* add wan vace t2v support
* add --vace-strength option
* add vace i2v support
* fix the processing of vace_context
* add vace v2v support
* update docs
* kontext: support ref images indices
* lora: support x_embedder
* update help message
* Support for negative indices
* support for OmniControl (offsets at index 0)
* c++11 compat
* add --increase-ref-index option
* simplify the logic and fix some issues
* update README.md
* remove unused variable
---------
Co-authored-by: leejet <leejet714@gmail.com>
* add wan vae suppport
* add wan model support
* add umt5 support
* add wan2.1 t2i support
* make flash attn work with wan
* make wan a little faster
* add wan2.1 t2v support
* add wan gguf support
* add offload params to cpu support
* add wan2.1 i2v support
* crop image before resize
* set default fps to 16
* add diff lora support
* fix wan2.1 i2v
* introduce sd_sample_params_t
* add wan2.2 t2v support
* add wan2.2 14B i2v support
* add wan2.2 ti2v support
* add high noise lora support
* sync: update ggml submodule url
* avoid build failure on linux
* avoid build failure
* update ggml
* update ggml
* fix sd_version_is_wan
* update ggml, fix cpu im2col_3d
* fix ggml_nn_attention_ext mask
* add cache support to ggml runner
* fix the issue of illegal memory access
* unify image loading processing
* add wan2.1/2.2 FLF2V support
* fix end_image mask
* update to latest ggml
* add GGUFReader
* update docs
* Conv2DDirect for VAE stage
* Enable only for Vulkan, reduced duplicated code
* Cmake option to use conv2d direct
* conv2d direct always on for opencl
* conv direct as a flag
* fix merge typo
* Align conv2d behavior to flash attention's
* fix readme
* add conv2d direct for controlnet
* add conv2d direct for esrgan
* clean code, use enable_conv2d_direct/get_all_blocks
* format code
---------
Co-authored-by: leejet <leejet714@gmail.com>
* repair flash attention in _ext
this does not fix the currently broken fa behind the define, which is only used by VAE
Co-authored-by: FSSRepo <FSSRepo@users.noreply.github.com>
* make flash attention in the diffusion model a runtime flag
no support for sd3 or video
* remove old flash attention option and switch vae over to attn_ext
* update docs
* format code
---------
Co-authored-by: FSSRepo <FSSRepo@users.noreply.github.com>
Co-authored-by: leejet <leejet714@gmail.com>
* add flux support
* avoid build failures in non-CUDA environments
* fix schnell support
* add k quants support
* add support for applying lora to quantized tensors
* add inplace conversion support for f8_e4m3 (#359)
in the same way it is done for bf16
like how bf16 converts losslessly to fp32,
f8_e4m3 converts losslessly to fp16
* add xlabs flux comfy converted lora support
* update docs
---------
Co-authored-by: Erik Scholz <Green-Sky@users.noreply.github.com>
Added NVIDEA's new "Align Your Steps" style scheduler in accordance with their
quick start guide. Currently has handling for SD1.5, SDXL, and SVD, using the
noise levels from their paper to generate the sigma values. Can be selected
using the --schedule ays command line switch. Updates the main.cpp help
message and README to reflect this option, also they now inform the user
of the --color switch as well.
---------
Co-authored-by: leejet <leejet714@gmail.com>
* first efforts at implementing photomaker; lots more to do
* added PhotoMakerIDEncoder model in SD
* fixed soem bugs; now photomaker model weights can be loaded into their tensor buffers
* added input id image loading
* added preprocessing inpit id images
* finished get_num_tensors
* fixed a bug in remove_duplicates
* add a get_learned_condition_with_trigger function to do photomaker stuff
* add a convert_token_to_id function for photomaker to extract trigger word's token id
* making progress; need to implement tokenizer decoder
* making more progress; finishing vision model forward
* debugging vision_model outputs
* corrected clip vision model output
* continue making progress in id fusion process
* finished stacked id embedding; to be tested
* remove garbage file
* debuging graph compute
* more progress; now alloc buffer failed
* fixed wtype issue; input images can only be 1 because issue with transformer when batch size > 1 (to be investigated)
* added delayed subject conditioning; now photomaker runs and generates images
* fixed stat_merge_step
* added photomaker lora model (to be tested)
* reworked pmid lora
* finished applying pmid lora; to be tested
* finalized pmid lora
* add a few print tensor; tweak in sample again
* small tweak; still not getting ID faces
* fixed a bug in FuseBlock forward; also remove diag_mask op in for vision transformer; getting better results
* disable pmid lora apply for now; 1 input image seems working; > 1 not working
* turn pmid lora apply back on
* fixed a decode bug
* fixed a bug in ggml's conv_2d, and now > 1 input images working
* add style_ratio as a cli param; reworked encode with trigger for attention weights
* merge commit fixing lora free param buffer error
* change default style ratio to 10%
* added an option to offload vae decoder to CPU for mem-limited gpus
* removing image normalization step seems making ID fidelity much higher
* revert default style ratio back ro 20%
* added an option for normalizing input ID images; cleaned up debugging code
* more clean up
* fixed bugs; now failed with cuda error; likely out-of-mem on GPU
* free pmid model params when required
* photomaker working properly now after merging and adapting to GGMLBlock API
* remove tensor renaming; fixing names in the photomaker model file
* updated README.md to include instructions and notes for running PhotoMaker
* a bit clean up
* remove -DGGML_CUDA_FORCE_MMQ; more clean up and README update
* add input image requirement in README
* bring back freeing pmid lora params buffer; simply pooled output of CLIPvision
* remove MultiheadAttention2; customized MultiheadAttention
* added a WIN32 get_files_from_dir; turn off Photomakder if receiving no input images
* update docs
* fix ci error
* make stable-diffusion.h a pure c header file
This reverts commit 27887b630db6a92f269f0aef8de9bc9832ab50a9.
* fix ci error
* format code
* reuse get_learned_condition
* reuse pad_tokens
* reuse CLIPVisionModel
* reuse LoraModel
* add --clip-on-cpu
* fix lora name conversion for SDXL
---------
Co-authored-by: bssrdf <bssrdf@gmail.com>
Co-authored-by: leejet <leejet714@gmail.com>
