
# ddpo-alignment

This model was finetuned from [Stable Diffusion v1-4](https:/CompVis/stable-diffusion-v1-4) using [DDPO](https://arxiv.org/abs/2305.13301) and a reward function that uses [LLaVA](https://llava-vl.github.io/) to measure prompt-image alignment. See [the project website](https://rl-diffusion.github.io/) for more details.

The model was finetuned for 200 iterations with a batch size of 256 samples per iteration. During finetuning, we used prompts of the form: "_a(n) \<animal\> \<activity\>_". We selected the animal and activity from the following lists, so try those for the best results. However, we also observed limited generalization to other prompts.

Activities:
- washing dishes
- playing chess
- riding a bike

Animals:
- cat
- dog
- horse
- monkey
- rabbit
- zebra
- spider
- bird
- sheep
- deer
- cow
- goat
- lion
- tiger
- bear
- raccoon
- fox
- wolf
- lizard
- beetle
- ant
- butterfly
- fish
- shark
- whale
- dolphin
- squirrel
- mouse
- rat
- snake
- turtle
- frog
- chicken
- duck
- goose
- bee
- pig
- turkey
- fly
- llama
- camel
- bat
- gorilla
- hedgehog
- kangaroo
{"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image"], "pipeline_tag": "text-to-image", "inference": {"parameters": {"num_inference_steps": 50, "guidance_scale": 5.0, "eta": 1.0}}, "widget": [{"text": "a horse playing chess", "example_title": "horse + chess"}, {"text": "a lion washing dishes", "example_title": "lion + dishes"}, {"text": "a goat riding a bike", "example_title": "goat + bike"}]}
diffusers stable-diffusion stable-diffusion-diffusers text-to-image en arxiv:2305.13301 license:creativeml-openrail-m endpoints_compatible diffusers:StableDiffusionPipeline region:us
TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #arxiv-2305.13301 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 


# ddpo-alignment

This model was finetuned from Stable Diffusion v1-4 using DDPO and a reward function that uses LLaVA to measure prompt-image alignment. See the project website for more details.

The model was finetuned for 200 iterations with a batch size of 256 samples per iteration. During finetuning, we used prompts of the form: "_a(n) \<animal\> \<activity\>_". We selected the animal and activity from the following lists, so try those for the best results. However, we also observed limited generalization to other prompts.

Activities:
- washing dishes
- playing chess
- riding a bike

Animals:
- cat
- dog
- horse
- monkey
- rabbit
- zebra
- spider
- bird
- sheep
- deer
- cow
- goat
- lion
- tiger
- bear
- raccoon
- fox
- wolf
- lizard
- beetle
- ant
- butterfly
- fish
- shark
- whale
- dolphin
- squirrel
- mouse
- rat
- snake
- turtle
- frog
- chicken
- duck
- goose
- bee
- pig
- turkey
- fly
- llama
- camel
- bat
- gorilla
- hedgehog
- kangaroo
# ddpo-alignment

This model was finetuned from Stable Diffusion v1-4 using DDPO and a reward function that uses LLaVA to measure prompt-image alignment. See the project website for more details.

The model was finetuned for 200 iterations with a batch size of 256 samples per iteration. During finetuning, we used prompts of the form: "_a(n) \<animal\> \<activity\>_". We selected the animal and activity from the following lists, so try those for the best results. However, we also observed limited generalization to other prompts.

Activities:
- washing dishes
- playing chess
- riding a bike

Animals:
- cat
- dog
- horse
- monkey
- rabbit
- zebra
- spider
- bird
- sheep
- deer
- cow
- goat
- lion
- tiger
- bear
- raccoon
- fox
- wolf
- lizard
- beetle
- ant
- butterfly
- fish
- shark
- whale
- dolphin
- squirrel
- mouse
- rat
- snake
- turtle
- frog
- chicken
- duck
- goose
- bee
- pig
- turkey
- fly
- llama
- camel
- bat
- gorilla
- hedgehog
- kangaroo
TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #arxiv-2305.13301 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 
 # ddpo-alignment

This model was finetuned from Stable Diffusion v1-4 using DDPO and a reward function that uses LLaVA to measure prompt-image alignment. See the project website for more details.

The model was finetuned for 200 iterations with a batch size of 256 samples per iteration. During finetuning, we used prompts of the form: "_a(n) \<animal\> \<activity\>_". We selected the animal and activity from the following lists, so try those for the best results. However, we also observed limited generalization to other prompts.

Activities:
- washing dishes
- playing chess
- riding a bike

Animals:
- cat
- dog
- horse
- monkey
- rabbit
- zebra
- spider
- bird
- sheep
- deer
- cow
- goat
- lion
- tiger
- bear
- raccoon
- fox
- wolf
- lizard
- beetle
- ant
- butterfly
- fish
- shark
- whale
- dolphin
- squirrel
- mouse
- rat
- snake
- turtle
- frog
- chicken
- duck
- goose
- bee
- pig
- turkey
- fly
- llama
- camel
- bat
- gorilla
- hedgehog
- kangaroo