* Experimental Multi-thread MP3
I used Deepseek V4 Flash to come up with a method of making the MP3 conversion with Ace-step multi-threaded.
I built and tested. Resulting MP3 doesnt have any glitches and generates almost instantly (compared to dozens of seconds previously)
* Implement WBRUNA suggestion
Updated as suggested
* further tidyup
Both issues fixed:
fprintf removed from encode_chunk (line 651) — it now just returns "" silently on failure. Error reporting moved to call sites:
Single-threaded (line 689-692): checks mp3_data.empty() and logs once
Multi-threaded (line 722-742): uses std::atomic<int> init_failures{0}, each thread does fetch_add(1) on failure, then after join a single fprintf reports the count. No interleaved output.
No leftover encode call outside threads — all chunks are encoded inside threads (lines 724-732). No results[0] = encode_chunk(...) exists between splitting and joining.
* Overlap by 1 frame
Overlapping by 1 frame from the previous chunk and discarding appears to have resolved the audible boundary glitch.
* Improve multi-threading logic in audio encoding
Refactor thread count calculation and chunk partitioning logic for audio encoding.
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Co-authored-by: LostRuins Concedo <39025047+LostRuins@users.noreply.github.com>
* sd: generalize internal interfaces to place generation on CPU
* sd: backend support for multi-device selection
* sd: frontend support for multi-device selection
* add deprecated flags to avoid breaking old cli args
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Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
* sd: sync to master-593-3d6064b
* sd: use the same sdtype_adapter object for all builds
Since master-592-b8079e2, no sd.cpp source depends on the ggml
backend build anymore.
* sd: fix main_gpu selection
* sd: report backend devices to the Python layer
All C++ handling code currently:
- build a comma-separated list from the info_vulkan array
- if GGML_VK_VISIBLE_DEVICES isn't set
- set GGML_VK_VISIBLE_DEVICES to the list
Once set, GGML_VK_VISIBLE_DEVICES affects the whole process. So this
can be done in the same way at the Python level, before all loading
functions.
Caveat: load_model had the default `inputs.vulkan_info = "0"`,
so the default GPU would be "0" only when loading a text model.
* sd: remove sampler alias handling from the C++ layer
It's already handled at the Python layer.
* sd: sync to master-580-7d33d4b
* sd: sync to master-582-7023fc4
* debug: allow loading backend libraries without normal arg parsing
This is just to be able to test backend functions directly, with e.g.:
>> import koboldcpp
>> koboldcpp.init_libraries()
>> koboldcpp.sd_get_info()
* sd: report all sampler aliases and centralize name mapping
* Pass img_min_params and img_max_params to ctx_clip_params
These values determine the minimum and maximum size (in
tokens) of vision embeddings. The default value of -1
uses a model-dependent default size, for example for
Gemma 4 the default is a 280 token embedding. For higher
quality results (at the cost of using more memory and
slower speed) you can increase the size of the embedding
to 1120 tokens.
* Change dict to mydict to match change to method
* sd: sync to master-540-f16a110
* tae post-merge fixes
* build fixes
* restore image mask for non-inpainting models
* sd: sync to master-551-99c1de3
* avoid nlohmann/json.hpp include diffs
* Euler A now works on Flux
* sd: sync to master-555-7397dda
avi_writer.h got removed upstream, but I've simply kept the local
copy for now.
* sd: sync to master-558-8afbeb6
* sd: sync to master-560-e8323ca
Concedo
Wagner Bruna
askmyteapot
Wagner Bruna
Llama