From 7ccd4614a56cfd6ecacba85605f338593f1059f0 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Tue, 7 Feb 2023 20:44:43 +0100
Subject: Add Lora
---
pipelines/stable_diffusion/vlpn_stable_diffusion.py | 14 ++++++++++++--
1 file changed, 12 insertions(+), 2 deletions(-)
(limited to 'pipelines/stable_diffusion')
diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index 3027421..dab7878 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -1,6 +1,6 @@
import inspect
import warnings
-from typing import List, Optional, Union, Callable
+from typing import List, Dict, Any, Optional, Union, Callable
import numpy as np
import torch
@@ -337,6 +337,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function invoked when calling the pipeline for generation.
@@ -379,6 +380,10 @@ class VlpnStableDiffusion(DiffusionPipeline):
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
+ `self.processor` in
+ [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
@@ -450,7 +455,12 @@ class VlpnStableDiffusion(DiffusionPipeline):
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
- noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=text_embeddings,
+ cross_attention_kwargs=cross_attention_kwargs,
+ ).sample
# perform guidance
if do_classifier_free_guidance:
--
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