fix: prevent z-image crash with reference images by wbruna · Pull Request #2185 · leejet/stable-diffusion.cpp

wbruna · 2026-10-22T04:51:44Z

Summary

Prevents a segfault when Z-Image receives reference images.

The change is intentionally minimal, just to prevent a hard crash. It could arguably be applied on other places instead (like sd_ctx_supports_ref_image_conditioning), but I'm not sure what should be done with the ZImageOmniPipeline support.

Related Issue / Discussion

Surprisingly, I couldn't find a bug report about this.

Checklist

I have read and confirmed this PR follows the contribution guidelines.

Summary by CodeRabbit

Bug Fixes
- Z-Image and Z-Image L2P now report an error and return an empty condition when reference images are provided. Text-only prompts continue to work as before.
- Boogu now uses the boogu_image default reference-image preset. Z-Image continues to use the z_image_omni preset.

coderabbitai · 2026-10-22T04:52:12Z

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src/pipeline/diffusion_engine.cpp

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📝 Walkthrough

For Z-Image and Z-Image L2P, prompts with reference images now produce an error log and an empty condition. Boogu Image and Z-Image reach the Omni preset through separate model-selection branches.

Changes: Z-Image handling

Layer / File(s)	Summary
Reject reference-image prompts `src/conditioning/conditioner.hpp`	When Z-Image or Z-Image L2P receives reference images, the function logs an error and returns an empty condition.
Separate model preset selection `src/pipeline/diffusion_engine.cpp`	The Boogu Image branch now checks only for Boogu Image. Z-Image continues to use the following branch to select the `z_image_omni` preset.

Priority: ➖ Normal

Estimated code review effort: 2 (Simple) | ~8 minutes

Change: Bug fix

Suggested reviewers: leejet, daniandtheweb

Merge Risk: 🟡 Moderate · up to f1403

Explicit VLM-enabled normal Z-Image reference requests are accepted by the public configuration path but now fail, so this needs clarification before merge.

Security architecture risk: 🔵 Low · up to f1403

Unsupported inputs now fail through the existing error path. No security regression was established, but behavior differs by configuration and runtime recovery has not been fully verified.

Retained concerns
No architecture-level concerns identified.

Security review details

Security Blast Radius

inferred — The inspected security-relevant effect is availability and failure containment within image generation. The new guard stops the prior Omni conditioning path for supplied references; it does not establish universal rejection or runtime safety for the remaining diffusion-reference route.

Trust Boundaries and Controls

observed — The public image-generation entrypoint uses ExecutionScope. Its ContextOperation holds a context mutex and rejects overlapping or reentrant operations; destruction clears the executing state. These existing controls remain applicable when conditioning fails early.

Resilience and Maintainability Implications

observed — The conditioner cleanup guard calls runner_end on early return, and empty conditions are excluded from the conditioning cache. Extension preparation resets runtime conditions before preparing a subsequent request. These controls support failure containment, although they do not replace runtime interruption and recovery validation.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 2 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly describes the main change: preventing a Z-Image crash when reference images are provided.
Description check		The description explains the crash-prevention change, addresses the related-issue section, and completes the checklist. The optional Additional Information section is omitted, but the description is o…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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Inline comments:
Review comments at @src/conditioning/conditioner.hpp:
- Around line 3048-3050: Update the reference-image rejection guard in the
Z-Image conditioning path to apply only to Z-Image L2P, allowing normal Z-Image
requests to continue to ZImageOmniPipeline prompt construction. Before the
existing loop indexes extra_prompts_attn_range, ensure it has an entry for every
extra_prompts item.

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src/conditioning/conditioner.hpp

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wbruna · 2026-10-22T10:25:54Z

This is with current master:

