From 82cfc227d735c140447d5b8dca29a71ee9bde127 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 20:23:17 +0300
Subject: [PATCH] added licenses screen to settings added footer removed unused
inpainting code
---
README.md | 2 +
html/footer.html | 9 +
html/licenses.html | 392 ++++++++++++++++++++++++++++++++
modules/sd_hijack_inpainting.py | 232 -------------------
modules/ui.py | 15 +-
style.css | 11 +
6 files changed, 427 insertions(+), 234 deletions(-)
create mode 100644 html/footer.html
create mode 100644 html/licenses.html
diff --git a/README.md b/README.md
index 556000fb..88250a6b 100644
--- a/README.md
+++ b/README.md
@@ -127,6 +127,8 @@ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
+Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
diff --git a/html/footer.html b/html/footer.html
new file mode 100644
index 00000000..a8f2adf7
--- /dev/null
+++ b/html/footer.html
@@ -0,0 +1,9 @@
+
diff --git a/html/licenses.html b/html/licenses.html
new file mode 100644
index 00000000..9eeaa072
--- /dev/null
+++ b/html/licenses.html
@@ -0,0 +1,392 @@
+
+
+
+Parts of CodeFormer code had to be copied to be compatible with GFPGAN.
+
+S-Lab License 1.0
+
+Copyright 2022 S-Lab
+
+Redistribution and use for non-commercial purpose in source and
+binary forms, with or without modification, are permitted provided
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+3. Neither the name of the copyright holder nor the names of its
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+source or binary forms, with or without modification is required,
+please contact the contributor(s) of the work.
+
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+
+
+Code for architecture and reading models copied.
+
+MIT License
+
+Copyright (c) 2021 victorca25
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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+Some code is copied to support ESRGAN models.
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+BSD 3-Clause License
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+Copyright (c) 2021, Xintao Wang
+All rights reserved.
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+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
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+Some code for compatibility with OSX is taken from lstein's repository.
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+MIT License
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+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+copies of the Software, and to permit persons to whom the Software is
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+copies or substantial portions of the Software.
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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+
+Code added by contirubtors, most likely copied from this repository.
+
+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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+Some small amounts of code borrowed and reworked.
+
+MIT License
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+Copyright (c) 2022 pharmapsychotic
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+Code added by contirubtors, most likely copied from this repository.
+
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diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 06b75772..3c214a35 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -12,191 +12,6 @@ from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ddim import DDIMSampler, noise_like
-# =================================================================================================
-# Monkey patch DDIMSampler methods from RunwayML repo directly.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
-# =================================================================================================
-@torch.no_grad()
-def sample_ddim(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
- if conditioning is not None:
- if isinstance(conditioning, dict):
- ctmp = conditioning[list(conditioning.keys())[0]]
- while isinstance(ctmp, list):
- ctmp = ctmp[0]
- cbs = ctmp.shape[0]
- if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
- else:
- if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
- self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
- # sampling
- C, H, W = shape
- size = (batch_size, C, H, W)
- print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
- samples, intermediates = self.ddim_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- )
- return samples, intermediates
-
-@torch.no_grad()
-def p_sample_ddim(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):
- b, *_, device = *x.shape, x.device
-
- 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)
-
- 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
- # 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
-
-
-# =================================================================================================
-# Monkey patch PLMSSampler methods.
-# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
-# Adapted from:
-# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
-# =================================================================================================
-@torch.no_grad()
-def sample_plms(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
- if conditioning is not None:
- if isinstance(conditioning, dict):
- ctmp = conditioning[list(conditioning.keys())[0]]
- while isinstance(ctmp, list):
- ctmp = ctmp[0]
- cbs = ctmp.shape[0]
- if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
- else:
- if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
- self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
- # sampling
- C, H, W = shape
- size = (batch_size, C, H, W)
- # print(f'Data shape for PLMS sampling is {size}') # remove unnecessary message
-
- samples, intermediates = self.plms_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- )
- return samples, intermediates
-
@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
@@ -280,44 +95,6 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
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):
ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
@@ -326,15 +103,6 @@ def should_hijack_inpainting(checkpoint_info):
def do_inpainting_hijack():
- # most of this stuff seems to no longer be needed because it is already included into SD2.0
# p_sample_plms is needed because PLMS can't work with dicts as conditionings
- # this file should be cleaned up later if everything turns out to work fine
-
- # 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
diff --git a/modules/ui.py b/modules/ui.py
index f2e7c0d6..d941cb5f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1529,8 +1529,10 @@ def create_ui():
with gr.Blocks(analytics_enabled=False) as settings_interface:
with gr.Row():
- settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
- restart_gradio = gr.Button(value='Restart UI', variant='primary', elem_id="settings_restart_gradio")
+ with gr.Column(scale=6):
+ settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+ with gr.Column():
+ restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
result = gr.HTML(elem_id="settings_result")
@@ -1574,6 +1576,11 @@ def create_ui():
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ if os.path.exists("html/licenses.html"):
+ with open("html/licenses.html", encoding="utf8") as file:
+ with gr.TabItem("Licenses"):
+ gr.HTML(file.read(), elem_id="licenses")
+
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
request_notifications.click(
@@ -1659,6 +1666,10 @@ def create_ui():
if os.path.exists(os.path.join(script_path, "notification.mp3")):
audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+ if os.path.exists("html/footer.html"):
+ with open("html/footer.html", encoding="utf8") as file:
+ gr.HTML(file.read(), elem_id="footer")
+
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
settings_submit.click(
fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
diff --git a/style.css b/style.css
index 7296ce91..2116ec3c 100644
--- a/style.css
+++ b/style.css
@@ -616,6 +616,17 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
padding-bottom: 0.5em;
}
+footer {
+ display: none !important;
+}
+
+#footer{
+ text-align: center;
+}
+
+#footer div{
+ display: inline-block;
+}
/* The following handles localization for right-to-left (RTL) languages like Arabic.
The rtl media type will only be activated by the logic in javascript/localization.js.