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56
inference/cli_demo.py
View File

@ -10,42 +10,20 @@ Run the script:
"""
import gc
import argparse
import tempfile
from typing import Union, List
import PIL
import imageio
import numpy as np
import torch
from diffusers import CogVideoXPipeline, CogVideoXDDIMScheduler
def export_to_video_imageio(
video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 8
) -> str:
"""
Export the video frames to a video file using imageio lib to Avoid "green screen" issue (for example CogVideoX)
"""
if output_video_path is None:
output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
if isinstance(video_frames[0], PIL.Image.Image):
video_frames = [np.array(frame) for frame in video_frames]
with imageio.get_writer(output_video_path, fps=fps) as writer:
for frame in video_frames:
writer.append_data(frame)
return output_video_path
from diffusers.utils import export_to_video
def generate_video(
prompt: str,
model_path: str,
output_path: str = "./output.mp4",
num_inference_steps: int = 50,
guidance_scale: float = 6.0,
num_videos_per_prompt: int = 1,
dtype: torch.dtype = torch.bloat16,
prompt: str,
model_path: str,
output_path: str = "./output.mp4",
num_inference_steps: int = 50,
guidance_scale: float = 6.0,
num_videos_per_prompt: int = 1,
dtype: torch.dtype = torch.bfloat16,
):
"""
Generates a video based on the given prompt and saves it to the specified path.
@ -72,20 +50,14 @@ def generate_video(
# We recommend using `CogVideoXDDIMScheduler` for better results.
pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# 3. Enable CPU offload for the model and reset the memory, enable tiling.
# 3. Enable CPU offload for the model, enable tiling.
pipe.enable_model_cpu_offload()
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_accumulated_memory_stats()
torch.cuda.reset_peak_memory_stats()
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.
# for diffusers `0.30.1` and after version, this should be 49.
video = pipe(
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt, # Number of videos to generate per prompt
@ -95,8 +67,8 @@ def generate_video(
generator=torch.Generator().manual_seed(42), # Set the seed for reproducibility
).frames[0]
# 5. Export the generated frames to a video file. fps must be 8
export_to_video_imageio(video, output_path, fps=8)
# 5. Export the generated frames to a video file. fps must be 8 for original video.
export_to_video(video, output_path, fps=8)
if __name__ == "__main__":
@ -119,7 +91,9 @@ if __name__ == "__main__":
args = parser.parse_args()
# Convert dtype argument to torch.dtype, NOT suggest BF16.
# Convert dtype argument to torch.dtype.
# For CogVideoX-2B model, use torch.float16.
# For CogVideoX-5B model, use torch.bfloat16.
dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
# main function to generate video.

167
inference/gradio_web_demo.py
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@ -1,23 +1,21 @@
import os
import tempfile
import threading
import time
import gradio as gr
import numpy as np
import torch
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
from datetime import datetime, timedelta
from openai import OpenAI
import imageio
import moviepy.editor as mp
from typing import List, Union
import PIL
dtype = torch.bfloat16
dtype = torch.float16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=dtype).to(device)
os.makedirs("./output", exist_ok=True)
os.makedirs("./gradio_tmp", exist_ok=True)
sys_prompt = """You are part of a team of bots that creates videos. You work with an assistant bot that will draw anything you say in square brackets.
