From 49463992f48ec25f2ea31b220a6cedac3466467a Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Wed, 26 Oct 2022 11:11:33 +0200
Subject: New Euler_a scheduler
---
.../stable_diffusion/vlpn_stable_diffusion.py | 27 +++++++++++-----------
1 file changed, 13 insertions(+), 14 deletions(-)
(limited to 'pipelines')
diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index e90528d..fc12355 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -11,7 +11,7 @@ from diffusers import AutoencoderKL, DiffusionPipeline, DDIMScheduler, LMSDiscre
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import logging
from transformers import CLIPTextModel, CLIPTokenizer
-from schedulers.scheduling_euler_a import EulerAScheduler
+from schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from models.clip.prompt import PromptProcessor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@@ -32,7 +32,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerAScheduler],
+ scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerAncestralDiscreteScheduler],
**kwargs,
):
super().__init__()
@@ -225,8 +225,13 @@ class VlpnStableDiffusion(DiffusionPipeline):
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
- timesteps = self.scheduler.timesteps[-init_timestep]
- timesteps = torch.tensor([timesteps] * batch_size, device=self.device)
+ if not isinstance(self.scheduler, DDIMScheduler) and not isinstance(self.scheduler, PNDMScheduler):
+ timesteps = torch.tensor(
+ [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
+ )
+ else:
+ timesteps = self.scheduler.timesteps[-init_timestep]
+ timesteps = torch.tensor([timesteps] * batch_size, device=self.device)
# add noise to latents using the timesteps
noise = torch.randn(latents.shape, generator=generator, device=self.device, dtype=latents_dtype)
@@ -259,16 +264,10 @@ class VlpnStableDiffusion(DiffusionPipeline):
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t, i)
- noise_pred = None
- if isinstance(self.scheduler, EulerAScheduler):
- c_out, c_in, sigma_in = self.scheduler.prepare_input(latent_model_input, t, batch_size)
- eps = self.unet(latent_model_input * c_in, sigma_in, encoder_hidden_states=text_embeddings).sample
- noise_pred = latent_model_input + eps * c_out
- else:
- # predict the noise residual
- noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+ # predict the noise residual
+ noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
@@ -276,7 +275,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+ latents = self.scheduler.step(noise_pred, t, i, latents, **extra_step_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
--
cgit v1.2.3-70-g09d2