Microsoft Windows [Version 10.0.26200.9457] (c) Microsoft Corporation. All rights reserved. C:\pinokio\api\wan.git\app>conda_hook & conda deactivate & conda deactivate & conda deactivate & conda activate base & C:\pinokio\api\wan.git\app\venv\Scripts\activate C:\pinokio\api\wan.git\app\venv && python wgp.py --multiple-images --advanced [GGUF][llama.cpp CUDA] kernels available. Switching to FP16 models when possible as GPU architecture doesn't support optimed BF16 Kernels [INT8] Backend: Comfy Kitchen CUDA (setting: auto). Downloading ffmpeg-9.0.2-essentials_build.zip: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115M/115M [01:15<00:00, 1.52MB/s] Loaded plugin: Motion Designer (from motion_designer) ██ Detached from Shell b90bdeeb-f8fd-4ae5-a925-48382f5d518b WanGP web authentication disabled. Deepy Web app: http://127.0.0.1:42003/deepy/ =================================================== # input.event [ "http://127.0.0.1:42003" ] =================================================== =================================================== # local variables { "url": "http://127.0.0.1:42003" } =================================================== * Running on local URL: http://127.0.0.1:42003 * To create a public link, set `share=True` in `launch()`. wan2.1_text2video_14B_quanto_mfp16_int8.safetensors: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 14.9G/14.9G [1:58:30<00:00, 2.10MB/s] special_tokens_map.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.62k/6.62k [00:00<00:00, 2.01MB/s] umt5-xxl/spiece.model: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.55M/4.55M [00:02<00:00, 1.60MB/s] umt5-xxl/tokenizer.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16.8M/16.8M [00:03<00:00, 4.89MB/s] tokenizer_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61.7k/61.7k [00:00<00:00, 39.9MB/s] xlm-roberta-large/models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors: 100%|█████████████████████████████████████████████████████████████████| 2.39G/2.39G [15:41<00:00, 2.54MB/s] xlm-roberta-large/sentencepiece.bpe.model: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.07M/5.07M [00:04<00:00, 1.05MB/s] special_tokens_map.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 280/280 [00:00}>>' qk = tl.dot(q, k).to(tl.float32) * q_scale * k_scale ^ C:\pinokio\api\wan.git\app\venv\Lib\site-packages\sageattention\attn_qk_int8_block_varlen.py:95:16: note: called from acc, l_i = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn, ^ module { tt.func public @_attn_fwd(%arg0: !tt.ptr {tt.divisibility = 16 : i32}, %arg1: !tt.ptr {tt.divisibility = 16 : i32}, %arg2: !tt.ptr {tt.divisibility = 16 : i32}, %arg3: !tt.ptr {tt.divisibility = 16 : i32}, %arg4: !tt.ptr {tt.divisibility = 16 : i32}, %arg5: !tt.ptr {tt.divisibility = 16 : i32}, %arg6: !tt.ptr {tt.divisibility = 16 : i32}, %arg7: !tt.ptr {tt.divisibility = 16 : i32}, %arg8: !tt.ptr {tt.divisibility = 16 : i32}, %arg9: !tt.ptr {tt.divisibility = 16 : i32}, %arg10: i32 {tt.divisibility = 16 : i32}, %arg11: i32 {tt.divisibility = 16 : i32}, %arg12: i32 {tt.divisibility = 16 : i32}, %arg13: i32 {tt.divisibility = 16 : i32}, %arg14: i32 {tt.divisibility = 16 : i32}, %arg15: i32 {tt.divisibility = 16 : i32}, %arg16: i32 {tt.divisibility = 16 : i32}, %arg17: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} { %cst = arith.constant dense<1.000000e+00> : tensor<128xf32> %cst_0 = arith.constant dense<0xFF800000> : tensor<128xf32> %c0_i32 = arith.constant 0 : i32 %c64_i32 = arith.constant 64 : i32 %cst_1 = arith.constant dense<0> : tensor<128x64xi32> %cst_2 = arith.constant dense<0.000000e+00> : tensor<128x128xf16> %cst_3 = arith.constant dense<0.000000e+00> : tensor<128x128xf32> %c40_i64 = arith.constant 40 : i64 %c40_i32 = arith.constant 40 : i32 %c128_i32 = arith.constant 128 : i32 %c1_i32 = arith.constant 1 : i32 %0 = tt.get_program_id x : i32 %1 = tt.get_program_id z : i32 %2 = arith.extsi %1 : i32 to i64 %3 = tt.get_program_id y : i32 %4 = arith.extsi %3 : i32 to i64 %5 = tt.addptr %arg3, %2 : !tt.ptr, i64 %6 = tt.load %5 : !tt.ptr %7 = tt.addptr %5, %c1_i32 : !tt.ptr, i32 %8 = tt.load %7 : !tt.ptr %9 = arith.subi %8, %6 : i32 %10 = arith.muli %0, %c128_i32 : i32 %11 = arith.cmpi sge, %10, %9 : i32 cf.cond_br %11, ^bb1, ^bb2 ^bb1: // pred: ^bb0 tt.return ^bb2: // pred: ^bb0 %12 = tt.addptr %arg7, %2 : !tt.ptr, i64 %13 = tt.load %12 : !tt.ptr %14 = tt.addptr %arg8, %2 : !tt.ptr, i64 %15 = tt.load %14 : !tt.ptr %16 = arith.muli %13, %c40_i64 : i64 %17 = arith.addi %16, %4 : i64 %18 = arith.muli %0, %c40_i32 : i32 %19 = arith.extsi %18 : i32 to i64 %20

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