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blocks_readout
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Starting up...
Started Invoke process with PID: 352201
[2025-01-06 01:38:11,222]::[InvokeAI]::INFO --> Loading node pack caption
[2025-01-06 01:38:11,493]::[InvokeAI]::INFO --> Loading node pack InvokeAI_NoiseAdjuster
[2025-01-06 01:38:11,503]::[InvokeAI]::INFO --> Loading node pack InvokeAI_ModularDenoiseNodes
[2025-01-06 01:38:12,149]::[InvokeAI]::INFO --> Registered extension InpaintMaskGuidance as InpaintMaskGuidance
[2025-01-06 01:38:12,156]::[InvokeAI]::INFO --> Registered extension FAM_FM_Guidance as FAM_FM
[2025-01-06 01:38:12,159]::[InvokeAI]::INFO --> Registered extension FAM_AM_Guidance as FAM_AM
[2025-01-06 01:38:12,164]::[InvokeAI]::INFO --> Registered extension RefDrop_Guidance as RefDrop
[2025-01-06 01:38:12,168]::[InvokeAI]::INFO --> Loading node pack color_palettes
[2025-01-06 01:38:12,174]::[InvokeAI]::INFO --> Loading node pack pixelize
[2025-01-06 01:38:12,179]::[InvokeAI]::INFO --> Loading node pack list_operators
[2025-01-06 01:38:12,185]::[InvokeAI]::INFO --> Loading node pack InvokeAI_Flux_Node
[2025-01-06 01:38:12,367]::[InvokeAI]::INFO --> Loading node pack invoke-random-booru
[2025-01-06 01:38:12,371]::[InvokeAI]::INFO --> Loading node pack InvokeAI_AlternateCLIP
[2025-01-06 01:38:12,371]::[InvokeAI]::INFO --> Loading node pack InvokeAI_RawDiffusers_1
[2025-01-06 01:38:12,377]::[InvokeAI]::INFO --> Loaded 10 node packs from /home/dunkeroni/Stable-Diffusion/invokeAI/nodes
[2025-01-06 01:38:12,688]::[InvokeAI]::INFO --> Patchmatch initialized
[2025-01-06 01:38:13,829]::[InvokeAI]::INFO --> Using torch device: NVIDIA GeForce RTX 4090
[2025-01-06 01:38:14,111]::[InvokeAI]::INFO --> cuDNN version: 90100
[2025-01-06 01:38:14,124]::[InvokeAI]::INFO --> InvokeAI version 5.5.0
[2025-01-06 01:38:14,125]::[InvokeAI]::INFO --> Root directory = /home/dunkeroni/Stable-Diffusion/invokeAI
[2025-01-06 01:38:14,125]::[InvokeAI]::INFO --> Initializing database at /home/dunkeroni/Stable-Diffusion/invokeAI/databases/invokeai.db
[2025-01-06 01:38:14,161]::[InvokeAI]::INFO --> Pruned 15 finished queue items
[2025-01-06 01:38:14,967]::[InvokeAI]::INFO --> Cleaned database (freed 0.86MB)
[2025-01-06 01:38:14,967]::[InvokeAI]::INFO --> Invoke running on http://0.0.0.0:9090 (Press CTRL+C to quit)
[2025-01-06 01:38:19,364]::[InvokeAI]::INFO --> Executing queue item 84446, session 51d7ef4b-f595-4544-b00d-a6531757a82b
Fetching 17 files: 0%| | 0/17 [00:00<?, ?it/s]
Fetching 17 files: 100%|██████████| 17/17 [00:00<00:00, 210333.83it/s]
Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]
Loading pipeline components...: 43%|████▎ | 3/7 [00:00<00:00, 14.30it/s]
Loading pipeline components...: 71%|███████▏ | 5/7 [00:00<00:00, 15.45it/s]
Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 11.42it/s]
Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 12.30it/s]
ModuleList(
(0): DownBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
(conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)
(norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
)
)
(1): CrossAttnDownBlock2D(
(attentions): ModuleList(
(0-1): 2 x Transformer2DModel(
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(proj_in): Linear(in_features=640, out_features=640, bias=True)
(transformer_blocks): ModuleList(
(0-1): 2 x BasicTransformerBlock(
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(to_q): Linear(in_features=640, out_features=640, bias=False)
(to_k): Linear(in_features=640, out_features=640, bias=False)
(to_v): Linear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): Linear(in_features=640, out_features=640, bias=False)
(to_k): Linear(in_features=2048, out_features=640, bias=False)
(to_v): Linear(in_features=2048, out_features=640, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(net): ModuleList(
(0): GEGLU(
(proj): Linear(in_features=640, out_features=5120, bias=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Linear(in_features=2560, out_features=640, bias=True)
)
)
)
)
(proj_out): Linear(in_features=640, out_features=640, bias=True)
)
)
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
(conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
)
)
(2): CrossAttnDownBlock2D(
(attentions): ModuleList(
(0-1): 2 x Transformer2DModel(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(transformer_blocks): ModuleList(
(0-9): 10 x BasicTransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(to_q): Linear(in_features=1280, out_features=1280, bias=False)
(to_k): Linear(in_features=1280, out_features=1280, bias=False)
(to_v): Linear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): Linear(in_features=1280, out_features=1280, bias=False)
(to_k): Linear(in_features=2048, out_features=1280, bias=False)
(to_v): Linear(in_features=2048, out_features=1280, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(net): ModuleList(
(0): GEGLU(
(proj): Linear(in_features=1280, out_features=10240, bias=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Linear(in_features=5120, out_features=1280, bias=True)
)
)
)
)
