chuckchen/supir-v0q 🔢🖼️📝✓❓ → 🖼️
About
Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild, using tiled VAE and Juggernaut XL to eliminate plastic skin and smooth textures.
Example Output
Output
Performance Metrics
329.39s
Prediction Time
626.24s
Total Time
All Input Parameters
{
"image": "https://replicate.delivery/pbxt/NaxRebrsD94RV65srqmQaX0l6R768CXtSnk10B5dUAA8LqCR/438590041_768566065223739_2474450681669884762_n.jpg",
"s_cfg": 2,
"s_churn": 5,
"s_noise": 1.003,
"upscale": 4,
"a_prompt": "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
"min_size": 1024,
"n_prompt": "painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
"s_stage1": -1,
"s_stage2": 1,
"edm_steps": 8,
"linear_CFG": false,
"output_format": "jpeg",
"color_fix_type": "Wavelet",
"output_quality": 90,
"spt_linear_CFG": 2,
"linear_s_stage2": false,
"spt_linear_s_stage2": 0
}
Input Parameters
- seed
- Random seed. Leave blank to randomize the seed
- image (required)
- Low quality input image.
- s_cfg
- Classifier-free guidance scale for prompts.
- s_churn
- Original churn hy-param of EDM.
- s_noise
- Original noise hy-param of EDM.
- upscale
- Upsampling ratio of given inputs.
- a_prompt
- Additive positive prompt for the inputs.
- min_size
- Minimum resolution of output images.
- n_prompt
- Negative prompt for the inputs.
- s_stage1
- Control Strength of Stage1 (negative means invalid).
- s_stage2
- Control Strength of Stage2.
- edm_steps
- Number of steps for EDM Sampling Schedule.
- linear_CFG
- Linearly (with sigma) increase CFG from 'spt_linear_CFG' to s_cfg.
- output_format
- Output image format.
- color_fix_type
- Color Fixing Type..
- output_quality
- Quality for JPEG output (1-100).
- spt_linear_CFG
- Start point of linearly increasing CFG.
- linear_s_stage2
- Linearly (with sigma) increase s_stage2 from 'spt_linear_s_stage2' to s_stage2.
- spt_linear_s_stage2
- Start point of linearly increasing s_stage2.
Output Schema
Output
Example Execution Logs
Using seed: 46814
[Tiled VAE]: input_size: torch.Size([1, 3, 4352, 4352]), tile_size: 3072, padding: 32
[Tiled VAE]: split to 2x2 = 4 tiles. Optimal tile size 2144x2144, original tile size 3072x3072
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[Tiled VAE]: Done in 28.650s, max VRAM alloc 15542.653 MB
[Tiled VAE]: input_size: torch.Size([1, 4, 544, 544]), tile_size: 256, padding: 11
[Tiled VAE]: split to 3x3 = 9 tiles. Optimal tile size 192x192, original tile size 256x256
[('conv_in', Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('store_res', <function resblock2task.<locals>.<lambda> at 0x72c8f812eb60>), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('silu', <function inplace_nonlinearity at 0x72c92e60f740>), ('conv1', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('silu', <function inplace_nonlinearity at 0x72c92e60f740>), ('conv2', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ['add_res', None], ('store_res', <function attn2task.<locals>.<lambda> at 0x72c8f812d8a0>), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('attn', <function attn2task.<locals>.<lambda> at 0x72c8f812e0c0>), ['add_res', None]]
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[Tiled VAE]: Done in 60.964s, max VRAM alloc 15683.634 MB
Seed set to 46814
[Tiled VAE]: input_size: torch.Size([1, 3, 4352, 4352]), tile_size: 3072, padding: 32
[Tiled VAE]: split to 2x2 = 4 tiles. Optimal tile size 2144x2144, original tile size 3072x3072
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[Tiled VAE]: Done in 28.691s, max VRAM alloc 15759.403 MB
[Tiled VAE]: input_size: torch.Size([1, 4, 544, 544]), tile_size: 256, padding: 11
[Tiled VAE]: split to 3x3 = 9 tiles. Optimal tile size 192x192, original tile size 256x256
[('conv_in', Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('store_res', <function resblock2task.<locals>.<lambda> at 0x72c8f812ef20>), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('silu', <function inplace_nonlinearity at 0x72c92e60f740>), ('conv1', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('silu', <function inplace_nonlinearity at 0x72c92e60f740>), ('conv2', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ['add_res', None], ('store_res', <function attn2task.<locals>.<lambda> at 0x72c8f812e0c0>), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('attn', <function attn2task.<locals>.<lambda> at 0x72c8f812e200>), ['add_res', None]]
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[Tiled VAE]: Done in 61.005s, max VRAM alloc 15900.384 MB
[Tiled VAE]: input_size: torch.Size([1, 3, 4352, 4352]), tile_size: 3072, padding: 32
[Tiled VAE]: split to 2x2 = 4 tiles. Optimal tile size 2144x2144, original tile size 3072x3072
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[Tiled VAE]: Done in 28.689s, max VRAM alloc 15978.653 MB
[Tiled VAE]: input_size: torch.Size([1, 4, 544, 544]), tile_size: 256, padding: 11
[Tiled VAE]: split to 3x3 = 9 tiles. Optimal tile size 192x192, original tile size 256x256
[('conv_in', Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('store_res', <function resblock2task.<locals>.<lambda> at 0x72c95c5db560>), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('silu', <function inplace_nonlinearity at 0x72c92e60f740>), ('conv1', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('silu', <function inplace_nonlinearity at 0x72c92e60f740>), ('conv2', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ['add_res', None], ('store_res', <function attn2task.<locals>.<lambda> at 0x72c8f812e2a0>), ('pre_norm', GroupNorm(32, 512, eps=1e-06, affine=True)), ('attn', <function attn2task.<locals>.<lambda> at 0x72c8f812e200>), ['add_res', None]]
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[Tiled VAE]: Executing Decoder Task Queue: 99%|█████████▉| 1099/1107 [01:00<00:00, 26.07it/s]
[Tiled VAE]: Executing Decoder Task Queue: 100%|█████████▉| 1105/1107 [01:00<00:00, 31.37it/s]
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[Tiled VAE]: Done in 61.040s, max VRAM alloc 16142.288 MB
Version Details
- Version ID
4e22384ed90d5a66cd1bbee71256178022f33db22343058871bbb72e2d1560b7- Version Created
- August 25, 2025