Merge branch 'CogVideoX_dev' of github.com:THUDM/CogVideo into CogVideoX_dev

README.md

@@ -22,6 +22,7 @@ Experience the CogVideoX-5B model online at <a href="https://huggingface.co/spac
 
 ## Project Updates
 
+- 🔥🔥 **News**: ```2024/9/25```: CogVideoX web demo is available on Replicate. Try the text-to-video model **CogVideoX-5B** here [![Replicate](https://replicate.com/chenxwh/cogvideox-t2v/badge)](https://replicate.com/chenxwh/cogvideox-t2v) and image-to-video model **CogVideoX-5B-I2V** here [![Replicate](https://replicate.com/chenxwh/cogvideox-i2v/badge)](https://replicate.com/chenxwh/cogvideox-i2v).
 - 🔥🔥 **News**: ```2024/9/19```: We have open-sourced the CogVideoX series image-to-video model **CogVideoX-5B-I2V**.
   This model can take an image as a background input and generate a video combined with prompt words, offering greater
   controllability. With this, the CogVideoX series models now support three tasks: text-to-video generation, video
@@ -358,6 +359,9 @@ This folder contains some tools for model conversion / caption generation, etc.
   Adapter.
 + [llm_flux_cogvideox](tools/llm_flux_cogvideox/llm_flux_cogvideox.py): Automatically generate videos using an
   open-source local large language model + Flux + CogVideoX.
++ [parallel_inference_xdit](tools/parallel_inference/parallel_inference_xdit.py):
+Supported by [xDiT](https://github.com/xdit-project/xDiT), parallelize the
+  video generation process on multiple GPUs.
 
 ## CogVideo(ICLR'23)
 

README_ja.md

@@ -329,6 +329,9 @@ pipe.vae.enable_tiling()
   をロードするためのツールコード。
 + [llm_flux_cogvideox](tools/llm_flux_cogvideox/llm_flux_cogvideox.py): オープンソースのローカル大規模言語モデル +
   Flux + CogVideoX を使用して自動的に動画を生成します。
++ [parallel_inference_xdit](tools/parallel_inference/parallel_inference_xdit.py)：
+[xDiT](https://github.com/xdit-project/xDiT)
+  によってサポートされ、ビデオ生成プロセスを複数の GPU で並列化します。
 
 ## CogVideo(ICLR'23)
 

README_zh.md

@@ -312,6 +312,9 @@ pipe.vae.enable_tiling()
 + [load_cogvideox_lora](tools/load_cogvideox_lora.py): 载入diffusers版微调Lora Adapter的工具代码。
 + [llm_flux_cogvideox](tools/llm_flux_cogvideox/llm_flux_cogvideox.py): 使用开源本地大语言模型 + Flux +
   CogVideoX实现自动化生成视频。
++ [parallel_inference_xdit](tools/parallel_inference/parallel_inference_xdit.py):
+在多个 GPU 上并行化视频生成过程，
+  由[xDiT](https://github.com/xdit-project/xDiT)提供支持。
 
 ## CogVideo(ICLR'23)
 

inference/cli_demo.py

@@ -133,7 +133,7 @@ def generate_video(
             video=video,  # The path of the video to be used as the background of the video
             num_videos_per_prompt=num_videos_per_prompt,
             num_inference_steps=num_inference_steps,
-            num_frames=49,
+            # num_frames=49,
             use_dynamic_cfg=True,
             guidance_scale=guidance_scale,
             generator=torch.Generator().manual_seed(seed),  # Set the seed for reproducibility

requirements.txt

@@ -12,3 +12,4 @@ imageio-ffmpeg>=0.5.1
 openai>=1.45.0
 moviepy>=1.0.3
 pillow==9.5.0
+scikit-video

