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pixart-alpha

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gradio_cached_examples/30/log.csv 0 → 100755
View file @ e2364931
Result,Seed,flag,username,timestamp
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model.properties 0 → 100644
View file @ e2364931
# 模型唯一标识
modelCode=604
# 模型名称
modelName=pixart-alpha_pytorch
# 模型描述
modelDescription=pixart-alpha是一种基于DiT的文生图模型
# 应用场景
appScenario=推理,AIGC,零售,广媒,教育
# 框架类型
frameType=pytorch
notebooks/PixArt_xl2_img512_internal_for_pokemon_sample_training.py 0 → 100755
View file @ e2364931
_base_ = ['/workspace/PixArt-alpha/configs/PixArt_xl2_internal.py']
data_root = '/workspace'
image_list_json = ['data_info.json',]
data = dict(type='InternalData', root='/workspace/pixart-pokemon', image_list_json=image_list_json, transform='default_train', load_vae_feat=True)
image_size = 512
# model setting
window_block_indexes = []
window_size=0
use_rel_pos=False
model = 'PixArt_XL_2'
fp32_attention = True
load_from = "/workspace/PixArt-alpha/output/pretrained_models/PixArt-XL-2-512x512.pth"
vae_pretrained = "output/pretrained_models/sd-vae-ft-ema"
lewei_scale = 1.0
# training setting
use_fsdp=False # if use FSDP mode
num_workers=10
train_batch_size = 38 # 32
num_epochs = 200 # 3
gradient_accumulation_steps = 1
grad_checkpointing = True
gradient_clip = 0.01
optimizer = dict(type='AdamW', lr=2e-5, weight_decay=3e-2, eps=1e-10)
lr_schedule_args = dict(num_warmup_steps=1000)
eval_sampling_steps = 200
log_interval = 20
save_model_steps=100
work_dir = 'output/debug'
notebooks/convert-checkpoint-to-diffusers.ipynb 0 → 100755
View file @ e2364931
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "2878bb5d-33a3-4a5b-b15c-c832c700129b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/workspace/PixArt-alpha\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
" self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
]
}
],
"source": [
"%cd PixArt-alpha"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7dd2d98c-3f8f-40f1-a9e1-bc916774afb3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of transformer parameters: 610856096\n"
]
}
],
"source": [
"!python tools/convert_pixart_alpha_to_diffusers.py \\\n",
" --orig_ckpt_path \"/workspace/PixArt-alpha/output/trained_model/checkpoints/epoch_5_step_110.pth\" \\\n",
" --dump_path \"/workspace/PixArt-alpha/output/diffusers_trained\" \\\n",
" --only_transformer=True \\\n",
" --image_size 512 \\\n",
" --multi_scale_train=False\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
notebooks/infer.ipynb 0 → 100755
View file @ e2364931
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8b2458c4-c461-4ddc-af94-fcd837357da4",
"metadata": {},
"outputs": [],
"source": [
"from diffusers import PixArtAlphaPipeline\n",
"import torch\n",
"from diffusers import Transformer2DModel"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81a5bc0f-682b-4ff9-92e9-43b68b3df8fc",
"metadata": {},
"outputs": [],
"source": [
"# for comparison\n",
"\n",
"orig_pipe = pipe = PixArtAlphaPipeline.from_pretrained(\"PixArt-alpha/PixArt-XL-2-512x512\", torch_dtype=torch.float16)\n",
"orig_pipe = orig_pipe.to(\"cuda\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "efc07821-5479-4ca3-a2c6-114ac484fd1e",
"metadata": {},
"outputs": [],
"source": [
"transformer = Transformer2DModel.from_pretrained(\"/workspace/PixArt-alpha/output/diffusers_trained/transformer\", torch_dtype=torch.float16)\n",
"pipe = PixArtAlphaPipeline.from_pretrained(\"PixArt-alpha/PixArt-XL-2-512x512\", torch_dtype=torch.float16, transformer=transformer)\n",
"pipe = pipe.to(\"cuda\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "57da873b-2c13-463b-b558-ee69522ccefc",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d69c7683773c4c25914764800ec1ef4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/20 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=512x512>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"A green pokemon on white background\"\n",
"image = pipe(prompt=prompt).images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
notebooks/train.ipynb 0 → 100755
View file @ e2364931
{
"cells": [
{
"cell_type": "markdown",
"id": "c423d2a1-475e-482e-b759-f16456fd6707",
"metadata": {},
"source": [
"# Install"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0440d6a7-78b9-49e9-98a2-9a5ed75e1a2f",
"metadata": {},
"outputs": [],
"source": [
"!git clone https://github.com/kopyl/PixArt-alpha.git"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0abadf51-a7e3-4091-bb02-0bdd8d28fb73",
"metadata": {},
"outputs": [],
"source": [
"%cd PixArt-alpha"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4df1af24-f439-485d-a946-966dbf16c49b",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117\n",
"!pip install -r requirements.txt\n",
"!pip install wandb"
]
},
{
"cell_type": "markdown",
"id": "d44474fd-0b92-48fc-b4cf-142b59d3917c",
"metadata": {},
"source": [
"## Download model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06b1c1c9-f8b1-4719-8564-2383eac9ff28",
"metadata": {},
"outputs": [],
"source": [
"!python tools/download.py --model_names \"PixArt-XL-2-512x512.pth\""
]
},
{
"cell_type": "markdown",
"id": "f298a89c-d2a5-4da7-8304-c1390da0ba58",
"metadata": {},
"source": [
"## Make dataset out of Hugginggface dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e17b8883-0a5c-4fa3-a7d0-e8ee95e42027",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from tqdm.notebook import tqdm\n",
"from datasets import load_dataset\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92957b2c-6765-48ee-9296-d6739066d74d",
"metadata": {},
"outputs": [],
"source": [
"dataset = load_dataset(\"lambdalabs/pokemon-blip-captions\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0095cdda-c31a-48ee-a115-076a5fc393c3",
"metadata": {},
"outputs": [],
"source": [
"root_dir = \"/workspace/pixart-pokemon\"\n",
"images_dir = \"images\"\n",
"captions_dir = \"captions\"\n",
"\n",
"images_dir_absolute = os.path.join(root_dir, images_dir)\n",
"captions_dir_absolute = os.path.join(root_dir, captions_dir)\n",
"\n",
"if not os.path.exists(root_dir):\n",
" os.makedirs(os.path.join(root_dir, images_dir))\n",
"\n",
"if not os.path.exists(os.path.join(root_dir, images_dir)):\n",
" os.makedirs(os.path.join(root_dir, images_dir))\n",
"if not os.path.exists(os.path.join(root_dir, captions_dir)):\n",
" os.makedirs(os.path.join(root_dir, captions_dir))\n",
"\n",
"image_format = \"png\"\n",
"json_name = \"partition/data_info.json\"\n",
"if not os.path.exists(os.path.join(root_dir, \"partition\")):\n",
" os.makedirs(os.path.join(root_dir, \"partition\"))\n",
"\n",
"absolute_json_name = os.path.join(root_dir, json_name)\n",
"data_info = []\n",
"\n",
"order = 0\n",
"for item in tqdm(dataset[\"train\"]): \n",
