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This commit is contained in:
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129
README.md
129
README.md
@ -1,16 +1,36 @@
|
||||
<h1 align='center'><b>Stable Diffusion Dream Script</b></h1>
|
||||
<div align="center">
|
||||
|
||||
<p align='center'>
|
||||
<img src="docs/assets/logo.png"/>
|
||||
</p>
|
||||
# Stable Diffusion Dream Script
|
||||
|
||||
<p align="center">
|
||||
<img src="https://img.shields.io/github/last-commit/lstein/stable-diffusion?logo=Python&logoColor=green&style=for-the-badge" alt="last-commit"/>
|
||||
<img src="https://img.shields.io/github/stars/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="stars"/>
|
||||
<br>
|
||||
<img src="https://img.shields.io/github/issues/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="issues"/>
|
||||
<img src="https://img.shields.io/github/issues-pr/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="pull-requests"/>
|
||||
</p>
|
||||
|
||||
|
||||
[![discord badge]][discord link]
|
||||
|
||||
[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
|
||||
|
||||
[![CI checks on main badge]][CI checks on main link] [![CI checks on dev badge]][CI checks on dev link] [![latest commit to dev badge]][latest commit to dev link]
|
||||
|
||||
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
|
||||
|
||||
[CI checks on dev badge]: https://flat.badgen.net/github/checks/lstein/stable-diffusion/development?label=CI%20status%20on%20dev&cache=900&icon=github
|
||||
[CI checks on dev link]: https://github.com/lstein/stable-diffusion/actions?query=branch%3Adevelopment
|
||||
[CI checks on main badge]: https://flat.badgen.net/github/checks/lstein/stable-diffusion/main?label=CI%20status%20on%20main&cache=900&icon=github
|
||||
[CI checks on main link]: https://github.com/lstein/stable-diffusion/actions/workflows/test-dream-conda.yml
|
||||
[discord badge]: https://flat.badgen.net/discord/members/htRgbc7e?icon=discord
|
||||
[discord link]: https://discord.com/invite/htRgbc7e
|
||||
[github forks badge]: https://flat.badgen.net/github/forks/lstein/stable-diffusion?icon=github
|
||||
[github forks link]: https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
|
||||
[github open issues badge]: https://flat.badgen.net/github/open-issues/lstein/stable-diffusion?icon=github
|
||||
[github open issues link]: https://github.com/lstein/stable-diffusion/issues?q=is%3Aissue+is%3Aopen
|
||||
[github open prs badge]: https://flat.badgen.net/github/open-prs/lstein/stable-diffusion?icon=github
|
||||
[github open prs link]: https://github.com/lstein/stable-diffusion/pulls?q=is%3Apr+is%3Aopen
|
||||
[github stars badge]: https://flat.badgen.net/github/stars/lstein/stable-diffusion?icon=github
|
||||
[github stars link]: https://github.com/lstein/stable-diffusion/stargazers
|
||||
[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/lstein/stable-diffusion/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
|
||||
[latest commit to dev link]: https://github.com/lstein/stable-diffusion/commits/development
|
||||
[latest release badge]: https://flat.badgen.net/github/release/lstein/stable-diffusion/development?icon=github
|
||||
[latest release link]: https://github.com/lstein/stable-diffusion/releases
|
||||
</div>
|
||||
|
||||
This is a fork of [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion), the open
|
||||
source text-to-image generator. It provides a streamlined process with various new features and
|
||||
@ -21,7 +41,7 @@ _Note: This fork is rapidly evolving. Please use the
|
||||
[Issues](https://github.com/lstein/stable-diffusion/issues) tab to report bugs and make feature
|
||||
requests. Be sure to use the provided templates. They will help aid diagnose issues faster._
|
||||
|
||||
**Table of Contents**
|
||||
## Table of Contents
|
||||
|
||||
1. [Installation](#installation)
|
||||
2. [Hardware Requirements](#hardware-requirements)
|
||||
@ -33,38 +53,38 @@ requests. Be sure to use the provided templates. They will help aid diagnose iss
|
||||
8. [Support](#support)
|
||||
9. [Further Reading](#further-reading)
|
||||
|
||||
## Installation
|
||||
### Installation
|
||||
|
||||
This fork is supported across multiple platforms. You can find individual installation instructions
|
||||
below.
