FP8 videos are working fine now btw

JJaviEM FP8 videos are working fine now btw

F

Furkan Gözükara SECourses•3/16/25, 3:10 PM

for FP8

F

Furkan Gözükara SECourses•3/16/25, 3:10 PM

increase number of parameters

F

Furkan Gözükara SECourses•3/16/25, 3:12 PM

720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB
1 is : 18326 MB
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB
16,000,000,000 is : 47030 MB
22,000,000,000 is : 47800 MB


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

3,000,000,000 is : 19763 MB
6,750,000,000 is : 23350 MB

720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB
1 is : 18326 MB
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB
16,000,000,000 is : 47030 MB
22,000,000,000 is : 47800 MB


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

3,000,000,000 is : 19763 MB
6,750,000,000 is : 23350 MB

720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB
1 is : 18326 MB
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB
16,000,000,000 is : 47030 MB
22,000,000,000 is : 47800 MB


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

3,000,000,000 is : 19763 MB
6,750,000,000 is : 23350 MB

720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB
1 is : 18326 MB
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB
16,000,000,000 is : 47030 MB
22,000,000,000 is : 47800 MB


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

3,000,000,000 is : 19763 MB
6,750,000,000 is : 23350 MB

F

Furkan Gözükara SECourses•3/16/25, 3:12 PM

i am doing some testing

J

JaviEMOP•3/16/25, 3:12 PM

nice, thanks

F

Furkan Gözükara SECourses•3/16/25, 3:30 PM

480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB
1 is : 8618 MB
1,000,000,000 is : x MB
1,500,000,000 is : x MB
2,000,000,000 is : x MB
3,000,000,000 is : x MB
3,500,000,000 is : x MB
4,000,000,000 is : x MB
8,000,000,000 is : 15392 MB
12,000,000,000 is : x MB
22,000,000,000 is : 23080 MB

480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB
1 is : 8618 MB
1,000,000,000 is : x MB
1,500,000,000 is : x MB
2,000,000,000 is : x MB
3,000,000,000 is : x MB
3,500,000,000 is : x MB
4,000,000,000 is : x MB
8,000,000,000 is : 15392 MB
12,000,000,000 is : x MB
22,000,000,000 is : 23080 MB

480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB
1 is : 8618 MB
1,000,000,000 is : x MB
1,500,000,000 is : x MB
2,000,000,000 is : x MB
3,000,000,000 is : x MB
3,500,000,000 is : x MB
4,000,000,000 is : x MB
8,000,000,000 is : 15392 MB
12,000,000,000 is : x MB
22,000,000,000 is : 23080 MB

480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB
1 is : 8618 MB
1,000,000,000 is : x MB
1,500,000,000 is : x MB
2,000,000,000 is : x MB
3,000,000,000 is : x MB
3,500,000,000 is : x MB
4,000,000,000 is : x MB
8,000,000,000 is : 15392 MB
12,000,000,000 is : x MB
22,000,000,000 is : 23080 MB

F

Furkan Gözükara SECourses•3/16/25, 3:30 PM

i am gonna make presets to support fp8 too

F

Furkan Gözükara SECourses•3/16/25, 3:41 PM

all done

F

Furkan Gözükara SECourses•3/16/25, 3:41 PM

GPU VRAM Usages Table With Respected To Number of Persistent Params+

All tests made on secondary GPU thus they are 100% accurate

Therefore, make sure that you have such empty VRAM before starting the Wan 2.1 SECourses App 

1280x720 px, 81 frames, 14b text to video model - BF16 precision (torch.bfloat16)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 15658 MB
2,000,000,000 is : 17664 MB
3,000,000,000 is : 20134 MB
3,750,000,000 is : 21134 MB
4,000,000,000 is : 21500 MB
4,250,000,000 is : 22000 MB - 24 GB GPUs
8,250,000,000 is : 29870 MB - 32 GB GPUs
22,000,000,000 is : 41700 MB - 48 GB GPUs

1280x720 px, 81 frames, 14b text to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 14918 MB
8,000,000,000 is : 21260 MB
8,750,000,000 is : 21980 MB - 24 GB GPUs
22,000,000,000 is : x MB