/tmp/test $ ./sd-cli --backend cpu  --diffusion-model ./z_image-Q4_K_M.gguf --vae ./flux_ae_bf16.safetensors --llm ./Qwen3-4B-UD-Q4_K_XL.gguf --cfg-scale 1 --steps 1 -p flower -W 512 -H 512 -r ref.png 
(...)
[I] loading diffusion model from './z_image-Q4_K_M.gguf' --- diffusion_engine.cpp:739
[I] load ./z_image-Q4_K_M.gguf using gguf format --- model_loader.cpp:216
[I] loading llm from './Qwen3-4B-UD-Q4_K_XL.gguf' --- diffusion_engine.cpp:796
[I] load ./Qwen3-4B-UD-Q4_K_XL.gguf using gguf format --- model_loader.cpp:216
[I] loading vae from './flux_ae_bf16.safetensors' --- diffusion_engine.cpp:810
[I] load ./flux_ae_bf16.safetensors using safetensors format --- model_loader.cpp:222
[I] Version: Z-Image  --- diffusion_engine.cpp:1017
(...)
[I] auto-fit: --backend "cpu" --params-backend "diffusion=cpu,te=cpu,vae=cpu" --- backend_fit.cpp:474
(...)
[I] Using 'z_image_omni' preset for reference images --- diffusion_engine.cpp:3261
(...)
[I] ZImageOmniPipeline --- conditioner.hpp:3050
[I] qwen3 build cached graph cut plan done (taking 2 ms) --- ggml_graph_cut.cpp:1032
  |##################################################| 386/386 - 692.02MB/s
[I] loading tensors completed, taking 3.40s (read: 3.28s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s) --- model_loader.cpp:1400
Segmentation fault (core dumped)
/tmp/test $

