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data/Captioned_ADE20K/data/train-00002-of-00023.parquet

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data/Captioned_ADE20K/data/train-00008-of-00023.parquet

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data/Captioned_ADE20K/data/train-00013-of-00023.parquet

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+size 260730749

scripts/autoregressive/extract_file_ade.sh

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+# !/bin/bash
+set -x
+
+# torchrun \
+# --nnodes=1 --nproc_per_node=8 --node_rank=0 \
+# --master_port=12336 \
+# autoregressive/train/extract_file_ade.py "$@"
+torchrun \
+--nnodes=1 --nproc_per_node=2 --node_rank=0 \
+--master_port=12336 \
+autoregressive/train/extract_file_ade.py "$@"

scripts/autoregressive/extract_file_cocostuff.sh

@@ -0,0 +1,7 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12336 \
+autoregressive/train/extract_file_cocostuff.py "$@"

scripts/autoregressive/extract_file_imagenet.sh

@@ -0,0 +1,7 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12336 \
+autoregressive/train/extract_file_imagenet.py "$@"

scripts/autoregressive/extract_file_multigen.sh

@@ -0,0 +1,10 @@
+# !/bin/bash
+
+# sleep 21600
+
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12336 \
+autoregressive/train/extract_file_multigen.py "$@"

scripts/autoregressive/sample_c2i.sh

@@ -0,0 +1,9 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=4 --node_rank=0 \
+--master_port=12346 \
+autoregressive/sample/sample_c2i_ddp.py \
+--vq-ckpt ./pretrained_models/vq_ds16_c2i.pt \
+"$@"

scripts/autoregressive/sample_t2i_coco.sh

@@ -0,0 +1,11 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12346 \
+autoregressive/sample/sample_t2i_ddp.py \
+--prompt-csv evaluations/t2i/coco_captions.csv \
+--sample-dir samples_coco \
+--vq-ckpt ./pretrained_models/vq_ds16_t2i.pt \
+"$@"

scripts/autoregressive/sample_t2i_parti.sh

@@ -0,0 +1,11 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12347 \
+autoregressive/sample/sample_t2i_ddp.py \
+--prompt-csv evaluations/t2i/PartiPrompts.tsv \
+--sample-dir samples_parti \
+--vq-ckpt ./pretrained_models/vq_ds16_t2i.pt \
+"$@"

scripts/autoregressive/test_c2i.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12349 \
+autoregressive/test/test_c2i.py \
+"$@"

scripts/autoregressive/test_t2i.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12349 \
+autoregressive/test/test_t2i.py \
+"$@"

scripts/autoregressive/train_c2i.sh

@@ -0,0 +1,7 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=4 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12345 \
+autoregressive/train/train_c2i.py "$@"

scripts/autoregressive/train_c2i_canny.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+# sleep 43200
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=4 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12345 \
+autoregressive/train/train_c2i_canny.py "$@"

scripts/autoregressive/train_c2i_depth.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+# sleep 39600
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=4 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12345 \
+autoregressive/train/train_c2i_depth.py "$@"

scripts/autoregressive/train_c2i_fsdp.sh

@@ -0,0 +1,7 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=$nnodes --nproc_per_node=$nproc_per_node --node_rank=$node_rank \
+--master_addr=$master_addr --master_port=$master_port \
+autoregressive/train/train_c2i_fsdp.py "$@"

scripts/autoregressive/train_t2i_canny.sh

@@ -0,0 +1,14 @@
+# !/bin/bash
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_canny.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 512 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+"$@"

scripts/autoregressive/train_t2i_depth.sh

@@ -0,0 +1,15 @@
+# !/bin/bash
+# sleep 36000
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_depth.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 512 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+"$@"

scripts/autoregressive/train_t2i_depth_multiscale.sh

@@ -0,0 +1,17 @@
+# !/bin/bash
+# sleep 36000
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_depth_multiscale.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 768 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+--no-compile
+# --adapter-size base \
+"$@"

scripts/autoregressive/train_t2i_hed.sh

@@ -0,0 +1,16 @@
+# !/bin/bash
+# sleep 36000
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_hed.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 512 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+--adapter-size base \
+"$@"

scripts/autoregressive/train_t2i_hed_multiscale.sh

@@ -0,0 +1,17 @@
+# !/bin/bash
+# sleep 36000
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_hed_multiscale.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 768 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+--no-compile
+# --adapter-size base \
+"$@"

scripts/autoregressive/train_t2i_lineart.sh

@@ -0,0 +1,15 @@
+# !/bin/bash
+# sleep 36000
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_lineart.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 512 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+"$@"

scripts/autoregressive/train_t2i_lineart_multiscale.sh

@@ -0,0 +1,17 @@
+# !/bin/bash
+# sleep 36000
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_lineart_multiscale.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 768 \
+--cloud-save-path output \
+--code-path data/MultiGen20M/train \
+--no-compile
+# --adapter-size base \
+"$@"

scripts/autoregressive/train_t2i_seg.sh

@@ -0,0 +1,15 @@
+# !/bin/bash
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_seg.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--dataset t2i_control \
+--image-size 512 \
+--cloud-save-path output \
+--code-path data/Captioned_ADE20K/train \
+--code-path2 data/Captioned_COCOStuff/train \
+"$@"

scripts/autoregressive/train_t2i_seg_multiscale.sh

@@ -0,0 +1,17 @@
+# !/bin/bash
+set -x
+export TOKENIZERS_PARALLELISM=true
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12346 \
+autoregressive/train/train_t2i_seg_multiscale.py \
+--vq-ckpt checkpoints/vq/vq_ds16_t2i.pt \
+--gpt-ckpt checkpoints/llamagen/t2i_XL_stage2_512.pt \
+--data-path /path/to/high_aesthetic_10M \
+--dataset t2i_control \
+--image-size 512 \
+--cloud-save-path output \
+--code-path data/Captioned_COCOStuff/train \
+--code-path2 data/Captioned_ADE20K/train \
+--no-compile
+"$@"

