[INFO ] stable-diffusion.cpp:5451 - generate_image 512x768
[DEBUG] stable-diffusion.cpp:2064 - lora ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors:1.00
[INFO ] stable-diffusion.cpp:2079 - apply_loras completed, taking 0.00s
[INFO ] stable-diffusion.cpp:3080 - Using 'cosmos_reference' preset for reference images
[INFO ] denoiser.hpp:1051 - get_sigmas with discrete scheduler
[INFO ] stable-diffusion.cpp:4280 - sampling using ER-SDE method
[DEBUG] conditioner.hpp:1718 - parse 'masterpiece, best quality, absurdres, ultra detailed, furina \(genshin impact\), blue eyes, heterochromia, white hair, long hair, ahoge, blue and white outfit, hat, gloves, 1girl, sitting atop a giant moon-shaped swing, gentle smile, star-filled sky, glowing clouds below, fantasy nightscape' to [['masterpiece, best quality, absurdres, ultra detailed, furina (genshin impact\), blue eyes, heterochromia, white hair, long hair, ahoge, blue and white outfit, hat, gloves, 1girl, sitting atop a giant moon-shaped swing, gentle smile, star-filled sky, glowing clouds below, fantasy nightscape', 1], ]
[DEBUG] bpe_tokenizer.cpp:208 - split prompt "masterpiece, best quality, absurdres, ultra detailed, furina (genshin impact\), blue eyes, heterochromia, white hair, long hair, ahoge, blue and white outfit, hat, gloves, 1girl, sitting atop a giant moon-shaped swing, gentle smile, star-filled sky, glowing clouds below, fantasy nightscape" to 72 tokens ["master", "piece", ",", "Ġbest", "Ġquality", ",", "Ġabsurd", "res", ",", "Ġultra", "Ġdetailed", ",", "Ġfur", "ina", "Ġ(", "gens", "hin", "Ġimpact", "\", "),", "Ġblue", "Ġeyes", ",", "Ġheter", "och", "rom", "ia", ",", "Ġwhite", "Ġhair", ",", "Ġlong", "Ġhair", ",", "Ġah", "oge", ",", "Ġblue", "Ġand", "Ġwhite", "Ġoutfit", ",", "Ġhat", ",", "Ġgloves", ",", "Ġ", "1", "girl", ",", "Ġsitting", "Ġatop", "Ġa", "Ġgiant", "Ġmoon", "-shaped", "Ġswing", ",", "Ġgentle", "Ġsmile", ",", "Ġstar", "-filled", "Ġsky", ",", "Ġglowing", "Ġclouds", "Ġbelow", ",", "Ġfantasy", "Ġnights", "cape", ]
[DEBUG] t5_unigram_tokenizer.cpp:336 - split prompt "masterpiece, best quality, absurdres, ultra detailed, furina (genshin impact\), blue eyes, heterochromia, white hair, long hair, ahoge, blue and white outfit, hat, gloves, 1girl, sitting atop a giant moon-shaped swing, gentle smile, star-filled sky, glowing clouds below, fantasy nightscape" to tokens ["▁masterpiece", ",", "▁best", "▁quality", ",", "▁absurd", "re", "s", ",", "▁ultra", "▁detailed", ",", "▁fur", "in", "a", "▁(", "gen", "s", "hin", "▁impact", "\", "),", "▁blue", "▁eyes", ",", "▁hetero", "chro", "m", "i", "a", ",", "▁white", "▁hair", ",", "▁long", "▁hair", ",", "▁", "a", "hog", "e", ",", "▁blue", "▁and", "▁white", "▁outfit", ",", "▁", "hat", ",", "▁gloves", ",", "▁1", "girl", ",", "▁sitting", "▁", "a", "top", "▁", "a", "▁giant", "▁moon", "-", "shaped", "▁swing", ",", "▁gentle", "▁smile", ",", "▁star", "-", "filled", "▁sky", ",", "▁glowing", "▁clouds", "▁below", ",", "▁fantasy", "▁night", "scape", ]
[DEBUG] model_loader.cpp:1038 - loading 310/310 tensors from /app/anima-base/qwen_3_06b_base.safetensors
|##################################################| 310/310 - 1.16GB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.96s (read: 0.08s, memcpy: 0.00s, convert: 0.02s, copy_to_backend: 0.20s)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (504.00 MB, 112 tensors, VRAM)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (929.75 MB, 198 tensors, VRAM)
[INFO ] model_loader.cpp:242 - load ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors using safetensors format
[DEBUG] model_loader.cpp:316 - init from './anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors', prefix = 'lora.'