@ -33,25 +31,6 @@ Video descriptions must have the same num of words as examples below. Extra word
"""
def export_to_video_imageio(
video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 8
) -> str:
"""
Export the video frames to a video file using imageio lib to Avoid "green screen" issue (for example CogVideoX)
"""
if output_video_path is None:
output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
if isinstance(video_frames[0], PIL.Image.Image):
video_frames = [np.array(frame) for frame in video_frames]
with imageio.get_writer(output_video_path, fps=fps) as writer:
for frame in video_frames:
writer.append_data(frame)
return output_video_path
def convert_prompt(prompt: str, retry_times: int = 3) -> str:
if not os.environ.get("OPENAI_API_KEY"):
return prompt
@ -104,22 +83,12 @@ def convert_prompt(prompt: str, retry_times: int = 3) -> str:
def infer(prompt: str, num_inference_steps: int, guidance_scale: float, progress=gr.Progress(track_tqdm=True)):
torch.cuda.empty_cache()
prompt_embeds, _ = pipe.encode_prompt(
prompt=prompt,
negative_prompt=None,
do_classifier_free_guidance=True,
num_videos_per_prompt=1,
max_sequence_length=226,
device=device,
dtype=dtype,
)
video = pipe(
prompt=prompt,
num_videos_per_prompt=1,
num_inference_steps=num_inference_steps,
num_frames=49,
guidance_scale=guidance_scale,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=torch.zeros_like(prompt_embeds),
).frames[0]
return video
@ -129,7 +98,7 @@ def save_video(tensor):
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
video_path = f"./output/{timestamp}.mp4"
os.makedirs(os.path.dirname(video_path), exist_ok=True)
export_to_video_imageio(tensor[1:], video_path)
export_to_video(tensor, video_path)
return video_path
@ -146,14 +115,16 @@ def delete_old_files():
while True:
now = datetime.now()
cutoff = now - timedelta(minutes=10)
output_dir = "./output"
for filename in os.listdir(output_dir):
file_path = os.path.join(output_dir, filename)
if os.path.isfile(file_path):
file_mtime = datetime.fromtimestamp(os.path.getmtime(file_path))
if file_mtime < cutoff:
os.remove(file_path)
time.sleep(600) # Sleep for 10 minutes
directories = ["./output", "./gradio_tmp"]
for directory in directories:
for filename in os.listdir(directory):
file_path = os.path.join(directory, filename)
if os.path.isfile(file_path):
file_mtime = datetime.fromtimestamp(os.path.getmtime(file_path))
if file_mtime < cutoff:
os.remove(file_path)
time.sleep(600)
threading.Thread(target=delete_old_files, daemon=True).start()
@ -164,8 +135,9 @@ with gr.Blocks() as demo:
CogVideoX-2B Huggingface Space🤗
</div>
<div style="text-align: center;">
<a href="https://huggingface.co/THUDM/CogVideoX-2b">🤗 Model Hub</a> |
<a href="https://github.com/THUDM/CogVideo">🌐 Github</a>
<a href="https://huggingface.co/THUDM/CogVideoX-2B">🤗 2B Model Hub</a> |
<a href="https://github.com/THUDM/CogVideo">🌐 Github</a> |
<a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
</div>
<div style="text-align: center; font-size: 15px; font-weight: bold; color: red; margin-bottom: 20px;">
@ -176,18 +148,17 @@ with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)
with gr.Row():
gr.Markdown(
"✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one."
)
"✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one.")
enhance_button = gr.Button("✨ Enhance Prompt(Optional)")
with gr.Column():
gr.Markdown(
"**Optional Parameters** (default values are recommended)<br>"
"Turn Inference Steps larger if you want more detailed video, but it will be slower.<br>"
"50 steps are recommended for most cases. will cause 120 seconds for inference.<br>"
)
gr.Markdown("**Optional Parameters** (default values are recommended)<br>"
"Increasing the number of inference steps will produce more detailed videos, but it will slow down the process.<br>"
"50 steps are recommended for most cases.<br>"
"For the 5B model, 50 steps will take approximately 350 seconds.")
