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import pandas as pd
import torch
import numpy as np
import random
from diffusers import StableDiffusionPipeline
from diffusers.utils import export_to_video
# Specify the GPU to use (adjust as needed)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def set_seed(seed: int = 42):
"""
Set random seed for reproducibility
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # For multi-GPU
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Set random seed
set_seed(42)
def generate_image(pipeline, prompt: str, output_path: str):
"""
Generate an image using the Stable Diffusion model and save it
"""
with torch.autocast("cuda"):
image = pipeline(prompt).images[0]
image.save(output_path)
import torch
from diffusers.utils import export_to_video # Ensure these methods are correctly imported
def generate_video(pipeline, pipeline_type: str, prompt: str, output_path: str, **kwargs):
"""
Generate a video using different video generation pipelines and save as mp4 or gif
Parameters:
pipeline: Loaded video generation pipeline
pipeline_type: Type of video model, options are "cogvideo", "ltx", "hunyuan", "animatediff"
prompt: Text description
output_path: Output video path (animatediff defaults to gif, others to mp4)
kwargs: Hyperparameter settings, e.g., width, height, num_frames, num_inference_steps, fps, guidance_scale, etc.
"""
if pipeline_type == "cogvideo":
# Example call for CogVideoX (some hyperparameters may only apply to this pipeline)
video = pipeline(
prompt=prompt,
num_videos_per_prompt=kwargs.get("num_videos_per_prompt", 1),
num_inference_steps=kwargs.get("num_inference_steps", 50),
num_frames=kwargs.get("num_frames", 49),
guidance_scale=kwargs.get("guidance_scale", 6),
generator=kwargs.get("generator", torch.Generator(device="cuda").manual_seed(42))
).frames[0]
export_to_video(video, output_path, fps=kwargs.get("fps", 8))
elif pipeline_type == "ltx":
# Example call for LTXPipeline
video = pipeline(
prompt=prompt,
negative_prompt=kwargs.get("negative_prompt", "worst quality, inconsistent motion, blurry, jittery, distorted"),
width=kwargs.get("width", 704),
height=kwargs.get("height", 480),
num_frames=kwargs.get("num_frames", 161),
num_inference_steps=kwargs.get("num_inference_steps", 50),
).frames[0]
export_to_video(video, output_path, fps=kwargs.get("fps", 15))
elif pipeline_type == "hunyuan":
# Example call for HunyuanVideoPipeline
video = pipeline(
prompt=prompt,
width=kwargs.get("width", 512),
height=kwargs.get("height", 320),
num_frames=kwargs.get("num_frames", 61),
num_inference_steps=kwargs.get("num_inference_steps", 30),
).frames[0]
export_to_video(video, output_path, fps=kwargs.get("fps", 15))
elif pipeline_type == "animatediff":
# Example call for AnimateDiff-Lightning (defaults to generating gif)
video = pipeline(
prompt=prompt,
guidance_scale=kwargs.get("guidance_scale", 1.0),
num_inference_steps=kwargs.get("num_inference_steps", 4) # Default step is 4, options are 1,2,4,8
).frames[0]
export_to_video(video, output_path)
else:
raise ValueError(f"Unknown pipeline type: {pipeline_type}")
def load_video_pipeline(pipeline_type: str):
"""
Load the corresponding video generation model based on pipeline_type
Parameters:
pipeline_type: Options are "cogvideo", "ltx", "hunyuan", "animatediff"
Returns:
Loaded and initialized video generation pipeline
"""
if pipeline_type == "cogvideo":
from diffusers import CogVideoXPipeline
print("Loading video generation model (CogVideoX-5b)...")
pipe = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-5b",
torch_dtype=torch.bfloat16
)
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
pipe.to("cuda")
return pipe
elif pipeline_type == "ltx":
from diffusers import LTXPipeline
print("Loading video generation model (LTX-Video)...")
pipe = LTXPipeline.from_pretrained(
"Lightricks/LTX-Video",
torch_dtype=torch.bfloat16
)
pipe.to("cuda")
return pipe
elif pipeline_type == "hunyuan":
from diffusers import BitsAndBytesConfig, HunyuanVideoTransformer3DModel, HunyuanVideoPipeline
from diffusers.hooks import apply_layerwise_casting
from transformers import LlamaModel
print("Loading video generation model (HunyuanVideo)...")
