--- license: openrail++ --- This repository contains offset versions of https://huggingface.co/mhdang/dpo-sdxl-text2image-v1 and https://huggingface.co/mhdang/dpo-sd1.5-text2image-v1. These can be added directly to any initialized UNet to inject DPO training into it. See the code below for usage (diffusers only.) ```py from __future__ import annotations from typing import TYPE_CHECKING if TYPE_CHECKING: from diffusers.models import UNet2DConditionModel def inject_dpo(unet: UNet2DConditionModel, dpo_offset_path: str, device: str, strict: bool = False) -> None: """ Injects DPO weights directly into your UNet. Args: unet (`UNet2DConditionModel`) The initialized UNet from your pipeline. dpo_offset_path (`str`) The path to the `.safetensors` file downloaded from https://huggingface.co/benjamin-paine/sd-dpo-offsets/. Make sure you're using the right file for the right base model. strict (`bool`, *optional*) Whether or not to raise errors when a weight cannot be applied. Defaults to false. """ from safetensors import safe_open with safe_open(dpo_offset_path, framework="pt", device=device) as f: for key in f.keys(): key_parts = key.split(".") current_layer = unet for key_part in key_parts[:-1]: current_layer = getattr(current_layer, key_part, None) if current_layer is None: break if current_layer is None: if strict: raise IOError(f"Couldn't find a layer to inject key {key} in.") continue layer_param = getattr(current_layer, key_parts[-1], None) if layer_param is None: if strict: raise IOError(f"Couldn't get weight parameter for key {key}") continue layer_param.data += f.get_tensor(key) ``` Now you can use this function like so: ```py from diffusers import StableDiffusionPipeline import huggingface_hub import torch # load sdv15 pipeline device = "cuda" model_id = "Lykon/dreamshaper-8" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.to(device) # make image prompt = "Two cats playing chess on a tree branch" generator = torch.Generator(device=device) generator.manual_seed(123456789) image = pipe(prompt, guidance_scale=7.5, generator=generator).images[0] image.save("cats_playing_chess.png") # download DPO offsets dpo_offset_path = huggingface_hub.hf_hub_download("benjamin-paine/sd-dpo-offsets", "sd_v15_unet_dpo_offset.safetensors") # inject inject_dpo(pipe.unet, dpo_offset_path, device) # make image again generator.manual_seed(123456789) image = pipe(prompt, guidance_scale=7.5, generator=generator).images[0] image.save("cats_playing_chess_dpo.png") ``` `cats_playing_chess.png` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/KqAohfKMXKVGTDpuBhhx6.png) `cats_playing_chess_dpo.png` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/fY9j1q8ZazyNP4JbD0TTU.png) Or for XL: ```py from diffusers import StableDiffusionXLPipeline import huggingface_hub import torch # load sdv15 pipeline device = "cuda" model_id = "Lykon/dreamshaper-xl-1-0" pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.to(device) # make image prompt = "Two cats playing chess on a tree branch" generator = torch.Generator(device=device) generator.manual_seed(123456789) image = pipe(prompt, guidance_scale=7.5, generator=generator).images[0] image.save("cats_playing_chess_xl.png") # download DPO offsets dpo_offset_path = huggingface_hub.hf_hub_download("benjamin-paine/sd-dpo-offsets", "sd_v15_unet_dpo_offset.safetensors") # inject inject_dpo(pipe.unet, dpo_offset_path, device) # make image again generator.manual_seed(123456789) image = pipe(prompt, guidance_scale=7.5, generator=generator).images[0] image.save("cats_playing_chess_xl_dpo.png") ``` `cats_playing_chess_xl.png` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/BufmVzFBsoYX_jipzErIo.png) `cats_playing_chess_xl_dpo.png` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/Rj9FXI-vmrMwvepMSLMe7.png)