with gr.Row():
num_inference_steps = gr.Number(label="Inference Steps", value=50)
guidance_scale = gr.Number(label="Guidance Scale", value=6.0)
@ -200,41 +171,43 @@ with gr.Blocks() as demo:
download_gif_button = gr.File(label="📥 Download GIF", visible=False)
gr.Markdown("""
<table border="1" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<th>Prompt</th>
<th>Video URL</th>
<th>Inference Steps</th>
<th>Guidance Scale</th>
</tr>
<tr>
<td>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.</td>
<td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/1.mp4">Video 1</a></td>
<td>50</td>
<td>6</td>
</tr>
<tr>
<td>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 it’s 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.</td>
<td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/2.mp4">Video 2</a></td>
<td>50</td>
<td>6</td>
</tr>
<tr>
<td>A street artist, clad in a worn-out denim jacket and a colorful bandana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall.</td>
<td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/3.mp4">Video 3</a></td>
<td>50</td>
<td>6</td>
</tr>
<tr>
<td>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.</td>
<td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/4.mp4">Video 4</a></td>
<td>50</td>
<td>6</td>
</tr>
</table>
<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<div style="text-align: center; font-size: 24px; font-weight: bold; margin-bottom: 20px;">
Demo Videos with 50 Inference Steps and 6.0 Guidance Scale.
</div>
<tr>
<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
<p>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.</p>
</td>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/ea3af39a-3160-4999-90ec-2f7863c5b0e9" width="100%" controls autoplay></video>
</td>
<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
<p>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.</p>
</td>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/9de41efd-d4d1-4095-aeda-246dd834e91d" width="100%" controls autoplay></video>
</td>
</tr>
<tr>
<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
<p>A street artist, clad in a worn-out denim jacket and a colorful bandana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall.</p>
</td>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/941d6661-6a8d-4a1b-b912-59606f0b2841" width="100%" controls autoplay></video>
</td>
<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
<p>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.</p>
</td>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/938529c4-91ae-4f60-b96b-3c3947fa63cb" width="100%" controls autoplay></video>
</td>
</tr>
</table>
""")
def generate(prompt, num_inference_steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
def generate(prompt, num_inference_steps, guidance_scale, model_choice, progress=gr.Progress(track_tqdm=True)):
tensor = infer(prompt, num_inference_steps, guidance_scale, progress=progress)
video_path = save_video(tensor)
video_update = gr.update(visible=True, value=video_path)
@ -243,16 +216,22 @@ with gr.Blocks() as demo:
return video_path, video_update, gif_update
def enhance_prompt_func(prompt):
return convert_prompt(prompt, retry_times=1)
generate_button.click(
generate,
inputs=[prompt, num_inference_steps, guidance_scale],
outputs=[video_output, download_video_button, download_gif_button],
outputs=[video_output, download_video_button, download_gif_button]
)
enhance_button.click(enhance_prompt_func, inputs=[prompt], outputs=[prompt])
enhance_button.click(
enhance_prompt_func,
inputs=[prompt],
outputs=[prompt]
)
if __name__ == "__main__":
demo.launch(server_name="127.0.0.1", server_port=7870, share=True)
demo.launch()

24
requirements.txt
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@ -1,14 +1,14 @@
git+https://github.com/huggingface/diffusers.git@main#egg=diffusers
transformers==4.44.0
git+https://github.com/huggingface/accelerate.git@main#egg=accelerate
sentencepiece==0.2.0 # T5
SwissArmyTransformer==0.4.12 # Inference
torch==2.4.0 # Tested in 2.2 2.3 2.4 and 2.5
torchvision==0.19.0
gradio==4.40.0 # For HF gradio demo
pillow==9.5.0 # For HF gradio demo
streamlit==1.37.0 # For streamlit web demo
diffusers>=0.30.1 #git+https://github.com/huggingface/diffusers.git@main#egg=diffusers is suggested
transformers>=4.44.0 # The development team is working on version 0.44.2
accelerate>=0.33.0 #git+https://github.com/huggingface/accelerate.git@main#egg=accelerate is suggested
sentencepiece>=0.2.0 # T5 used
SwissArmyTransformer>=0.4.12 # Inference
torch>=2.4.0 # Tested in 2.2 2.3 2.4 and 2.5, The development team is working on version 2.4.0.
torchvision>=0.19.0 # The development team is working on version 0.19.0.
