awoo
Browse filesSigned-off-by: Balazs Horvath <[email protected]>
README.md
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@@ -42,8 +42,8 @@ The Yiff Toolkit is a comprehensive set of tools designed to enhance your creati
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- [`--dataset_repeats`](#--dataset_repeats)
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- [`--max_train_steps`](#--max_train_steps)
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- [`--shuffle_caption`](#--shuffle_caption)
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-
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-
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- [CosXL Training](#cosxl-training)
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- [Embeddings for 1.5 and SDXL](#embeddings-for-15-and-sdxl)
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- [ComfyUI Walkthrough any%](#comfyui-walkthrough-any)
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git clone https://huggingface.co/k4d3/ponydiffusers
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```
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#### Sample Prompt File
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A sample prompt file is used during training to sample images. A sample prompt for example might look like this for Pony:
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score_9, score_8_up, score_7_up, score_6_up, rating_explicit, source_furry, solo, anthro female wolf, sexy pose, standing, gray fur, brown fur, canine pussy, black nose, blue eyes, pink areola, pink nipples, detailed background, amazing_background, realistic, photo --n low quality, worst quality, blurred background, blurry, simple background --w 1024 --h 1024 --d 1 --l 6.0 --s 40
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```
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Please note that sample prompts should not exceed 77 tokens, you can use
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---
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---
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-
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The choice between `--xformers` or `--mem_eff_attn` and `--spda` will depend on your GPU. You can benchmark it by repeating a training with them!
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-
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You have the option of generating images during training so you can check the progress, the argument let's you pick between different samplers, by default it is on `ddim`, so you better change it!
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ddim, pndm, lms, euler, euler_a, heun, dpm_2, dpm_2_a, dpmsolver, dpmsolver++, dpmsingle, k_lms, k_euler, k_euler_a, k_dpm_2, k_dpm_2_a
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```
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### CosXL Training
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The only difference between CosXL training is that you need to enable `--v_parameterization`, and you can't sample the images. 😹 I also don't recommend using the `block_dims` and `block_alphas` from Pony.
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---
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- [`--dataset_repeats`](#--dataset_repeats)
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- [`--max_train_steps`](#--max_train_steps)
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- [`--shuffle_caption`](#--shuffle_caption)
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- [`--sdpa` or `--xformers` or `--mem_eff_attn`](#--sdpa-or---xformers-or---mem_eff_attn)
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- [`--sample_prompts` and `--sample_sampler` and `--sample_every_n_steps`](#--sample_prompts-and---sample_sampler-and---sample_every_n_steps)
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- [CosXL Training](#cosxl-training)
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- [Embeddings for 1.5 and SDXL](#embeddings-for-15-and-sdxl)
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- [ComfyUI Walkthrough any%](#comfyui-walkthrough-any)
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git clone https://huggingface.co/k4d3/ponydiffusers
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```
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---
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#### Sample Prompt File
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A sample prompt file is used during training to sample images. A sample prompt for example might look like this for Pony:
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score_9, score_8_up, score_7_up, score_6_up, rating_explicit, source_furry, solo, anthro female wolf, sexy pose, standing, gray fur, brown fur, canine pussy, black nose, blue eyes, pink areola, pink nipples, detailed background, amazing_background, realistic, photo --n low quality, worst quality, blurred background, blurry, simple background --w 1024 --h 1024 --d 1 --l 6.0 --s 40
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```
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Please note that sample prompts should not exceed 77 tokens, you can use [Count Tokens in Sample Prompts](https://huggingface.co/k4d3/yiff_toolkit/blob/main/dataset_tools/Count%20Tokens%20in%20Sample%20Prompts.ipynb) from [/dataset_tools](https://huggingface.co/k4d3/yiff_toolkit/tree/main/dataset_tools) to analyze your prompts.
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If you are training with multiple GPUs, ensure that the total number of prompts is divisible by the number of GPUs without any remainder.
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---
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---
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##### `--sdpa` or `--xformers` or `--mem_eff_attn`
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The choice between `--xformers` or `--mem_eff_attn` and `--spda` will depend on your GPU. You can benchmark it by repeating a training with them!
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---
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##### `--sample_prompts` and `--sample_sampler` and `--sample_every_n_steps`
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You have the option of generating images during training so you can check the progress, the argument let's you pick between different samplers, by default it is on `ddim`, so you better change it!
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ddim, pndm, lms, euler, euler_a, heun, dpm_2, dpm_2_a, dpmsolver, dpmsolver++, dpmsingle, k_lms, k_euler, k_euler_a, k_dpm_2, k_dpm_2_a
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```
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---
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### CosXL Training
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<!--
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The only difference between CosXL training is that you need to enable `--v_parameterization`, and you can't sample the images. 😹 I also don't recommend using the `block_dims` and `block_alphas` from Pony.
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-->
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---
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