full log

/tmp/test $ ./sd-cli --backend cpu  --diffusion-model ./z_image-Q4_K_M.gguf --vae ./flux_ae_bf16.safetensors --llm ./Qwen3-4B-UD-Q4_K_XL.gguf --cfg-scale 1 --steps 1 -p flower -W 512 -H 512 -r ref.png 
ggml_cuda_init: found 2 ROCm devices (Total VRAM: 39406 MiB):
  Device 0: AMD Radeon RX 7600 XT, gfx1102 (0x1102), VMM: no, Wave Size: 32, VRAM: 16368 MiB
  Device 1: AMD Radeon Vega 11 Graphics, gfx902:xnack- (0x902), VMM: no, Wave Size: 64, VRAM: 23038 MiB
ggml_vulkan: Found 2 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon Vega 11 Graphics (RADV RAVEN) (radv) | uma: 1 | fp16: 1 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 0 | matrix cores: none
ggml_vulkan: 1 = AMD Radeon RX 7600 XT (RADV NAVI33) (radv) | uma: 0 | fp16: 1 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
[W] Using upstream GGML: INT8 tensorwise/convrot is disabled and FP8 weights are converted to F16 at load time. Some operators may be unsupported and performance may be lower than with patched GGML. --- diffusion_engine.cpp:934
[I] loading diffusion model from './z_image-Q4_K_M.gguf' --- diffusion_engine.cpp:739
[I] load ./z_image-Q4_K_M.gguf using gguf format --- model_loader.cpp:216
[I] loading llm from './Qwen3-4B-UD-Q4_K_XL.gguf' --- diffusion_engine.cpp:796
[I] load ./Qwen3-4B-UD-Q4_K_XL.gguf using gguf format --- model_loader.cpp:216
[I] loading vae from './flux_ae_bf16.safetensors' --- diffusion_engine.cpp:810
[I] load ./flux_ae_bf16.safetensors using safetensors format --- model_loader.cpp:222
[I] Version: Z-Image  --- diffusion_engine.cpp:1017
[I] auto-fit plan: --- backend_fit.cpp:323
[I]   devices: --- backend_fit.cpp:324
[I]     ROCm0        AMD Radeon RX 7600 XT            free  16334 MiB, budget  15822 MiB --- backend_fit.cpp:326
[I]     ROCm1        AMD Radeon Vega 11 Graphics      free  23034 MiB, budget  22522 MiB --- backend_fit.cpp:326
[I]     Vulkan0      AMD Radeon Vega 11 Graphics (RADV RAVEN) free  16142 MiB, budget  15630 MiB --- backend_fit.cpp:326
[I]     Vulkan1      AMD Radeon RX 7600 XT (RADV NAVI33) free  16342 MiB, budget  15830 MiB --- backend_fit.cpp:326
[I]     RAM          free  23751 MiB, params budget  21376 MiB --- backend_fit.cpp:333
[I]   compute-device weight cache priority: diffusion > te > vae --- backend_fit.cpp:336
[I]   components (params: compute device -> RAM -> other GPU -> disk): --- backend_fit.cpp:337
[I]     DiT          params   4832 MiB, compute reserve  2048 MiB -> compute CPU, params cpu --- backend_fit.cpp:346
[I]     Conditioner  params   2422 MiB, compute reserve  2048 MiB -> compute CPU, params cpu --- backend_fit.cpp:346
[I]     VAE          params    159 MiB, compute reserve  1024 MiB -> compute CPU, params cpu --- backend_fit.cpp:346
[I] auto-fit: --backend "cpu" --params-backend "diffusion=cpu,te=cpu,vae=cpu" --- backend_fit.cpp:474
[I] Weight type stat:                      f32: 390  |    q4_K: 241  |    q5_K: 88   |    q6_K: 84   |  iq4_xs: 20   |    bf16: 272   --- diffusion_engine.cpp:1650
[I] Conditioner weight type stat:          f32: 145  |    q4_K: 154  |    q5_K: 30   |    q6_K: 49   |  iq4_xs: 20    --- diffusion_engine.cpp:1651
[I] Diffusion model weight type stat:      f32: 245  |    q4_K: 87   |    q5_K: 58   |    q6_K: 35   |    bf16: 28    --- diffusion_engine.cpp:1652
[I] VAE weight type stat:                 bf16: 244   --- diffusion_engine.cpp:1653
[I] using VAE for encoding / decoding --- model_builders.cpp:664
[I] vae decoder: ch = 128 --- auto_encoder_kl.hpp:529
[I] flash attention auto: CPU -> on (CPU) --- flash_attn.cpp:66
[I] conv2d direct auto: CPU -> off (no implicit GEMM path) --- conv_direct.cpp:90
[I] conv3d direct auto: CPU -> off (no implicit GEMM path) --- conv_direct.cpp:90
[I] total params memory size = 7416.40MB (VRAM 0.00MB, RAM 7416.40MB): text_encoders 2422.70MB(RAM), diffusion_model 4833.70MB(RAM), vae 160.00MB(RAM), controlnet 0.00MB(N/A), extensions 0.00MB(N/A) --- diffusion_engine.cpp:1444
[I] running in FLOW mode --- diffusion_engine.cpp:1590
[I] preprocess ref[0]: 512x512 crop=(0,0,512,512) resize=512x512 pad=(0,0) output=512x512 filter=auto(0) antialias=false --- image_preprocess.cpp:302
[I] generate_image 512x512 --- image.cpp:822
[I] Using 'z_image_omni' preset for reference images --- diffusion_engine.cpp:3261