scripts/autoregressive/train_t2i_stage1.sh

@@ -0,0 +1,13 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=$nnodes --nproc_per_node=$nproc_per_node --node_rank=$node_rank \
+--master_addr=$master_addr --master_port=$master_port \
+autoregressive/train/train_t2i.py \
+--vq-ckpt ./pretrained_models/vq_ds16_t2i.pt \
+--data-path /path/to/laion_coco50M \
+--t5-feat-path /path/to/laion_coco50M_flan_t5_xl \
+--dataset t2i \
+--image-size 256 \
+"$@"

scripts/autoregressive/train_t2i_stage2.sh

@@ -0,0 +1,14 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=$nnodes --nproc_per_node=$nproc_per_node --node_rank=$node_rank \
+--master_addr=$master_addr --master_port=$master_port \
+autoregressive/train/train_t2i.py \
+--vq-ckpt ./pretrained_models/vq_ds16_t2i.pt \
+--data-path /path/to/high_aesthetic_10M \
+--t5-feat-path /path/to/high_aesthetic_10M_flan_t5_xl \
+--short-t5-feat-path /path/to/high_aesthetic_10M_trunc_flan_t5_xl \
+--dataset t2i \
+--image-size 512 \
+"$@"

scripts/language/extract_flan_t5_feat_laion_coco_stage1.sh

@@ -0,0 +1,11 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12337 \
+language/extract_t5_feature.py \
+--data-path /path/to/laion_coco50M \
+--t5-path /path/to/laion_coco50M_flan_t5_xl \
+--caption-key blip \
+"$@"

scripts/language/extract_flan_t5_feat_stage2.sh

@@ -0,0 +1,10 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12337 \
+language/extract_t5_feature.py \
+--data-path /path/to/high_aesthetic_10M \
+--t5-path /path/to/high_aesthetic_10M_flan_t5_xl \
+"$@"

scripts/language/extract_flan_t5_feat_trunc_stage2.sh

@@ -0,0 +1,11 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12337 \
+language/extract_t5_feature.py \
+--data-path /path/to/high_aesthetic_10M \
+--t5-path /path/to/high_aesthetic_10M_trunc_flan_t5_xl \
+--trunc-caption \
+"$@"

scripts/tokenizer/reconstruction_consistency_decoder.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12344 \
+tokenizer/consistencydecoder/reconstruction_cd_ddp.py \
+"$@"

scripts/tokenizer/reconstruction_vae.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12344 \
+tokenizer/vae/reconstruction_vae_ddp.py \
+"$@"

scripts/tokenizer/reconstruction_vq.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=1 --node_rank=0 \
+--master_port=12344 \
+tokenizer/tokenizer_image/reconstruction_vq_ddp.py \
+"$@"

scripts/tokenizer/reconstruction_vqgan.sh

@@ -0,0 +1,8 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=8 --node_rank=0 \
+--master_port=12344 \
+tokenizer/vqgan/reconstruction_vqgan_ddp.py \
+"$@"

scripts/tokenizer/train_vq.sh

@@ -0,0 +1,7 @@
+# !/bin/bash
+set -x
+
+torchrun \
+--nnodes=1 --nproc_per_node=4 --node_rank=0 \
+--master_addr=127.0.0.1 --master_port=12345 \
+tokenizer/tokenizer_image/vq_train.py "$@"

tokenizer/consistencydecoder/cd_demo.py

@@ -0,0 +1,57 @@
+import argparse
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+from diffusers import ConsistencyDecoderVAE
+
+
+def main(args):
+    # Setup PyTorch:
+    torch.manual_seed(args.seed)
+    torch.set_grad_enabled(False)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # create and load model
+    vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16).to(device)
+
+    # load image
+    img_path = args.image_path
+    out_path = args.image_path.replace('.jpg', '_cd.jpg').replace('.jpeg', '_cd.jpeg').replace('.png', '_cd.png')
+    input_size = args.image_size
+    img = Image.open(img_path).convert("RGB")
+
+    # preprocess
+    size_org = img.size
+    img = img.resize((input_size, input_size))
+    img = np.array(img) / 255.
+    x = 2.0 * img - 1.0 # x value is between [-1, 1]
+    x = torch.tensor(x)
+    x = x.unsqueeze(dim=0)
+    x = torch.einsum('nhwc->nchw', x)
+    x_input = x.half().to(device)
+
+    # inference
+    with torch.no_grad():
+        # Map input images to latent space + normalize latents:
+        latent = vae.encode(x_input).latent_dist.sample().mul_(0.18215)
+        # reconstruct:
+        output = vae.decode(latent / 0.18215).sample # output value is between [-1, 1]
+
+    # postprocess
+    output = F.interpolate(output, size=[size_org[1], size_org[0]], mode='bilinear').permute(0, 2, 3, 1)[0]
+    sample = torch.clamp(127.5 * output + 128.0, 0, 255).to("cpu", dtype=torch.uint8).numpy()
+
+    # save        
+    Image.fromarray(sample).save(out_path)
+    print("Reconstructed image is saved to {}".format(out_path))
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image-path", type=str, default="assets/example.jpg")
+    parser.add_argument("--image-size", type=int, choices=[256, 512, 1024], default=512)
+    parser.add_argument("--seed", type=int, default=0)
+    args = parser.parse_args()
+    main(args)