" image = item[\"image\"]\n",
" image.save(f\"{images_dir_absolute}/{order}.{image_format}\")\n",
" with open(f\"{captions_dir_absolute}/{order}.txt\", \"w\") as text_file:\n",
" text_file.write(item[\"text\"])\n",
" \n",
" width, height = 512, 512\n",
" ratio = 1\n",
" data_info.append({\n",
" \"height\": height,\n",
" \"width\": width,\n",
" \"ratio\": ratio,\n",
" \"path\": f\"images/{order}.{image_format}\",\n",
" \"prompt\": item[\"text\"],\n",
" })\n",
" \n",
" order += 1\n",
"\n",
"with open(absolute_json_name, \"w\") as json_file:\n",
" json.dump(data_info, json_file)"
]
},
{
"cell_type": "markdown",
"id": "25be1c03",
"metadata": {},
"source": [
"## Extract features"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f07a4f5-1873-48bf-86d0-9304942de5d3",
"metadata": {},
"outputs": [],
"source": [
"!python /workspace/PixArt-alpha/tools/extract_features.py \\\n",
" --img_size 512 \\\n",
" --json_path \"/workspace/pixart-pokemon/partition/data_info.json\" \\\n",
" --t5_save_root \"/workspace/pixart-pokemon/caption_feature_wmask\" \\\n",
" --vae_save_root \"/workspace/pixart-pokemon/img_vae_features\" \\\n",
" --pretrained_models_dir \"/workspace/PixArt-alpha/output/pretrained_models\" \\\n",
" --dataset_root \"/workspace/pixart-pokemon\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fc653d0",
"metadata": {},
"outputs": [],
"source": [
"!wandb login REPLACE_THIS_WITH_YOUR_AUTH_TOKEN_OF_WANDB"
]
},
{
"cell_type": "markdown",
"id": "2cf1fd1a",
"metadata": {},
"source": [
"## Train model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea0e9dab-17bc-45ed-9c81-b670bbb8de47",
"metadata": {},
"outputs": [],
"source": [
"!python -m torch.distributed.launch \\\n",
" train_scripts/train.py \\\n",
" /workspace/PixArt-alpha/notebooks/PixArt_xl2_img512_internal_for_pokemon_sample_training.py \\\n",
" --work-dir output/trained_model \\\n",
" --report_to=\"wandb\" \\\n",
" --loss_report_name=\"train_loss\""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
quick_inference_with_code.py 0 → 100755
View file @ e2364931
import torch
import sys
from diffusers import PixArtAlphaPipeline, ConsistencyDecoderVAE, AutoencoderKL
# You can replace the checkpoint id with "PixArt-alpha/PixArt-XL-2-512x512" too.
pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16, use_safetensors=True,
cache_dir="./pretrained_models/hub")
# If use DALL-E 3 Consistency Decoder
# pipe.vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
# If use SA-Solver sampler
# from diffusion.sa_solver_diffusers import SASolverScheduler
# pipe.scheduler = SASolverScheduler.from_config(pipe.scheduler.config, algorithm_type='data_prediction')
# If loading a LoRA model
# transformer = Transformer2DModel.from_pretrained("PixArt-alpha/PixArt-LCM-XL-2-1024-MS", subfolder="transformer", torch_dtype=torch.float16)
# transformer = PeftModel.from_pretrained(transformer, "Your-LoRA-Model-Path")
# pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-LCM-XL-2-1024-MS", transformer=transformer, torch_dtype=torch.float16, use_safetensors=True)
# del transformer
# Enable memory optimizations.
pipe.enable_model_cpu_offload()
# prompt = "A small cactus with a happy face in the Sahara desert."
prompt = str(sys.argv[1])
print(prompt)
image = pipe(prompt).images[0]
image.save("./catcus.png")
\ No newline at end of file
requirements.txt 0 → 100755
View file @ e2364931
# torch==2.1.1
# torchaudio==2.1.1
# torchvision==0.16.1
# diffusers
mmcv==1.7.0
# git+https://github.com/huggingface/diffusers
# timm==0.6.12
accelerate
tensorboard
tensorboardX
transformers
sentencepiece~=0.1.99
ftfy
beautifulsoup4
protobuf==3.20.2
gradio==4.1.1
yapf==0.40.1
opencv-python
bs4
einops
# xformers
optimum
peft==0.6.2
\ No newline at end of file
scripts/infer_pixart_8_bits.py 0 → 100755
View file @ e2364931
# pip install -U accelerate transformers bitsandbytes
# pip install -U git+https://github.com/huggingface/diffusers
from transformers import T5EncoderModel
from diffusers import PixArtAlphaPipeline
import torch
import gc
def flush():
gc.collect()
torch.cuda.empty_cache()
def bytes_to_giga_bytes(bytes):
return bytes / 1024 / 1024 / 1024
# Loading in 8 bits needs `bitsandbytes`.
text_encoder = T5EncoderModel.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
subfolder="text_encoder",
load_in_8bit=True,
device_map="auto",
)
pipe = PixArtAlphaPipeline.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
text_encoder=text_encoder,
transformer=None,
device_map="auto"
)
with torch.no_grad():
prompt = "cute cat"
prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
del text_encoder
del pipe
flush()
pipe = PixArtAlphaPipeline.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
text_encoder=None,
torch_dtype=torch.float16,
).to("cuda")
latents = pipe(
negative_prompt=None,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
num_images_per_prompt=1,
output_type="latent",
).images
del pipe.transformer
flush()
with torch.no_grad():
image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
image = pipe.image_processor.postprocess(image, output_type="pil")
image[0].save("cat.png")
print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
\ No newline at end of file
scripts/inference.py 0 → 100755
View file @ e2364931
import os
import sys
from pathlib import Path
current_file_path = Path(__file__).resolve()
sys.path.insert(0, str(current_file_path.parent.parent))
import warnings
warnings.filterwarnings("ignore") # ignore warning
import re
import argparse
from datetime import datetime
from tqdm import tqdm
import torch
from torchvision.utils import save_image
from diffusers.models import AutoencoderKL
from diffusion.model.utils import prepare_prompt_ar
from diffusion import IDDPM, DPMS, SASolverSampler
from tools.download import find_model
from diffusion.model.nets import PixArtMS_XL_2, PixArt_XL_2
from diffusion.model.t5 import T5Embedder
from diffusion.data.datasets import get_chunks, ASPECT_RATIO_512_TEST, ASPECT_RATIO_1024_TEST
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--image_size', default=1024, type=int)
parser.add_argument('--t5_path', default='output/pretrained_models/t5_ckpts', type=str)
parser.add_argument('--tokenizer_path', default='output/pretrained_models/sd-vae-ft-ema', type=str)
parser.add_argument('--txt_file', default='asset/samples.txt', type=str)
parser.add_argument('--model_path', default='output/pretrained_models/PixArt-XL-2-1024x1024.pth', type=str)
parser.add_argument('--bs', default=1, type=int)
parser.add_argument('--cfg_scale', default=4.5, type=float)
parser.add_argument('--sampling_algo', default='dpm-solver', type=str, choices=['iddpm', 'dpm-solver', 'sa-solver'])
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--dataset', default='custom', type=str)
parser.add_argument('--step', default=-1, type=int)
