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update README_zh.md
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tunglinwood
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sat/README_zh.md
206
sat/README_zh.md
@ -10,13 +10,13 @@
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## 推理模型
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1. 确保你已经正确安装本文件夹中的要求的依赖
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### 1. 确保你已经正确安装本文件夹中的要求的依赖
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```shell
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pip install -r requirements.txt
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```
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2. 下载模型权重
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### 2. 下载模型权重
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首先，前往 SAT 镜像下载依赖。
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@ -43,10 +43,17 @@ unzip transformer.zip
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└── 3d-vae.pt
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```
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接着，克隆 T5 模型，该模型不用做训练和微调，但是必须使用。
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由于模型的权重档案较大，建议使用`git lfs`。`git lfs`安装参见[这里](https://github.com/git-lfs/git-lfs?tab=readme-ov-file#installing)
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```shell
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git lfs install
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```
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git clone https://huggingface.co/THUDM/CogVideoX-2b.git
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接着，克隆 T5 模型，该模型不用做训练和微调，但是必须使用。
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> 克隆模型的时候也可以使用[Modelscope](https://modelscope.cn/models/ZhipuAI/CogVideoX-2b)上的模型文件位置。
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```shell
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git clone https://huggingface.co/THUDM/CogVideoX-2b.git #从huggingface下载模型
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# git clone https://www.modelscope.cn/ZhipuAI/CogVideoX-2b.git #从modelscope下载模型
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mkdir t5-v1_1-xxl
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mv CogVideoX-2b/text_encoder/* CogVideoX-2b/tokenizer/* t5-v1_1-xxl
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```
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@ -66,29 +73,182 @@ mv CogVideoX-2b/text_encoder/* CogVideoX-2b/tokenizer/* t5-v1_1-xxl
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0 directories, 8 files
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```
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3. 修改`configs/cogvideox_2b_infer.yaml`中的文件。
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### 3. 修改`configs/cogvideox_2b.yaml`中的文件。
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```yaml
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load: "{your_CogVideoX-2b-sat_path}/transformer" ## Transformer 模型路径
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model:
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scale_factor: 1.15258426
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disable_first_stage_autocast: true
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log_keys:
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- txt
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conditioner_config:
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target: sgm.modules.GeneralConditioner
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params:
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emb_models:
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- is_trainable: false
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input_key: txt
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ucg_rate: 0.1
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target: sgm.modules.encoders.modules.FrozenT5Embedder
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denoiser_config:
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target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
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params:
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num_idx: 1000
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quantize_c_noise: False
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weighting_config:
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target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
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scaling_config:
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target: sgm.modules.diffusionmodules.denoiser_scaling.VideoScaling
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discretization_config:
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target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
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params:
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model_dir: "google/t5-v1_1-xxl" ## T5 模型路径
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max_length: 226
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shift_scale: 3.0
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first_stage_config:
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target: sgm.models.autoencoder.VideoAutoencoderInferenceWrapper
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params:
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cp_size: 1
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ckpt_path: "{your_CogVideoX-2b-sat_path}/vae/3d-vae.pt" ## VAE 模型路径
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network_config:
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target: dit_video_concat.DiffusionTransformer
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params:
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time_embed_dim: 512
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elementwise_affine: True
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num_frames: 49
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time_compressed_rate: 4
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latent_width: 90
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latent_height: 60
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num_layers: 30
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patch_size: 2
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in_channels: 16
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out_channels: 16
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hidden_size: 1920
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adm_in_channels: 256
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num_attention_heads: 30
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transformer_args:
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checkpoint_activations: True ## using gradient checkpointing
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vocab_size: 1
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max_sequence_length: 64
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layernorm_order: pre
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skip_init: false
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model_parallel_size: 1
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is_decoder: false
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modules:
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pos_embed_config:
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target: dit_video_concat.Basic3DPositionEmbeddingMixin
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params:
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text_length: 226
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height_interpolation: 1.875
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width_interpolation: 1.875
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patch_embed_config:
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target: dit_video_concat.ImagePatchEmbeddingMixin
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params:
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text_hidden_size: 4096
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adaln_layer_config:
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target: dit_video_concat.AdaLNMixin
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params:
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qk_ln: True
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final_layer_config:
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target: dit_video_concat.FinalLayerMixin
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conditioner_config:
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target: sgm.modules.GeneralConditioner
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params:
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emb_models:
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- is_trainable: false
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input_key: txt
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ucg_rate: 0.1
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target: sgm.modules.encoders.modules.FrozenT5Embedder
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params:
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model_dir: "{absolute_path/to/your/t5-v1_1-xxl}/t5-v1_1-xxl" # CogVideoX-2b/t5-v1_1-xxl权重文件夹的绝对路径
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max_length: 226
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first_stage_config:
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target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
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params:
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cp_size: 1
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ckpt_path: "{absolute_path/to/your/t5-v1_1-xxl}/CogVideoX-2b-sat/vae/3d-vae.pt" # CogVideoX-2b-sat/vae/3d-vae.pt文件夹的绝对路径
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ignore_keys: [ 'loss' ]
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loss_config:
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target: torch.nn.Identity
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regularizer_config:
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target: vae_modules.regularizers.DiagonalGaussianRegularizer
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encoder_config:
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target: vae_modules.cp_enc_dec.ContextParallelEncoder3D
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params:
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double_z: true
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z_channels: 16
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [ 1, 2, 2, 4 ]
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attn_resolutions: [ ]
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num_res_blocks: 3
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dropout: 0.0
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gather_norm: True
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decoder_config:
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target: vae_modules.cp_enc_dec.ContextParallelDecoder3D
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params:
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double_z: True
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z_channels: 16
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [ 1, 2, 2, 4 ]
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attn_resolutions: [ ]
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num_res_blocks: 3
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dropout: 0.0
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gather_norm: False
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loss_fn_config:
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target: sgm.modules.diffusionmodules.loss.VideoDiffusionLoss
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params:
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offset_noise_level: 0
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sigma_sampler_config:
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target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
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params:
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uniform_sampling: True
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num_idx: 1000
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discretization_config:
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target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
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params:
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shift_scale: 3.0
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sampler_config:
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target: sgm.modules.diffusionmodules.sampling.VPSDEDPMPP2MSampler
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params:
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num_steps: 50
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verbose: True
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discretization_config:
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target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
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params:
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shift_scale: 3.0
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guider_config:
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target: sgm.modules.diffusionmodules.guiders.DynamicCFG
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params:
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scale: 6
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exp: 5
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num_steps: 50
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```
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### 4. 修改`configs/inference.yaml`中的文件。
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```yaml
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args:
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latent_channels: 16
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mode: inference
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load: "{absolute_path/to/your}/transformer" # CogVideoX-2b-sat/transformer文件夹的绝对路径
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# load: "{your lora folder} such as zRzRzRzRzRzRzR/lora-disney-08-20-13-28" # This is for Full model without lora adapter
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batch_size: 1
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input_type: txt #可以选择txt纯文字档作为输入，或者改成cli命令行作为输入
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input_file: configs/test.txt #纯文字档，可以对此做编辑
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sampling_num_frames: 13 # Must be 13, 11 or 9
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sampling_fps: 8
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fp16: True # For CogVideoX-2B
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# bf16: True # For CogVideoX-5B
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output_dir: outputs/
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force_inference: True
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```
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+ 如果使用 txt 保存多个提示词，请参考`configs/test.txt`
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@ -109,7 +269,7 @@ output_dir: outputs/
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默认保存在`.outputs/`文件夹下。
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4. 运行推理代码,即可推理
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### 5. 运行推理代码, 即可推理
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```shell
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bash inference.sh
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