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Krea realtime video (#1011)
* krea-realtime-video * Add Krea real-time video inference and training support * Delete .gitignore * update README * update README --------- Co-authored-by: Artiprocher <wangye87v5@hotmail.com> Co-authored-by: Jintao Huang <huangjintao.hjt@alibaba-inc.com> Co-authored-by: Zhongjie Duan <35051019+Artiprocher@users.noreply.github.com>
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@@ -76,6 +76,8 @@ save_video(video, "video1.mp4", fps=15, quality=5)
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|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B.py)|[code](./model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
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|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-14B.py)|[code](./model_training/full/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./model_training/lora/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-14B.py)|
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|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
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|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](./model_inference/krea-realtime-video.py)|[code](./model_training/full/krea-realtime-video.sh)|[code](./model_training/validate_full/krea-realtime-video.py)|[code](./model_training/lora/krea-realtime-video.sh)|[code](./model_training/validate_lora/krea-realtime-video.py)|
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## Model Inference
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@@ -76,6 +76,8 @@ save_video(video, "video1.mp4", fps=15, quality=5)
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|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B.py)|[code](./model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
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|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-14B.py)|[code](./model_training/full/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./model_training/lora/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-14B.py)|
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|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
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|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](./model_inference/krea-realtime-video.py)|[code](./model_training/full/krea-realtime-video.sh)|[code](./model_training/validate_full/krea-realtime-video.py)|[code](./model_training/lora/krea-realtime-video.sh)|[code](./model_training/validate_lora/krea-realtime-video.py)|
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## 模型推理
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25
examples/wanvideo/model_inference/krea-realtime-video.py
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examples/wanvideo/model_inference/krea-realtime-video.py
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import torch
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from diffsynth import save_video
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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pipe.enable_vram_management()
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# Text-to-video
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video = pipe(
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prompt="a cat sitting on a boat",
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num_inference_steps=6, num_frames=81,
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seed=0, tiled=True,
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cfg_scale=1,
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sigma_shift=20,
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)
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save_video(video, "video1.mp4", fps=15, quality=5)
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12
examples/wanvideo/model_training/full/krea-realtime-video.sh
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examples/wanvideo/model_training/full/krea-realtime-video.sh
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accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "krea/krea-realtime-video:krea-realtime-video-14b.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-5 \
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--num_epochs 2 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/krea-realtime-video_full" \
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--trainable_models "dit"
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14
examples/wanvideo/model_training/lora/krea-realtime-video.sh
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examples/wanvideo/model_training/lora/krea-realtime-video.sh
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accelerate launch examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "krea/krea-realtime-video:krea-realtime-video-14b.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/krea-realtime-video_lora" \
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--lora_base_model "dit" \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32
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@@ -0,0 +1,28 @@
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData, load_state_dict
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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state_dict = load_state_dict("models/train/krea-realtime-video_full/epoch-1.safetensors")
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pipe.dit.load_state_dict(state_dict)
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pipe.enable_vram_management()
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# Text-to-video
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video = pipe(
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prompt="a cat sitting on a boat",
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num_inference_steps=6, num_frames=81,
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seed=0, tiled=True,
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cfg_scale=1,
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sigma_shift=20,
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)
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save_video(video, "output.mp4", fps=15, quality=5)
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData, load_state_dict
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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pipe.load_lora(pipe.dit, "models/train/krea-realtime-video_lora/epoch-4.safetensors", alpha=1)
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pipe.enable_vram_management()
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# Text-to-video
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video = pipe(
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prompt="a cat sitting on a boat",
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num_inference_steps=6, num_frames=81,
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seed=0, tiled=True,
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cfg_scale=1,
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sigma_shift=20,
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)
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save_video(video, "output.mp4", fps=15, quality=5)
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