parser.add_argument('--save_name', default='test_sample', type=str)
return parser.parse_args()
def set_env(seed=0):
torch.manual_seed(seed)
torch.set_grad_enabled(False)
for _ in range(30):
torch.randn(1, 4, args.image_size, args.image_size)
@torch.inference_mode()
def visualize(items, bs, sample_steps, cfg_scale):
for chunk in tqdm(list(get_chunks(items, bs)), unit='batch'):
prompts = []
if bs == 1:
prompt_clean, _, hw, ar, custom_hw = prepare_prompt_ar(chunk[0], base_ratios, device=device, show=False) # ar for aspect ratio
if args.image_size == 1024:
latent_size_h, latent_size_w = int(hw[0, 0] // 8), int(hw[0, 1] // 8)
else:
hw = torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1)
ar = torch.tensor([[1.]], device=device).repeat(bs, 1)
latent_size_h, latent_size_w = latent_size, latent_size
prompts.append(prompt_clean.strip())
else:
hw = torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1)
ar = torch.tensor([[1.]], device=device).repeat(bs, 1)
for prompt in chunk:
prompts.append(prepare_prompt_ar(prompt, base_ratios, device=device, show=False)[0].strip())
latent_size_h, latent_size_w = latent_size, latent_size
null_y = model.y_embedder.y_embedding[None].repeat(len(prompts), 1, 1)[:, None]
with torch.no_grad():
caption_embs, emb_masks = t5.get_text_embeddings(prompts)
caption_embs = caption_embs.float()[:, None]
print('finish embedding')
if args.sampling_algo == 'iddpm':
# Create sampling noise:
n = len(prompts)
z = torch.randn(n, 4, latent_size_h, latent_size_w, device=device).repeat(2, 1, 1, 1)
model_kwargs = dict(y=torch.cat([caption_embs, null_y]),
cfg_scale=cfg_scale, data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
diffusion = IDDPM(str(sample_steps))
# Sample images:
samples = diffusion.p_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True,
device=device
)
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
elif args.sampling_algo == 'dpm-solver':
# Create sampling noise:
n = len(prompts)
z = torch.randn(n, 4, latent_size_h, latent_size_w, device=device)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
dpm_solver = DPMS(model.forward_with_dpmsolver,
condition=caption_embs,
uncondition=null_y,
cfg_scale=cfg_scale,
model_kwargs=model_kwargs)
samples = dpm_solver.sample(
z,
steps=sample_steps,
order=2,
skip_type="time_uniform",
method="multistep",
)
elif args.sampling_algo == 'sa-solver':
# Create sampling noise:
n = len(prompts)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
sa_solver = SASolverSampler(model.forward_with_dpmsolver, device=device)
samples = sa_solver.sample(
S=25,
batch_size=n,
shape=(4, latent_size_h, latent_size_w),
eta=1,
conditioning=caption_embs,
unconditional_conditioning=null_y,
unconditional_guidance_scale=cfg_scale,
model_kwargs=model_kwargs,
)[0]
samples = vae.decode(samples / 0.18215).sample
torch.cuda.empty_cache()
# Save images:
os.umask(0o000) # file permission: 666; dir permission: 777
for i, sample in enumerate(samples):
save_path = os.path.join(save_root, f"{prompts[i][:100]}.jpg")
print("Saving path: ", save_path)
save_image(sample, save_path, nrow=1, normalize=True, value_range=(-1, 1))
if __name__ == '__main__':
args = get_args()
# Setup PyTorch:
seed = args.seed
set_env(seed)
device = "cuda" if torch.cuda.is_available() else "cpu"
assert args.sampling_algo in ['iddpm', 'dpm-solver', 'sa-solver']
# only support fixed latent size currently
latent_size = args.image_size // 8
lewei_scale = {512: 1, 1024: 2} # trick for positional embedding interpolation
sample_steps_dict = {'iddpm': 100, 'dpm-solver': 20, 'sa-solver': 25}
sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo]
weight_dtype = torch.float16
print(f"Inference with {weight_dtype}")
# model setting
if args.image_size == 512:
model = PixArt_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device)
else:
model = PixArtMS_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device)
print(f"Generating sample from ckpt: {args.model_path}")
state_dict = find_model(args.model_path)
del state_dict['state_dict']['pos_embed']
missing, unexpected = model.load_state_dict(state_dict['state_dict'], strict=False)
print('Missing keys: ', missing)
print('Unexpected keys', unexpected)
model.eval()
model.to(weight_dtype)
base_ratios = eval(f'ASPECT_RATIO_{args.image_size}_TEST')
vae = AutoencoderKL.from_pretrained(args.tokenizer_path).to(device)
t5 = T5Embedder(device="cuda", local_cache=True, cache_dir=args.t5_path, torch_dtype=torch.float)
work_dir = os.path.join(*args.model_path.split('/')[:-2])
work_dir = f'/{work_dir}' if args.model_path[0] == '/' else work_dir
# data setting
with open(args.txt_file, 'r') as f:
items = [item.strip() for item in f.readlines()]
# img save setting
try:
epoch_name = re.search(r'.*epoch_(\d+).*.pth', args.model_path).group(1)
step_name = re.search(r'.*step_(\d+).*.pth', args.model_path).group(1)
except Exception:
epoch_name = 'unknown'
step_name = 'unknown'
img_save_dir = os.path.join(work_dir, 'vis')
os.umask(0o000) # file permission: 666; dir permission: 777
os.makedirs(img_save_dir, exist_ok=True)
save_root = os.path.join(img_save_dir, f"{datetime.now().date()}_{args.dataset}_epoch{epoch_name}_step{step_name}_scale{args.cfg_scale}_step{sample_steps}_size{args.image_size}_bs{args.bs}_samp{args.sampling_algo}_seed{seed}")
os.makedirs(save_root, exist_ok=True)
visualize(items, args.bs, sample_steps, args.cfg_scale)
\ No newline at end of file
scripts/inference_lcm.py 0 → 100755
View file @ e2364931
import os
import sys
from pathlib import Path
current_file_path = Path(__file__).resolve()
sys.path.insert(0, str(current_file_path.parent.parent))
import warnings
warnings.filterwarnings("ignore") # ignore warning
import re
import argparse
from datetime import datetime
from tqdm import tqdm
import torch
from torchvision.utils import save_image
from diffusers.models import AutoencoderKL
from diffusion.model.utils import prepare_prompt_ar
from tools.download import find_model
from diffusion.model.nets import PixArtMS_XL_2, PixArt_XL_2
from diffusion.model.t5 import T5Embedder
from diffusion.data.datasets import get_chunks
from diffusion.lcm_scheduler import LCMScheduler
from diffusion.data.datasets import ASPECT_RATIO_512_TEST, ASPECT_RATIO_1024_TEST
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--image_size', default=1024, type=int)
parser.add_argument('--t5_path', default='output/pretrained_models/t5_ckpts', type=str)
parser.add_argument('--tokenizer_path', default='output/pretrained_models/sd-vae-ft-ema', type=str)
parser.add_argument('--txt_file', default='asset/samples.txt', type=str)
parser.add_argument('--model_path', default='output/pretrained_models/PixArt-XL-2-1024x1024.pth', type=str)
parser.add_argument('--bs', default=1, type=int)
parser.add_argument('--cfg_scale', default=4.5, type=float)