[INFO ] lora.hpp:47 - loading LoRA from './anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors'
[DEBUG] model_loader.cpp:228 - using 4 threads for model loading
[DEBUG] model_loader.cpp:1038 - loading 1016/1016 tensors from ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
|##################################################| 1016/1016 - 0.00MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.00s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s)
[DEBUG] model_loader.cpp:1038 - loading 1016/1016 tensors from ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
|##################################################| 1016/1016 - 705.85MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.01s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.06s)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (141.88 MB, 1016 tensors, VRAM)
[DEBUG] lora.hpp:123 - finished loaded lora
[DEBUG] ggml_extend.hpp:2193 - lora compute buffer size: 0.00 MB(VRAM)
[INFO ] lora.hpp:997 - (0 / 1016) LoRA tensors have been applied, lora_file_path = ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
[DEBUG] model_manager.cpp:981 - model manager releasing params backend buffer (141.88 MB, 1016 tensors, VRAM)
[DEBUG] ggml_extend.hpp:2193 - qwen3 compute buffer size: 2.83 MB(VRAM)
[DEBUG] conditioner.hpp:1776 - computing condition graph completed, taking 2339 ms
[INFO ] stable-diffusion.cpp:5131 - get_learned_condition completed, taking 2.34s
[INFO ] stable-diffusion.cpp:5502 - generating image: 1/1 - seed 43
[DEBUG] model_loader.cpp:1038 - loading 685/685 tensors from /app/anima-base/anima-base-v1.0.safetensors
|##################################################| 685/685 - 2.96GB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 1.32s (read: 0.35s, memcpy: 0.00s, convert: 0.01s, copy_to_backend: 0.68s)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (1020.23 MB, 224 tensors, VRAM)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (1012.02 MB, 156 tensors, VRAM)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (1020.02 MB, 158 tensors, VRAM)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (999.01 MB, 147 tensors, VRAM)
[INFO ] model_loader.cpp:242 - load ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors using safetensors format
[DEBUG] model_loader.cpp:316 - init from './anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors', prefix = 'lora.'
[INFO ] lora.hpp:47 - loading LoRA from './anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors'
[DEBUG] model_loader.cpp:228 - using 4 threads for model loading
[DEBUG] model_loader.cpp:1038 - loading 1016/1016 tensors from ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
|##################################################| 1016/1016 - 0.00MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.00s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s)
[DEBUG] model_loader.cpp:1038 - loading 1016/1016 tensors from ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
|##################################################| 1016/1016 - 705.85MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.01s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.04s)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (141.88 MB, 1016 tensors, VRAM)
[DEBUG] lora.hpp:123 - finished loaded lora
[DEBUG] ggml_extend.hpp:2193 - lora compute buffer size: 194.25 MB(VRAM)
[INFO ] lora.hpp:997 - (1016 / 1016) LoRA tensors have been applied, lora_file_path = ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
[DEBUG] model_manager.cpp:981 - model manager releasing params backend buffer (141.88 MB, 1016 tensors, VRAM)
[DEBUG] ggml_extend.hpp:2193 - anima compute buffer size: 78.31 MB(VRAM)
|==================================================| 8/8 - 1.06s/it
[INFO ] stable-diffusion.cpp:5534 - sampling completed, taking 15.98s
[INFO ] stable-diffusion.cpp:5546 - generating 1 latent images completed, taking 15.98s
[INFO ] stable-diffusion.cpp:5156 - decoding 1 latents
[DEBUG] model_loader.cpp:1038 - loading 64/128 tensors from /app/vae/taew2_1.safetensors
|##################################################| 64/64 - 93.42MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.00s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer ( 18.79 MB, 64 tensors, VRAM)
[INFO ] model_loader.cpp:242 - load ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors using safetensors format
[DEBUG] model_loader.cpp:316 - init from './anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors', prefix = 'lora.'