|
||||
|
||||
- ### [Linux](docs/installation/INSTALL_LINUX.md)
|
||||
- #### [Linux](docs/installation/INSTALL_LINUX.md)
|
||||
|
||||
- ### [Windows](docs/installation/INSTALL_WINDOWS.md)
|
||||
- #### [Windows](docs/installation/INSTALL_WINDOWS.md)
|
||||
|
||||
- ### [Macintosh](docs/installation/INSTALL_MAC.md)
|
||||
- #### [Macintosh](docs/installation/INSTALL_MAC.md)
|
||||
|
||||
## Hardware Requirements
|
||||
### Hardware Requirements
|
||||
|
||||
**System**
|
||||
#### System
|
||||
|
||||
You wil need one of the following:
|
||||
|
||||
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
|
||||
- An Apple computer with an M1 chip.
|
||||
|
||||
**Memory**
|
||||
#### Memory
|
||||
|
||||
- At least 12 GB Main Memory RAM.
|
||||
|
||||
**Disk**
|
||||
#### Disk
|
||||
|
||||
- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
|
||||
|
||||
**Note**
|
||||
|
||||
If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
|
||||
full-precision mode as shown below.
|
||||
> Note
|
||||
>
|
||||
> If you have an Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
|
||||
> full-precision mode as shown below.
|
||||
|
||||
Similarly, specify full-precision mode on Apple M1 hardware.
|
||||
|
||||
@ -74,43 +94,30 @@ To run in full-precision mode, start `dream.py` with the `--full_precision` flag
|
||||
(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
|
||||
```
|
||||
|
||||
## Features
|
||||
### Features
|
||||
|
||||
### Major Features
|
||||
#### Major Features
|
||||
|
||||
- #### [Interactive Command Line Interface](docs/features/CLI.md)
|
||||
- [Interactive Command Line Interface](docs/features/CLI.md)
|
||||
- [Image To Image](docs/features/IMG2IMG.md)
|
||||
- [Inpainting Support](docs/features/INPAINTING.md)
|
||||
- [GFPGAN and Real-ESRGAN Support](docs/features/UPSCALE.md)
|
||||
- [Seamless Tiling](docs/features/OTHER.md#seamless-tiling)
|
||||
- [Google Colab](docs/features/OTHER.md#google-colab)
|
||||
- [Web Server](docs/features/WEB.md)
|
||||
- [Reading Prompts From File](docs/features/OTHER.md#reading-prompts-from-a-file)
|
||||
- [Shortcut: Reusing Seeds](docs/features/OTHER.md#shortcuts-reusing-seeds)
|
||||
- [Weighted Prompts](docs/features/OTHER.md#weighted-prompts)
|
||||
- [Variations](docs/features/VARIATIONS.md)
|
||||
- [Personalizing Text-to-Image Generation](docs/features/TEXTUAL_INVERSION.md)
|
||||
- [Simplified API for text to image generation](docs/features/OTHER.md#simplified-api)
|
||||
|
||||
- #### [Image To Image](docs/features/IMG2IMG.md)
|
||||
#### Other Features
|
||||
|
||||
- #### [Inpainting Support](docs/features/INPAINTING.md)
|
||||
- [Creating Transparent Regions for Inpainting](docs/features/INPAINTING.md#creating-transparent-regions-for-inpainting)
|
||||
- [Preload Models](docs/features/OTHER.md#preload-models)
|
||||
|
||||
- #### [GFPGAN and Real-ESRGAN Support](docs/features/UPSCALE.md)
|
||||
|
||||
- #### [Seamless Tiling](docs/features/OTHER.md#seamless-tiling)
|
||||
|
||||
- #### [Google Colab](docs/features/OTHER.md#google-colab)
|
||||
|
||||
- #### [Web Server](docs/features/WEB.md)
|
||||
|
||||
- #### [Reading Prompts From File](docs/features/OTHER.md#reading-prompts-from-a-file)
|
||||
|
||||
- #### [Shortcut: Reusing Seeds](docs/features/OTHER.md#shortcuts-reusing-seeds)
|
||||
|
||||
- #### [Weighted Prompts](docs/features/OTHER.md#weighted-prompts)
|
||||
|
||||
- #### [Variations](docs/features/VARIATIONS.md)
|
||||
|
||||
- #### [Personalizing Text-to-Image Generation](docs/features/TEXTUAL_INVERSION.md)
|
||||
|
||||
- #### [Simplified API for text to image generation](docs/features/OTHER.md#simplified-api)
|
||||
|
||||
### Other Features
|
||||
|
||||
- #### [Creating Transparent Regions for Inpainting](docs/features/INPAINTING.md#creating-transparent-regions-for-inpainting)
|
||||
|
||||
- #### [Preload Models](docs/features/OTHER.md#preload-models)
|
||||
|
||||
## Latest Changes
|
||||
### Latest Changes
|
||||
|
||||
- v1.14 (11 September 2022)
|
||||
|
||||
@ -142,12 +149,12 @@ To run in full-precision mode, start `dream.py` with the `--full_precision` flag
|
||||
|
||||
For older changelogs, please visit the **[CHANGELOG](docs/features/CHANGELOG.md)**.