tools/parallel_inference/parallel_inference_xdit.py

@@ -0,0 +1,105 @@
+"""
+This is a parallel inference script for CogVideo. The original script
+can be found from the xDiT project at
+
+https://github.com/xdit-project/xDiT/blob/main/examples/cogvideox_example.py
+
+By using this code, the inference process is parallelized on multiple GPUs,
+and thus speeded up.
+
+Usage:
+1. pip install xfuser
+2. mkdir results
+3. run the following command to generate video
+torchrun --nproc_per_node=4 parallel_inference_xdit.py \
+    --model <cogvideox-model-path> --ulysses_degree 1 --ring_degree 2 \
+    --use_cfg_parallel --height 480 --width 720 --num_frames 9 \
+    --prompt 'A small dog.'
+
+You can also use the run.sh file in the same folder to automate running this
+code for batch generation of videos, by running:
+
+sh ./run.sh
+
+"""
+
+import time
+import torch
+import torch.distributed
+from diffusers import AutoencoderKLTemporalDecoder
+from xfuser import xFuserCogVideoXPipeline, xFuserArgs
+from xfuser.config import FlexibleArgumentParser
+from xfuser.core.distributed import (
+    get_world_group,
+    get_data_parallel_rank,
+    get_data_parallel_world_size,
+    get_runtime_state,
+    is_dp_last_group,
+)
+from diffusers.utils import export_to_video
+
+
+def main():
+    parser = FlexibleArgumentParser(description="xFuser Arguments")
+    args = xFuserArgs.add_cli_args(parser).parse_args()
+    engine_args = xFuserArgs.from_cli_args(args)
+
+    # Check if ulysses_degree is valid
+    num_heads = 30
+    if engine_args.ulysses_degree > 0 and num_heads % engine_args.ulysses_degree != 0:
+        raise ValueError(
+            f"ulysses_degree ({engine_args.ulysses_degree}) must be a divisor of the number of heads ({num_heads})"
+        )
+
+    engine_config, input_config = engine_args.create_config()
+    local_rank = get_world_group().local_rank
+
+    pipe = xFuserCogVideoXPipeline.from_pretrained(
+        pretrained_model_name_or_path=engine_config.model_config.model,
+        engine_config=engine_config,
+        torch_dtype=torch.bfloat16,
+    )
+    if args.enable_sequential_cpu_offload:
+        pipe.enable_model_cpu_offload(gpu_id=local_rank)
+        pipe.vae.enable_tiling()
+    else:
+        device = torch.device(f"cuda:{local_rank}")
+        pipe = pipe.to(device)
+
+    torch.cuda.reset_peak_memory_stats()
+    start_time = time.time()
+
+    output = pipe(
+        height=input_config.height,
+        width=input_config.width,
+        num_frames=input_config.num_frames,
+        prompt=input_config.prompt,
+        num_inference_steps=input_config.num_inference_steps,
+        generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
+        guidance_scale=6,
+    ).frames[0]
+
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    peak_memory = torch.cuda.max_memory_allocated(device=f"cuda:{local_rank}")
+
+    parallel_info = (
+        f"dp{engine_args.data_parallel_degree}_cfg{engine_config.parallel_config.cfg_degree}_"
+        f"ulysses{engine_args.ulysses_degree}_ring{engine_args.ring_degree}_"
+        f"tp{engine_args.tensor_parallel_degree}_"
+        f"pp{engine_args.pipefusion_parallel_degree}_patch{engine_args.num_pipeline_patch}"
+    )
+    if is_dp_last_group():
+        world_size = get_data_parallel_world_size()
+        resolution = f"{input_config.width}x{input_config.height}"
+        output_filename = f"results/cogvideox_{parallel_info}_{resolution}.mp4"
+        export_to_video(output, output_filename, fps=8)
+        print(f"output saved to {output_filename}")
+
+    if get_world_group().rank == get_world_group().world_size - 1:
+        print(f"epoch time: {elapsed_time:.2f} sec, memory: {peak_memory/1e9} GB")
+    get_runtime_state().destory_distributed_env()
+
+
+if __name__ == "__main__":
+    main()

tools/parallel_inference/run.sh

@@ -0,0 +1,51 @@
+set -x
+
+export PYTHONPATH=$PWD:$PYTHONPATH
+
+# Select the model type
+# The model is downloaded to a specified location on disk, 
+# or you can simply use the model's ID on Hugging Face, 
+# which will then be downloaded to the default cache path on Hugging Face.
+
+export MODEL_TYPE="CogVideoX"
+# Configuration for different model types
+# script, model_id, inference_step
+declare -A MODEL_CONFIGS=(
+    ["CogVideoX"]="parallel_inference_xdit.py /cfs/dit/CogVideoX-2b 20"
+)
+
+if [[ -v MODEL_CONFIGS[$MODEL_TYPE] ]]; then
+    IFS=' ' read -r SCRIPT MODEL_ID INFERENCE_STEP <<< "${MODEL_CONFIGS[$MODEL_TYPE]}"
+    export SCRIPT MODEL_ID INFERENCE_STEP
+else
+    echo "Invalid MODEL_TYPE: $MODEL_TYPE"
+    exit 1
+fi
+
+mkdir -p ./results
+
+# task args
+if [ "$MODEL_TYPE" = "CogVideoX" ]; then
+  TASK_ARGS="--height 480 --width 720 --num_frames 9"
+fi
+
+# CogVideoX asserts sp_degree == ulysses_degree*ring_degree <= 2. Also, do not set the pipefusion degree.
+if [ "$MODEL_TYPE" = "CogVideoX" ]; then
+N_GPUS=4
+PARALLEL_ARGS="--ulysses_degree 2 --ring_degree 1"
+CFG_ARGS="--use_cfg_parallel"
+fi
+
+
+torchrun --nproc_per_node=$N_GPUS ./$SCRIPT \
+--model $MODEL_ID \
+$PARALLEL_ARGS \
+$TASK_ARGS \
+$PIPEFUSION_ARGS \
+$OUTPUT_ARGS \
+--num_inference_steps $INFERENCE_STEP \
+--warmup_steps 0 \
+--prompt "A small dog." \
+$CFG_ARGS \
+$PARALLLEL_VAE \
+$COMPILE_FLAG

