[docs] refactor docs for easier info parsing

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)