parser.add_argument('--sample_steps', default=4, type=int)
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--dataset', default='custom', type=str)
parser.add_argument('--step', default=-1, type=int)
parser.add_argument('--save_name', default='test_sample', type=str)
return parser.parse_args()
def set_env(seed=0):
torch.manual_seed(seed)
torch.set_grad_enabled(False)
for _ in range(30):
torch.randn(1, 4, args.image_size, args.image_size)
@torch.inference_mode()
def visualize(items, bs, sample_steps, cfg_scale):
# 4. Prepare timesteps
scheduler.set_timesteps(sample_steps, 50)
timesteps = scheduler.timesteps
for chunk in tqdm(list(get_chunks(items, bs)), unit='batch'):
prompts = []
if bs == 1:
prompt_clean, _, hw, ar, custom_hw = prepare_prompt_ar(chunk[0], base_ratios, device=device, show=False) # ar for aspect ratio
if args.image_size == 1024:
latent_size_h, latent_size_w = int(hw[0, 0] // 8), int(hw[0, 1] // 8)
else:
hw = torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1)
ar = torch.tensor([[1.]], device=device).repeat(bs, 1)
latent_size_h, latent_size_w = latent_size, latent_size
prompts.append(prompt_clean.strip())
else:
hw = torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1)
ar = torch.tensor([[1.]], device=device).repeat(bs, 1)
prompts.append(prepare_prompt_ar(prompt, base_ratios, device=device, show=False)[0].strip())
latent_size_h, latent_size_w = latent_size, latent_size
with torch.no_grad():
caption_embs, emb_masks = t5.get_text_embeddings(prompts)
caption_embs = caption_embs.float()[:, None]
print('finish embedding')
# Create sampling noise:
n = len(prompts)
latents = torch.randn(n, 4, latent_size_h, latent_size_w, device=device)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
# 7. LCM MultiStep Sampling Loop:
for i, t in tqdm(list(enumerate(timesteps))):
ts = torch.full((bs,), t, device=device, dtype=torch.long)
# model prediction (v-prediction, eps, x)
model_pred = model(latents, ts, caption_embs, **model_kwargs)[:, :4]
# compute the previous noisy sample x_t -> x_t-1
latents, denoised = scheduler.step(model_pred, i, t, latents, return_dict=False)
samples = vae.decode(denoised / 0.18215).sample
torch.cuda.empty_cache()
# Save images:
os.umask(0o000) # file permission: 666; dir permission: 777
for i, sample in enumerate(samples):
save_path = os.path.join(save_root, f"{prompts[i][:100]}.jpg")
print("Saving path: ", save_path)
save_image(sample, save_path, nrow=1, normalize=True, value_range=(-1, 1))
if __name__ == '__main__':
args = get_args()
# Setup PyTorch:
seed = args.seed
set_env(seed)
device = "cuda" if torch.cuda.is_available() else "cpu"
# only support fixed latent size currently
latent_size = args.image_size // 8
lewei_scale = {512: 1, 1024: 2} # trick for positional embedding interpolation
sample_steps = args.sample_steps
# Initalize Scheduler:
scheduler = LCMScheduler(beta_start=0.0001, beta_end=0.02, beta_schedule="linear", prediction_type="epsilon")
# model setting
if args.image_size == 512:
model = PixArt_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device)
else:
model = PixArtMS_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device)
print(f"Generating sample from ckpt: {args.model_path}")
state_dict = find_model(args.model_path)
del state_dict['state_dict']['pos_embed']
missing, unexpected = model.load_state_dict(state_dict['state_dict'], strict=False)
print('Missing keys: ', missing)
print('Unexpected keys', unexpected)
model.eval()
base_ratios = eval(f'ASPECT_RATIO_{args.image_size}_TEST')
vae = AutoencoderKL.from_pretrained(args.tokenizer_path).to(device)
t5 = T5Embedder(device="cuda", local_cache=True, cache_dir=args.t5_path, torch_dtype=torch.float)
work_dir = os.path.join(*args.model_path.split('/')[:-2])
work_dir = f'/{work_dir}' if args.model_path[0] == '/' else work_dir
# data setting
with open(args.txt_file, 'r') as f:
items = [item.strip() for item in f.readlines()]
# img save setting
try:
epoch_name = re.search(r'.*epoch_(\d+).*.pth', args.model_path).group(1)
step_name = re.search(r'.*step_(\d+).*.pth', args.model_path).group(1)
except Exception:
epoch_name = 'unknown'
step_name = 'unknown'
img_save_dir = os.path.join(work_dir, 'vis')
os.umask(0o000) # file permission: 666; dir permission: 777
os.makedirs(img_save_dir, exist_ok=True)
save_root = os.path.join(img_save_dir, f"{datetime.now().date()}_{args.dataset}_epoch{epoch_name}_step{step_name}_scale{args.cfg_scale}_step{sample_steps}_size{args.image_size}_bs{args.bs}_sampLCM_seed{seed}")
os.makedirs(save_root, exist_ok=True)
visualize(items, args.bs, sample_steps, args.cfg_scale)
scripts/interface.py 0 → 100755
View file @ e2364931
import argparse
import sys
from pathlib import Path
current_file_path = Path(__file__).resolve()
sys.path.insert(0, str(current_file_path.parent.parent))
import os
import random
import torch
from torchvision.utils import save_image
from diffusion import IDDPM, DPMS, SASolverSampler
from diffusers.models import AutoencoderKL
from tools.download import find_model
from datetime import datetime
from typing import List, Union
import gradio as gr
import numpy as np
from gradio.components import Textbox, Image
from diffusion.model.utils import prepare_prompt_ar, resize_and_crop_tensor
from diffusion.model.nets import PixArtMS_XL_2, PixArt_XL_2
from diffusion.model.t5 import T5Embedder
from torchvision.utils import _log_api_usage_once, make_grid
from diffusion.data.datasets import ASPECT_RATIO_512_TEST, ASPECT_RATIO_1024_TEST
from asset.examples import examples
MAX_SEED = np.iinfo(np.int32).max
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--image_size', default=1024, type=int)
parser.add_argument('--model_path', default='output/pretrained_models/PixArt-XL-2-1024-MS.pth', type=str)
parser.add_argument('--t5_path', default='output/pretrained_models', type=str)
parser.add_argument('--tokenizer_path', default='output/pretrained_models/sd-vae-ft-ema', type=str)
parser.add_argument('--llm_model', default='t5', type=str)
parser.add_argument('--port', default=7788, type=int)
return parser.parse_args()
@torch.no_grad()
def ndarr_image(tensor: Union[torch.Tensor, List[torch.Tensor]], **kwargs,) -> None:
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(save_image)
grid = make_grid(tensor, **kwargs)
# Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
return grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
def set_env(seed=0):
torch.manual_seed(seed)
torch.set_grad_enabled(False)
for _ in range(30):
torch.randn(1, 4, args.image_size, args.image_size)
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@torch.inference_mode()
def generate_img(prompt, sampler, sample_steps, scale, seed=0, randomize_seed=False):
seed = int(randomize_seed_fn(seed, randomize_seed))
set_env(seed)
os.makedirs(f'output/demo/online_demo_prompts/', exist_ok=True)
save_promt_path = f'output/demo/online_demo_prompts/tested_prompts{datetime.now().date()}.txt'
with open(save_promt_path, 'a') as f:
f.write(prompt + '\n')
print(prompt)
prompt_clean, prompt_show, hw, ar, custom_hw = prepare_prompt_ar(prompt, base_ratios, device=device) # ar for aspect ratio
prompt_clean = prompt_clean.strip()
if isinstance(prompt_clean, str):
prompts = [prompt_clean]
caption_embs, emb_masks = llm_embed_model.get_text_embeddings(prompts)
caption_embs = caption_embs[:, None]
null_y = model.y_embedder.y_embedding[None].repeat(len(prompts), 1, 1)[:, None]
latent_size_h, latent_size_w = int(hw[0, 0]//8), int(hw[0, 1]//8)
# Sample images:
if sampler == 'iddpm':
# Create sampling noise:
n = len(prompts)
z = torch.randn(n, 4, latent_size_h, latent_size_w, device=device).repeat(2, 1, 1, 1)
model_kwargs = dict(y=torch.cat([caption_embs, null_y]),
cfg_scale=scale, data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
diffusion = IDDPM(str(sample_steps))
samples = diffusion.p_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True,
device=device
)
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
elif sampler == 'dpm-solver':
# Create sampling noise:
n = len(prompts)
z = torch.randn(n, 4, latent_size_h, latent_size_w, device=device)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
dpm_solver = DPMS(model.forward_with_dpmsolver,
condition=caption_embs,
uncondition=null_y,
cfg_scale=scale,
model_kwargs=model_kwargs)
samples = dpm_solver.sample(
z,
steps=sample_steps,
order=2,
skip_type="time_uniform",
method="multistep",
)
elif sampler == 'sa-solver':
# Create sampling noise:
n = len(prompts)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
sa_solver = SASolverSampler(model.forward_with_dpmsolver, device=device)
samples = sa_solver.sample(
S=sample_steps,
batch_size=n,
shape=(4, latent_size_h, latent_size_w),
eta=1,
conditioning=caption_embs,
unconditional_conditioning=null_y,
unconditional_guidance_scale=scale,
model_kwargs=model_kwargs,
)[0]
samples = vae.decode(samples / 0.18215).sample
torch.cuda.empty_cache()
samples = resize_and_crop_tensor(samples, custom_hw[0,1], custom_hw[0,0])
display_model_info = f'Model path: {args.model_path},\nBase image size: {args.image_size}, \nSampling Algo: {sampler}'
return ndarr_image(samples, normalize=True, value_range=(-1, 1)), prompt_show, display_model_info, seed
if __name__ == '__main__':
from diffusion.utils.logger import get_root_logger
args = get_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
logger = get_root_logger()
assert args.image_size in [512, 1024], "We only provide pre-trained models for 256x256, 512x512 and 1024x1024 resolutions."
lewei_scale = {512: 1, 1024: 2}
latent_size = args.image_size // 8
t5_device = {512: 'cuda', 1024: 'cuda'}
if args.image_size == 512:
model = PixArt_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device)
else:
model = PixArtMS_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device)
state_dict = find_model(args.model_path)
del state_dict['state_dict']['pos_embed']
missing, unexpected = model.load_state_dict(state_dict['state_dict'], strict=False)
logger.warning(f'Missing keys: {missing}')
logger.warning(f'Unexpected keys: {unexpected}')
model.eval()
base_ratios = eval(f'ASPECT_RATIO_{args.image_size}_TEST')
vae = AutoencoderKL.from_pretrained(args.tokenizer_path).to(device)
if args.llm_model == 't5':
llm_embed_model = T5Embedder(device=t5_device[args.image_size], local_cache=True, cache_dir=args.t5_path, torch_dtype=torch.float)
else:
print('We support t5 only, please initialize the llm again')
sys.exit()
title = f"""
'' Unleashing your Creativity \n ''
<div style='display: flex; align-items: center; justify-content: center; text-align: center;'>
<img src='https://raw.githubusercontent.com/PixArt-alpha/PixArt-alpha.github.io/master/static/images/logo.png' style='width: 400px; height: auto; margin-right: 10px;' />
{args.image_size}px
</div>
"""
DESCRIPTION = """# PixArt-Alpha 1024px
## If PixArt-Alpha is helpful, please help to ⭐ the [Github Repo](https://github.com/PixArt-alpha/PixArt) and recommend it to your friends 😊'
#### [PixArt-Alpha 1024px](https://github.com/PixArt-alpha/PixArt-alpha) is a transformer-based text-to-image diffusion system trained on text embeddings from T5. This demo uses the [PixArt-alpha/PixArt-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS) checkpoint.
#### English prompts ONLY; 提示词仅限英文
Don't want to queue? Try [OpenXLab](https://openxlab.org.cn/apps/detail/PixArt-alpha/PixArt-alpha) or [Google Colab Demo](https://colab.research.google.com/drive/1jZ5UZXk7tcpTfVwnX33dDuefNMcnW9ME?usp=sharing).
"""
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
demo = gr.Interface(
fn=generate_img,
inputs=[Textbox(label="Note: If you want to specify a aspect ratio or determine a customized height and width, "
"use --ar h:w (or --aspect_ratio h:w) or --hw h:w. If no aspect ratio or hw is given, all setting will be default.",
placeholder="Please enter your prompt. \n"),
gr.Radio(
choices=["iddpm", "dpm-solver"],
label=f"Sampler",
interactive=True,
value='dpm-solver',
),
gr.Slider(
label='Sample Steps',
minimum=1,
maximum=100,
value=14,
step=1
),
gr.Slider(
label='Guidance Scale',
minimum=0.1,
maximum=30.0,
value=4.5,
step=0.1
),
gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
),
gr.Checkbox(label="Randomize seed", value=True),
],
outputs=[Image(type="numpy", label="Img"),
Textbox(label="clean prompt"),
Textbox(label="model info"),
gr.Slider(label='seed')],
title=title,
description=DESCRIPTION,
examples=examples,
)
demo.launch(server_name="0.0.0.0", server_port=args.port, debug=True)
\ No newline at end of file
scripts/interface_controlnet.py 0 → 100755
View file @ e2364931
import argparse
import os
from datetime import datetime
import numpy as np
import sys
from pathlib import Path
from typing import List, Union
current_file_path = Path(__file__).resolve()
sys.path.insert(0, str(current_file_path.parent.parent))
import gradio as gr
from gradio.components import Textbox, Image, Slider
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from torchvision.utils import _log_api_usage_once, make_grid, save_image
from diffusion import IDDPM, DPMS, SASolverSampler
from diffusion.data.datasets import *
from diffusion.model.hed import HEDdetector
from diffusion.model.nets import PixArtMS_XL_2, ControlPixArtHalf, ControlPixArtMSHalf
from diffusion.model.t5 import T5Embedder
from diffusion.model.utils import prepare_prompt_ar, resize_and_crop_tensor
from diffusion.utils.misc import read_config
from diffusers.models import AutoencoderKL
from tools.download import find_model
vae_scale = 0.18215
DESCRIPTION = """![Logo](https://raw.githubusercontent.com/PixArt-alpha/PixArt-alpha.github.io/master/static/images/logo.png)
