diff --git a/sat/README_zh.md b/sat/README_zh.md index 807b133..114fb82 100644 --- a/sat/README_zh.md +++ b/sat/README_zh.md @@ -10,13 +10,13 @@ ## 推理模型 -1. 确保你已经正确安装本文件夹中的要求的依赖 +### 1. 确保你已经正确安装本文件夹中的要求的依赖 ```shell pip install -r requirements.txt ``` -2. 下载模型权重 +### 2. 下载模型权重 首先，前往 SAT 镜像下载依赖。 @@ -43,10 +43,17 @@ unzip transformer.zip └── 3d-vae.pt ``` -接着，克隆 T5 模型，该模型不用做训练和微调，但是必须使用。 - +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) +```shell +git lfs install ``` -git clone https://huggingface.co/THUDM/CogVideoX-2b.git + +接着，克隆 T5 模型，该模型不用做训练和微调，但是必须使用。 +> 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。 + +```shell +git clone https://huggingface.co/THUDM/CogVideoX-2b.git #从huggingface下载模型 +# git clone https://www.modelscope.cn/ZhipuAI/CogVideoX-2b.git #从modelscope下载模型 mkdir t5-v1_1-xxl mv CogVideoX-2b/text_encoder/* CogVideoX-2b/tokenizer/* t5-v1_1-xxl ``` @@ -66,29 +73,182 @@ mv CogVideoX-2b/text_encoder/* CogVideoX-2b/tokenizer/* t5-v1_1-xxl 0 directories, 8 files ``` -3. 修改`configs/cogvideox_2b_infer.yaml`中的文件。 +### 3. 修改`configs/cogvideox_2b.yaml`中的文件。 ```yaml -load: "{your_CogVideoX-2b-sat_path}/transformer" ## Transformer 模型路径 +model: + scale_factor: 1.15258426 + disable_first_stage_autocast: true + log_keys: + - txt -conditioner_config: - target: sgm.modules.GeneralConditioner - params: - emb_models: - - is_trainable: false - input_key: txt - ucg_rate: 0.1 - target: sgm.modules.encoders.modules.FrozenT5Embedder + denoiser_config: + target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser + params: + num_idx: 1000 + quantize_c_noise: False + + weighting_config: + target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + target: sgm.modules.diffusionmodules.denoiser_scaling.VideoScaling + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization params: - model_dir: "google/t5-v1_1-xxl" ## T5 模型路径 - max_length: 226 + shift_scale: 3.0 -first_stage_config: - target: sgm.models.autoencoder.VideoAutoencoderInferenceWrapper - params: - cp_size: 1 - ckpt_path: "{your_CogVideoX-2b-sat_path}/vae/3d-vae.pt" ## VAE 模型路径 + network_config: + target: dit_video_concat.DiffusionTransformer + params: + time_embed_dim: 512 + elementwise_affine: True + num_frames: 49 + time_compressed_rate: 4 + latent_width: 90 + latent_height: 60 + num_layers: 30 + patch_size: 2 + in_channels: 16 + out_channels: 16 + hidden_size: 1920 + adm_in_channels: 256 + num_attention_heads: 30 + transformer_args: + checkpoint_activations: True ## using gradient checkpointing + vocab_size: 1 + max_sequence_length: 64 + layernorm_order: pre + skip_init: false + model_parallel_size: 1 + is_decoder: false + + modules: + pos_embed_config: + target: dit_video_concat.Basic3DPositionEmbeddingMixin + params: + text_length: 226 + height_interpolation: 1.875 + width_interpolation: 1.875 + + patch_embed_config: + target: dit_video_concat.ImagePatchEmbeddingMixin + params: + text_hidden_size: 4096 + + adaln_layer_config: + target: dit_video_concat.AdaLNMixin + params: + qk_ln: True + + final_layer_config: + target: dit_video_concat.FinalLayerMixin + + conditioner_config: + target: sgm.modules.GeneralConditioner + params: + emb_models: + - is_trainable: false + input_key: txt + ucg_rate: 0.1 + target: sgm.modules.encoders.modules.FrozenT5Embedder + params: + 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 + max_length: 226 + + first_stage_config: + target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper + params: + cp_size: 1 + 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 + ignore_keys: [ 'loss' ] + + loss_config: + target: torch.nn.Identity + + regularizer_config: + target: vae_modules.regularizers.DiagonalGaussianRegularizer + + encoder_config: + target: vae_modules.cp_enc_dec.ContextParallelEncoder3D + params: + double_z: true + z_channels: 16 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1, 2, 2, 4 ] + attn_resolutions: [ ] + num_res_blocks: 3 + dropout: 0.0 + gather_norm: True + + decoder_config: + target: vae_modules.cp_enc_dec.ContextParallelDecoder3D + params: + double_z: True + z_channels: 16 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1, 2, 2, 4 ] + attn_resolutions: [ ] + num_res_blocks: 3 + dropout: 0.0 + gather_norm: False + + loss_fn_config: + target: sgm.modules.diffusionmodules.loss.VideoDiffusionLoss + params: + offset_noise_level: 0 + sigma_sampler_config: + target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling + params: + uniform_sampling: True + num_idx: 1000 + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization + params: + shift_scale: 3.0 + + sampler_config: + target: sgm.modules.diffusionmodules.sampling.VPSDEDPMPP2MSampler + params: + num_steps: 50 + verbose: True + + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization + params: + shift_scale: 3.0 + + guider_config: + target: sgm.modules.diffusionmodules.guiders.DynamicCFG + params: + scale: 6 + exp: 5 + num_steps: 50 +``` + +### 4. 修改`configs/inference.yaml`中的文件。 +```yaml +args: + latent_channels: 16 + mode: inference + load: "{absolute_path/to/your}/transformer" # CogVideoX-2b-sat/transformer文件夹的绝对路径 + # load: "{your lora folder} such as zRzRzRzRzRzRzR/lora-disney-08-20-13-28" # This is for Full model without lora adapter + + batch_size: 1 + 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 + input_file: configs/test.txt #纯文字档，可以对此做编辑 + sampling_num_frames: 13 # Must be 13, 11 or 9 + sampling_fps: 8 + fp16: True # For CogVideoX-2B +# bf16: True # For CogVideoX-5B + output_dir: outputs/ + force_inference: True ``` + 如果使用 txt 保存多个提示词，请参考`configs/test.txt` @@ -109,7 +269,7 @@ output_dir: outputs/ 默认保存在`.outputs/`文件夹下。 -4. 运行推理代码,即可推理 +### 5. 运行推理代码, 即可推理 ```shell bash inference.sh