480x832 px, 81 frames, 14b 480p image to video model - BF16 precision (torch.bfloat16)

0 is : 8618 MB - Below 12 GB GPUs
1,000,000,000 is : 9354 MB
1,500,000,000 is : 10090 MB - 12 GB GPUs
2,000,000,000 is : 11370 MB
3,000,000,000 is : 13080 MB
3,500,000,000 is : 14038 MB - 16 GB GPUs
4,000,000,000 is : 15140 MB
8,000,000,000 is : 22100 MB - 24 GB GPUs
12,000,000,000 is : 30060 MB - 32 GB GPUs
22,000,000,000 is : 38300 MB - 48 GB GPUs


480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB - Below 12 GB GPUs
2,500,000,000 is : 9794 MB - 12 GB GPUs
7,500,000,000 is : 14850 MB - 16 GB GPUs
8,000,000,000 is : 15392 MB
15,000,000,000 is : 21700 MB - 24 GB GPUs
22,000,000,000 is : 23080 MB - 32 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB - Below 24 GB GPUs
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB - 24 GB GPUs
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB - 32 GB GPUs
16,000,000,000 is : 47030 MB - 48 GB GPUs
22,000,000,000 is : 47800 MB - 80 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 18326 MB - Below 24 GB GPUs
3,000,000,000 is : 19763 MB
6,000,000,000 is : 22020 MB - 24 GB GPUs
14,000,000,000 is : 29840 MB
15,000,000,000 is : 30790 MB - 32 GB GPUs
22,000,000,000 is : 32013 MB - 48 GB GPUs

GPU VRAM Usages Table With Respected To Number of Persistent Params+

All tests made on secondary GPU thus they are 100% accurate

Therefore, make sure that you have such empty VRAM before starting the Wan 2.1 SECourses App 

1280x720 px, 81 frames, 14b text to video model - BF16 precision (torch.bfloat16)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 15658 MB
2,000,000,000 is : 17664 MB
3,000,000,000 is : 20134 MB
3,750,000,000 is : 21134 MB
4,000,000,000 is : 21500 MB
4,250,000,000 is : 22000 MB - 24 GB GPUs
8,250,000,000 is : 29870 MB - 32 GB GPUs
22,000,000,000 is : 41700 MB - 48 GB GPUs

1280x720 px, 81 frames, 14b text to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 14918 MB
8,000,000,000 is : 21260 MB
8,750,000,000 is : 21980 MB - 24 GB GPUs
22,000,000,000 is : x MB


480x832 px, 81 frames, 14b 480p image to video model - BF16 precision (torch.bfloat16)

0 is : 8618 MB - Below 12 GB GPUs
1,000,000,000 is : 9354 MB
1,500,000,000 is : 10090 MB - 12 GB GPUs
2,000,000,000 is : 11370 MB
3,000,000,000 is : 13080 MB
3,500,000,000 is : 14038 MB - 16 GB GPUs
4,000,000,000 is : 15140 MB
8,000,000,000 is : 22100 MB - 24 GB GPUs
12,000,000,000 is : 30060 MB - 32 GB GPUs
22,000,000,000 is : 38300 MB - 48 GB GPUs


480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB - Below 12 GB GPUs
2,500,000,000 is : 9794 MB - 12 GB GPUs
7,500,000,000 is : 14850 MB - 16 GB GPUs
8,000,000,000 is : 15392 MB
15,000,000,000 is : 21700 MB - 24 GB GPUs
22,000,000,000 is : 23080 MB - 32 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB - Below 24 GB GPUs
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB - 24 GB GPUs
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB - 32 GB GPUs
16,000,000,000 is : 47030 MB - 48 GB GPUs
22,000,000,000 is : 47800 MB - 80 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 18326 MB - Below 24 GB GPUs
3,000,000,000 is : 19763 MB
6,000,000,000 is : 22020 MB - 24 GB GPUs
14,000,000,000 is : 29840 MB
15,000,000,000 is : 30790 MB - 32 GB GPUs
22,000,000,000 is : 32013 MB - 48 GB GPUs