|
||||
|
||||
## Troubleshooting
|
||||
### Troubleshooting
|
||||
|
||||
Please check out our **[Q&A](docs/help/TROUBLESHOOT.md)** to get solutions for common installation
|
||||
problems and other issues.
|
||||
|
||||
## Contributing
|
||||
### Contributing
|
||||
|
||||
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
|
||||
cleanup, testing, or code reviews, is very much encouraged to do so. If you are unfamiliar with how
|
||||
@ -159,13 +166,13 @@ important thing is to **make your pull request against the "development" branch*
|
||||
"main". This will help keep public breakage to a minimum and will allow you to propose more radical
|
||||
changes.
|
||||
|
||||
## Contributors
|
||||
### Contributors
|
||||
|
||||
This fork is a combined effort of various people from across the world.
|
||||
[Check out the list of all these amazing people](docs/other/CONTRIBUTORS.md). We thank them for
|
||||
their time, hard work and effort.
|
||||
|
||||
## Support
|
||||
### Support
|
||||
|
||||
For support, please use this repository's GitHub Issues tracking service. Feel free to send me an
|
||||
email if you use and like the script.
|
||||
@ -173,7 +180,7 @@ email if you use and like the script.
|
||||
Original portions of the software are Copyright (c) 2020
|
||||
[Lincoln D. Stein](https://github.com/lstein)
|
||||
|
||||
## Further Reading
|
||||
### Further Reading
|
||||
|
||||
Please see the original README for more information on this software and underlying algorithm,
|
||||
located in the file [README-CompViz.md](docs/other/README-CompViz.md).
|
||||
|
||||
@ -20,7 +20,7 @@ dependencies:
|
||||
- realesrgan==0.2.5.0
|
||||
- test-tube>=0.7.5
|
||||
- streamlit==1.12.0
|
||||
- pillow==6.2.0
|
||||
- pillow==9.2.0
|
||||
- einops==0.3.0
|
||||
- torch-fidelity==0.3.0
|
||||
- transformers==4.19.2
|
||||
|
||||
@ -2,7 +2,10 @@
|
||||
|
||||
The Args class parses both the command line (shell) arguments, as well as the
|
||||
command string passed at the dream> prompt. It serves as the definitive repository
|
||||
of all the arguments used by Generate and their default values.
|
||||
of all the arguments used by Generate and their default values, and implements the
|
||||
preliminary metadata standards discussed here:
|
||||
|
||||
https://github.com/lstein/stable-diffusion/issues/266
|
||||
|
||||
To use:
|
||||
opt = Args()
|
||||
@ -52,15 +55,38 @@ you wish to apply logic as to which one to use. For example:
|
||||
To add new attributes, edit the _create_arg_parser() and
|
||||
_create_dream_cmd_parser() methods.
|
||||
|
||||
We also export the function build_metadata
|
||||
**Generating and retrieving sd-metadata**
|
||||
|
||||
To generate a dict representing RFC266 metadata:
|
||||
|
||||
metadata = metadata_dumps(opt,<seeds,model_hash,postprocesser>)
|
||||
|
||||
This will generate an RFC266 dictionary that can then be turned into a JSON
|
||||
and written to the PNG file. The optional seeds, weights, model_hash and
|
||||
postprocesser arguments are not available to the opt object and so must be
|
||||
provided externally. See how dream.py does it.
