mirror of https://github.com/hpcaitech/ColossalAI
fix README
Fazzie
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# ColoDiffusion: Stable Diffusion with Colossal-AI
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Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).
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<p id="diffusion_train" align="center">
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@ -57,14 +56,19 @@ pip install transformers==4.19.2 diffusers invisible-watermark
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pip install -e .
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```
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##### Step 2: install lightning
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#### Step 2: install lightning
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Install Lightning version later than 2022.01.04. We suggest you install lightning from source.
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https://github.com/Lightning-AI/lightning.git
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```
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git clone https://github.com/Lightning-AI/lightning.git
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pip install -r requirements.txt
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python setup.py install
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```
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#### Step 3:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website
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##### Step 3:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website
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##### From pip
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For example, you can install v0.1.12 from our official website.
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@ -72,6 +76,16 @@ For example, you can install v0.1.12 from our official website.
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pip install colossalai==0.1.12+torch1.12cu11.3 -f https://release.colossalai.org
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```
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##### From source
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```
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git clone https://github.com/hpcaitech/ColossalAI.git
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cd ColossalAI
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# install colossalai
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CUDA_EXT=1 pip install .
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```
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### Option #2: Use Docker
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To use the stable diffusion Docker image, you can either build using the provided the [Dockerfile](./docker/Dockerfile) or pull a Docker image from our Docker hub.
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@ -122,6 +136,12 @@ It is important for you to configure your volume mapping in order to get the bes
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## Download the model checkpoint from pretrained
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### stable-diffusion-v2-base
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```
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wget https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt
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```
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### stable-diffusion-v1-4
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Our default model config use the weight from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4?text=A+mecha+robot+in+a+favela+in+expressionist+style)
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@ -110,6 +110,7 @@ lightning:
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enable_distributed_storage: True
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placement_policy: cuda
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force_outputs_fp32: true
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min_chunk_size: 64
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log_every_n_steps: 2
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logger: True
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@ -107,6 +107,7 @@ lightning:
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enable_distributed_storage: True
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placement_policy: cuda
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force_outputs_fp32: true
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min_chunk_size: 64
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log_every_n_steps: 2
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logger: True
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@ -111,6 +111,7 @@ lightning:
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enable_distributed_storage: True
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placement_policy: cuda
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force_outputs_fp32: true
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min_chunk_size: 64
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log_every_n_steps: 2
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logger: True
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@ -1,120 +0,0 @@
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model:
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base_learning_rate: 1.0e-4
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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parameterization: "v"
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: image
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cond_stage_key: txt
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image_size: 64
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channels: 4
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cond_stage_trainable: false
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False # we set this to false because this is an inference only config
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1.e-4 ]
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f_min: [ 1.e-10 ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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use_checkpoint: True
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use_fp16: True
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_head_channels: 64 # need to fix for flash-attn
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use_spatial_transformer: True
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use_linear_in_transformer: True
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transformer_depth: 1
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context_dim: 1024
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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#attn_type: "vanilla-xformers"
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double_z: true
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z_channels: 4
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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:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
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params:
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freeze: True
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layer: "penultimate"
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 32
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wrap: False
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train:
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target: ldm.data.pokemon.PokemonDataset
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# params:
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# file_path: "/data/scratch/diffuser/laion_part0/"
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# world_size: 1
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# rank: 0
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lightning:
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trainer:
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accelerator: 'gpu'
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devices: 1
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log_gpu_memory: all
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max_epochs: 2
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precision: 16
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auto_select_gpus: False
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strategy:
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target: strategies.ColossalAIStrategy
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params:
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use_chunk: True
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enable_distributed_storage: True
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placement_policy: cuda
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force_outputs_fp32: true
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log_every_n_steps: 2
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logger: True
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default_root_dir: "/tmp/diff_log/"
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# profiler: pytorch
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logger_config:
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wandb:
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target: loggers.WandbLogger
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params:
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name: nowname
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save_dir: "/tmp/diff_log/"
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offline: opt.debug
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id: nowname
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@ -1,6 +1,5 @@
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python scripts/txt2img.py --prompt "Teyvat, Name:Layla, Element: Cryo, Weapon:Sword, Region:Sumeru, Model type:Medium Female, Description:a woman in a blue outfit holding a sword" --plms \
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--outdir ./output \
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--config /home/lcmql/data2/Genshin/2022-11-18T16-38-46_train_colossalai_teyvattest/checkpoints/last.ckpt \
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--ckpt /home/lcmql/data2/Genshin/2022-11-18T16-38-46_train_colossalai_teyvattest/configs/2022-11-18T16-38-46-project.yaml \
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--ckpt /tmp/2022-11-18T16-38-46_train_colossalai/checkpoints/last.ckpt \
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--config /tmp/2022-11-18T16-38-46_train_colossalai/configs/2022-11-18T16-38-46-project.yaml \
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--n_samples 4
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@ -2,4 +2,4 @@ HF_DATASETS_OFFLINE=1
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TRANSFORMERS_OFFLINE=1
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DIFFUSERS_OFFLINE=1
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python main.py --logdir /tmp -t -b /configs/train_colossalai.yaml
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python main.py --logdir /tmp -t -b configs/train_colossalai.yaml
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