GPU VRAM Usages Table With Respected To Number of Persistent Params+

All tests made on secondary GPU thus they are 100% accurate

Therefore, make sure that you have such empty VRAM before starting the Wan 2.1 SECourses App 

1280x720 px, 81 frames, 14b text to video model - BF16 precision (torch.bfloat16)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 15658 MB
2,000,000,000 is : 17664 MB
3,000,000,000 is : 20134 MB
3,750,000,000 is : 21134 MB
4,000,000,000 is : 21500 MB
4,250,000,000 is : 22000 MB - 24 GB GPUs
8,250,000,000 is : 29870 MB - 32 GB GPUs
22,000,000,000 is : 41700 MB - 48 GB GPUs

1280x720 px, 81 frames, 14b text to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 14918 MB
8,000,000,000 is : 21260 MB
8,750,000,000 is : 21980 MB - 24 GB GPUs
22,000,000,000 is : x MB


480x832 px, 81 frames, 14b 480p image to video model - BF16 precision (torch.bfloat16)

0 is : 8618 MB - Below 12 GB GPUs
1,000,000,000 is : 9354 MB
1,500,000,000 is : 10090 MB - 12 GB GPUs
2,000,000,000 is : 11370 MB
3,000,000,000 is : 13080 MB
3,500,000,000 is : 14038 MB - 16 GB GPUs
4,000,000,000 is : 15140 MB
8,000,000,000 is : 22100 MB - 24 GB GPUs
12,000,000,000 is : 30060 MB - 32 GB GPUs
22,000,000,000 is : 38300 MB - 48 GB GPUs


480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB - Below 12 GB GPUs
2,500,000,000 is : 9794 MB - 12 GB GPUs
7,500,000,000 is : 14850 MB - 16 GB GPUs
8,000,000,000 is : 15392 MB
15,000,000,000 is : 21700 MB - 24 GB GPUs
22,000,000,000 is : 23080 MB - 32 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB - Below 24 GB GPUs
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB - 24 GB GPUs
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB - 32 GB GPUs
16,000,000,000 is : 47030 MB - 48 GB GPUs
22,000,000,000 is : 47800 MB - 80 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 18326 MB - Below 24 GB GPUs
3,000,000,000 is : 19763 MB
6,000,000,000 is : 22020 MB - 24 GB GPUs
14,000,000,000 is : 29840 MB
15,000,000,000 is : 30790 MB - 32 GB GPUs
22,000,000,000 is : 32013 MB - 48 GB GPUs

GPU VRAM Usages Table With Respected To Number of Persistent Params+

All tests made on secondary GPU thus they are 100% accurate

Therefore, make sure that you have such empty VRAM before starting the Wan 2.1 SECourses App 

1280x720 px, 81 frames, 14b text to video model - BF16 precision (torch.bfloat16)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 15658 MB
2,000,000,000 is : 17664 MB
3,000,000,000 is : 20134 MB
3,750,000,000 is : 21134 MB
4,000,000,000 is : 21500 MB
4,250,000,000 is : 22000 MB - 24 GB GPUs
8,250,000,000 is : 29870 MB - 32 GB GPUs
22,000,000,000 is : 41700 MB - 48 GB GPUs

1280x720 px, 81 frames, 14b text to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 14168 MB - Below 24 GB GPUs
1,000,000,000 is : 14918 MB
8,000,000,000 is : 21260 MB
8,750,000,000 is : 21980 MB - 24 GB GPUs
22,000,000,000 is : x MB


480x832 px, 81 frames, 14b 480p image to video model - BF16 precision (torch.bfloat16)

0 is : 8618 MB - Below 12 GB GPUs
1,000,000,000 is : 9354 MB
1,500,000,000 is : 10090 MB - 12 GB GPUs
2,000,000,000 is : 11370 MB
3,000,000,000 is : 13080 MB
3,500,000,000 is : 14038 MB - 16 GB GPUs
4,000,000,000 is : 15140 MB
8,000,000,000 is : 22100 MB - 24 GB GPUs
12,000,000,000 is : 30060 MB - 32 GB GPUs
22,000,000,000 is : 38300 MB - 48 GB GPUs


