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|
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""" |
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This script runs a Gradio App for the Open-Sora model. |
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|
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Usage: |
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python demo.py <config-path> |
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""" |
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|
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import argparse |
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import datetime |
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import importlib |
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import os |
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import subprocess |
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import sys |
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from tempfile import NamedTemporaryFile |
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|
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import spaces |
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import torch |
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|
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import gradio as gr |
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|
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MODEL_TYPES = ["v1.2-stage3"] |
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WATERMARK_PATH = "./assets/images/watermark/watermark.png" |
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CONFIG_MAP = { |
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"v1.2-stage3": "configs/opensora-v1-2/inference/sample.py", |
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} |
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HF_STDIT_MAP = {"v1.2-stage3": "hpcai-tech/OpenSora-STDiT-v3"} |
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|
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def install_dependencies(enable_optimization=False): |
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""" |
|
Install the required dependencies for the demo if they are not already installed. |
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""" |
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|
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def _is_package_available(name) -> bool: |
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try: |
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importlib.import_module(name) |
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return True |
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except (ImportError, ModuleNotFoundError): |
|
return False |
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if enable_optimization: |
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|
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if not _is_package_available("flash_attn"): |
|
subprocess.run( |
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f"{sys.executable} -m pip install flash-attn --no-build-isolation", |
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env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, |
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shell=True, |
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) |
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|
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if not _is_package_available("apex"): |
|
subprocess.run( |
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f'{sys.executable} -m pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git', |
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shell=True, |
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) |
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|
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if not _is_package_available("ninja"): |
|
subprocess.run(f"{sys.executable} -m pip install ninja", shell=True) |
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|
|
if not _is_package_available("xformers"): |
|
subprocess.run( |
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f"{sys.executable} -m pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers", |
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shell=True, |
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) |
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|
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def read_config(config_path): |
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""" |
|
Read the configuration file. |
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""" |
|
from mmengine.config import Config |
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return Config.fromfile(config_path) |
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def build_models(model_type, config, enable_optimization=False): |
|
""" |
|
Build the models for the given model type and configuration. |
|
""" |
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|
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from opensora.registry import MODELS, build_module |
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vae = build_module(config.vae, MODELS).cuda() |
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text_encoder = build_module(config.text_encoder, MODELS) |
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text_encoder.t5.model = text_encoder.t5.model.cuda() |
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from opensora.models.stdit.stdit3 import STDiT3 |
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stdit = STDiT3.from_pretrained(HF_STDIT_MAP[model_type]) |
