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Add All Multimodal Source Code Part 2: Text to image, x to nerf (#7970)
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* Update README.md: output_path --> output_manifest_filepath (#7442)

Signed-off-by: Samuele Cornell <cornellsamuele@gmail.com>

* Updating FlashAttention API to match FlashAttentionV2

* Multiple fixes for mm

* Fix CI inductor issue and update to torch compile

* Remove suppress error

* Fix when conversion config uses fp16 and it complains about precision plugin

* Fixing FAv2 API usage

* Initial release of content filtering model

* Added synthetic dataloader for precached and online mode

* Mingyuanm/dreambooth opt

* Add llama2 support in neva training

* Fix sampler length

* Fix all precision issues in nemo multimodal

* Add rope dynamic linear scaling (#7437)

* Add dynamic linear scaling

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* Fix bug

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* Fix None dataloader issue in PTL2.0 (#7455)

* Fix None dataloader issue in PTL2.0

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* updating values of self._validation_dl and self._test_dl as well

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* updating values of self._validation_dl and self._test_dl as well

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* [ASR] Confidence measure -> method renames (#7434)

* measure -> method

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* Add steps for document of getting dataset 'SF Bilingual Speech' (#7378)

* Add steps for document of getting dataset 'SF Bilingual Speech'

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* Update datasets.rst

added a link from a tutorial demonstrating detailed data prep steps.

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* RNN-T confidence and alignment bugfix (#7381)

* new frame_confidence and alignments lists are now always created after the while loop

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* tests added

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* Fix resume from checkpoint in exp_manager (#7424) (#7426)

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* Fix checking of cuda/cpu device for inputs of Decoder (#7444)

* Fix checking of cuda/cpu device for inputs of Decoder

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* Update tacotron2.py

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* Fix failure of ljspeech's get_data.py (#7430)

* Fix failure of ljspeech's get_data.py

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* [TTS] Fix audio codec type checks (#7373)

* [TTS] Fix audio codec type checks

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* [TTS] Fix audio codec tests

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* [TTS] Add dataset to path of logged artifacts (#7462)

* [TTS] Add dataset to path of logged artifacts

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* [TTS] Revert axis name back to Audio Frames

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* Fix sft dataset truncation (#7464)

* Add fix

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* Automatic Lip Reading Recognition (ALR) - ASR/CV (Visual ASR) (#7330)

* striding_conv1d_k5 and dw_striding_conv1d_k5 subsampling

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* video manifest

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* add collection classes

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* add test_step_outputs

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* correct manifest bug when having only audio or only videos

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* correct manifest bug when having only audio or only videos

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* clean references

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* freeze unfreeze transcribe cv models

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* correct manifest get_full_path bug

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* update for PR

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* guard torchvision

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* Update nemo/collections/cv/data/video_to_text_dataset.py

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* _video_speech_collate_fn in cv/data/video_to_text.py

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* add self.out = None to asr subsampling

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* cv -> multimodal/speech_cv branch

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* HF StarCoder to NeMo conversion script (#7421)

* Script to convert HF StarCoder checkpoint to NeMo

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* StarCoder conversion test

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* Fix test

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* Don't abbreviate args for clarity

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* fix bug when loading dist ckpt in peft (#7452)

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* Fix adding positional embeddings in-place in transformer module (#7440)

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* Fix (#7478)

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* add sleep (#7498) (#7499)

* add sleep



* add sleep onto config instead



* add comment



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* Fix exp manager check for sleep (#7503) (#7504)

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* bugfix: trainer.accelerator=auto from None. (#7492) (#7493)

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* [doc] fix broken link (#7481)

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* [TTS] Read audio as int32 to avoid flac read errors (#7477)

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* Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS (#7409)

* Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS
* Train 'AISHELL-3' dataset with multi-speakers

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* Update get_data.py

update copyright header

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* Add new configuration file for AISHELL3 with multispeaker of fastpitch

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* dllogger - log on rank 0 only (#7513)

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* Fix TTS FastPitch tutorial (#7494) (#7516)

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* Fix get_dist() tensor dimension (#7506) (#7515)

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* fix (#7511)

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* Add the `strategy` argument to `MegatronGPTModel.generate()` (#7264)

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2 changes: 1 addition & 1 deletion examples/multimodal/convert_ckpt_to_nemo.py
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Expand Up @@ -36,7 +36,7 @@
from nemo.collections.multimodal.models.text_to_image.imagen import MegatronImagen
from nemo.collections.multimodal.models.text_to_image.instruct_pix2pix.ldm.ddpm_edit import MegatronLatentDiffusionEdit
from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion
from nemo.collections.multimodal.models.vision_language_foundation.clip import MegatronCLIPModel
from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel
from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder
from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
from nemo.utils import AppState, logging
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name: stable-diffusion-train

infer:
unconditional_guidance_scale: 3
num_images_per_prompt: 4
hint_image_size: 512
height: 512
width: 512
down_factor: 8
inference_steps: 50
sampler_type: 'DDIM'
eta: 0
output_type: 'pil'
save_to_file: True
out_path: 'controlnet'
seed: 355
prompts:
- high quality picture of a house in oil painting style
control:
- /datasets/coco-stuff/house.png #images/val2017/000000001584.jpg
# Depending on the input control, if the input control is already the conditioning image, null should be passed here
# If a reconstruction target is used as control, then preprocessing function that turns it into a conditioning image needs to be specified
control_image_preprocess:

trainer:
devices: 1
num_nodes: 1
accelerator: gpu
precision: 16
logger: False # logger provided by exp_manager

model:
restore_from_path: /ckpts/controlnet/30k.nemo
precision: ${trainer.precision}
strength: 2.0
guess_mode: False
222 changes: 222 additions & 0 deletions examples/multimodal/text_to_image/controlnet/conf/controlnet_v1-5.yaml
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trainer:
devices: 2
num_nodes: 1
accelerator: gpu
precision: 16
logger: False # logger provided by exp_manager
enable_checkpointing: False
use_distributed_sampler: True
max_epochs: 3 # PTL default. In practice, max_steps will be reached first.
max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
log_every_n_steps: 10
accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models
gradient_clip_val: 1.0
benchmark: False
enable_model_summary: True
limit_val_batches: 0


exp_manager:
explicit_log_dir: null
exp_dir: null
name: controlnet
create_wandb_logger: False
wandb_logger_kwargs:
project: stable-diffusion
group: controlnet
name: controlnet-v1.5
resume: True
create_checkpoint_callback: True
create_tensorboard_logger: True
checkpoint_callback_params:
save_top_k: -1
every_n_train_steps: 5000
every_n_epochs: 0
monitor: reduced_train_loss
filename: 'controlnet--{reduced_train_loss:.2f}-{step}-{consumed_samples}'
resume_if_exists: True
resume_ignore_no_checkpoint: True
resume_from_checkpoint: ${model.resume_from_checkpoint}
ema:
enable: False
decay: 0.9999
validate_original_weights: False
every_n_steps: 1
cpu_offload: False




model:
precision: ${trainer.precision}
# specify micro_batch_size, global_batch_size, and model parallelism
# gradient accumulation will be done automatically based on data_parallel_size
micro_batch_size: 4 # limited by GPU memory
global_batch_size: 8

linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: images
cond_stage_key: captions
control_key: hint
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scale_by_std: False
ckpt_path:
ignore_keys: [ ]
parameterization: eps
clip_denoised: True
load_only_unet: False
cosine_s: 8e-3
given_betas:
original_elbo_weight: 0
v_posterior: 0
l_simple_weight: 1
use_positional_encodings: False
learn_logvar: False
logvar_init: 0
beta_schedule: linear
loss_type: l2
learning_rate: 1.0e-04
concat_mode: True
cond_stage_forward:
text_embedding_dropout_rate: 0.0
fused_opt: True
inductor: False
inductor_cudagraphs: False
capture_cudagraph_iters: -1 # -1 to disable
channels_last: True
only_mid_control: False
sd_locked: True

control_stage_config:
_target_: nemo.collections.multimodal.models.controlnet.controlnet.ControlNet
params:
from_pretrained_unet: /ckpts/v1-5-pruned.ckpt
from_NeMo: True
image_size: 32 # unused
in_channels: 4
hint_channels: 3
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
use_linear_in_transformer: False
transformer_depth: 1
context_dim: 768
use_checkpoint: False
legacy: False
use_flash_attention: False

unet_config:
_target_: nemo.collections.multimodal.models.controlnet.controlnet.ControlledUnetModel
from_pretrained: /ckpts/v1-5-pruned.ckpt
from_NeMo: True
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions:
- 4
- 2
- 1
num_res_blocks: 2
channel_mult:
- 1
- 2
- 4
- 4
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: False
legacy: False
use_flash_attention: False

first_stage_config:
_target_: nemo.collections.multimodal.models.stable_diffusion.ldm.autoencoder.AutoencoderKL
from_pretrained: /ckpts/vae.bin
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity

cond_stage_config:
_target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder
version: openai/clip-vit-large-patch14
device: cuda
max_length: 77

data:
num_workers: 16
synthetic_data: False # dataset_path and local_root_path can be empty when using synthetic data
synthetic_data_length: 10000
train:
dataset_path:
#- /datasets/tarfiles/fill50k.pkl
- /datasets/coco-stuff/coco-stuff-tarfiles/wdinfo-coco-stuff.pkl
augmentations:
resize_smallest_side: 512
center_crop_h_w: 512, 512
horizontal_flip: False
filterings:

webdataset:
infinite_sampler: False
local_root_path: /datasets/coco-stuff/coco-stuff-tarfiles

optim:
name: fused_adam
lr: 2e-5
weight_decay: 0.
betas:
- 0.9
- 0.999
sched:
name: WarmupHoldPolicy
warmup_steps: 0
hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant

# Nsys profiling options
nsys_profile:
enabled: False
start_step: 10 # Global batch to start profiling
end_step: 10 # Global batch to end profiling
ranks: [ 0 ] # Global rank IDs to profile
gen_shape: False # Generate model and kernel details including input shapes

image_logger:
batch_frequency: 1000
max_images: 4

#miscellaneous
seed: 1234
resume_from_checkpoint: null # manually set the checkpoint file to load from
apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this
gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
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