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refactor: remove backported img2img.get_timesteps

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now that we can use it directly from diffusers 0.8.1
This commit is contained in:
Kevin Turner 2022-11-23 20:30:06 -08:00
parent ceb53ccdfb
commit b7864aa1a7
1 changed files with 4 additions and 14 deletions
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18
ldm/invoke/generator/diffusers_pipeline.py
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@ -5,7 +5,7 @@ from typing import List, Optional, Union, Callable
import PIL.Image
import torch
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
@ -260,8 +260,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess(init_image.convert('RGB'))
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self._diffusers08_get_timesteps(num_inference_steps, strength)
img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
@ -292,17 +293,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
return self.scheduler.add_noise(init_latents, noise, timestep)
def _diffusers08_get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps
def check_for_safety(self, output, dtype):
with torch.inference_mode():
screened_images, has_nsfw_concept = self.run_safety_checker(