update README.md
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README.md
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README.md
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@ -126,7 +126,7 @@ cmake .. -DSD_METAL=ON
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cmake --build . --config Release
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```
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### Using Flash Attention
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##### Using Flash Attention
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Enabling flash attention reduces memory usage by at least 400 MB. At the moment, it is not supported when CUBLAS is enabled because the kernel implementation is missing.
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@ -142,7 +142,7 @@ usage: ./bin/sd [arguments]
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arguments:
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-h, --help show this help message and exit
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-M, --mode [txt2img or img2img] generation mode (default: txt2img)
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-M, --mode [MODEL] run mode (txt2img or img2img or convert, default: txt2img)
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-t, --threads N number of threads to use during computation (default: -1).
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If threads <= 0, then threads will be set to the number of CPU physical cores
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-m, --model [MODEL] path to model
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@ -168,7 +168,8 @@ arguments:
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-s SEED, --seed SEED RNG seed (default: 42, use random seed for < 0)
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-b, --batch-count COUNT number of images to generate.
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--schedule {discrete, karras} Denoiser sigma schedule (default: discrete)
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--clip-skip N number of layers to skip of clip model (default: 0)
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--clip-skip N ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1)
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<= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
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--vae-tiling process vae in tiles to reduce memory usage
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-v, --verbose print extra info
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```
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@ -183,6 +184,16 @@ You can specify the model weight type using the `--type` parameter. The weights
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- `q5_0` or `q5_1` for 5-bit integer quantization
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- `q4_0` or `q4_1` for 4-bit integer quantization
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#### Convert to GGUF
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You can also convert weights in the formats `ckpt/safetensors/diffusers` to gguf and perform quantization in advance, avoiding the need for quantization every time you load them.
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For example:
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```sh
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./bin/sd -M convert -m ../models/v1-5-pruned-emaonly.safetensors -o ../models/v1-5-pruned-emaonly.q8_0.gguf -v --type q8_0
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```
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#### txt2img example
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```sh
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@ -240,7 +251,7 @@ Here's a simple example:
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| ---- |---- |
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| ![](./assets/without_lcm.png) |![](./assets/with_lcm.png) |
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## Using TAESD to faster decoding
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#### Using TAESD to faster decoding
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You can use TAESD to accelerate the decoding of latent images by following these steps:
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@ -258,7 +269,7 @@ curl -L -O https://huggingface.co/madebyollin/taesd/blob/main/diffusion_pytorch_
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sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors
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```
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## Using ESRGAN to upscale results
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#### Using ESRGAN to upscale results
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You can use ESRGAN to upscale the generated images. At the moment, only the [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) model is supported. Support for more models of this architecture will be added soon.
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