[I] get_sigmas with discrete scheduler --- denoiser.hpp:1129
[I] sampling using Euler method --- request.cpp:422
[I] EDIT mode --- image.cpp:274
[I] vae build cached graph cut plan done (taking 0 ms) --- ggml_graph_cut.cpp:1032
  |##################################################| 106/106 - 325.24MB/s
[I] loading tensors completed, taking 0.20s (read: 0.01s, memcpy: 0.00s, convert: 0.05s, copy_to_backend: 0.00s) --- model_loader.cpp:1400
[I] encode_first_stage completed, taking 48.88s --- image.cpp:406
[I] ZImageOmniPipeline --- conditioner.hpp:3050
[I] qwen3 build cached graph cut plan done (taking 2 ms) --- ggml_graph_cut.cpp:1032
  |##################################################| 386/386 - 692.02MB/s
[I] loading tensors completed, taking 3.40s (read: 3.28s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s) --- model_loader.cpp:1400
Segmentation fault (core dumped)
/tmp/test $ ./sd-cli --backend cpu  --diffusion-model ./z_image-Q4_K_M.gguf --vae ./flux_ae_bf16.safetensors --llm ./Qwen3-4B-UD-Q4_K_XL.gguf --cfg-scale 1 --steps 1 -p flower -W 512 -H 512 -r ref.png -v
[V] version: stable-diffusion.cpp version master-987-a77d362, commit a77d3623 --- main.cpp:702
ggml_cuda_init: found 2 ROCm devices (Total VRAM: 39406 MiB):
  Device 0: AMD Radeon RX 7600 XT, gfx1102 (0x1102), VMM: no, Wave Size: 32, VRAM: 16368 MiB
  Device 1: AMD Radeon Vega 11 Graphics, gfx902:xnack- (0x902), VMM: no, Wave Size: 64, VRAM: 23038 MiB
ggml_vulkan: Found 2 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon Vega 11 Graphics (RADV RAVEN) (radv) | uma: 1 | fp16: 1 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 0 | matrix cores: none
ggml_vulkan: 1 = AMD Radeon RX 7600 XT (RADV NAVI33) (radv) | uma: 0 | fp16: 1 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
[V] System Info: 
   SSE3 = 1 |    SSSE3 = 1 |    AVX = 1 |    AVX2 = 1 |    F16C = 1 |    FMA = 1 |    BMI2 = 1 |    OPENMP = 1 |    REPACK = 1 |  --- main.cpp:703
[W] Using upstream GGML: INT8 tensorwise/convrot is disabled and FP8 weights are converted to F16 at load time. Some operators may be unsupported and performance may be lower than with patched GGML. --- diffusion_engine.cpp:934
[V] using 4 threads for model loading --- model_loader.cpp:190
[I] loading diffusion model from './z_image-Q4_K_M.gguf' --- diffusion_engine.cpp:739
[I] load ./z_image-Q4_K_M.gguf using gguf format --- model_loader.cpp:216
[V] init from './z_image-Q4_K_M.gguf' --- model_loader.cpp:262
[V] GGUF v3, tensor_count=453, metadata_kv_count=3 --- gguf_reader_ext.h:219
[I] loading llm from './Qwen3-4B-UD-Q4_K_XL.gguf' --- diffusion_engine.cpp:796
[I] load ./Qwen3-4B-UD-Q4_K_XL.gguf using gguf format --- model_loader.cpp:216
[V] init from './Qwen3-4B-UD-Q4_K_XL.gguf' --- model_loader.cpp:262
[V] GGUF v3, tensor_count=398, metadata_kv_count=32 --- gguf_reader_ext.h:219
[I] loading vae from './flux_ae_bf16.safetensors' --- diffusion_engine.cpp:810
[I] load ./flux_ae_bf16.safetensors using safetensors format --- model_loader.cpp:222
[V] init from './flux_ae_bf16.safetensors', prefix = 'vae.' --- model_loader.cpp:290
[I] Version: Z-Image  --- diffusion_engine.cpp:1017
[I] auto-fit plan: --- backend_fit.cpp:323
[I]   devices: --- backend_fit.cpp:324
[I]     ROCm0        AMD Radeon RX 7600 XT            free  16334 MiB, budget  15822 MiB --- backend_fit.cpp:326
[I]     ROCm1        AMD Radeon Vega 11 Graphics      free  23034 MiB, budget  22522 MiB --- backend_fit.cpp:326
[I]     Vulkan0      AMD Radeon Vega 11 Graphics (RADV RAVEN) free  16129 MiB, budget  15617 MiB --- backend_fit.cpp:326
[I]     Vulkan1      AMD Radeon RX 7600 XT (RADV NAVI33) free  16341 MiB, budget  15829 MiB --- backend_fit.cpp:326
[I]     RAM          free  23571 MiB, params budget  21214 MiB --- backend_fit.cpp:333
[I]   compute-device weight cache priority: diffusion > te > vae --- backend_fit.cpp:336
[I]   components (params: compute device -> RAM -> other GPU -> disk): --- backend_fit.cpp:337
[I]     DiT          params   4832 MiB, compute reserve  2048 MiB -> compute CPU, params cpu --- backend_fit.cpp:346
[I]     Conditioner  params   2422 MiB, compute reserve  2048 MiB -> compute CPU, params cpu --- backend_fit.cpp:346
[I]     VAE          params    159 MiB, compute reserve  1024 MiB -> compute CPU, params cpu --- backend_fit.cpp:346