# PixArt-Alpha 1024px + ControlNet. This is the demo for ControlNet combined with 1024px PixArt-Alpha.
# The input reference image need to be around 1024x1024. And descriptive prompts also need to be provided.
# You may change the random seed, if you didn't get satisfied results.
"""
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("config", type=str, help="config")
parser.add_argument('--num_sampling_steps', default=14, type=int)
parser.add_argument('--cfg_scale', default=4.5, type=int)
parser.add_argument('--image_size', default=1024, type=int)
parser.add_argument('--model_path', type=str)
parser.add_argument('--tokenizer_path', default='output/pretrained_models/sd-vae-ft-ema', type=str)
parser.add_argument('--llm_model', default='t5', type=str)
parser.add_argument('--sampling_algo', default='dpm-solver', type=str, choices=['iddpm', 'dpm-solver', 'sa-solver'])
parser.add_argument('--port', default=7788, type=int)
parser.add_argument('--condition_strength', default=1, type=float)
return parser.parse_args()
@torch.no_grad()
def ndarr_image(tensor: Union[torch.Tensor, List[torch.Tensor]], **kwargs, ) -> None:
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(save_image)
grid = make_grid(tensor, **kwargs)
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
return ndarr
def set_env():
torch.manual_seed(0)
torch.set_grad_enabled(False)
@torch.inference_mode()
def generate_img(prompt, given_image, seed):
torch.manual_seed(seed)
torch.cuda.empty_cache()
strength = 1.0
c_vis = given_image
save_promt_path = f'{save_prompt_path}/tested_prompts{datetime.now().date()}.txt'
with open(save_promt_path, 'a') as f:
f.write(prompt + '\n')
prompt_clean, prompt_show, hw, ar, custom_hw = prepare_prompt_ar(prompt, base_ratios, device=device) # ar for aspect ratio
prompt_clean = prompt_clean.strip()
if isinstance(prompt_clean, str):
prompts = [prompt_clean]
caption_embs, emb_masks = llm_embed_model.get_text_embeddings(prompts)
caption_embs = caption_embs[:, None]
null_y = model.y_embedder.y_embedding[None].repeat(len(prompts), 1, 1)[:, None]
# condition process
if given_image is not None:
ar = torch.tensor([given_image.size[1] / given_image.size[0]], device=device)[None]
custom_hw = torch.tensor([given_image.size[1], given_image.size[0]], device=device)[None]
closest_hw = base_ratios[min(base_ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))]
hw = torch.tensor(closest_hw, device=device)[None]
condition_transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB')),
T.Resize(int(min(closest_hw))),
T.CenterCrop([int(closest_hw[0]), int(closest_hw[1])]),
T.ToTensor(),
])
given_image = condition_transform(given_image).unsqueeze(0).to(device)
hed_edge = hed(given_image) * strength
hed_edge = TF.normalize(hed_edge, [.5], [.5])
hed_edge = hed_edge.repeat(1, 3, 1, 1)
posterior = vae.encode(hed_edge).latent_dist
condition = posterior.sample()
c = condition * vae_scale
c_vis = vae.decode(condition)['sample']
c_vis = torch.clamp(127.5 * c_vis + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()[0]
else:
c = None
latent_size_h, latent_size_w = int(hw[0, 0] // 8), int(hw[0, 1] // 8)
# Sample images:
if args.sampling_algo == 'iddpm':
# Create sampling noise:
n = len(prompts)
z = torch.randn(n, 4, latent_size, latent_size, device=device).repeat(2, 1, 1, 1)
model_kwargs = dict(y=torch.cat([caption_embs, null_y]), cfg_scale=args.cfg_scale,
data_info={'img_hw': hw, 'aspect_ratio': ar},
mask=emb_masks, c=c)
diffusion = IDDPM(str(args.num_sampling_steps))
samples = diffusion.p_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True,
device=device
)
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
elif args.sampling_algo == 'dpm-solver':
# Create sampling noise:
n = len(prompts)
z = torch.randn(n, 4, latent_size_h, latent_size_w, device=device)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks, c=c)
dpm_solver = DPMS(model.forward_with_dpmsolver,
condition=caption_embs,
uncondition=null_y,
cfg_scale=args.cfg_scale,
model_kwargs=model_kwargs)
samples = dpm_solver.sample(
z,
steps=args.num_sampling_steps,
order=2,
skip_type="time_uniform",
method="multistep",
)
elif args.sampling_algo == 'sa-solver':
# Create sampling noise:
n = len(prompts)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks, c=c)
sas_solver = SASolverSampler(model.forward_with_dpmsolver, device=device)
samples = sas_solver.sample(
S=args.num_sampling_steps,
batch_size=n,
shape=(4, latent_size_h, latent_size_w),
eta=1,
conditioning=caption_embs,
unconditional_conditioning=null_y,
unconditional_guidance_scale=args.cfg_scale,
model_kwargs=model_kwargs,
)[0]
samples = vae.decode(samples / vae_scale).sample
torch.cuda.empty_cache()
samples = resize_and_crop_tensor(samples, custom_hw[0, 1], custom_hw[0, 0])
return ndarr_image(samples, normalize=True, value_range=(-1, 1)), c_vis, prompt_show
if __name__ == '__main__':
args = get_args()
config = read_config(args.config)
set_env()
device = "cuda" if torch.cuda.is_available() else "cpu"
save_prompt_path = 'output/demo/online_demo_prompts/'
os.makedirs(save_prompt_path, exist_ok=True)
assert args.image_size in [512, 1024], "We only provide pre-trained models for 512x512 and 1024x1024 resolutions."
lewei_scale = {512: 1, 1024: 2}
latent_size = args.image_size // 8
weight_dtype = torch.float16
print(f"Inference with {weight_dtype}")
model = PixArtMS_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size])
if config.image_size == 512:
print('model architecture ControlPixArtHalf and image size is 512')
model = ControlPixArtHalf(model).to(device)
elif config.image_size == 1024:
print('model architecture ControlPixArtMSHalf and image size is 1024')
model = ControlPixArtMSHalf(model).to(device)
state_dict = find_model(args.model_path)['state_dict']
if 'pos_embed' in state_dict:
del state_dict['pos_embed']
elif 'base_model.pos_embed' in state_dict:
del state_dict['base_model.pos_embed']
missing, unexpected = model.load_state_dict(state_dict, strict=False)
print('Missing keys (missing pos_embed is normal): ', missing)
print('Unexpected keys', unexpected)
model.eval()
model.to(weight_dtype)
display_model_info = f'model path: {args.model_path},\n base image size: {args.image_size}'
base_ratios = eval(f'ASPECT_RATIO_{args.image_size}_TEST')
vae = AutoencoderKL.from_pretrained(args.tokenizer_path).to(device)
hed = HEDdetector(False).to(device)
if args.llm_model == 't5':
print("begin load t5")
llm_embed_model = T5Embedder(device=device, local_cache=True, cache_dir='data/t5_ckpts', torch_dtype=torch.float)
print("finish load t5")
else:
print(f'We support t5 only, please initialize the llm again')
sys.exit()
gr.Markdown(DESCRIPTION)
demo = gr.Interface(fn=generate_img,
inputs=[
Textbox(label="Enter a reference image, the resolution of image need around 1024 x 1024",
placeholder="Please enter your prompt. \n"),
Image(type="pil", label="Condition"),
Slider(minimum=0., maximum=10000., value=0, step=2, label='seed'),
],
outputs=[Image(type="numpy", label="Img"),
Image(type="numpy", label="HED Edge Map"),
Textbox(label="clean prompt"),]
)
demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=args.port, debug=True)
tools/VLM_caption_lightning.py 0 → 100755
View file @ e2364931
# {'model': 'LLaVA-7B-v0', 'prompt': 'You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab.You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.Follow the instructions carefully and explain your answers in detail.###Human: Hi!###Assistant: Hi there! How can I help you today?\n###Human: ?\n<image>###Assistant:', 'temperature': 0.2, 'max_new_tokens': 512, 'stop': '###', 'images': "List of 1 images: ['793f00027d3dc5bd69445a388a2f289c']"}
import sys
from pathlib import Path
current_file_path = Path(__file__).resolve()
sys.path.insert(0, str(current_file_path.parent.parent))
import argparse
import torch
from transformers import AutoTokenizer, CLIPImageProcessor, CLIPVisionModel, AutoConfig
from diffusion.model.llava import LlavaMPTForCausalLM
from PIL import Image
from tqdm import tqdm
from os import path, makedirs
from torch.utils.data import Dataset, DataLoader
