昨日尝试训练了一个拜年动作的XL大模型, 并使用lora分层控制 Lora Block Weight,使两个原本会互相影响的模型能融洽的一同使用, 这里案例使用的是拜年动作lora和迪奥娜lora, 这是测试不同分区下拜年动作的作用情况 可以看到在SD-MOUT:1,0,0,0,0,0,1,1,1,1,1,1,1,1,0.5,0,0/SD-MOUT 此时即可获得较好的...
有大佬知道lora-..或者说支持小马模型吗?我发现好像这个插件没法准确控制小马的lora,就算输入的参数是ALL:1和不用插件出来的都不是一张图。
SDXL LoRA模型训练参数配置-炼丹新手入门喂 云上A4000训练失败,错误信息 size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]). size mismatch ...
SDXL_Spatial Transformer_X模块:由GroupNorm+Linear+X个BasicTransformer Block+Linear构成,同时ResNet模型的“残差结构”依旧没有缺席。 SDXL_DownBlock模块:由两个ResNetBlock+一个DownSample组成。 SDXL_UpBlock_X模块:由X个ResNetBlock模块组成。 CrossAttnDownBlock_X_K模块:是Stable Diffusion XL Base U-Net...
cell3:进行lora训练: !accelerate launch /workspace/diffusers/examples/dreambooth/train_dreambooth_lora_sdxl.py \ --pretrained_model_name_or_path=stabilityai \ --instance_data_dir=/project/data/toy-jensen \ --output_dir=/project/models/tuned-toy-jensen \ ...
num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps, ) optimizer_class = paddle.optimizer.AdamW optimizer = optimizer_class( learning_rate=lr_scheduler, parameters=unet.mid_block.parameters(), beta1=args.adam_beta1, beta2=args.adam_beta2, weight_decay=0.01, epsi...
- See [LoRA Block Weight](https://github.com/hako-mikan/sd-webui-lora-block-weight) by hako-mikan for details on LBW. - These will be included in the next release.- `sdxl_merge_lora.py` がOFT をサポートされました。PR [#1580](https://github.com/kohya-ss/sd-scripts/pull/1580...
param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight" return param_name in state_dict def is_animatediff_xl(self, state_dict): param_name = "up_blocks.2.motion_modules.2.temporal_transformer.transformer_blocks.0.ff_norm.weight" return param_name in state_dict def...
apply_snr_weight, prepare_scheduler_for_custom_training, scale_v_prediction_loss_like_noise_prediction, add_v_prediction_like_loss, ) from library.sdxl_original_unet import SdxlUNet2DConditionModel UNET_NUM_BLOCKS_FOR_BLOCK_LR = 23 def...
mid_block.to(dtype) @property def guidance_scale(self): return self._guidance_scale @property def clip_skip(self): return self._clip_skip # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/...