what did you name this metadata category/type? its specific to your Supir script no?
what did you name this metadata category/type? its specific to your Supir script no?
VSUPIR seems to be amazing and the 1-click installer too! Great job! -Is SDXL model with Juggernaut XL - V9 only used to generate the initial image that is then upscaled, so I do not need SDXL if I use other images to upscale ?
Fit should work with all images that uses same kind of metadata
Fno it is used during upscale
Fdo you need other images metadata too? i may try to make it more robust
Fi am testing output folders parameter right now
Rso, I use an called Allusion which sees the metadata from Auto1111 and Forge
Rhere's an example
R
R
Fok i will try to make it more robust
Ryou get all the data from the generation
Ri think this metadata that auto1111 saves is called Parameters
RSo if you use the same, i think it will be universal
Ffixing errors
Ptrying to use Supor and at 2nd stage getting:
[Tiled VAE]: split to 4x2 = 8 tiles. Optimal tile size 480x448, original tile size 512x512
[Tiled VAE]: Executing Encoder Task Queue: 100%|████████████████████████████████████| 728/728 [00:01<00:00, 405.80it/s]
[Tiled VAE]: Done in 2.045s, max VRAM alloc 9757.875 MB
P:\SUPIR_v7\SUPIR\venv\lib\site-packages\torch\nn\functional.py:5476: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ..\aten\src\ATen\native\transformers\cuda\sdp_utils.cpp:263.)
attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
[Tiled VAE]: split to 4x2 = 8 tiles. Optimal tile size 480x448, original tile size 512x512
[Tiled VAE]: Executing Encoder Task Queue: 100%|████████████████████████████████████| 728/728 [00:01<00:00, 405.80it/s]
[Tiled VAE]: Done in 2.045s, max VRAM alloc 9757.875 MB
P:\SUPIR_v7\SUPIR\venv\lib\site-packages\torch\nn\functional.py:5476: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ..\aten\src\ATen\native\transformers\cuda\sdp_utils.cpp:263.)
attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
F
Fyes not an error
Pit was stuck for a very long time. but in the end it did upscale. I thought it was dead xD
F
Fyou must be using shared vram
Fcheck and let me know 
Rsame metadata type as auto1111?
Khopefully fp8 with supir soon
Fi dont know
Fbut works on both
Fye would be great
Ffp8 added for auto1111
Pyou mean Nvidia offload to ram? its turned off. i have 4090 using 1-1 options to reduce vram usage
Ri can test it if you send me the image
Fcan you check task manager
Fand show shared vram
P0.5/31.9
Fok then must be working
Fwhat is output resolution?
Falso hit enter on cmd
Fcould be frozen
P1536x2688
Pyeah it wors now. takes about 88 seconds. 1st run was forever. something didnt work right probably
Pwhat is the diference between Model Selection
v0-Q
v0-F
v0-Q
v0-F
Pwitch one is better?
Ri've been doing some tests with the two models
Rit's hard to say
Ri think i like F better
Rthe documentation says Q is more general, F better detail and avoids degradation (hallucination)
Pyeah i think f gives more detailes
RI find that this applies mildly
Pwaht checkpoint you are using?
RI find that x1 upscale is prone to artifacts like monstruous teeth and overkill eyelashes on lower quality images in midshot to fullbody
Rso x2 is almost completely necessary if you're running a big batch with lots of small faces
Rjuggerv9