[I] auto-fit: --backend "cpu" --params-backend "diffusion=cpu,te=cpu,vae=cpu" --- backend_fit.cpp:474
[V] Initializing backend: CPU --- ggml_extend_backend.cpp:420
[V] using 4 threads for model loading --- model_loader.cpp:190
[I] Weight type stat:                      f32: 390  |    q4_K: 241  |    q5_K: 88   |    q6_K: 84   |  iq4_xs: 20   |    bf16: 272   --- diffusion_engine.cpp:1650
[I] Conditioner weight type stat:          f32: 145  |    q4_K: 154  |    q5_K: 30   |    q6_K: 49   |  iq4_xs: 20    --- diffusion_engine.cpp:1651
[I] Diffusion model weight type stat:      f32: 245  |    q4_K: 87   |    q5_K: 58   |    q6_K: 35   |    bf16: 28    --- diffusion_engine.cpp:1652
[I] VAE weight type stat:                 bf16: 244   --- diffusion_engine.cpp:1653
[V] llm: num_layers = 36, vocab_size = 151936, hidden_size = 2560, intermediate_size = 9728 --- llm.hpp:442
[V] z_image: num_layers = 30, num_refiner_layers = 2, hidden_size = 3840, num_heads = 30, num_kv_heads = 30, in_channels = 16, out_channels = 16 --- z_image.hpp:116
[I] using VAE for encoding / decoding --- model_builders.cpp:664
[I] vae decoder: ch = 128 --- auto_encoder_kl.hpp:529
[I] flash attention auto: CPU -> on (CPU) --- flash_attn.cpp:66
[I] conv2d direct auto: CPU -> off (no implicit GEMM path) --- conv_direct.cpp:90
[I] conv3d direct auto: CPU -> off (no implicit GEMM path) --- conv_direct.cpp:90
[V] validating model metadata --- diffusion_engine.cpp:1328
[V] model metadata validated; weights will be prepared lazily --- diffusion_engine.cpp:1398
[I] total params memory size = 7416.40MB (VRAM 0.00MB, RAM 7416.40MB): text_encoders 2422.70MB(RAM), diffusion_model 4833.70MB(RAM), vae 160.00MB(RAM), controlnet 0.00MB(N/A), extensions 0.00MB(N/A) --- diffusion_engine.cpp:1444
[I] running in FLOW mode --- diffusion_engine.cpp:1590
[I] preprocess ref[0]: 512x512 crop=(0,0,512,512) resize=512x512 pad=(0,0) output=512x512 filter=auto(0) antialias=false --- image_preprocess.cpp:302
[I] generate_image 512x512 --- image.cpp:822
[V] using 4 threads for model loading --- model_loader.cpp:190
[I] Using 'z_image_omni' preset for reference images --- diffusion_engine.cpp:3261
[I] get_sigmas with discrete scheduler --- denoiser.hpp:1129
[I] sampling using Euler method --- request.cpp:422
[I] EDIT mode --- image.cpp:274
[V] auto resize ref images --- image.cpp:284
[V] resize vae ref image 0 from 512x512 to 512x512 --- image.cpp:316
[I] vae build cached graph cut plan done (taking 0 ms) --- ggml_graph_cut.cpp:1032
[V] loading 106/244 tensors from ./flux_ae_bf16.safetensors --- model_loader.cpp:1130
  |##################################################| 106/106 - 325.24MB/s
[I] loading tensors completed, taking 0.20s (read: 0.01s, memcpy: 0.00s, convert: 0.05s, copy_to_backend: 0.00s) --- model_loader.cpp:1400
[V] model manager prepared params backend buffers ( 65.43 MB, 106 tensors, 1 blocks, RAM) on CPU --- model_manager.cpp:490
[V] vae compute buffer size: 848.50 MB(RAM) on CPU (peak across 1 segment) --- ggml_runner.cpp:1019
[V] computing vae encode graph completed, taking 43.10s --- vae.hpp:310
[I] encode_first_stage completed, taking 43.12s --- image.cpp:406
[I] ZImageOmniPipeline --- conditioner.hpp:3050
[V] parse '<|im_start|>user\n<|vision_start|>' to [['<|im_start|>user\n<|vision_start|>', 1], ] --- conditioner.hpp:2183
[V] split prompt "<|im_start|>user\n<|vision_start|>" to 4 tokens ["<|im_start|>", "user", "Ċ", "<|vision_start|>", ] --- bpe_tokenizer.cpp:245
[I] qwen3 build cached graph cut plan done (taking 2 ms) --- ggml_graph_cut.cpp:1032
[V] loading 386/398 tensors from ./Qwen3-4B-UD-Q4_K_XL.gguf --- model_loader.cpp:1130
  |##################################################| 386/386 - 653.42MB/s
[I] loading tensors completed, taking 3.60s (read: 3.51s, memcpy: 0.00s, convert: 0.00s, copy_to_backend: 0.00s) --- model_loader.cpp:1400
[V] model manager prepared params backend buffers (2354.26 MB, 386 tensors, 1 blocks, RAM) on CPU --- model_manager.cpp:490
[V] qwen3 compute buffer size: 0.38 MB(RAM) on CPU (peak across 1 segment) --- ggml_runner.cpp:1019
Segmentation fault (core dumped)
/tmp/test $ 
/tmp/test $ ./sd-cli --version
stable-diffusion.cpp version master-987-a77d362, commit a77d3623
/tmp/test $

