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Merge branch 'main' into dev/installer

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Lincoln Stein 2023-02-02 13:53:11 -05:00 committed by GitHub
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3 changed files with 16 additions and 14 deletions
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2
environments-and-requirements/requirements-lin-cuda.txt
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@ -2,5 +2,5 @@
-r environments-and-requirements/requirements-base.txt
torch>=1.13.1
torchvision>=0.14.1
xformers~=0.16
xformers~=0.0.16
-e .

2
environments-and-requirements/requirements-win-colab-cuda.txt
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@ -3,5 +3,5 @@
--extra-index-url https://download.pytorch.org/whl/cu117 --trusted-host https://download.pytorch.org
torch==1.13.1
torchvision==0.14.1
xformers~=0.16
xformers~=0.0.16
-e .

26
ldm/invoke/generator/diffusers_pipeline.py
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@ -17,10 +17,8 @@ import PIL.Image
import einops
import torch
import torchvision.transforms as T
from diffusers.models import attention
from diffusers.utils.import_utils import is_xformers_available
from ...models.diffusion import cross_attention_control
from ...models.diffusion.cross_attention_map_saving import AttentionMapSaver
from ...modules.prompt_to_embeddings_converter import WeightedPromptFragmentsToEmbeddingsConverter
@ -506,11 +504,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
strength,
noise: torch.Tensor, run_id=None, callback=None
) -> InvokeAIStableDiffusionPipelineOutput:
device = self.unet.device
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)
timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength, self.unet.device)
result_latents, result_attention_maps = self.latents_from_embeddings(
initial_latents, num_inference_steps, conditioning_data,
timesteps=timesteps,
@ -526,6 +520,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
return self.check_for_safety(output, dtype=conditioning_data.dtype)
def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device) -> (torch.Tensor, int):
img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
assert img2img_pipeline.scheduler is self.scheduler
img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, adjusted_steps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
# Workaround for low strength resulting in zero timesteps.
# TODO: submit upstream fix for zero-step img2img
if timesteps.numel() == 0:
timesteps = self.scheduler.timesteps[-1:]
adjusted_steps = timesteps.numel()
return timesteps, adjusted_steps
def inpaint_from_embeddings(
self,
init_image: torch.FloatTensor,
@ -549,11 +555,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if init_image.dim() == 3:
init_image = init_image.unsqueeze(0)
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)
assert img2img_pipeline.scheduler is self.scheduler
timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength, device=device)
# 6. Prepare latent variables
# can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents