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[docs] refactor docs for easier info parsing (#175)

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* refactor docs for easier info parsing

* refactor readme

* anonym paths

* updates

* remove notes.

* add a toc.

* minor typos.

* move cog.md -> cogvideox.md

* add a note about the model-specific docs in training readme.

* add memory usage for CogVideoX.

Co-authored-by: a-r-r-o-w <contact.aryanvs@gmail.com>

* change to 5b from 2b for CogVideoX.

Co-authored-by: a-r-r-o-w <contact.aryanvs@gmail.com>

* more appropriate names.

* add headers to the model docs.

* fix adapter name

* minor

* updates

* fix cog training command example

---------

Co-authored-by: a-r-r-o-w <contact.aryanvs@gmail.com>
This commit is contained in:
Sayak Paul
2025-01-05 20:50:30 +05:30
committed by GitHub
parent b3d0ba8e12
commit eeb4dd7afa
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docs/training/README.md
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This directory contains the training-related specifications for all the models we support in `finetrainers`. Each model page has:
* an example training command
* inference example
* numbers on memory consumption
By default, we don't include any validation-related arguments in the example training commands. To enable validation inference, one can pass:
```diff
+ --validation_prompts "$ID_TOKEN A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions.@@@49x512x768:::$ID_TOKEN A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage@@@49x512x768" \
+ --num_validation_videos 1 \
+ --validation_steps 100
```
## Model-specific docs
* [CogVideoX](./cogvideox.md)
* [LTX-Video](./ltx_video.md)
* [HunyuanVideo](./hunyuan_video.md)
docs/training/cogvideox.md
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# CogVideoX
## Training
```bash
#!/bin/bash
export WANDB_MODE="offline"
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
export FINETRAINERS_LOG_LEVEL=DEBUG
GPU_IDS="0,1"
DATA_ROOT="/path/to/dataset"
CAPTION_COLUMN="prompt.txt"
VIDEO_COLUMN="videos.txt"
OUTPUT_DIR="/path/to/models/cog/"
ID_TOKEN="BW_STYLE"
# Model arguments
model_cmd="--model_name cogvideox \
--pretrained_model_name_or_path THUDM/CogVideoX-5b"
# Dataset arguments
dataset_cmd="--data_root $DATA_ROOT \
--video_column $VIDEO_COLUMN \
--caption_column $CAPTION_COLUMN \
--id_token $ID_TOKEN \
--video_resolution_buckets 49x480x720 \
--caption_dropout_p 0.05"
# Dataloader arguments
dataloader_cmd="--dataloader_num_workers 4"
# Training arguments
training_cmd="--training_type lora \
--seed 42 \
--mixed_precision bf16 \
--batch_size 1 \
--precompute_conditions \
--train_steps 1000 \
--rank 128 \
--lora_alpha 128 \
--target_modules to_q to_k to_v to_out.0 \
--gradient_accumulation_steps 1 \
--gradient_checkpointing \
--checkpointing_steps 200 \
--checkpointing_limit 2 \
--resume_from_checkpoint=latest \
--enable_slicing \
--enable_tiling"
# Optimizer arguments
optimizer_cmd="--optimizer adamw \
--use_8bit_bnb \
--lr 3e-5 \
--lr_scheduler constant_with_warmup \
--lr_warmup_steps 100 \
--lr_num_cycles 1 \
--beta1 0.9 \
--beta2 0.95 \
--weight_decay 1e-4 \
--epsilon 1e-8 \
--max_grad_norm 1.0"
# Miscellaneous arguments
miscellaneous_cmd="--tracker_name finetrainers-cog \
--output_dir $OUTPUT_DIR \
--nccl_timeout 1800 \
--report_to wandb"
cmd="accelerate launch --config_file accelerate_configs/deepspeed.yaml --gpu_ids $GPU_IDS train.py \
$model_cmd \
$dataset_cmd \
$dataloader_cmd \
$training_cmd \
$optimizer_cmd \
$miscellaneous_cmd"
echo "Running command: $cmd"
eval $cmd
echo -ne "-------------------- Finished executing script --------------------\n\n"
```
## Memory Usage
LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, `49x480x720` resolutions, **with precomputation**:
```
Training configuration: {
"trainable parameters": 132120576,
"total samples": 69,
"train epochs": 1,
"train steps": 10,
"batches per device": 1,
"total batches observed per epoch": 69,
"train batch size": 1,
"gradient accumulation steps": 1
}
```
| stage | memory_allocated | max_memory_reserved |
|:-----------------------------:|:-----------------:|:-------------------:|
| after precomputing conditions | 8.880 | 8.941 |
| after precomputing latents | 9.300 | 12.441 |
| before training start | 10.622 | 20.701 |
| after epoch 1 | 11.145 | 20.701 |
| before validation start | 11.145 | 20.702 |
| after validation end | 11.145 | 28.324 |
| after training end | 11.144 | 11.592 |
## Inference
Assuming your LoRA is saved and pushed to the HF Hub, and named `my-awesome-name/my-awesome-lora`, we can now use the finetuned model for inference:
```diff
import torch
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
pipe = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16
).to("cuda")
+ pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="cogvideox-lora")
+ pipe.set_adapters(["cogvideox-lora"], [0.75])
video = pipe("<my-awesome-prompt>").frames[0]
export_to_video(video, "output.mp4")
```
You can refer to the following guides to know more about performing LoRA inference in `diffusers`:
* [Load LoRAs for inference](https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference)
* [Merge LoRAs](https://huggingface.co/docs/diffusers/main/en/using-diffusers/merge_loras)
docs/training/hunyuan_video.md
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# HunyuanVideo
## Training
```bash
#!/bin/bash
export WANDB_MODE="offline"
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
export FINETRAINERS_LOG_LEVEL=DEBUG
GPU_IDS="0,1"
DATA_ROOT="/path/to/dataset"
CAPTION_COLUMN="prompts.txt"
VIDEO_COLUMN="videos.txt"
OUTPUT_DIR="/path/to/models/hunyuan-video/"
ID_TOKEN="afkx"
# Model arguments
model_cmd="--model_name hunyuan_video \
--pretrained_model_name_or_path hunyuanvideo-community/HunyuanVideo"
# Dataset arguments
dataset_cmd="--data_root $DATA_ROOT \
--video_column $VIDEO_COLUMN \
--caption_column $CAPTION_COLUMN \
--id_token $ID_TOKEN \
--video_resolution_buckets 17x512x768 49x512x768 61x512x768 \
--caption_dropout_p 0.05"
# Dataloader arguments
dataloader_cmd="--dataloader_num_workers 0"
# Diffusion arguments
diffusion_cmd=""
# Training arguments
training_cmd="--training_type lora \
--seed 42 \
--mixed_precision bf16 \
--batch_size 1 \
--train_steps 500 \
--rank 128 \
--lora_alpha 128 \
--target_modules to_q to_k to_v to_out.0 \
--gradient_accumulation_steps 1 \
--gradient_checkpointing \
--checkpointing_steps 500 \
--checkpointing_limit 2 \
--enable_slicing \
--enable_tiling"
# Optimizer arguments
optimizer_cmd="--optimizer adamw \
--lr 2e-5 \
--lr_scheduler constant_with_warmup \
--lr_warmup_steps 100 \
--lr_num_cycles 1 \
--beta1 0.9 \
--beta2 0.95 \
--weight_decay 1e-4 \
--epsilon 1e-8 \
--max_grad_norm 1.0"
# Miscellaneous arguments
miscellaneous_cmd="--tracker_name finetrainers-hunyuan-video \
--output_dir $OUTPUT_DIR \
--nccl_timeout 1800 \
--report_to wandb"
cmd="accelerate launch --config_file accelerate_configs/uncompiled_8.yaml --gpu_ids $GPU_IDS train.py \
$model_cmd \
$dataset_cmd \
$dataloader_cmd \
$diffusion_cmd \
$training_cmd \
$optimizer_cmd \
$miscellaneous_cmd"
echo "Running command: $cmd"
eval $cmd
echo -ne "-------------------- Finished executing script --------------------\n\n"
```
## Memory Usage
LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, `49x512x768` resolutions, **without precomputation**:
```
Training configuration: {
"trainable parameters": 163577856,
"total samples": 69,
"train epochs": 1,
"train steps": 10,
"batches per device": 1,
"total batches observed per epoch": 69,
"train batch size": 1,
"gradient accumulation steps": 1
}
```
| stage | memory_allocated | max_memory_reserved |
|:-----------------------:|:----------------:|:-------------------:|
| before training start | 38.889 | 39.020 |
| before validation start | 39.747 | 56.266 |
| after validation end | 39.748 | 58.385 |
| after epoch 1 | 39.748 | 40.910 |
| after training end | 25.288 | 40.910 |
Note: requires about `59` GB of VRAM when validation is performed.
LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, `49x512x768` resolutions, **with precomputation**:
```
Training configuration: {
"trainable parameters": 163577856,
"total samples": 1,
"train epochs": 10,
"train steps": 10,
"batches per device": 1,
"total batches observed per epoch": 1,
"train batch size": 1,
"gradient accumulation steps": 1
}
```
| stage | memory_allocated | max_memory_reserved |
|:-----------------------------:|:----------------:|:-------------------:|
| after precomputing conditions | 14.232 | 14.461 |
| after precomputing latents | 14.717 | 17.244 |
| before training start | 24.195 | 26.039 |
| after epoch 1 | 24.83 | 42.387 |
| before validation start | 24.842 | 42.387 |
| after validation end | 39.558 | 46.947 |
| after training end | 24.842 | 41.039 |
Note: requires about `47` GB of VRAM with validation. If validation is not performed, the memory usage is reduced to about `42` GB.
## Inference
Assuming your LoRA is saved and pushed to the HF Hub, and named `my-awesome-name/my-awesome-lora`, we can now use the finetuned model for inference:
```py
import torch
from diffusers import HunyuanVideoPipeline
import torch
from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
from diffusers.utils import export_to_video
model_id = "hunyuanvideo-community/HunyuanVideo"
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
model_id, subfolder="transformer", torch_dtype=torch.bfloat16
)
pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16)
pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="hunyuanvideo-lora")
pipe.set_adapters(["hunyuanvideo-lora"], [0.6])
pipe.vae.enable_tiling()
pipe.to("cuda")
output = pipe(
prompt="A cat walks on the grass, realistic",
height=320,
width=512,
num_frames=61,
num_inference_steps=30,
).frames[0]
export_to_video(output, "output.mp4", fps=15)
```
You can refer to the following guides to know more about performing LoRA inference in `diffusers`:
* [Load LoRAs for inference](https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference)
* [Merge LoRAs](https://huggingface.co/docs/diffusers/main/en/using-diffusers/merge_loras)
docs/training/ltx_video.md
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# LTX-Video
## Training
Provided you have a dataset:
```bash
#!/bin/bash
export WANDB_MODE="offline"
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
export FINETRAINERS_LOG_LEVEL=DEBUG
GPU_IDS="0,1"
DATA_ROOT="/path/to/dataset"
CAPTION_COLUMN="prompts.txt"
VIDEO_COLUMN="videos.txt"
OUTPUT_DIR="/path/to/models/ltx-video/"
ID_TOKEN="BW_STYLE"
# Model arguments
model_cmd="--model_name ltx_video \
--pretrained_model_name_or_path Lightricks/LTX-Video"
# Dataset arguments
dataset_cmd="--data_root $DATA_ROOT \
--video_column $VIDEO_COLUMN \
--caption_column $CAPTION_COLUMN \
--id_token $ID_TOKEN \
--video_resolution_buckets 49x512x768 \
--caption_dropout_p 0.05"
# Dataloader arguments
dataloader_cmd="--dataloader_num_workers 0"
# Diffusion arguments
diffusion_cmd="--flow_resolution_shifting"
# Training arguments
training_cmd="--training_type lora \
--seed 42 \
--mixed_precision bf16 \
--batch_size 1 \
--train_steps 1200 \
--rank 128 \
--lora_alpha 128 \
--target_modules to_q to_k to_v to_out.0 \
--gradient_accumulation_steps 1 \
--gradient_checkpointing \
--checkpointing_steps 500 \
--checkpointing_limit 2 \
--enable_slicing \
--enable_tiling"
# Optimizer arguments
optimizer_cmd="--optimizer adamw \
--lr 3e-5 \
--lr_scheduler constant_with_warmup \
--lr_warmup_steps 100 \
--lr_num_cycles 1 \
--beta1 0.9 \
--beta2 0.95 \
--weight_decay 1e-4 \
--epsilon 1e-8 \
--max_grad_norm 1.0"
# Miscellaneous arguments
miscellaneous_cmd="--tracker_name finetrainers-ltxv \
--output_dir $OUTPUT_DIR \
--nccl_timeout 1800 \
--report_to wandb"
cmd="accelerate launch --config_file accelerate_configs/uncompiled_2.yaml --gpu_ids $GPU_IDS train.py \
$model_cmd \
$dataset_cmd \
$dataloader_cmd \
$diffusion_cmd \
$training_cmd \
$optimizer_cmd \
$miscellaneous_cmd"
echo "Running command: $cmd"
eval $cmd
echo -ne "-------------------- Finished executing script --------------------\n\n"
```
## Memory Usage
LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, `49x512x768` resolution, **without precomputation**:
```
Training configuration: {
"trainable parameters": 117440512,
"total samples": 69,
"train epochs": 1,
"train steps": 10,
"batches per device": 1,
"total batches observed per epoch": 69,
"train batch size": 1,
"gradient accumulation steps": 1
}
```
| stage | memory_allocated | max_memory_reserved |
|:-----------------------:|:----------------:|:-------------------:|
| before training start | 13.486 | 13.879 |
| before validation start | 14.146 | 17.623 |
| after validation end | 14.146 | 17.623 |
| after epoch 1 | 14.146 | 17.623 |
| after training end | 4.461 | 17.623 |
Note: requires about `18` GB of VRAM without precomputation.
LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, `49x512x768` resolution, **with precomputation**:
```
Training configuration: {
"trainable parameters": 117440512,
"total samples": 1,
"train epochs": 10,
"train steps": 10,
"batches per device": 1,
"total batches observed per epoch": 1,
"train batch size": 1,
"gradient accumulation steps": 1
}
```
| stage | memory_allocated | max_memory_reserved |
|:-----------------------------:|:----------------:|:-------------------:|
| after precomputing conditions | 8.88 | 8.920 |
| after precomputing latents | 9.684 | 11.613 |
| before training start | 3.809 | 10.010 |
| after epoch 1 | 4.26 | 10.916 |
| before validation start | 4.26 | 10.916 |
| after validation end | 13.924 | 17.262 |
| after training end | 4.26 | 14.314 |
Note: requires about `17.5` GB of VRAM with precomputation. If validation is not performed, the memory usage is reduced to `11` GB.
## Inference
Assuming your LoRA is saved and pushed to the HF Hub, and named `my-awesome-name/my-awesome-lora`, we can now use the finetuned model for inference:
```diff
import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video
pipe = LTXPipeline.from_pretrained(
"Lightricks/LTX-Video", torch_dtype=torch.bfloat16
).to("cuda")
+ pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="ltxv-lora")
+ pipe.set_adapters(["ltxv-lora"], [0.75])
video = pipe("<my-awesome-prompt>").frames[0]
export_to_video(video, "output.mp4", fps=8)
```
You can refer to the following guides to know more about performing LoRA inference in `diffusers`:
* [Load LoRAs for inference](https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference)
* [Merge LoRAs](https://huggingface.co/docs/diffusers/main/en/using-diffusers/merge_loras)
docs/training/optimization.md
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To lower memory requirements during training:
- Use a DeepSpeed config to launch training (refer to [`accelerate_configs/deepspeed.yaml`](./accelerate_configs/deepspeed.yaml) as an example).
- Pass `--precompute_conditions` when launching training.
- Pass `--gradient_checkpointing` when launching training.
- Pass `--use_8bit_bnb` when launching training. Note that this is only applicable to Adam and AdamW optimizers.
- Do not perform validation/testing. This saves a significant amount of memory, which can be used to focus solely on training if you're on smaller VRAM GPUs.
We will continue to add more features that help to reduce memory consumption.