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94598a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | #!/usr/bin/env python3
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import argparse
import os
import torch
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from diffusers import Cosmos2_5_PredictBasePipeline
from diffusers.utils import export_to_video, load_image
IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}
class ImageDataset(Dataset):
"""Dataset that loads images and their corresponding text prompts.
Expects a directory with:
<filename>.jpg / .jpeg / .png — the conditioning image
<filename>.txt — the prompt text
"""
def __init__(self, data_dir: str):
self.data_dir = data_dir
self.samples = []
for filename in sorted(os.listdir(data_dir)):
stem, ext = os.path.splitext(filename)
if ext.lower() not in IMAGE_EXTENSIONS:
continue
img_path = os.path.join(data_dir, filename)
txt_path = os.path.join(data_dir, stem + ".txt")
if not os.path.exists(txt_path):
print(f"WARNING: no prompt file found for {img_path}, skipping.")
continue
self.samples.append((img_path, txt_path, stem))
if len(self.samples) == 0:
raise ValueError(f"No valid image/prompt pairs found in {data_dir}")
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
img_path, txt_path, stem = self.samples[idx]
image = load_image(img_path)
with open(txt_path) as f:
prompt = f.read().strip()
return {
"image": image,
"prompt": prompt,
"stem": stem,
}
def collate_fn(batch):
"""Keep images as a list (PIL images can't be stacked into a tensor)."""
return {
"images": [item["image"] for item in batch],
"prompts": [item["prompt"] for item in batch],
"stems": [item["stem"] for item in batch],
}
def parse_args():
parser = argparse.ArgumentParser(description="Eval Cosmos Predict 2.5 with optional LoRA weights.")
parser.add_argument("--data_dir", type=str, required=True, help="Directory with image/prompt pairs.")
parser.add_argument("--output_dir", type=str, required=True, help="Directory to save generated outputs.")
parser.add_argument(
"--model_id", type=str, default="nvidia/Cosmos-Predict2.5-2B", help="HuggingFace model repository."
)
parser.add_argument(
"--revision",
type=str,
default="diffusers/base/post-trained",
choices=["diffusers/base/post-trained", "diffusers/base/pre-trained"],
)
parser.add_argument("--lora_dir", type=str, default=None, help="Path to LoRA weights directory.")
parser.add_argument("--num_output_frames", type=int, default=93, help="1 for image output, 93 for video output.")
parser.add_argument("--num_steps", type=int, default=36, help="Number of inference steps.")
parser.add_argument("--height", type=int, default=704, help="Output height in pixels (must be divisible by 16).")
parser.add_argument("--width", type=int, default=1280, help="Output width in pixels (must be divisible by 16).")
parser.add_argument("--seed", type=int, default=0, help="Random seed.")
parser.add_argument("--device", type=str, default="cuda", help="Device to use.")
parser.add_argument("--batch_size", type=int, default=1, help="Number of samples per batch.")
parser.add_argument("--num_workers", type=int, default=4, help="DataLoader worker processes.")
parser.add_argument(
"--negative_prompt",
type=str,
default=None,
help="Negative prompt. Defaults to the pipeline's built-in negative prompt.",
)
return parser.parse_args()
def main():
args = parse_args()
os.makedirs(args.output_dir, exist_ok=True)
dataset = ImageDataset(args.data_dir)
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
print(f"Found {len(dataset)} examples.")
class MockSafetyChecker:
def to(self, *args, **kwargs):
return self
def check_text_safety(self, *args, **kwargs):
return True
def check_video_safety(self, video):
return video
pipe = Cosmos2_5_PredictBasePipeline.from_pretrained(
args.model_id,
revision=args.revision,
device_map=args.device,
torch_dtype=torch.bfloat16,
safety_checker=MockSafetyChecker(),
)
if args.lora_dir is not None:
pipe.load_lora_weights(args.lora_dir)
pipe.fuse_lora(lora_scale=1.0)
print(f"Loaded LoRA weights from {args.lora_dir}")
progress = tqdm(total=len(dataset), desc="Generating")
for batch in dataloader:
images = batch["images"]
prompts = batch["prompts"]
stems = batch["stems"]
for image, prompt, stem in zip(images, prompts, stems):
frames = pipe(
image=image,
prompt=prompt,
negative_prompt=args.negative_prompt,
num_frames=args.num_output_frames,
num_inference_steps=args.num_steps,
height=args.height,
width=args.width,
).frames[0] # NOTE: batch_size == 1
out_path = os.path.join(args.output_dir, f"{stem}.mp4")
export_to_video(frames, out_path, fps=16)
tqdm.write(f" Saved to: {out_path}")
progress.update(1)
if __name__ == "__main__":
main()
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