import json
import pathlib
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
# only fixed for py < 3.9
def remove_suffix(input_string, suffix):
if suffix and input_string.endswith(suffix):
return input_string[:-len(suffix)]
return input_string
def expand2square(pil_img, background_color=(122, 116, 104)):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def pad2square(image):
max_hw, min_hw = max(image.size), min(image.size)
aspect_ratio = max_hw / min_hw
max_len, min_len = 800, 400
shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
longest_edge = int(shortest_edge * aspect_ratio)
W, H = image.size
if H > W:
H, W = longest_edge, shortest_edge
else:
H, W = shortest_edge, longest_edge
image = image.resize((W, H))
return image
def load_model(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMPTForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
vision_tower = model.get_model().vision_tower[0]
if vision_tower.device.type == 'meta':
vision_tower = CLIPVisionModel.from_pretrained(
vision_tower.config._name_or_path, torch_dtype=torch.float16, low_cpu_mem_usage=True).cuda()
model.get_model().vision_tower[0] = vision_tower
else:
vision_tower.to(device='cuda', dtype=torch.float16)
vision_config = vision_tower.config
vision_config.im_patch_token = tokenizer.convert_tokens_to_ids(
[DEFAULT_IMAGE_PATCH_TOKEN])[0]
vision_config.use_im_start_end = mm_use_im_start_end
if mm_use_im_start_end:
vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids(
[DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
model.cuda()
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, context_len
class SanitizedLaion(Dataset):
def __init__(self, root_dir, index_file, prompt, config, img_extension='.jpg', caption=True) -> None:
super().__init__()
self.root_dir = root_dir
self.image_processor = CLIPImageProcessor.from_pretrained(AutoConfig.from_pretrained(config).mm_vision_tower, torch_dtype=torch.float16)
self.prompt = prompt
self.img_extension = img_extension
self.caption=caption
if '.txt' in index_file:
with open(index_file, 'r') as f:
self.lines = f.readlines()
elif '.json' in index_file:
with open(index_file, 'r') as f:
self.lines = json.load(f)
else:
raise ValueError(f'{index_file} format not supported')
def __len__(self):
return len(self.lines)
def __getitem__(self, idx):
item = self.lines[idx]
caption = item['prompt'].strip()
prompt = self.prompt.format(caption) if self.caption else self.prompt
with open(path.join(self.root_dir, item['path']), 'rb') as f:
img = pad2square(Image.open(f).convert('RGB'))
return self.image_processor(img, return_tensors='pt')['pixel_values'].squeeze(), prompt, item['path'].split(self.img_extension)[0]
@torch.no_grad()
def caption(tokenizer, model, context_len, images, prompt, prefix):
images = images.to(model.device, dtype=torch.float16)
# HACK: 256 is the max image token length hacked
replace_token = DEFAULT_IMAGE_PATCH_TOKEN * 256
if getattr(model.config, 'mm_use_im_start_end', False):
replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
prompt = list(map(lambda p: p.replace(DEFAULT_IMAGE_TOKEN, replace_token), prompt))
temperature = 0.2
max_new_tokens = 1024
stop_str = '<|im_end|>'
max_src_len = context_len - max_new_tokens - 8
input_ids = tokenizer(prompt).input_ids
input_ids = list(map(lambda input_id: input_id[-max_src_len:], input_ids))
lens = list(map(lambda x: len(x), input_ids))
longest = max(lens)
input_ids = list(map(lambda x: x if len(x) == longest else [tokenizer.pad_token_id] * (longest - len(x)) + x, input_ids))
pred_ids = torch.zeros([images.shape[0], 0], device=model.device, dtype=torch.long)
past_key_values = None
finish = [False] * images.shape[0]
for i in tqdm(range(max_new_tokens), leave=False):
if i == 0:
out = model(
torch.as_tensor(input_ids).cuda(),
use_cache=True,
images=images)
del images
else:
attention_mask = torch.ones(1, past_key_values[0][0].shape[-2] + 1, device="cuda")
out = model(input_ids=token,
use_cache=True,
attention_mask=attention_mask,
past_key_values=past_key_values)
past_key_values = out.past_key_values
logits = out.logits
last_token_logits = logits[:, -1]
if temperature < 1e-4:
token = torch.argmax(last_token_logits)
else:
probs = torch.softmax(last_token_logits / temperature, dim=-1)
token = torch.multinomial(probs, num_samples=1)
pred_ids = torch.concatenate([pred_ids, token], dim=1)
for ii in torch.nonzero(token.cpu() == tokenizer.eos_token_id, as_tuple=True)[0]:
if finish[ii]:
continue
ii = int(ii)
# output = tokenizer.decode(pred_ids[ii][:-1]).removesuffix(stop_str)
output = remove_suffix(tokenizer.decode(pred_ids[ii][:-1]), stop_str)
finish[ii] = True
yield output, prefix[ii]
if all(finish):
break
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="liuhaotian/LLaVA-Lightning-MPT-7B-preview")
parser.add_argument("--data-root", type=str, required=True)
parser.add_argument('--index', type=str, required=True)
parser.add_argument('--output', type=str, required=True)
args = parser.parse_args()
prompt = """<|im_start|>system
- You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab.
- You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.
- You should follow the instructions carefully and explain your answers in detail.<|im_end|><|im_start|>user
Given the caption of this image "{}", describe this image in a very detailed manner
<image><|im_end|><|im_start|>assistant\n"""
prompt_nocap = """<|im_start|>system
- You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab.
- You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.
- You should follow the instructions carefully and explain your answers in detail.<|im_end|><|im_start|>user
Describe this image in a very detailed manner
<image><|im_end|><|im_start|>assistant\n"""
d = SanitizedLaion(args.data_root, args.index, prompt, args.model_path, img_extension='.png')
l = DataLoader(d, batch_size=32, pin_memory=True, num_workers=10)
tokenizer, model, context_len = load_model(args.model_path)
# model = torch.compile(model)
for b in tqdm(l):
for c, p in caption(tokenizer, model, context_len, *b):
o = path.join(args.output, f'{p}.txt')
makedirs(path.dirname(o), exist_ok=True, mode=0o755)
with open(o, 'w') as k:
k.write(c)
tools/convert_pixart_alpha_to_diffusers.py 0 → 100755
View file @ e2364931
import argparse
import os
import torch
from transformers import T5EncoderModel, T5Tokenizer
from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtAlphaPipeline, Transformer2DModel
ckpt_id = "PixArt-alpha/PixArt-alpha"
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/scripts/inference.py#L125
interpolation_scale = {256: 0.5, 512: 1, 1024: 2}
def main(args):
all_state_dict = torch.load(args.orig_ckpt_path, map_location='cpu')
state_dict = all_state_dict.pop("state_dict")
converted_state_dict = {}
# Patch embeddings.
converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight")
converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias")
# Caption projection.
converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight")
converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias")
converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight")
converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias")
# AdaLN-single LN
converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop(
"t_embedder.mlp.0.weight"
)
converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias")
converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop(
"t_embedder.mlp.2.weight"
)
converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias")
if args.image_size == 1024 and args.multi_scale_train:
# Resolution.
converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.weight"] = state_dict.pop(
"csize_embedder.mlp.0.weight"
)
converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.bias"] = state_dict.pop(
"csize_embedder.mlp.0.bias"
)
converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.weight"] = state_dict.pop(
"csize_embedder.mlp.2.weight"
)
converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.bias"] = state_dict.pop(
"csize_embedder.mlp.2.bias"
)
