File size: 3,087 Bytes
c3c1b48 | 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 | # Copyright (c) 2025 SandAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from typing import Optional
from inference.common import MagiConfig, print_rank_0, set_random_seed
from inference.infra.distributed import dist_init
from inference.model.dit import get_dit
from .prompt_process import get_txt_embeddings
from .video_generate import generate_per_chunk
from .video_process import post_chunk_process, process_image, process_prefix_video, save_video_to_disk
class MagiPipeline:
def __init__(self, config_path, residual_stats_path: Optional[str] = None, l1_rel_stats_path: Optional[str] = None):
self.config = MagiConfig.from_json(config_path)
self.residual_stats_path = residual_stats_path
self.l1_rel_stats_path = l1_rel_stats_path
set_random_seed(self.config.runtime_config.seed)
dist_init(self.config)
print_rank_0(self.config)
def run_text_to_video(self, prompt: str, output_path: str):
self._run(prompt, None, output_path)
def run_image_to_video(self, prompt: str, image_path: str, output_path: str):
prefix_video = process_image(image_path, self.config)
self._run(prompt, prefix_video, output_path)
def run_video_to_video(self, prompt: str, prefix_video_path: str, output_path: str):
prefix_video = process_prefix_video(prefix_video_path, self.config)
self._run(prompt, prefix_video, output_path)
def _run(self, prompt: str, prefix_video: torch.Tensor, output_path: str):
caption_embs, emb_masks = get_txt_embeddings(prompt, self.config) # caption_embs: [1, 1, 800, 4096], emb_masks: [1, 800]
dit = get_dit(self.config)
videos = torch.cat(
[
post_chunk_process(chunk, self.config)
for chunk in generate_per_chunk(
model=dit,
prefix_video=prefix_video,
caption_embs=caption_embs,
emb_masks=emb_masks,
residual_stats_path=self.residual_stats_path,
l1_rel_stats_path=self.l1_rel_stats_path,
)
],
dim=0,
)
save_video_to_disk(videos, output_path, fps=self.config.runtime_config.fps)
mem_allocated_gb = torch.cuda.max_memory_allocated() / 1024**3
mem_reserved_gb = torch.cuda.max_memory_reserved() / 1024**3
print_rank_0(
f"Finish MagiPipeline, max memory allocated: {mem_allocated_gb:.2f} GB, max memory reserved: {mem_reserved_gb:.2f} GB"
)
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