update sth

Dockerfile

-FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04 AS base
+FROM nvidia/cuda:12.8.1-cudnn-devel-ubuntu22.04 AS base
 
 WORKDIR /workspace
 

README.md

@@ -30,10 +30,10 @@ conda create -n lightx2v python=3.11 && conda activate lightx2v
 pip install -r requirements.txt
 
 # install flash-attention 2
-RUN cd lightx2v/3rd/flash-attention && pip install --no-cache-dir -v -e .
+cd lightx2v/3rd/flash-attention && pip install --no-cache-dir -v -e .
 
 # install flash-attention 3, only if hopper
-RUN cd lightx2v/3rd/flash-attention/hopper && pip install --no-cache-dir -v -e .
+cd lightx2v/3rd/flash-attention/hopper && pip install --no-cache-dir -v -e .
 
 # modify the parameters of the running script
 bash scripts/run_hunyuan_t2v.sh

scripts/run_hunyuan_i2v.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -35,4 +35,4 @@ python -m lightx2v \
 --config_json ${lightx2v_path}/configs/hunyuan_i2v.json \
 --prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
 --image_path ${lightx2v_path}/assets/inputs/imgs/img_1.jpg \
---save_video_path ./output_lightx2v_hy_i2v.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_hy_i2v.mp4

scripts/run_hunyuan_t2v.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -34,4 +34,4 @@ python -m lightx2v \
 --model_path $model_path \
 --config_json ${lightx2v_path}/configs/hunyuan_t2v.json \
 --prompt "A cat walks on the grass, realistic style." \
---save_video_path ./output_lightx2v_hy_t2v.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_hy_t2v.mp4

scripts/run_hunyuan_t2v_dist.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -33,7 +33,7 @@ torchrun --nproc_per_node=4 ${lightx2v_path}/lightx2v/__main__.py \
 --model_path $model_path \
 --config_json ${lightx2v_path}/configs/dist/hunyuan_t2v_dist_ulysses.json \
 --prompt "A cat walks on the grass, realistic style." \
---save_video_path ./output_lightx2v_hunyuan_t2v_dist_ulysses.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_hunyuan_t2v_dist_ulysses.mp4
 
 torchrun --nproc_per_node=4 ${lightx2v_path}/lightx2v/__main__.py \
 --model_cls hunyuan \
@@ -41,4 +41,4 @@ torchrun --nproc_per_node=4 ${lightx2v_path}/lightx2v/__main__.py \
 --model_path $model_path \
 --config_json ${lightx2v_path}/configs/dist/hunyuan_t2v_dist_ring.json \
 --prompt "A cat walks on the grass, realistic style." \
---save_video_path ./output_lightx2v_hunyuan_t2v_dist_ring.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_hunyuan_t2v_dist_ring.mp4

scripts/run_hunyuan_t2v_taylorseer.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -33,4 +33,4 @@ python -m lightx2v \
 --model_path $model_path \
 --config_json ${lightx2v_path}/configs/caching/hunyuan_t2v_TaylorSeer.json \
 --prompt "A cat walks on the grass, realistic style." \
---save_video_path ./output_lightx2v_hy_t2v.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_hy_t2v.mp4

scripts/run_wan_i2v.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -36,4 +36,4 @@ python -m lightx2v \
 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
 --negative_prompt 色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走 \
 --image_path ${lightx2v_path}/assets/inputs/imgs/img_0.jpg \
---save_video_path ./output_lightx2v_wan_i2v.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_i2v.mp4

scripts/run_wan_i2v_with_lora.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
-lora_path=""
+lightx2v_path=
+model_path=
+lora_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -40,7 +40,7 @@ python -m lightx2v \
 --attention_type flash_attn3 \
 --seed 42 \
 --negative_prompt "画面过曝，模糊，文字，字幕" \
---save_video_path ./output_lightx2v_wan_i2v.mp4 \
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_i2v.mp4 \
 --sample_guide_scale 5 \
 --sample_shift 5 \
 --image_path ${lightx2v_path}/assets/inputs/imgs/img_0.jpg \

scripts/run_wan_t2v.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -35,4 +35,4 @@ python -m lightx2v \
 --config_json ${lightx2v_path}/configs/wan_t2v.json \
 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
 --negative_prompt 色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走 \
---save_video_path ./output_lightx2v_wan_t2v.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_t2v.mp4

scripts/run_wan_t2v_dist.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=""
-model_path=""
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -43,7 +43,7 @@ torchrun --nproc_per_node=4 ${lightx2v_path}/lightx2v/__main__.py \
 --sample_shift 8 \
 --parallel_attn_type ring \
 --parallel_vae \
---save_video_path ./output_lightx2v_wan_t2v_dist_ring.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_t2v_dist_ring.mp4
 
 torchrun --nproc_per_node=4 ${lightx2v_path}/lightx2v/__main__.py \
 --model_cls wan2.1 \
@@ -61,4 +61,4 @@ torchrun --nproc_per_node=4 ${lightx2v_path}/lightx2v/__main__.py \
 --sample_shift 8 \
 --parallel_attn_type ulysses \
 --parallel_vae \
---save_video_path ./output_lightx2v_wan_t2v_dist_ulysses.mp4
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_t2v_dist_ulysses.mp4