Merge branch 'master' into training-help-text
AUTOMATIC1111
3 years ago
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GitHub
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@ -1,32 +0,0 @@
|
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---
|
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name: Bug report
|
||||
about: Create a report to help us improve
|
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title: ''
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labels: bug-report
|
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assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Describe the bug**
|
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A clear and concise description of what the bug is.
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|
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**To Reproduce**
|
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Steps to reproduce the behavior:
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1. Go to '...'
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2. Click on '....'
|
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3. Scroll down to '....'
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4. See error
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|
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**Expected behavior**
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A clear and concise description of what you expected to happen.
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**Screenshots**
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If applicable, add screenshots to help explain your problem.
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**Desktop (please complete the following information):**
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- OS: [e.g. Windows, Linux]
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- Browser [e.g. chrome, safari]
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- Commit revision [looks like this: e68484500f76a33ba477d5a99340ab30451e557b; can be seen when launching webui.bat, or obtained manually by running `git rev-parse HEAD`]
|
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|
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**Additional context**
|
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Add any other context about the problem here.
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@ -0,0 +1,81 @@
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name: Bug Report
|
||||
description: You think somethings is broken in the UI
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||||
title: "[Bug]: "
|
||||
labels: ["bug-report"]
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||||
|
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body:
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- type: checkboxes
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attributes:
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label: Is there an existing issue for this?
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description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
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options:
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- label: I have searched the existing issues and checked the recent builds/commits
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required: true
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- type: markdown
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attributes:
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value: |
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*Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
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- type: textarea
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id: what-did
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attributes:
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label: What happened?
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description: Tell us what happened in a very clear and simple way
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validations:
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required: true
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id: steps
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attributes:
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label: Steps to reproduce the problem
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description: Please provide us with precise step by step information on how to reproduce the bug
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value: |
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1. Go to ....
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2. Press ....
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3. ...
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required: true
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id: what-should
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attributes:
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label: What should have happened?
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description: tell what you think the normal behavior should be
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validations:
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required: true
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- type: input
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id: commit
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attributes:
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label: Commit where the problem happens
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description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
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- type: dropdown
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id: platforms
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attributes:
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label: What platforms do you use to access UI ?
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multiple: true
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options:
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id: cmdargs
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attributes:
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label: Command Line Arguments
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description: Are you using any launching parameters/command line arguments (modified webui-user.py) ? If yes, please write them below
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id: misc
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description: Please provide us with any relevant additional info, context or log output.
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@ -1,20 +0,0 @@
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---
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name: Feature request
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about: Suggest an idea for this project
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title: ''
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labels: 'suggestion'
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assignees: ''
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|
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---
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**Is your feature request related to a problem? Please describe.**
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A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
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**Describe the solution you'd like**
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**Describe alternatives you've considered**
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A clear and concise description of any alternative solutions or features you've considered.
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**Additional context**
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Add any other context or screenshots about the feature request here.
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@ -0,0 +1,40 @@
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name: Feature request
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description: Suggest an idea for this project
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title: "[Feature Request]: "
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labels: ["suggestion"]
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|
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body:
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- type: checkboxes
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attributes:
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label: Is there an existing issue for this?
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description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
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options:
|
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- label: I have searched the existing issues and checked the recent builds/commits
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required: true
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- type: markdown
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attributes:
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value: |
|
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*Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
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id: feature
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attributes:
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label: What would your feature do ?
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description: Tell us about your feature in a very clear and simple way, and what problem it would solve
|
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validations:
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required: true
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- type: textarea
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id: workflow
|
||||
attributes:
|
||||
label: Proposed workflow
|
||||
description: Please provide us with step by step information on how you'd like the feature to be accessed and used
|
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value: |
|
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1. Go to ....
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2. Press ....
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3. ...
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validations:
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required: true
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- type: textarea
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id: misc
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attributes:
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label: Additional information
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description: Add any other context or screenshots about the feature request here.
|
||||
@ -0,0 +1,331 @@
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import torch
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from einops import repeat
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from omegaconf import ListConfig
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import ldm.models.diffusion.ddpm
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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|
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from ldm.models.diffusion.ddpm import LatentDiffusion
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from ldm.models.diffusion.plms import PLMSSampler
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from ldm.models.diffusion.ddim import DDIMSampler, noise_like
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|
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# =================================================================================================
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# Monkey patch DDIMSampler methods from RunwayML repo directly.
|
||||
# Adapted from:
|
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# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
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# =================================================================================================
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@torch.no_grad()
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def sample_ddim(self,
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S,
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batch_size,
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shape,
|
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conditioning=None,
|
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callback=None,
|
||||
normals_sequence=None,
|
||||
img_callback=None,
|
||||
quantize_x0=False,
|
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eta=0.,
|
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mask=None,
|
||||
x0=None,
|
||||
temperature=1.,
|
||||
noise_dropout=0.,
|
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score_corrector=None,
|
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corrector_kwargs=None,
|
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verbose=True,
|
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x_T=None,
|
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log_every_t=100,
|
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unconditional_guidance_scale=1.,
|
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unconditional_conditioning=None,
|
||||
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
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**kwargs
|
||||
):
|
||||
if conditioning is not None:
|
||||
if isinstance(conditioning, dict):
|
||||
ctmp = conditioning[list(conditioning.keys())[0]]
|
||||
while isinstance(ctmp, list):
|
||||
ctmp = ctmp[0]
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cbs = ctmp.shape[0]
|
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if cbs != batch_size:
|
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
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else:
|
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if conditioning.shape[0] != batch_size:
|
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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|
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self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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print(f'Data shape for DDIM sampling is {size}, eta {eta}')
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|
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samples, intermediates = self.ddim_sampling(conditioning, size,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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mask=mask, x0=x0,
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ddim_use_original_steps=False,
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noise_dropout=noise_dropout,
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temperature=temperature,
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score_corrector=score_corrector,
|
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corrector_kwargs=corrector_kwargs,
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x_T=x_T,
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log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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)
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return samples, intermediates
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|
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@torch.no_grad()
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def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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unconditional_guidance_scale=1., unconditional_conditioning=None):
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b, *_, device = *x.shape, x.device
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if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
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e_t = self.model.apply_model(x, t, c)
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else:
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t] * 2)
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if isinstance(c, dict):
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assert isinstance(unconditional_conditioning, dict)
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c_in = dict()
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for k in c:
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if isinstance(c[k], list):
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c_in[k] = [
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torch.cat([unconditional_conditioning[k][i], c[k][i]])
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for i in range(len(c[k]))
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]
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else:
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c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
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else:
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c_in = torch.cat([unconditional_conditioning, c])
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e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
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e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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if score_corrector is not None:
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assert self.model.parameterization == "eps"
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e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
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alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
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sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
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|
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised:
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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# direction pointing to x_t
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dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
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noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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if noise_dropout > 0.:
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noise = torch.nn.functional.dropout(noise, p=noise_dropout)
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
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return x_prev, pred_x0
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# =================================================================================================
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# Monkey patch PLMSSampler methods.