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stdit = stdit.cuda() |
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from opensora.registry import SCHEDULERS |
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scheduler = build_module(config.scheduler, SCHEDULERS) |
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text_encoder.y_embedder = stdit.y_embedder |
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vae = vae.to(torch.bfloat16).eval() |
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text_encoder.t5.model = text_encoder.t5.model.eval() |
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stdit = stdit.to(torch.bfloat16).eval() |
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|
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torch.cuda.empty_cache() |
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return vae, text_encoder, stdit, scheduler |
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|
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def parse_args(): |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument( |
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"--model-type", |
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default="v1.2-stage3", |
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choices=MODEL_TYPES, |
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help=f"The type of model to run for the Gradio App, can only be {MODEL_TYPES}", |
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) |
|
parser.add_argument("--output", default="./outputs", type=str, help="The path to the output folder") |
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parser.add_argument("--port", default=None, type=int, help="The port to run the Gradio App on.") |
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parser.add_argument("--host", default="0.0.0.0", type=str, help="The host to run the Gradio App on.") |
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parser.add_argument("--share", action="store_true", help="Whether to share this gradio demo.") |
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parser.add_argument( |
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"--enable-optimization", |
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action="store_true", |
|
help="Whether to enable optimization such as flash attention and fused layernorm", |
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) |
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return parser.parse_args() |
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args = parse_args() |
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config = read_config(CONFIG_MAP[args.model_type]) |
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torch.backends.cuda.matmul.allow_tf32 = True |
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torch.backends.cudnn.allow_tf32 = True |
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|
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os.makedirs(args.output, exist_ok=True) |
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torch.jit._state.disable() |
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install_dependencies(enable_optimization=args.enable_optimization) |
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from opensora.datasets import IMG_FPS, save_sample |
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from opensora.datasets.aspect import get_image_size, get_num_frames |
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from opensora.models.text_encoder.t5 import text_preprocessing |
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from opensora.utils.inference_utils import ( |
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add_watermark, |
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append_generated, |
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append_score_to_prompts, |
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apply_mask_strategy, |
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collect_references_batch, |
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dframe_to_frame, |
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extract_json_from_prompts, |
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extract_prompts_loop, |
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get_random_prompt_by_openai, |
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has_openai_key, |
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merge_prompt, |
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prepare_multi_resolution_info, |
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refine_prompts_by_openai, |
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split_prompt, |
|
has_openai_key |
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) |
|
from opensora.utils.misc import to_torch_dtype |
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|
dtype = to_torch_dtype(config.dtype) |
|
device = torch.device("cuda") |
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|
|
vae, text_encoder, stdit, scheduler = build_models( |
|
args.model_type, config, enable_optimization=args.enable_optimization |
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) |
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|
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|
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def run_inference( |
|
mode, |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
): |
|
if prompt_text is None or prompt_text == "": |
|
gr.Warning("Your prompt is empty, please enter a valid prompt") |
|
return None |
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|
|
torch.manual_seed(seed) |
|
with torch.inference_mode(): |
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image_size = get_image_size(resolution, aspect_ratio) |
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|
if mode == "Text2Image": |
|
num_frames = 1 |
|
fps = IMG_FPS |
|
else: |
|
num_frames = config.num_frames |
|
num_frames = get_num_frames(length) |
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|
|
condition_frame_length = int(num_frames / 17 * 5 / 3) |
|
condition_frame_edit = 0.0 |
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|
|
input_size = (num_frames, *image_size) |
|
latent_size = vae.get_latent_size(input_size) |
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multi_resolution = "OpenSora" |
|
align = 5 |
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|
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|
|
if mode == "Text2Image": |
|
mask_strategy = [None] |
|
elif mode == "Text2Video": |
|
if reference_image is not None: |
|
mask_strategy = ["0"] |
|
else: |
|
mask_strategy = [None] |
|
else: |
|
raise ValueError(f"Invalid mode: {mode}") |
|
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|
|
if mode == "Text2Image": |
|
refs = [""] |
|
elif mode == "Text2Video": |
|
if reference_image is not None: |
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|
|
from PIL import Image |
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|
|
im = Image.fromarray(reference_image) |
|
temp_file = NamedTemporaryFile(suffix=".png") |
|
im.save(temp_file.name) |
|
refs = [temp_file.name] |
|
else: |
|
refs = [""] |
|
else: |
|
raise ValueError(f"Invalid mode: {mode}") |
|
|
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|
|
batch_prompts = [prompt_text] |
|
batch_prompts, refs, mask_strategy = extract_json_from_prompts(batch_prompts, refs, mask_strategy) |
|
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|
|
refs = collect_references_batch(refs, vae, image_size) |
|
|
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|
|
model_args = prepare_multi_resolution_info( |
|
multi_resolution, len(batch_prompts), image_size, num_frames, fps, device, dtype |
|
) |
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|
|
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|
|
batched_prompt_segment_list = [] |
|
batched_loop_idx_list = [] |
|
for prompt in batch_prompts: |
|
prompt_segment_list, loop_idx_list = split_prompt(prompt) |
|
batched_prompt_segment_list.append(prompt_segment_list) |
|
batched_loop_idx_list.append(loop_idx_list) |
|
|
|
|
|
if refine_prompt: |
|
|
|
if not has_openai_key(): |
|
gr.Warning("OpenAI API key is not provided, the prompt will not be enhanced.") |
|
else: |
|
for idx, prompt_segment_list in enumerate(batched_prompt_segment_list): |
|
batched_prompt_segment_list[idx] = refine_prompts_by_openai(prompt_segment_list) |
|
|
|
|
|
aesthetic_score = aesthetic_score if use_aesthetic_score else None |
|
motion_strength = motion_strength if use_motion_strength and mode != "Text2Image" else None |
|
camera_motion = None if camera_motion == "none" or mode == "Text2Image" else camera_motion |
|
|
|
for idx, prompt_segment_list in enumerate(batched_prompt_segment_list): |
|
batched_prompt_segment_list[idx] = append_score_to_prompts( |
|
prompt_segment_list, |
|
aes=aesthetic_score, |
|
flow=motion_strength, |
|
camera_motion=camera_motion, |
|
) |
|
|
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|
|
for idx, prompt_segment_list in enumerate(batched_prompt_segment_list): |
|
batched_prompt_segment_list[idx] = [text_preprocessing(prompt) for prompt in prompt_segment_list] |
|
|
|
|
|
batch_prompts = [] |
|
for prompt_segment_list, loop_idx_list in zip(batched_prompt_segment_list, batched_loop_idx_list): |
|
batch_prompts.append(merge_prompt(prompt_segment_list, loop_idx_list)) |
|
|
|
|
|
|
|
|
|
video_clips = [] |
|
|
|
for loop_i in range(num_loop): |
|
|
|
batch_prompts_loop = extract_prompts_loop(batch_prompts, loop_i) |
|
|
|
|
|
if loop_i > 0: |
|
refs, mask_strategy = append_generated( |
|
vae, video_clips[-1], refs, mask_strategy, loop_i, condition_frame_length, condition_frame_edit |
|
) |
|
|
|
|
|
z = torch.randn(len(batch_prompts), vae.out_channels, *latent_size, device=device, dtype=dtype) |
|
masks = apply_mask_strategy(z, refs, mask_strategy, loop_i, align=align) |
|
|
|
|
|
|
|
scheduler_kwargs = config.scheduler.copy() |
|
scheduler_kwargs.pop("type") |
|
scheduler_kwargs["num_sampling_steps"] = sampling_steps |
|
scheduler_kwargs["cfg_scale"] = cfg_scale |
|
|
|
scheduler.__init__(**scheduler_kwargs) |
|
samples = scheduler.sample( |
|
stdit, |
|
text_encoder, |
|
z=z, |
|
prompts=batch_prompts_loop, |
|
device=device, |
|
additional_args=model_args, |
|
progress=True, |
|
mask=masks, |
|
) |
|
samples = vae.decode(samples.to(dtype), num_frames=num_frames) |
|
video_clips.append(samples) |
|
|
|
|
|
|
|
|
|
video_clips = [val[0] for val in video_clips] |
|
for i in range(1, num_loop): |
|
video_clips[i] = video_clips[i][:, dframe_to_frame(condition_frame_length) :] |
|
video = torch.cat(video_clips, dim=1) |
|
current_datetime = datetime.datetime.now() |
|
timestamp = current_datetime.timestamp() |
|
save_path = os.path.join(args.output, f"output_{timestamp}") |
|
saved_path = save_sample(video, save_path=save_path, fps=24) |
|
torch.cuda.empty_cache() |
|
|
|
|
|
|
|
if mode != "Text2Image" and os.path.exists(WATERMARK_PATH): |
|
watermarked_path = saved_path.replace(".mp4", "_watermarked.mp4") |
|
success = add_watermark(saved_path, WATERMARK_PATH, watermarked_path) |
|
if success: |
|
return watermarked_path |
|
else: |
|
return saved_path |
|
else: |
|
return saved_path |
|
|
|
|
|
@spaces.GPU(duration=200) |
|
def run_image_inference( |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
): |
|
return run_inference( |
|
"Text2Image", |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