# Aspect ratio.
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.weight"] = state_dict.pop(
"ar_embedder.mlp.0.weight"
)
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.bias"] = state_dict.pop(
"ar_embedder.mlp.0.bias"
)
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.weight"] = state_dict.pop(
"ar_embedder.mlp.2.weight"
)
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.bias"] = state_dict.pop(
"ar_embedder.mlp.2.bias"
)
# Shared norm.
converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight")
converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias")
for depth in range(28):
# Transformer blocks.
converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
f"blocks.{depth}.scale_shift_table"
)
# Attention is all you need 🤘
# Self attention.
q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0)
q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.bias"), 3, dim=0)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias
# Projection.
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop(
f"blocks.{depth}.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop(
f"blocks.{depth}.attn.proj.bias"
)
# Feed-forward.
converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.fc1.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.fc1.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.fc2.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.fc2.bias"
)
# Cross-attention.
q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight")
q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias")
k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0)
k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop(
f"blocks.{depth}.cross_attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop(
f"blocks.{depth}.cross_attn.proj.bias"
)
# Final block.
converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight")
converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias")
converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table")
# DiT XL/2
transformer = Transformer2DModel(
sample_size=args.image_size // 8,
num_layers=28,
attention_head_dim=72,
in_channels=4,
out_channels=8,
patch_size=2,
attention_bias=True,
num_attention_heads=16,
cross_attention_dim=1152,
activation_fn="gelu-approximate",
num_embeds_ada_norm=1000,
norm_type="ada_norm_single",
norm_elementwise_affine=False,
norm_eps=1e-6,
caption_channels=4096,
)
transformer.load_state_dict(converted_state_dict, strict=True)
assert transformer.pos_embed.pos_embed is not None
state_dict.pop("pos_embed")
state_dict.pop("y_embedder.y_embedding")
assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}"
num_model_params = sum(p.numel() for p in transformer.parameters())
print(f"Total number of transformer parameters: {num_model_params}")
if args.only_transformer:
transformer.save_pretrained(os.path.join(args.dump_path, "transformer"))
else:
scheduler = DPMSolverMultistepScheduler()
vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="sd-vae-ft-ema")
tokenizer = T5Tokenizer.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl")
text_encoder = T5EncoderModel.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl")
pipeline = PixArtAlphaPipeline(
tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler
)
pipeline.save_pretrained(args.dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# set multi_scale_train=True if using PixArtMS structure during training else set it to False
parser.add_argument("--multi_scale_train", default=True, type=str, required=True, help="If use Multi-Scale PixArtMS structure during training.")
parser.add_argument("--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert.")
parser.add_argument(
"--image_size",
default=1024,
type=int,
choices=[256, 512, 1024],
required=False,
help="Image size of pretrained model, either 512 or 1024.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
parser.add_argument("--only_transformer", default=True, type=bool, required=True)
args = parser.parse_args()
main(args)
tools/convert_png_to_jpg.py 0 → 100644
View file @ e2364931
from PIL import Image
from glob import glob
from pathlib import Path
from tqdm import tqdm
import os
def main(args):
if not os.path.exists(args.tgt_folder):
os.makedirs(args.tgt_folder)
print("图像转换")
for ipath in tqdm(glob(f"{args.src_folder}/*.png")):
image = Image.open(ipath)
tgt_name = Path(ipath).resolve().name.replace("png", "jpg")
image.save(os.path.join(args.tgt_folder, tgt_name), "JPEG")
print("图像转换完成")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--src_folder", type=str, help="")
parser.add_argument("--tgt_folder", type=str, help="")
args = parser.parse_args()
main(args)
\ No newline at end of file
tools/download.py 0 → 100755
View file @ e2364931
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Functions for downloading pre-trained PixArt models
"""
from torchvision.datasets.utils import download_url
import torch
import os
import argparse
pretrained_models = {'PixArt-XL-2-512x512.pth', 'PixArt-XL-2-1024-MS.pth'}
vae_models = {
'sd-vae-ft-ema/config.json',
'sd-vae-ft-ema/diffusion_pytorch_model.bin'
}
t5_models = {
't5-v1_1-xxl/config.json', 't5-v1_1-xxl/pytorch_model-00001-of-00002.bin',
't5-v1_1-xxl/pytorch_model-00002-of-00002.bin', 't5-v1_1-xxl/pytorch_model.bin.index.json',
't5-v1_1-xxl/special_tokens_map.json', 't5-v1_1-xxl/spiece.model',
't5-v1_1-xxl/tokenizer_config.json',
}
def find_model(model_name):
"""
Finds a pre-trained G.pt model, downloading it if necessary. Alternatively, loads a model from a local path.
"""
if model_name in pretrained_models:
return download_model(model_name)
assert os.path.isfile(model_name), f'Could not find PixArt checkpoint at {model_name}'
return torch.load(model_name, map_location=lambda storage, loc: storage)
def download_model(model_name):
"""
Downloads a pre-trained PixArt model from the web.
"""
assert model_name in pretrained_models
local_path = f'output/pretrained_models/{model_name}'
if not os.path.isfile(local_path):
os.makedirs('output/pretrained_models', exist_ok=True)
web_path = f'https://huggingface.co/PixArt-alpha/PixArt-alpha/resolve/main/{model_name}'
download_url(web_path, 'output/pretrained_models')
return torch.load(local_path, map_location=lambda storage, loc: storage)
def download_other(model_name, model_zoo, output_dir):
"""
Downloads a pre-trained PixArt model from the web.
"""
assert model_name in model_zoo
local_path = os.path.join(output_dir, model_name)
if not os.path.isfile(local_path):
os.makedirs(output_dir, exist_ok=True)
web_path = f'https://huggingface.co/PixArt-alpha/PixArt-alpha/resolve/main/{model_name}'
print(web_path)
download_url(web_path, os.path.join(output_dir, model_name.split('/')[0]))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model_names', nargs='+', type=str, default=pretrained_models)
args = parser.parse_args()
model_names = args.model_names
model_names = set(model_names)
# Download PixArt checkpoints
for t5_model in t5_models:
download_other(t5_model, t5_models, 'output/pretrained_models/t5_ckpts')
for vae_model in vae_models:
download_other(vae_model, vae_models, 'output/pretrained_models/')
for model in model_names:
download_model(model) # for vae_model in vae_models:
print('Done.')
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