Kevin Black
commited on
Commit
·
e102578
1
Parent(s):
0ac0437
update scheduler
Browse files- README.md +49 -2
- model_index.json +1 -1
- scheduler/scheduler_config.json +6 -2
README.md
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# ddpo-aesthetic
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This model was finetuned from [Stable Diffusion v1-5](https:/runwayml/stable-diffusion-v1-5) using [DDPO](https://arxiv.org/abs/2305.13301)
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# ddpo-aesthetic
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This model was finetuned from [Stable Diffusion v1-5](https:/runwayml/stable-diffusion-v1-5) using [DDPO](https://arxiv.org/abs/2305.13301) and a reward function based on the [LAION aesthetic predictor](https://laion.ai/blog/laion-aesthetics/). See [the project website](https://rl-diffusion.github.io/) for more details.
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The model was finetuned for 50 iterations with a batch size of 256 samples per iteration. It was only prompted with the following list of 45 common animals during finetuning, so try those for the best results. However, we also observed limited generalization to other prompts.
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Prompts:
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- cat
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- dog
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- horse
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- monkey
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- rabbit
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- zebra
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- spider
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- bird
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- sheep
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- deer
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- cow
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- goat
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- lion
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- tiger
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- bear
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- raccoon
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- fox
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- wolf
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- lizard
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- beetle
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- ant
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- butterfly
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- fish
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- shark
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- whale
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- dolphin
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- squirrel
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- mouse
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- rat
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- snake
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- turtle
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- frog
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- chicken
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- duck
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- goose
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- bee
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- pig
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- turkey
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- fly
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- llama
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- camel
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- bat
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- gorilla
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- hedgehog
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- kangaroo
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model_index.json
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],
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"scheduler": [
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"diffusers",
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],
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"text_encoder": [
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"transformers",
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],
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"scheduler": [
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"diffusers",
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"DDIMScheduler"
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],
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"text_encoder": [
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"transformers",
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scheduler/scheduler_config.json
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{
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"_class_name": "
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"_diffusers_version": "0.16.1",
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"beta_end": 0.012,
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"num_train_timesteps": 1000,
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"prediction_type": "epsilon",
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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"steps_offset": 1,
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"trained_betas": null
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}
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{
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"_class_name": "DDIMScheduler",
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"_diffusers_version": "0.16.1",
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"beta_end": 0.012,
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"clip_sample": true,
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"clip_sample_range": 1.0,
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"dynamic_thresholding_ratio": 0.995,
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"num_train_timesteps": 1000,
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"prediction_type": "epsilon",
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"sample_max_value": 1.0,
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"set_alpha_to_one": false,
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"steps_offset": 1,
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"thresholding": false,
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"trained_betas": null
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}
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