Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 2,534 Bytes
7f921f4 | 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 | #!/bin/bash
export CUDA_VISIBLE_DEVICES=4
# 定义日志文件名
LOG_FILE="eval_cons_diode.txt"
# 清空旧日志(可选,若想保留历史可删除此行)
> "$LOG_FILE"
# 定义要执行的所有命令(数组形式,每个元素是完整的 python 命令)
COMMANDS=(
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 2700 --state_dict models/train/kontext/flux/uni/step-2700.safetensors --datasets diode --model_root /mnt/nfs/share_model/FLUX.1-dev"
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 1800 --state_dict models/train/kontext/flux/sqrt/step-1800.safetensors --datasets diode --model_root /mnt/nfs/share_model/FLUX.1-dev"
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 3050 --state_dict models/train/kontext/flux/uni_cons/step-3050.safetensors --datasets diode --model_root /mnt/nfs/share_model/FLUX.1-dev"
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 2200 --state_dict models/train/kontext/flux/sqrt/step-2200.safetensors --datasets diode --model_root /mnt/nfs/share_model/FLUX.1-dev"
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 3800 --state_dict models/train/kontext/bs64_mask/step-3800.safetensors --datasets diode"
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 1850 --state_dict models/train/kontext/bs64_sqrt_mask/step-1850.safetensors --datasets diode"
"python diffsynth/utils/eval_multiple_datasets.py --cur_step 4000 --state_dict models/train/kontext/bs64_mask/step-4000.safetensors --datasets diode"
# "python diffsynth/utils/eval_multiple_datasets.py --cur_step 5300 --state_dict models/train/kontext/bs64_sqrt_cons/step-5300.safetensors --datasets diode"
)
# 循环执行每个命令
for cmd in "${COMMANDS[@]}"; do
# 从命令中提取 state_dict 的值(正则匹配 --state_dict 后的路径)
state_dict=$(echo "$cmd" | grep -oP '(?<=--state_dict\s)\S+')
# 打印提示信息(可选,方便实时查看进度)
echo "正在执行:$cmd"
echo "对应的 state_dict:$state_dict"
# 执行命令,捕获所有输出(stdout + stderr),并按格式追加到日志文件
# 格式:[state_dict 值] 原始输出内容(每行都添加前缀)
$cmd 2>&1 | while IFS= read -r line; do
echo "[$state_dict] $line" >> "$LOG_FILE"
done
# 打印分隔符(可选,方便日志文件中区分不同命令的输出)
echo "----------------------------------------" >> "$LOG_FILE"
done
echo "所有命令执行完毕,日志已保存到 $LOG_FILE" |