480x832 px, 81 frames, 14b 480p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 8618 MB - Below 12 GB GPUs
2,500,000,000 is : 9794 MB - 12 GB GPUs
7,500,000,000 is : 14850 MB - 16 GB GPUs
8,000,000,000 is : 15392 MB
15,000,000,000 is : 21700 MB - 24 GB GPUs
22,000,000,000 is : 23080 MB - 32 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - BF16 precision (torch.bfloat16)

0 is : 18326 MB - Below 24 GB GPUs
2,000,000,000 is : 20360 MB
3,000,000,000 is : 22010 MB - 24 GB GPUs
6,000,000,000 is : 28380 MB
6,500,000,000 is : 29330 MB
6,750,000,000 is : 29790 MB - 32 GB GPUs
16,000,000,000 is : 47030 MB - 48 GB GPUs
22,000,000,000 is : 47800 MB - 80 GB GPUs


720x1280 px, 81 frames, 14b 720p image to video model - FP8 precision (torch.float8_e4m3fn)

0 is : 18326 MB - Below 24 GB GPUs
3,000,000,000 is : 19763 MB
6,000,000,000 is : 22020 MB - 24 GB GPUs
14,000,000,000 is : 29840 MB
15,000,000,000 is : 30790 MB - 32 GB GPUs
22,000,000,000 is : 32013 MB - 48 GB GPUs

F

Furkan Gözükara SECourses•3/16/25, 3:41 PM

time to update the app

J

JaviEMOP•3/16/25, 4:07 PM

I have updated but preset doesn't change for FP8. 16GB preset still use 3,500,000,000 on FP8

J

JaviEMOP•3/16/25, 4:21 PM

With 7,500,000,000 is using 0,4GB of shared VRAM on a 16GB VRAM GPU. I will try lowering it to 7,000,000,000 or 6,500,000,000

F

Furkan Gözükara SECourses•3/16/25, 4:33 PM

i havent updated app yet still few missing

J

JaviEMOP•3/16/25, 4:58 PM

FP8 7,500,000,000: 1278.54 seconds
FP8 7,000,000,000: 1137.68 seconds

J

JaviEMOP•3/16/25, 5:17 PM

BF16 3,500,000,000: 1002.77 seconds

J

JaviEMOP•3/16/25, 5:17 PM

FP8 has no sense on a 16GB VRAM GPU

J

JaviEMOP•3/16/25, 5:18 PM

Img to video 480p model*

J

JaviEMOP•3/16/25, 5:33 PM

10 videos generated this afternoon and no glitches or image corruption with teacache. I only had one big colour change with FP8. Very nice updates today.

JJaviEM 10 videos generated this afternoon and no glitches or image corruption with teac...

F

Furkan Gözükara SECourses•3/16/25, 5:46 PM

awesome so fixed finally

F

Furkan Gözükara SECourses•3/16/25, 6:44 PM

VRAM usages Wan 2.1, APP updated : https://www.reddit.com/r/SECourses/comments/1jcse0z/wan_21_vram_usages_very_detailed_chart_for_4_8_10/

From the SECourses community on Reddit: Wan 2.1 VRAM usages very de...

Explore this post and more from the SECourses community

F

Furkan Gözükara SECourses•3/16/25, 7:04 PM

sana 1.5 published but i will wait diffusers

J

JaviEMOP•3/16/25, 7:51 PM

@Dr. Furkan Gözükara are you using the new use_ret_steps in your implementation of teacache in wan 2.1? According to the developers it has more speed and quality in generation

J

JaviEMOP•3/16/25, 7:52 PM

The info is here, at the bottom https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1

GitHub

TeaCache/TeaCache4Wan2.1 at main · ali-vilab/TeaCache

Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model - ali-vilab/TeaCache

JJaviEM @Dr. Furkan Gözükara are you using the new use_ret_steps in your implementation ...

F

Furkan Gözükara SECourses•3/16/25, 7:53 PM

i think ours matching

F

Furkan Gözükara SECourses•3/16/25, 7:53 PM

JJaviEM @Dr. Furkan Gözükara are you using the new use_ret_steps in your implementation ...