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(conv1): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
)
UNetMidBlock2DCrossAttn(
(attentions): ModuleList(
(0): Transformer2DModel(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(transformer_blocks): ModuleList(
(0-9): 10 x BasicTransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(to_q): Linear(in_features=1280, out_features=1280, bias=False)
(to_k): Linear(in_features=1280, out_features=1280, bias=False)
(to_v): Linear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): Linear(in_features=1280, out_features=1280, bias=False)
(to_k): Linear(in_features=2048, out_features=1280, bias=False)
(to_v): Linear(in_features=2048, out_features=1280, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(net): ModuleList(
(0): GEGLU(
(proj): Linear(in_features=1280, out_features=10240, bias=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Linear(in_features=5120, out_features=1280, bias=True)
)
)
)
)
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
ModuleList(
(0): CrossAttnUpBlock2D(
(attentions): ModuleList(
(0-2): 3 x Transformer2DModel(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(transformer_blocks): ModuleList(
(0-9): 10 x BasicTransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(to_q): Linear(in_features=1280, out_features=1280, bias=False)
(to_k): Linear(in_features=1280, out_features=1280, bias=False)
(to_v): Linear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): Linear(in_features=1280, out_features=1280, bias=False)
(to_k): Linear(in_features=2048, out_features=1280, bias=False)
(to_v): Linear(in_features=2048, out_features=1280, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(net): ModuleList(
(0): GEGLU(
(proj): Linear(in_features=1280, out_features=10240, bias=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Linear(in_features=5120, out_features=1280, bias=True)
)
)
)
)
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)
(conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))
)
(2): ResnetBlock2D(
(norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
(conv1): Conv2d(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(1920, 1280, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(1): CrossAttnUpBlock2D(
(attentions): ModuleList(
(0-2): 3 x Transformer2DModel(
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(proj_in): Linear(in_features=640, out_features=640, bias=True)
(transformer_blocks): ModuleList(
(0-1): 2 x BasicTransformerBlock(
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(to_q): Linear(in_features=640, out_features=640, bias=False)
(to_k): Linear(in_features=640, out_features=640, bias=False)
(to_v): Linear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): Linear(in_features=640, out_features=640, bias=False)
(to_k): Linear(in_features=2048, out_features=640, bias=False)
(to_v): Linear(in_features=2048, out_features=640, bias=False)
(to_out): ModuleList(
(0): Linear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(net): ModuleList(
(0): GEGLU(
(proj): Linear(in_features=640, out_features=5120, bias=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Linear(in_features=2560, out_features=640, bias=True)
)
)
)
)
(proj_out): Linear(in_features=640, out_features=640, bias=True)
)
)
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
(conv1): Conv2d(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(1920, 640, kernel_size=(1, 1), stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(conv1): Conv2d(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(1280, 640, kernel_size=(1, 1), stride=(1, 1))
)
(2): ResnetBlock2D(
(norm1): GroupNorm(32, 960, eps=1e-05, affine=True)
(conv1): Conv2d(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(960, 640, kernel_size=(1, 1), stride=(1, 1))
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(2): UpBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 960, eps=1e-05, affine=True)
(conv1): Conv2d(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)
(norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(conv1): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)
(norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1))
)
)
)
)
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[2025-01-06 01:38:28,813]::[InvokeAI]::INFO --> Graph stats: 51d7ef4b-f595-4544-b00d-a6531757a82b
Node Calls Seconds VRAM Used
image 1 0.054s 0.000G
vae_loader 1 0.001s 0.000G
i2l 1 0.711s 0.550G
RefDrop_extInvocation 1 0.001s 0.169G
sdxl_model_loader 1 0.001s 0.169G
string 2 0.001s 0.169G
string_join 2 0.001s 0.169G
sdxl_compel_prompt 2 1.714s 1.311G
rand_int 1 0.001s 0.169G
noise 1 0.004s 0.169G
exposed_denoise_latents 1 6.219s 6.039G
l2i 1 0.682s 4.890G
TOTAL GRAPH EXECUTION TIME: 9.389s
TOTAL GRAPH WALL TIME: 9.405s
RAM used by InvokeAI process: 8.62G (+7.785G)
RAM used to load models: 6.46G
VRAM in use: 0.169G
RAM cache statistics:
Model cache hits: 12
Model cache misses: 2
Models cached: 8
Models cleared from cache: 0
Cache high water mark: 6.62/15.00G
We'll activate the virtual environment for the install at /home/dunkeroni/Stable-Diffusion/invokeAI.