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# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
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# Adapted from:
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# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
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# =================================================================================================
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@torch.no_grad()
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def sample_plms(self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
|
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
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**kwargs
|
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):
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if conditioning is not None:
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if isinstance(conditioning, dict):
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ctmp = conditioning[list(conditioning.keys())[0]]
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while isinstance(ctmp, list):
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ctmp = ctmp[0]
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cbs = ctmp.shape[0]
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if cbs != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
|
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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|
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self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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print(f'Data shape for PLMS sampling is {size}')
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samples, intermediates = self.plms_sampling(conditioning, size,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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mask=mask, x0=x0,
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ddim_use_original_steps=False,
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noise_dropout=noise_dropout,
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temperature=temperature,
|
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score_corrector=score_corrector,
|
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corrector_kwargs=corrector_kwargs,
|
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x_T=x_T,
|
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log_every_t=log_every_t,
|
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
|
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)
|
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return samples, intermediates
|
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|
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|
||||
@torch.no_grad()
|
||||
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
|
||||
b, *_, device = *x.shape, x.device
|
||||
|
||||
def get_model_output(x, t):
|
||||
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
||||
e_t = self.model.apply_model(x, t, c)
|
||||
else:
|
||||
x_in = torch.cat([x] * 2)
|
||||
t_in = torch.cat([t] * 2)
|
||||
|
||||
if isinstance(c, dict):
|
||||
assert isinstance(unconditional_conditioning, dict)
|
||||
c_in = dict()
|
||||
for k in c:
|
||||
if isinstance(c[k], list):
|
||||
c_in[k] = [
|
||||
torch.cat([unconditional_conditioning[k][i], c[k][i]])
|
||||
for i in range(len(c[k]))
|
||||
]
|
||||
else:
|
||||
c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
|
||||
else:
|
||||
c_in = torch.cat([unconditional_conditioning, c])
|
||||
|
||||
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
|
||||
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
||||
|
||||
if score_corrector is not None:
|
||||
assert self.model.parameterization == "eps"
|
||||
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
||||
|
||||
return e_t
|
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
||||
|
||||
def get_x_prev_and_pred_x0(e_t, index):
|
||||
# select parameters corresponding to the currently considered timestep
|
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
||||
|
||||
# current prediction for x_0
|
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
||||
if quantize_denoised:
|
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
||||
# direction pointing to x_t
|
||||
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
||||
if noise_dropout > 0.:
|
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
||||
return x_prev, pred_x0
|
||||
|
||||
e_t = get_model_output(x, t)
|
||||
if len(old_eps) == 0:
|
||||
# Pseudo Improved Euler (2nd order)
|
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
|
||||
e_t_next = get_model_output(x_prev, t_next)
|
||||
e_t_prime = (e_t + e_t_next) / 2
|
||||
elif len(old_eps) == 1:
|
||||
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
|
||||
e_t_prime = (3 * e_t - old_eps[-1]) / 2
|
||||
elif len(old_eps) == 2:
|
||||
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
|
||||
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
|
||||
elif len(old_eps) >= 3:
|
||||
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
|
||||
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
|
||||
|
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
|
||||
|
||||
return x_prev, pred_x0, e_t
|
||||
|
||||
# =================================================================================================
|
||||
# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
|
||||
# Adapted from:
|
||||
# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
|
||||
# =================================================================================================
|
||||
|
||||
@torch.no_grad()
|
||||
def get_unconditional_conditioning(self, batch_size, null_label=None):
|
||||
if null_label is not None:
|
||||
xc = null_label
|
||||
if isinstance(xc, ListConfig):
|
||||
xc = list(xc)
|
||||
if isinstance(xc, dict) or isinstance(xc, list):
|
||||
c = self.get_learned_conditioning(xc)
|
||||
else:
|
||||
if hasattr(xc, "to"):
|
||||
xc = xc.to(self.device)
|
||||
c = self.get_learned_conditioning(xc)
|
||||
else:
|
||||
# todo: get null label from cond_stage_model
|
||||
raise NotImplementedError()
|
||||
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
|
||||
return c
|
||||
|
||||
|
||||
class LatentInpaintDiffusion(LatentDiffusion):
|
||||
def __init__(
|
||||
self,
|
||||
concat_keys=("mask", "masked_image"),
|
||||
masked_image_key="masked_image",
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.masked_image_key = masked_image_key
|
||||
assert self.masked_image_key in concat_keys
|
||||
self.concat_keys = concat_keys
|
||||
|
||||
|
||||
def should_hijack_inpainting(checkpoint_info):
|
||||
return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
|
||||
|
||||
|
||||
def do_inpainting_hijack():
|
||||
ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
|
||||
ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
|
||||
|
||||
ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
|
||||
ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
|
||||
|
||||
ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
|
||||
ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
|
||||
Loading…
Reference in New Issue