gradio>=4.42.0 # For HF gradio demo
streamlit>=1.37.1 # For streamlit web demo
imageio==2.34.2 # For diffusers inference export video
imageio-ffmpeg==0.5.1 # For diffusers inference export video
openai==1.40.6 # For prompt refiner
moviepy==1.0.3 # For export video
openai>=1.42.0 # For prompt refiner
moviepy==1.0.3 # For export video
pillow==9.5.0 # For moviepy

153
sat/configs/cogvideox_5b.yaml Normal file
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@ -0,0 +1,153 @@
model:
scale_factor: 0.7 # different from cogvideox_2b_infer.yaml
disable_first_stage_autocast: true
log_keys:
- txt
denoiser_config:
target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
params:
num_idx: 1000
quantize_c_noise: False
weighting_config:
target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
scaling_config:
target: sgm.modules.diffusionmodules.denoiser_scaling.VideoScaling
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
params:
shift_scale: 1.0 # different from cogvideox_2b_infer.yaml
network_config:
target: dit_video_concat.DiffusionTransformer
params:
time_embed_dim: 512
elementwise_affine: True
num_frames: 49
time_compressed_rate: 4
latent_width: 90
latent_height: 60
num_layers: 42 # different from cogvideox_2b_infer.yaml
patch_size: 2
in_channels: 16
out_channels: 16
hidden_size: 3072 # different from cogvideox_2b_infer.yaml
adm_in_channels: 256
num_attention_heads: 48 # different from cogvideox_2b_infer.yaml
transformer_args:
checkpoint_activations: True
vocab_size: 1
max_sequence_length: 64
layernorm_order: pre
skip_init: false
model_parallel_size: 1
is_decoder: false
modules:
pos_embed_config:
target: dit_video_concat.Rotary3DPositionEmbeddingMixin # different from cogvideox_2b_infer.yaml
params:
hidden_size_head: 64
text_length: 226
patch_embed_config:
target: dit_video_concat.ImagePatchEmbeddingMixin
params:
text_hidden_size: 4096
adaln_layer_config:
target: dit_video_concat.AdaLNMixin
params:
qk_ln: True
final_layer_config:
target: dit_video_concat.FinalLayerMixin
conditioner_config:
target: sgm.modules.GeneralConditioner
params:
emb_models:
- is_trainable: false
input_key: txt
ucg_rate: 0.1
target: sgm.modules.encoders.modules.FrozenT5Embedder
params:
model_dir: "/share/official_pretrains/hf_home/t5-v1_1-xxl"
max_length: 226
first_stage_config:
target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
params:
cp_size: 1
ckpt_path: "/share/official_pretrains/sat_home/cogvideox-5b-sat/vae/3d-vae.pt"
ignore_keys: [ 'loss' ]
loss_config:
target: torch.nn.Identity
regularizer_config:
target: vae_modules.regularizers.DiagonalGaussianRegularizer
encoder_config:
target: vae_modules.cp_enc_dec.ContextParallelEncoder3D
params:
double_z: true
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1, 2, 2, 4 ]
attn_resolutions: [ ]
num_res_blocks: 3
dropout: 0.0
gather_norm: True
decoder_config:
target: vae_modules.cp_enc_dec.ContextParallelDecoder3D
params:
double_z: True
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1, 2, 2, 4 ]
attn_resolutions: [ ]
num_res_blocks: 3
dropout: 0.0
gather_norm: False
loss_fn_config:
target: sgm.modules.diffusionmodules.loss.VideoDiffusionLoss
params:
offset_noise_level: 0
sigma_sampler_config:
target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
params:
uniform_sampling: True
num_idx: 1000
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
params:
shift_scale: 1.0 # different from cogvideox_2b_infer.yaml
sampler_config:
target: sgm.modules.diffusionmodules.sampling.VPSDEDPMPP2MSampler
params:
num_steps: 50
verbose: True
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
params:
shift_scale: 1.0 # different from cogvideox_2b_infer.yaml
guider_config:
target: sgm.modules.diffusionmodules.guiders.DynamicCFG
params:
scale: 6
exp: 5
num_steps: 50

159
sat/configs/cogvideox_5b_lora.yaml Normal file
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@ -0,0 +1,159 @@
model:
scale_factor: 0.7 # different from cogvideox_2b_infer.yaml
disable_first_stage_autocast: true
not_trainable_prefixes: ['all'] ## Using Lora
log_keys:
- txt
denoiser_config:
target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
params:
num_idx: 1000
quantize_c_noise: False
weighting_config:
target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
scaling_config:
target: sgm.modules.diffusionmodules.denoiser_scaling.VideoScaling
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
params:
shift_scale: 1.0 # different from cogvideox_2b_infer.yaml
network_config:
target: dit_video_concat.DiffusionTransformer
params:
time_embed_dim: 512
elementwise_affine: True
num_frames: 49
time_compressed_rate: 4
latent_width: 90
latent_height: 60
num_layers: 42 # different from cogvideox_2b_infer.yaml
patch_size: 2
in_channels: 16
out_channels: 16
hidden_size: 3072 # different from cogvideox_2b_infer.yaml
adm_in_channels: 256
num_attention_heads: 48 # different from cogvideox_2b_infer.yaml
transformer_args:
checkpoint_activations: True
vocab_size: 1
max_sequence_length: 64
layernorm_order: pre
skip_init: false
model_parallel_size: 1
is_decoder: false
modules:
pos_embed_config:
target: dit_video_concat.Rotary3DPositionEmbeddingMixin # different from cogvideox_2b_infer.yaml
params:
hidden_size_head: 64
text_length: 226
lora_config: ## Using Lora
target: sat.model.finetune.lora2.LoraMixin
params:
r: 128
patch_embed_config:
target: dit_video_concat.ImagePatchEmbeddingMixin
params:
text_hidden_size: 4096
adaln_layer_config:
target: dit_video_concat.AdaLNMixin
params:
qk_ln: True
final_layer_config:
target: dit_video_concat.FinalLayerMixin
conditioner_config:
target: sgm.modules.GeneralConditioner
params:
emb_models:
- is_trainable: false
input_key: txt
ucg_rate: 0.1
target: sgm.modules.encoders.modules.FrozenT5Embedder
params:
model_dir: "t5-v1_1-xxl"
max_length: 226
first_stage_config:
target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
params:
cp_size: 1
ckpt_path: "cogvideox-5b-sat/vae/3d-vae.pt"
ignore_keys: [ 'loss' ]
loss_config:
target: torch.nn.Identity
regularizer_config:
target: vae_modules.regularizers.DiagonalGaussianRegularizer
encoder_config:
target: vae_modules.cp_enc_dec.ContextParallelEncoder3D
params:
double_z: true
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1, 2, 2, 4 ]
attn_resolutions: [ ]
num_res_blocks: 3
dropout: 0.0
gather_norm: True
decoder_config:
target: vae_modules.cp_enc_dec.ContextParallelDecoder3D
params:
double_z: True
z_channels: 16
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1, 2, 2, 4 ]
attn_resolutions: [ ]
num_res_blocks: 3
dropout: 0.0
gather_norm: False
loss_fn_config:
target: sgm.modules.diffusionmodules.loss.VideoDiffusionLoss
params:
offset_noise_level: 0
sigma_sampler_config:
target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
params:
uniform_sampling: True
num_idx: 1000
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
params:
shift_scale: 1.0 # different from cogvideox_2b_infer.yaml
sampler_config:
target: sgm.modules.diffusionmodules.sampling.VPSDEDPMPP2MSampler
params:
num_steps: 50
verbose: True
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
params:
shift_scale: 1.0
guider_config:
target: sgm.modules.diffusionmodules.guiders.DynamicCFG
params:
scale: 6
exp: 5
num_steps: 50