model_id = "hunyuanvideo-community/HunyuanVideo"
quantization_config = BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16
)
text_encoder = LlamaModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.float16)
apply_layerwise_casting(text_encoder, storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.float16)
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
model_id,
subfolder="transformer",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
)
pipe = HunyuanVideoPipeline.from_pretrained(
model_id, transformer=transformer, text_encoder=text_encoder, torch_dtype=torch.float16
)
pipe.vae.enable_tiling()
pipe.enable_model_cpu_offload()
return pipe
elif pipeline_type == "animatediff":
from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
print("Loading video generation model (AnimateDiff-Lightning)...")
device = "cuda"
dtype = torch.float16
step = 4 # Options: [1,2,4,8], default is 4
repo = "ByteDance/AnimateDiff-Lightning"
ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
base = "emilianJR/epiCRealism" # Choose base model as preferred
adapter = MotionAdapter().to(device, dtype)
# Download and load weights
adapter.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear"
)
return pipe
else:
raise ValueError(f"Unknown pipeline type: {pipeline_type}")
def main():
# ============ 1. Load/Initialize Models ============
# (1) Image generation model: Stable Diffusion
print("Loading image generation model (Stable Diffusion)...")
pipe_image = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
)
pipe_image.to("cuda")
# Enable xformers acceleration if needed
# pipe_image.enable_xformers_memory_efficient_attention()
# (2) Video generation model: Choose "cogvideo", "ltx", or "hunyuan"
video_pipeline_type = "ltx" # Change here to select other models: "ltx" or "hunyuan" animatediff
# ============ 2. Define Task List ============
tasks1 = [
{
"csv_file": "output_prompt_rag_more/prompt_ai_concrete_rag_10_testset.csv",
"image_dir": "output_ai_covers_concrete_rag_10_testset",
"video_dir": "output_ai_videos_concrete_rag_10_testset_ltx"
},
{
"csv_file": "output_prompt_rag_more/prompt_ai_abstract_rag_10_testset.csv",
"image_dir": "output_ai_covers_abstract_rag_10_testset",
"video_dir": "output_ai_videos_abstract_rag_10_testset_ltx"
}
]
# Only the first task is used in the example
#tasks = [tasks[-4],tasks[-2]]
#tasks=tasks_ablation_abstract_5b+tasks_ablation_concrete_5b
#tasks= tasks_ablation_concrete2
tasks = tasks1
pipe_video = load_video_pipeline(video_pipeline_type)
# ============ 3. Iterate over CSV files to generate images and videos ============
for task in tasks:
csv_file = task["csv_file"]
image_dir = task["image_dir"]
video_dir = task["video_dir"]
os.makedirs(image_dir, exist_ok=True)
print(f"Ensuring directory exists: {image_dir}")
os.makedirs(video_dir, exist_ok=True)
print(f"Ensuring directory exists: {video_dir}")
if not os.path.exists(csv_file):
print(f"Error: CSV file {csv_file} not found, please check the path.")
continue
df = pd.read_csv(csv_file)
for idx, row in df.iterrows():
user_prompt = str(row["user prompt"])
title = str(row["title"])
cover_prompt = str(row["cover prompt"])
video_prompt = str(row["video prompt"])
# Generate filenames
image_filename = os.path.join(image_dir, f"{user_prompt}.png")
video_filename = os.path.join(video_dir, f"{user_prompt}.mp4")
print("-" * 50)
print(f"[CSV: {csv_file}] - [{idx}] Starting generation: {user_prompt}")
print(f"Title: {title}")
print(f"Cover Prompt: {cover_prompt}")
print(f"Video Prompt: {video_prompt}")
if os.path.exists(image_filename) and os.path.exists(video_filename):
print(f"File already exists, skipping generation: {video_filename}")
continue
# 4. Generate image
try:
generate_image(pipe_image, cover_prompt, image_filename)
print(f"Image saved to {image_filename}")
except Exception as e:
print(f"Image generation failed: {e}")
# 5. Generate video (customize hyperparameters by passing additional arguments)
try:
generate_video(
pipe_video,
pipeline_type=video_pipeline_type,
prompt=video_prompt,
output_path=video_filename
# To modify hyperparameters, pass them here, e.g.:
# num_inference_steps=60, num_frames=50, fps=10, width=640, height=360, guidance_scale=7, ...
)
print(f"Video saved to {video_filename}")
except Exception as e:
print(f"Video generation failed: {e}")
print("All generation tasks completed!")
if __name__ == "__main__":
main()
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