wbruna · 2026-10-22T10:48:21Z

Got an agent to extract the real reason: in conditioner.hpp, LLMEmbedder::get_learned_condition:

https://github.com/leejet/stable-diffusion.cpp/blob/master-987-a77d362/src/conditioning/conditioner.hpp#L2418

Confirmed: extra_prompts_attn_range is declared (line 2423) and read at (line 3247), but is never written to anywhere in the codebase. The z_image branch pushes two entries into extra_prompts (lines 3053, 3055, 3056), so at line 3247:
  for (int i = 0; i < extra_prompts.size(); i++) {
      auto extra_hidden_states = encode_prompt(n_threads,
                                               extra_prompts[i],
                                               extra_prompts_attn_range[i],   // ← UB: empty vector
extra_prompts_attn_range[0] on an empty vector is undefined behavior — in practice it's a null-dereference segfault (in a vector's operator[], this->_M_start[0] where _M_start == nullptr).

This is the crash. It's happening inside LLMEmbedder::get_learned_condition, i.e., before the diffusion model's compute() is ever called — which is exactly why the user's guard in ZImageRunner::compute never gets a chance to fire. The crash is in the conditioner, not the DiT.

(this is referring to a tentative fix at another point)

condition_params.ref_images is only set when ref_image_params.pass_to_vlm is true (line 442 of image.cpp: if (ref_image_params.pass_to_vlm) condition_params.ref_images = &latents->ref_images;). The z_image_omni preset (the default for z_image) has pass_to_vlm = true. So yes, for z_image with -r and the default preset, ref_images gets passed to the conditioner → z_image branch → extra_prompts gets populated → crash at line 3247.

So the whole "ZImageOmniPipeline" branch is a half-finished feature: it was built for Z-Image-Omni (a VLM-based variant), but it crashes on any z_image with a ref image. Because extra_prompts_attn_range is never populated (UB / segfault).

Currently, a Z-Image model with a reference image calls `LLMEmbedder::get_learned_condition` with a non-empty `extra_prompts` vector. This causes the `extra_prompts_attn_range` vector to be accessed out of bounds, leading to a crash (UB).

fix: prevent z-image crash with reference images

9da83f2

coderabbitai Bot reviewed Oct 22, 2026

View reviewed changes

Comment thread src/conditioning/conditioner.hpp

fix: disable z_image_omni for Z-Image models

f1403e9

Currently, a Z-Image model with a reference image calls `LLMEmbedder::get_learned_condition` with a non-empty `extra_prompts` vector. This causes the `extra_prompts_attn_range` vector to be accessed out of bounds, leading to a crash (UB).

leejet merged commit 530fa39 into leejet:master Oct 22, 2026
11 checks passed

danielhanchen mentioned this pull request Oct 23, 2026

Sync upstream master (fd21ecf): server cancel, Z-Image reference crash fix, ggml Vulkan small-BAR placement, one placeholder per image unslothai/stable-diffusion.cpp#35

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leehack mentioned this pull request Oct 23, 2026

Qualify post-v0.2 upstream changes with context ABI and FP8 CPU safety checks leehack/stable-diffusion-native#20

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fix: prevent z-image crash with reference images - #2185

fix: prevent z-image crash with reference images#2185
leejet merged 2 commits into
leejet:masterfrom
wbruna:sd_zit_crash_ref

wbruna commented Oct 22, 2026 •

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coderabbitai Bot commented Oct 22, 2026 •

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wbruna commented Oct 22, 2026

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wbruna commented Oct 22, 2026 •

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wbruna commented Oct 22, 2026 • edited by coderabbitai Bot Loading Uh oh! There was an error while loading. Please reload this page.

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coderabbitai Bot commented Oct 22, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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wbruna commented Oct 22, 2026

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wbruna commented Oct 22, 2026 •

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wbruna commented Oct 22, 2026 •

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