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