tools/replicate/cog.yaml

@@ -0,0 +1,37 @@
+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.11' or '3.11.4'
+  python_version: "3.11"
+
+  # a list of packages in the format <package-name>==<version>
+  python_packages:
+    - diffusers>=0.30.3
+    - accelerate>=0.34.2
+    - transformers>=4.44.2
+    - numpy==1.26.0
+    - torch>=2.4.0
+    - torchvision>=0.19.0
+    - sentencepiece>=0.2.0
+    - SwissArmyTransformer>=0.4.12
+    - imageio>=2.35.1
+    - imageio-ffmpeg>=0.5.1
+    - openai>=1.45.0
+    - moviepy>=1.0.3
+    - pillow==9.5.0
+    - pydantic==1.10.7
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.8.2/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "predict_t2v.py:Predictor"
+# predict: "predict_i2v.py:Predictor"

tools/replicate/predict_i2v.py

@@ -0,0 +1,89 @@
+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+import os
+import subprocess
+import time
+import torch
+from diffusers import CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from cog import BasePredictor, Input, Path
+
+
+MODEL_CACHE = "model_cache_i2v"
+MODEL_URL = (
+    f"https://weights.replicate.delivery/default/THUDM/CogVideo/{MODEL_CACHE}.tar"
+)
+os.environ["HF_DATASETS_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+os.environ["HF_HOME"] = MODEL_CACHE
+os.environ["TORCH_HOME"] = MODEL_CACHE
+os.environ["HF_DATASETS_CACHE"] = MODEL_CACHE
+os.environ["TRANSFORMERS_CACHE"] = MODEL_CACHE
+os.environ["HUGGINGFACE_HUB_CACHE"] = MODEL_CACHE
+
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+
+        if not os.path.exists(MODEL_CACHE):
+            download_weights(MODEL_URL, MODEL_CACHE)
+
+        # model_id: THUDM/CogVideoX-5b-I2V
+        self.pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+            MODEL_CACHE, torch_dtype=torch.bfloat16
+        ).to("cuda")
+
+        self.pipe.enable_model_cpu_offload()
+        self.pipe.vae.enable_tiling()
+
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Input prompt", default="Starry sky slowly rotating."
+        ),
+        image: Path = Input(description="Input image"),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps", ge=1, le=500, default=50
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance", ge=1, le=20, default=6
+        ),
+        num_frames: int = Input(
+            description="Number of frames for the output video", default=49
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+    ) -> Path:
+        """Run a single prediction on the model"""
+
+        if seed is None:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(f"Using seed: {seed}")
+
+        img = load_image(image=str(image))
+
+        video = self.pipe(
+            prompt=prompt,
+            image=img,
+            num_videos_per_prompt=1,
+            num_inference_steps=num_inference_steps,
+            num_frames=num_frames,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator(device="cuda").manual_seed(seed),
+        ).frames[0]
+
+        out_path = "/tmp/out.mp4"
+
+        export_to_video(video, out_path, fps=8)
+        return Path(out_path)

tools/replicate/predict_t2v.py

@@ -0,0 +1,87 @@
+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+import os
+import subprocess
+import time
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+from cog import BasePredictor, Input, Path
+
+
+MODEL_CACHE = "model_cache"
+MODEL_URL = (
+    f"https://weights.replicate.delivery/default/THUDM/CogVideo/{MODEL_CACHE}.tar"
+)
+os.environ["HF_DATASETS_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+os.environ["HF_HOME"] = MODEL_CACHE
+os.environ["TORCH_HOME"] = MODEL_CACHE
+os.environ["HF_DATASETS_CACHE"] = MODEL_CACHE
+os.environ["TRANSFORMERS_CACHE"] = MODEL_CACHE
+os.environ["HUGGINGFACE_HUB_CACHE"] = MODEL_CACHE
+
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+
+        if not os.path.exists(MODEL_CACHE):
+            download_weights(MODEL_URL, MODEL_CACHE)
+
+        # model_id: THUDM/CogVideoX-5b
+        self.pipe = CogVideoXPipeline.from_pretrained(
+            MODEL_CACHE,
+            torch_dtype=torch.bfloat16,
+        ).to("cuda")
+
+        self.pipe.enable_model_cpu_offload()
+        self.pipe.vae.enable_tiling()
+
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Input prompt",
+            default="A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.",
+        ),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps", ge=1, le=500, default=50
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance", ge=1, le=20, default=6
+        ),
+        num_frames: int = Input(
+            description="Number of frames for the output video", default=49
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+    ) -> Path:
+        """Run a single prediction on the model"""
+
+        if seed is None:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(f"Using seed: {seed}")
+
+        video = self.pipe(
+            prompt=prompt,
+            num_videos_per_prompt=1,
+            num_inference_steps=num_inference_steps,
+            num_frames=num_frames,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator(device="cuda").manual_seed(seed),
+        ).frames[0]
+
+        out_path = "/tmp/out.mp4"
+
+        export_to_video(video, out_path, fps=8)
+        return Path(out_path)