python test_device_map_pipelines.py --num_inference_steps=50 VAE: tensor([0.2964, 0.2983, 0.3008...
[ INFO] - Already cached /home/aistudio/.paddlenlp/models/runwayml/stable-diffusion-v1-5/tokenizer/special_tokens_map.json [2023-03-23 17:07:17,541] [ INFO] - Already cached /home/aistudio/.paddlenlp/models/runwayml/stable-diffusion-v1-5/tokenizer/tokenizer_config.json [2023-03-23 ...