[INFO ] lora.hpp:47 - loading LoRA from './anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors'
[DEBUG] model_loader.cpp:228 - using 4 threads for model loading
[DEBUG] model_loader.cpp:1038 - loading 1016/1016 tensors from ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
|##################################################| 1016/1016 - 0.00MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.00s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s)
[DEBUG] model_loader.cpp:1038 - loading 1016/1016 tensors from ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
|##################################################| 1016/1016 - 709.37MB/s
[INFO ] model_loader.cpp:1300 - loading tensors completed, taking 0.20s (read: 0.01s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.03s)
[DEBUG] model_manager.cpp:406 - model manager prepared params backend buffer (141.88 MB, 1016 tensors, VRAM)
[DEBUG] lora.hpp:123 - finished loaded lora
[DEBUG] ggml_extend.hpp:2193 - lora compute buffer size: 0.00 MB(VRAM)
[INFO ] lora.hpp:997 - (0 / 1016) LoRA tensors have been applied, lora_file_path = ./anima-base/lora-speed/anima-turbo-lora-v0.2.safetensors
[DEBUG] model_manager.cpp:981 - model manager releasing params backend buffer (141.88 MB, 1016 tensors, VRAM)
[DEBUG] ggml_extend.hpp:2193 - taehv compute buffer size: 2592.38 MB(VRAM)
[DEBUG] vae.hpp:219 - computing vae decode graph completed, taking 0.96s
[INFO ] stable-diffusion.cpp:5222 - latent 1 decoded, taking 0.96s
[INFO ] stable-diffusion.cpp:5226 - decode_first_stage completed, taking 0.96s
[INFO ] stable-diffusion.cpp:5684 - generate_image completed in 19.29s
Git commit
$docker run --rm -it --entrypoint="/sd-server" ghcr.io/leejet/stable-diffusion.cpp:master-vulkan --version
stable-diffusion.cpp version unknown, commit db99efd
Operating System & Version
Ubuntu 24.04, Win10 22H2
GGML backends
Vulkan
Command-line arguments used
-v --diffusion-model /app/anima-base/anima-base-v1.0.safetensors --tae /app/vae/taew2_1.safetensors --llm /app/anima-base/qwen_3_06b_base.safetensors --cfg-scale 1 --steps 8 --sampling-method er_sde --fa
Steps to reproduce
Case 1
Start the server with:
Generate an image with a LoRA enabled.
Change the LoRA configuration:
Generate image again.
Result: the generated image is completely black.
Result: the server crashes, exit code 139.
Case 2
Start the server with:
Generate an image without any LoRA.
Add a LoRA.
Generate image again.
Result: the generated image is completely black.
Result: the server crashes, exit code 139.
What you expected to happen
Changing LoRA parameters should only affect the generated image output.
The server should continue generating images normally without producing black images or crashing.
What actually happened
After server startup, the first generation always succeeds.
If the LoRA configuration is changed after the first generation, including:
the next generated image becomes completely black.
Any subsequent generation attempt then crashes the server process (exit code 139).
The issue reproduces consistently on both:
This suggests the issue is not vendor-specific and may be related to LoRA reload handling when TAEHV is used.
Logs / error messages / stack trace
server startup logs:
Details
generate first image with lora:
Details
change lora configuration, generate again, cause black image:
Details
generate one more image cause crash after black image:
Details
Additional context / environment details
Did not check upscaler yet.
Flags won't affect result:
--fa--vae-tiling--rng cpu--mmap--eager-loadsystem 1:
run inside docker via docker compose:
system 2: