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update cli_demo

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zR 2024-08-14 20:40:31 +08:00
parent 62a1b7050e
commit 25531820ea
3 changed files with 7 additions and 6 deletions
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7
inference/cli_demo.py
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@ -2,7 +2,7 @@
This script demonstrates how to generate a video from a text prompt using CogVideoX with 🤗Huggingface Diffusers Pipeline.
Note:
This script requires the `diffusers>=0.30.0` library to be installed.
This script requires the `diffusers>=0.31.0` library to be installed.
Run the script:
$ python cli_demo.py --prompt "A girl ridding a bike." --model_path THUDM/CogVideoX-2b
@ -83,11 +83,14 @@ def generate_video(
# pipe.vae.enable_tiling()
# 4. Generate the video frames based on the prompt.
# `num_frames` is the Number of frames to generate.
# This is the default value for 6 seconds video and 8 fps,so 48 frames and will plus 1 frame for the first frame.
# for diffusers version `0.30.0`, this should be 48. and for `0.31.0` and after, this should be 49.
video = pipe(
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt, # Number of videos to generate per prompt
num_inference_steps=num_inference_steps, # Number of inference steps
num_frames=48, # Number of frames to generate
num_frames=49,
guidance_scale=guidance_scale, # Guidance scale for classifier-free guidance
generator=torch.Generator().manual_seed(42), # Set the seed for reproducibility
).frames[0]

4
sat/configs/test.txt
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@ -1,3 +1 @@
In the haunting backdrop of a war-torn city, where ruins and crumbled walls tell a story of devastation, a poignant close-up frames a young girl. Her face is smudged with ash, a silent testament to the chaos around her. Her eyes glistening with a mix of sorrow and resilience, capturing the raw emotion of a world that has lost its innocence to the ravages of conflict.
The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope, dust kicks up from its tires, the sunlight shines on the SUV as it speeds along the dirt road, casting a warm glow over the scene. The dirt road curves gently into the distance, with no other cars or vehicles in sight. The trees on either side of the road are redwoods, with patches of greenery scattered throughout. The car is seen from the rear following the curve with ease, making it seem as if it is on a rugged drive through the rugged terrain. The dirt road itself is surrounded by steep hills and mountains, with a clear blue sky above with wispy clouds.
A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
In the haunting backdrop of a war-torn city, where ruins and crumbled walls tell a story of devastation, a poignant close-up frames a young girl. Her face is smudged with ash, a silent testament to the chaos around her. Her eyes glistening with a mix of sorrow and resilience, capturing the raw emotion of a world that has lost its innocence to the ravages of conflict.

2
sat/requirements.txt
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@ -13,4 +13,4 @@ imageio==2.34.2
scipy==1.14.0
decord==0.6.0
wandb==0.17.5
deepspeed==0.14.4
deepspeed==0.14.4