F

Furkan Gözükara SECourses•3/16/25, 7:54 PM

what we use here https://github.com/modelscope/DiffSynth-Studio/pull/448

GitHub

support teacache in wan by Artiprocher · Pull Request #448 · models...

J

JaviEMOP•3/16/25, 7:55 PM

nice

F

Furkan Gözükara SECourses•3/16/25, 9:16 PM

i did setup wan 2.1 for a client on runpod and it took freaking 65 min to install

F

Furkan Gözükara SECourses•3/16/25, 9:16 PM

shameless disk speed

F

Furkan Gözükara SECourses•3/16/25, 9:52 PM

just fixed FP8 + LoRA issue

F

Furkan Gözükara SECourses•3/16/25, 9:52 PM

whoever gets that run update

FFurkan Gözükara SECourses VRAM usages Wan 2.1, APP updated : https://www.reddit.com/r/SECourses/comments/1...

J

J2_Nedo•3/16/25, 10:25 PM

thank you Doc. but you know its Sunday, don't you have some time off, its important too

JJ2_Nedo thank you Doc. but you know its Sunday, don't you have some time off, its import...

F

Furkan Gözükara SECourses•3/16/25, 10:30 PM

i have been working since woken up

F

Furkan Gözükara SECourses•3/16/25, 10:30 PM

still working lol

J

J2_Nedo•3/16/25, 10:33 PM

you have to take care of you Doc.! stress kills

F

Furkan Gözükara SECourses•3/16/25, 10:34 PM

F

Furkan Gözükara SECourses•3/16/25, 10:34 PM

true

T

Tomfoolery•3/16/25, 10:40 PM

@Dr. Furkan Gözükara - you see skip-layer guidance has been implement for WAN in Comfy? Thoughts?

https://github.com/kijai/ComfyUI-KJNodes/commit/7c488a16ef420acf0276a4f8e31fc024a969d24b

GitHub

Add SkipLayerGuidanceWanVideo · kijai/ComfyUI-KJNodes@7c488a1

TTomfoolery @Dr. Furkan Gözükara - you see skip-layer guidance has been implement for WAN in...

F

Furkan Gözükara SECourses•3/16/25, 10:41 PM

some placebo effect

F

Furkan Gözükara SECourses•3/16/25, 10:41 PM

0 explanation :d

T

Tomfoolery•3/16/25, 10:42 PM

I know it was pretty well tested in Hunyuan video, that skipping layers 9 and 10 resulted in 'smoother' videos. Curious to see what the end results are for Wan

X

xlads•3/16/25, 10:47 PM

X

xlads•3/16/25, 10:48 PM

Going to try a 30 sec image to video 720p tomorrow.

Xxlads Click to see attachment

F

Furkan Gözükara SECourses•3/16/25, 10:59 PM

nice

Xxlads Going to try a 30 sec image to video 720p tomorrow.

J

JaviEMOP•3/16/25, 11:03 PM

you need a lot of VRAM. Today I tried to make a 7,5s video and ran out of VRAM

FFurkan Gözükara SECourses some placebo effect

O

OmegaBrain•3/16/25, 11:41 PM

Calling it placebo is dismissive when there’s a clear rationale: SLG adjusts how noise is removed across layers, affecting detail retention and structure consistency. Many users, including myself, report improved results—I’ve seen this across hundreds of generations, even with SD1.5—which aligns with how diffusion models handle denoising. Plus, tweaking SLG allows fine-tuning a scene that already follows a fixed seed without breaking its coherence. If it’s truly placebo, proper testing would confirm or disprove it… so why not let the results speak for themselves?

PPelolisu did you try this ? https://huggingface.co/spaces/primecai/diffusion-self-distill...

M

masood•3/17/25, 4:46 AM

tried it buddy, a bit too much. the ouput doesnt look like the input at all

FFurkan Gözükara SECourses you can use google gemini flash

M

masood•3/17/25, 4:47 AM

life saver, dr

M

masood•3/17/25, 4:47 AM

@Dr. Furkan Gözükara

M

masood•3/17/25, 4:48 AM

one more thing, suppose i have trained a person now i want that person to hold an item ( a bag for example ) do i have to train the model on that item as well? or is there any moe effective way

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