) |
|
|
|
|
|
@spaces.GPU(duration=200) |
|
def run_video_inference( |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
): |
|
|
|
|
|
|
|
|
|
return run_inference( |
|
"Text2Video", |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
) |
|
|
|
|
|
def generate_random_prompt(): |
|
if "OPENAI_API_KEY" not in os.environ: |
|
gr.Warning("Your prompt is empty and the OpenAI API key is not provided, please enter a valid prompt") |
|
return None |
|
else: |
|
prompt_text = get_random_prompt_by_openai() |
|
return prompt_text |
|
|
|
|
|
def main(): |
|
|
|
with gr.Blocks() as demo: |
|
with gr.Row(): |
|
with gr.Column(): |
|
gr.HTML( |
|
""" |
|
<div style='text-align: center;'> |
|
<p align="center"> |
|
<img src="https://github.com/hpcaitech/Open-Sora/raw/main/assets/readme/icon.png" width="250"/> |
|
</p> |
|
<div style="display: flex; gap: 10px; justify-content: center;"> |
|
<a href="https://github.com/hpcaitech/Open-Sora/stargazers"><img src="https://img.shields.io/github/stars/hpcaitech/Open-Sora?style=social"></a> |
|
<a href="https://hpcaitech.github.io/Open-Sora/"><img src="https://img.shields.io/badge/Gallery-View-orange?logo=&"></a> |
|
<a href="https://discord.gg/kZakZzrSUT"><img src="https://img.shields.io/badge/Discord-join-blueviolet?logo=discord&"></a> |
|
<a href="https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-247ipg9fk-KRRYmUl~u2ll2637WRURVA"><img src="https://img.shields.io/badge/Slack-ColossalAI-blueviolet?logo=slack&"></a> |
|
<a href="https://twitter.com/yangyou1991/status/1769411544083996787?s=61&t=jT0Dsx2d-MS5vS9rNM5e5g"><img src="https://img.shields.io/badge/Twitter-Discuss-blue?logo=twitter&"></a> |
|
<a href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png"><img src="https://img.shields.io/badge/微信-小助手加群-green?logo=wechat&"></a> |
|
<a href="https://hpc-ai.com/blog/open-sora-v1.0"><img src="https://img.shields.io/badge/Open_Sora-Blog-blue"></a> |
|
</div> |
|
<h1 style='margin-top: 5px;'>Open-Sora: Democratizing Efficient Video Production for All</h1> |
|
</div> |
|
""" |
|
) |
|
|
|
with gr.Row(): |
|
with gr.Column(): |
|
prompt_text = gr.Textbox(label="Prompt", placeholder="Describe your video here", lines=4) |
|
refine_prompt = gr.Checkbox(value=has_openai_key(), label="Refine prompt with GPT4o", interactive=has_openai_key()) |
|
random_prompt_btn = gr.Button("Random Prompt By GPT4o", interactive=has_openai_key()) |
|
|
|
gr.Markdown("## Basic Settings") |
|
resolution = gr.Radio( |
|
choices=["144p", "240p", "360p", "480p", "720p"], |
|
value="240p", |
|
label="Resolution", |
|
) |
|
aspect_ratio = gr.Radio( |
|
choices=["9:16", "16:9", "3:4", "4:3", "1:1"], |
|
value="9:16", |
|
label="Aspect Ratio (H:W)", |
|
) |
|
length = gr.Radio( |
|
choices=["2s", "4s", "8s", "16s"], |
|
value="2s", |
|
label="Video Length", |
|
info="only effective for video generation, 8s may fail as Hugging Face ZeroGPU has the limitation of max 200 seconds inference time.", |
|
) |
|
|
|
with gr.Row(): |
|
seed = gr.Slider(value=1024, minimum=1, maximum=2048, step=1, label="Seed") |
|
|
|
sampling_steps = gr.Slider(value=30, minimum=1, maximum=200, step=1, label="Sampling steps") |
|
cfg_scale = gr.Slider(value=7.0, minimum=0.0, maximum=10.0, step=0.1, label="CFG Scale") |
|
|
|
with gr.Row(): |
|
with gr.Column(): |
|
motion_strength = gr.Slider( |
|
value=5, |
|
minimum=0, |
|
maximum=100, |
|
step=1, |
|
label="Motion Strength", |
|
info="only effective for video generation", |
|
) |
|
use_motion_strength = gr.Checkbox(value=False, label="Enable") |
|
|
|
with gr.Column(): |
|
aesthetic_score = gr.Slider( |
|
value=6.5, |
|
minimum=4, |
|
maximum=7, |
|
step=0.1, |
|
label="Aesthetic", |
|
info="effective for text & video generation", |
|
) |
|
use_aesthetic_score = gr.Checkbox(value=True, label="Enable") |
|
|
|
camera_motion = gr.Radio( |
|
value="none", |
|
label="Camera Motion", |
|
choices=["none", "pan right", "pan left", "tilt up", "tilt down", "zoom in", "zoom out", "static"], |
|
interactive=True, |
|
) |
|
|
|
gr.Markdown("## Advanced Settings") |
|
with gr.Row(): |
|
fps = gr.Slider( |
|
value=24, |
|
minimum=1, |
|
maximum=60, |
|
step=1, |
|
label="FPS", |
|
info="This is the frames per seconds for video generation, keep it to 24 if you are not sure", |
|
) |
|
num_loop = gr.Slider( |
|
value=1, |
|
minimum=1, |
|
maximum=20, |
|
step=1, |
|
label="Number of Loops", |
|
info="This will change the length of the generated video, keep it to 1 if you are not sure", |
|
) |
|
|
|
gr.Markdown("## Reference Image") |
|
reference_image = gr.Image(label="Image (optional)", show_download_button=True) |
|
|
|
with gr.Column(): |
|
output_video = gr.Video(label="Output Video", height="100%") |
|
|
|
with gr.Row(): |
|
image_gen_button = gr.Button("Generate image") |
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video_gen_button = gr.Button("Generate video") |
|
|
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image_gen_button.click( |
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fn=run_image_inference, |
|
inputs=[ |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
], |
|
outputs=reference_image, |
|
) |
|
video_gen_button.click( |
|
fn=run_video_inference, |
|
inputs=[ |
|
prompt_text, |
|
resolution, |
|
aspect_ratio, |
|
length, |
|
motion_strength, |
|
aesthetic_score, |
|
use_motion_strength, |
|
use_aesthetic_score, |
|
camera_motion, |
|
reference_image, |
|
refine_prompt, |
|
fps, |
|
num_loop, |
|
seed, |
|
sampling_steps, |
|
cfg_scale, |
|
], |
|
outputs=output_video, |
|
) |
|
random_prompt_btn.click(fn=generate_random_prompt, outputs=prompt_text) |
|
|
|
|
|
demo.launch(server_port=args.port, server_name=args.host, share=args.share) |
|
|
|
|
|
if __name__ == "__main__": |
|
main() |
|
|