wehub-resource-sync
83 lines
4.0 KiB
Bash
83 lines
4.0 KiB
Bash
#!/bin/bash
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set -e
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python scripts/inference.py \
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--config=configs/sana_config/1024ms/Sana_600M_img1024.yaml \
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--model_path=hf://Efficient-Large-Model/Sana_600M_1024px/checkpoints/Sana_600M_1024px_MultiLing.pth
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python scripts/inference.py \
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--config=configs/sana_config/1024ms/Sana_1600M_img1024.yaml \
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--model_path=hf://Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth
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mkdir -p tools/controlnet/annotator/ckpts
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hf download lllyasviel/Annotators ControlNetHED.pth --local-dir tools/controlnet/annotator/ckpts
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python tools/controlnet/inference_controlnet.py \
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--config=configs/sana_controlnet_config/Sana_600M_img1024_controlnet.yaml \
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--model_path=hf://Efficient-Large-Model/Sana_600M_1024px_ControlNet_HED/checkpoints/Sana_600M_1024px_ControlNet_HED.pth \
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--json_file=asset/controlnet/samples_controlnet.json
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python scripts/inference_sana_sprint.py \
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--config=configs/sana_sprint_config/1024ms/SanaSprint_1600M_1024px_allqknorm_bf16_scm_ladd.yaml \
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--model_path=hf://Lawrence-cj/Sana_Sprint_1600M_1024px/Sana_Sprint_1600M_1024px_36K.pth \
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--txt_file=asset/samples/samples_mini.txt
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python inference_video_scripts/inference_sana_video.py \
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--config=configs/sana_video_config/Sana_2000M_256px_AdamW_fsdp.yaml \
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--model_path=hf://Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth \
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--debug=true
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python inference_video_scripts/inference_sana_video.py \
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--config=configs/sana_video_config/Sana_2000M_480px_adamW_fsdp_longsana.yaml \
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--model_path=hf://Efficient-Large-Model/SANA-Video_2B_480p_LongLive/checkpoints/SANA_Video_2B_480p_LongLive.pth \
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--cfg_scale=1.0 --debug=true
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python inference_video_scripts/wm/inference_sana_wm.py \
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--image=asset/sana_wm/demo_0.png \
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--prompt=asset/sana_wm/demo_0.txt \
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--action=w-641 \
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--output_dir=results/sana_wm_ci \
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--name=demo_0 \
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--num_frames=641 \
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--step=4
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python inference_video_scripts/wm/inference_sana_wm.py \
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--config=configs/sana_wm/sana_wm_chunk_causal_1600m_720p.yaml \
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--model_path=hf://Efficient-Large-Model/SANA-WM_chunk_causal/dit/sana_wm_chunk_causal_1600m_720p.safetensors \
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--image=asset/sana_wm/demo_0.png \
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--prompt=asset/sana_wm/demo_0.txt \
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--action=w-25 \
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--intrinsics=asset/sana_wm/demo_0_intrinsics.npy \
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--output_dir=results/sana_wm_chunk_causal_ci \
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--name=demo_0_chunk_causal \
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--num_frames=25 \
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--step=4 \
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--no_refiner
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python inference_video_scripts/wm/inference_sana_wm_streaming.py \
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--image=asset/sana_wm/demo_0.png \
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--prompt=asset/sana_wm/demo_0.txt \
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--action=w-25 \
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--intrinsics=asset/sana_wm/demo_0_intrinsics.npy \
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--output_dir=results/sana_wm_streaming_ci \
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--name=demo_0_streaming \
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--num_frames=25 \
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--no_compile \
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--streaming_preset=ultrafast
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python inference_video_scripts/v2v/inference_sana_streaming.py \
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--mode=bidirectional_short \
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--config=configs/sana_streaming/sana_streaming_bidirectional_2b_720p.yaml \
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--model_path=hf://Efficient-Large-Model/SANA-Streaming_bidirectional/dit/sana_bidirectional_short.pth \
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--prompt="Remove the thick, textured gold hoop earrings from the woman's ears. Carefully reconstruct the exposed earlobes to match her natural skin tone and texture. Ensure the lighting and soft shadows on the newly bare ears blend seamlessly with the rest of her face, leaving no trace or reflection of the metallic jewelry behind." \
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--video_path=hf://Efficient-Large-Model/SANA-Streaming/source/00_local_editing_source.mp4 \
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--output_dir=results/sana_streaming_bidirectional_ci
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python inference_video_scripts/v2v/inference_sana_streaming.py \
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--mode=long_streaming \
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--config=configs/sana_streaming/sana_streaming_2b_720p.yaml \
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--model_path=hf://Efficient-Large-Model/SANA-Streaming/dit/sana_streaming_ar.pth \
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--prompt="Transform the entire scene into a breathtaking Sci-Fi Art digital painting." \
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--video_path=hf://Efficient-Large-Model/SANA-Streaming/source/09_style_transfer_source.mp4 \
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--output_dir=results/sana_streaming_long_ci
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