wan gguf works but needs 8gb ram free on 12gb Ram device so ull have to disable alot of apps and bloatware on your android

wan 2.1 13b sell forcing q4 gguf and umt5-xxl-encoder-Q4_K_S.gguf with wan 2.1 fp8 models work and frames 9 fps 6 u get 90 its/s per step and u only need 6-8 steps it works 👍

Stable-Diffusion-CCP-Termux-Android

I've Made An Easy to Follow install Guide For Sd.ccp its uses way less ram than fastsdcpu and u can use any model and lora or vae it supports sd14,sd15,sdxl,and sd3

Before installing virtual environment make sure u install ubuntu in termux first

Looks like Python 3.12 actually Works!! U have to create a virtual environment so here is the guide:

To run sdcpp in a separate environment on Termux with Python 3.10.11, follow these steps:

Install Required Packages

First, ensure your Termux is updated and install necessary packages:

apt update -y && apt upgrade -y apt install python3-full git ffmpeg

Install & Setup a Virtual Environment

Create and activate a virtual environment:

python3 -m venv sdcpp-env source sdcpp-env/bin/activate

AAAAAA YOO! you can install vulkan now it will use the android graphics for gpu acceleration I've updated the guide to install vulkan.

Update: Flux is now Supported

HERES AN EASY SD CCP INSTALL GUIDE THIS BABY RUNS WAY LESS RAM I EVEN CAN USE SDXL AND SD3!!! U CAN EVEN PICK AMOUNT OF THREADS YOUR CPU HAS.

you can use this to quantize any . model you want if u got limited ram just quantize the model but do note the lower you quantize the lower the quality of images you get.

Update: I currently tried to quantize aura flow 2 but I didn't have enough RAM to save the output. but it did successfully qaunt it.

pkg update -y && pkg install wget curl proot tar -y && wget https://raw.githubusercontent.com/AndronixApp/AndronixOrigin/master/Installer/Ubuntu22/ubuntu22.sh -O ubuntu22.sh && chmod +x ubuntu22.sh && bash ubuntu22.sh

apt update && apt upgrade -y && apt-get install curl git gcc make build-essential python3 python3-dev python3-pip python3-venv python-is-python3 -y && pip install ffmpeg && apt dist-upgrade -y && apt install wget && apt-get install libgl1 libglib2.0-0 libsm6 libxrender1 libxext6 -y && apt-get install google-perftools && apt install libgoogle-perftools-dev && pip install moviepy==1.0.3 && pip install cmake && apt install build-essential libvulkan-dev vulkan-tools mesa-vulkan-drivers -y

Install & Setup a Virtual Environment Create and activate a virtual environment:

python3 -m venv sdccp-env

source sdccp-env/bin/activate

install required packages for vulkan it will take an hour depending upon your phone

git clone https://github.com/google/shaderc.git

cd shaderc

python3 utils/git-sync-deps cmake -S . -B build cmake --build build cmake --install build

after you install the vulkan libs make sure you cd out of the folder before continuing to step 3 restart termux

git clone --recursive https://github.com/leejet/stable-diffusion.cpp

cd stable-diffusion.cpp

git pull origin master

git submodule init

git submodule update

mkdir build

cd build

cmake ..

cmake --build . --config Release

12 if this Command doesn't work go to the original respiratory and copy it from there

cmake .. -DSD_VULKAN=ON cmake --build . --config Release

12.5 Using OpenBLAS

cmake .. -DGGML_OPENBLAS=ON cmake --build . --config Release

TO RUN

i used marco file manager to create the models folder in the build folder.

cd ubuntu-in-termux && ./startubuntu.sh

cd stable-diffusion.cpp && cd build

./bin/sd -m /root/stable-diffusion.cpp/build/models/portray_v10.safetensors -p "a lovely cat"

HERE IS ALL THE COMMAND ARGS YOU NEED TO RUN THE MODELS AND EVEN LORAS

usage: ./bin/sd [arguments]

Options: -m, --model path to full model --clip_l path to the clip-l text encoder --clip_g path to the clip-g text encoder --clip_vision path to the clip-vision encoder --t5xxl path to the t5xxl text encoder --qwen2vl path to the qwen2vl text encoder --qwen2vl_vision path to the qwen2vl vit --diffusion-model path to the standalone diffusion model --high-noise-diffusion-model path to the standalone high noise diffusion model --vae path to standalone vae model --taesd path to taesd. Using Tiny AutoEncoder for fast decoding (low quality) --control-net path to control net model --embd-dir embeddings directory --lora-model-dir lora model directory -i, --init-img path to the init image --end-img path to the end image, required by flf2v --tensor-type-rules weight type per tensor pattern (example: "^vae.=f16,model.=q8_0") --photo-maker path to PHOTOMAKER model --pm-id-images-dir path to PHOTOMAKER input id images dir --pm-id-embed-path path to PHOTOMAKER v2 id embed --mask path to the mask image --control-image path to control image, control net --control-video path to control video frames, It must be a directory path. The video frames inside should be stored as images in lexicographical (character) order. For example, if the control video path is frames, the directory contain images such as 00.png, 01.png, ... etc. -o, --output path to write result image to (default: ./output.png) -p, --prompt the prompt to render -n, --negative-prompt the negative prompt (default: "") --upscale-model path to esrgan model. -t, --threads number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of CPU physical cores --upscale-repeats Run the ESRGAN upscaler this many times (default: 1) -H, --height image height, in pixel space (default: 512) -W, --width image width, in pixel space (default: 512) --steps number of sample steps (default: 20) --high-noise-steps (high noise) number of sample steps (default: -1 = auto) --clip-skip ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x -b, --batch-count batch count --chroma-t5-mask-pad t5 mask pad size of chroma --video-frames video frames (default: 1) --fps fps (default: 24) --timestep-shift shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for NitroSD-Vibrant --cfg-scale unconditional guidance scale: (default: 7.0) --img-cfg-scale image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale) --guidance distilled guidance scale for models with guidance input (default: 3.5) --slg-scale skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5 medium --skip-layer-start SLG enabling point (default: 0.01) --skip-layer-end SLG disabling point (default: 0.2) --eta eta in DDIM, only for DDIM and TCD (default: 0) --high-noise-cfg-scale (high noise) unconditional guidance scale: (default: 7.0) --high-noise-img-cfg-scale (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale) --high-noise-guidance (high noise) distilled guidance scale for models with guidance input (default: 3.5) --high-noise-slg-scale (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0) --high-noise-skip-layer-start (high noise) SLG enabling point (default: 0.01) --high-noise-skip-layer-end (high noise) SLG disabling point (default: 0.2) --high-noise-eta (high noise) eta in DDIM, only for DDIM and TCD (default: 0) --strength strength for noising/unnoising (default: 0.75) --pm-style-strength --control-strength strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image --moe-boundary timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if --high-noise-steps is set to -1 --flow-shift shift value for Flow models like SD3.x or WAN (default: auto) --vace-strength wan vace strength --vae-tile-overlap tile overlap for vae tiling, in fraction of tile size (default: 0.5) --vae-tiling process vae in tiles to reduce memory usage --force-sdxl-vae-conv-scale force use of conv scale on sdxl vae --offload-to-cpu place the weights in RAM to save VRAM, and automatically load them into VRAM when needed --control-net-cpu keep controlnet in cpu (for low vram) --clip-on-cpu keep clip in cpu (for low vram) --vae-on-cpu keep vae in cpu (for low vram) --diffusion-fa use flash attention in the diffusion model --diffusion-conv-direct use ggml_conv2d_direct in the diffusion model --vae-conv-direct use ggml_conv2d_direct in the vae model --canny apply canny preprocessor (edge detection) -v, --verbose print extra info --color colors the logging tags according to level --chroma-disable-dit-mask disable dit mask for chroma --chroma-enable-t5-mask enable t5 mask for chroma --increase-ref-index automatically increase the indices of references images based on the order they are listed (starting with 1). --disable-auto-resize-ref-image disable auto resize of ref images -M, --mode run mode, one of [img_gen, vid_gen, upscale, convert], default: img_gen --type weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the type of the weight file --rng RNG, one of [std_default, cuda], default: cuda -s, --seed RNG seed (default: 42, use random seed for < 0) --sampling-method sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise) --prediction prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow] --scheduler denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple], default: discrete --skip-layers layers to skip for SLG steps (default: [7,8,9]) --high-noise-sampling-method (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise --high-noise-scheduler (high noise) denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple], default: discrete --high-noise-skip-layers (high noise) layers to skip for SLG steps (default: [7,8,9]) -r, --ref-image reference image for Flux Kontext models (can be used multiple times) -h, --help show this help message and exit --vae-tile-size tile size for vae tiling, format [X]x[Y] (default: 32x32) --vae-relative-tile-size relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1 (overrides --vae-tile-size) encoder --vae [VAE] path to vae --taesd [TAESD_PATH] path to taesd. Using Tiny AutoEncoder for fast decoding (low quality) --control-net [CONTROL_PATH] path to control net model --embd-dir [EMBEDDING_PATH] path to embeddings --stacked-id-embd-dir [DIR] path to PHOTOMAKER stacked id embeddings --input-id-images-dir [DIR] path to PHOTOMAKER input id images dir --normalize-input normalize PHOTOMAKER input id images --upscale-model [ESRGAN_PATH] path to esrgan model. Upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now --upscale-repeats Run the ESRGAN upscaler this many times (default 1) --type [TYPE] weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K) If not specified, the default is the type of the weight file --tensor-type-rules [EXPRESSION] weight type per tensor pattern (example: "^vae.=f16,model.=q8_0") --lora-model-dir [DIR] lora model directory -i, --init-img [IMAGE] path to the init image, required by img2img --mask [MASK] path to the mask image, required by img2img with mask -i, --end-img [IMAGE] path to the end image, required by flf2v --control-image [IMAGE] path to image condition, control net -r, --ref-image [PATH] reference image for Flux Kontext models (can be used multiple times) --increase-ref-index automatically increase the indices of references images based on the order they are listed (starting with 1). -o, --output OUTPUT path to write result image to (default: ./output.png) -p, --prompt [PROMPT] the prompt to render -n, --negative-prompt PROMPT the negative prompt (default: "") --cfg-scale SCALE unconditional guidance scale: (default: 7.0) --img-cfg-scale SCALE image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale) --guidance SCALE distilled guidance scale for models with guidance input (default: 3.5) --slg-scale SCALE skip layer guidance (SLG) scale, only for DiT models: (default: 0) 0 means disabled, a value of 2.5 is nice for sd3.5 medium --eta SCALE eta in DDIM, only for DDIM and TCD: (default: 0) --skip-layers LAYERS Layers to skip for SLG steps: (default: [7,8,9]) --skip-layer-start START SLG enabling point: (default: 0.01) --skip-layer-end END SLG disabling point: (default: 0.2) --scheduler {discrete, karras, exponential, ays, gits} Denoiser sigma scheduler (default: discrete) --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd} sampling method (default: "euler_a") --steps STEPS number of sample steps (default: 20) --high-noise-cfg-scale SCALE (high noise) unconditional guidance scale: (default: 7.0) --high-noise-img-cfg-scale SCALE (high noise) image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale) --high-noise-guidance SCALE (high noise) distilled guidance scale for models with guidance input (default: 3.5) --high-noise-slg-scale SCALE (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0) 0 means disabled, a value of 2.5 is nice for sd3.5 medium --high-noise-eta SCALE (high noise) eta in DDIM, only for DDIM and TCD: (default: 0) --high-noise-skip-layers LAYERS (high noise) Layers to skip for SLG steps: (default: [7,8,9]) --high-noise-skip-layer-start (high noise) SLG enabling point: (default: 0.01) --high-noise-skip-layer-end END (high noise) SLG disabling point: (default: 0.2) --high-noise-scheduler {discrete, karras, exponential, ays, gits} Denoiser sigma scheduler (default: discrete) --high-noise-sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd} (high noise) sampling method (default: "euler_a") --high-noise-steps STEPS (high noise) number of sample steps (default: -1 = auto) SLG will be enabled at step int([STEPS][START]) and disabled at int([STEPS][END]) --strength STRENGTH strength for noising/unnoising (default: 0.75) --style-ratio STYLE-RATIO strength for keeping input identity (default: 20) --control-strength STRENGTH strength to apply Control Net (default: 0.9) 1.0 corresponds to full destruction of information in init image -H, --height H image height, in pixel space (default: 512) -W, --width W image width, in pixel space (default: 512) --rng {std_default, cuda} RNG (default: cuda) -s SEED, --seed SEED RNG seed (default: 42, use random seed for < 0) -b, --batch-count COUNT number of images to generate --clip-skip N ignore last_dot_pos layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1) <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x --vae-tiling process vae in tiles to reduce memory usage --vae-on-cpu keep vae in cpu (for low vram) --clip-on-cpu keep clip in cpu (for low vram) --diffusion-fa use flash attention in the diffusion model (for low vram) Might lower quality, since it implies converting k and v to f16. This might crash if it is not supported by the backend. --diffusion-conv-direct use Conv2d direct in the diffusion model This might crash if it is not supported by the backend. --vae-conv-direct use Conv2d direct in the vae model (should improve the performance) This might crash if it is not supported by the backend. --control-net-cpu keep controlnet in cpu (for low vram) --canny apply canny preprocessor (edge detection) --color colors the logging tags according to level --chroma-disable-dit-mask disable dit mask for chroma --chroma-enable-t5-mask enable t5 mask for chroma --chroma-t5-mask-pad PAD_SIZE t5 mask pad size of chroma --video-frames video frames (default: 1) --fps fps (default: 24) --moe-boundary BOUNDARY timestep boundary for Wan2.2 MoE model. (default: 0.875) only enabled if --high-noise-steps is set to -1 --flow-shift SHIFT shift value for Flow models like SD3.x or WAN (default: auto) -v, --verbose print extra info

WANT TO SEE YOUR IMAGE AFTER IT GENERATES INSTALL THIS.PUT THE COMMAND AT THE END OF YOUR ARGS

pip install termvisage

COMMAND>

&& termvisage /root/stable-diffusion.cpp/build/output.png

KintCark/Stable-Diffusion-CCP-Termux-Android

Stable-Diffusion-CCP-Termux-Android