|
||||
|
||||
Note that this function was originally called format_metadata() and a wrapper
|
||||
is provided that issues a deprecation notice.
|
||||
|
||||
To retrieve a (series of) opt objects corresponding to the metadata, do this:
|
||||
|
||||
opt_list = metadata_loads(metadata)
|
||||
|
||||
The metadata should be pulled out of the PNG image. pngwriter has a method
|
||||
retrieve_metadata that will do this.
|
||||
|
||||
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from argparse import Namespace
|
||||
import shlex
|
||||
import json
|
||||
import hashlib
|
||||
import os
|
||||
import copy
|
||||
import base64
|
||||
from ldm.dream.conditioning import split_weighted_subprompts
|
||||
|
||||
SAMPLER_CHOICES = [
|
||||
@ -105,6 +131,7 @@ class Args(object):
|
||||
try:
|
||||
elements = shlex.split(command)
|
||||
except ValueError:
|
||||
import sys, traceback
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
return
|
||||
switches = ['']
|
||||
@ -141,7 +168,7 @@ class Args(object):
|
||||
a = vars(self)
|
||||
a.update(kwargs)
|
||||
switches = list()
|
||||
switches.append(f'"{a["prompt"]}')
|
||||
switches.append(f'"{a["prompt"]}"')
|
||||
switches.append(f'-s {a["steps"]}')
|
||||
switches.append(f'-W {a["width"]}')
|
||||
switches.append(f'-H {a["height"]}')
|
||||
@ -150,15 +177,13 @@ class Args(object):
|
||||
switches.append(f'-S {a["seed"]}')
|
||||
if a['grid']:
|
||||
switches.append('--grid')
|
||||
if a['iterations'] and a['iterations']>0:
|
||||
switches.append(f'-n {a["iterations"]}')
|
||||
if a['seamless']:
|
||||
switches.append('--seamless')
|
||||
if a['init_img'] and len(a['init_img'])>0:
|
||||
switches.append(f'-I {a["init_img"]}')
|
||||
if a['fit']:
|
||||
switches.append(f'--fit')
|
||||
if a['strength'] and a['strength']>0:
|
||||
if a['init_img'] and a['strength'] and a['strength']>0:
|
||||
switches.append(f'-f {a["strength"]}')
|
||||
if a['gfpgan_strength']:
|
||||
switches.append(f'-G {a["gfpgan_strength"]}')
|
||||
@ -189,10 +214,10 @@ class Args(object):
|
||||
pass
|
||||
|
||||
if cmd_switches and arg_switches and name=='__dict__':
|
||||
a = arg_switches.__dict__
|
||||
a.update(cmd_switches.__dict__)
|
||||
return a
|
||||
|
||||
return self._merge_dict(
|
||||
arg_switches.__dict__,
|
||||
cmd_switches.__dict__,
|
||||
)
|
||||
try:
|
||||
return object.__getattribute__(self,name)
|
||||
except AttributeError:
|
||||
@ -216,13 +241,8 @@ class Args(object):
|
||||
# the arg value. For example, the --grid and --individual options are a little
|
||||
# funny because of their push/pull relationship. This is how to handle it.
|
||||
if name=='grid':
|
||||
return value_arg or value_cmd # arg supersedes cmd
|
||||
if name=='individual':
|
||||
return value_cmd or value_arg # cmd supersedes arg
|
||||
if value_cmd is not None:
|
||||
return value_cmd
|
||||
else:
|
||||
return value_arg
|
||||
return not cmd_switches.individual and value_arg # arg supersedes cmd
|
||||
return value_cmd if value_cmd is not None else value_arg
|
||||
|
||||
def __setattr__(self,name,value):
|
||||
if name.startswith('_'):
|
||||
@ -230,6 +250,14 @@ class Args(object):
|
||||
else:
|
||||
self._cmd_switches.__dict__[name] = value
|
||||
|
||||
def _merge_dict(self,dict1,dict2):
|
||||
new_dict = {}
|
||||
for k in set(list(dict1.keys())+list(dict2.keys())):
|
||||
value1 = dict1.get(k,None)
|
||||
value2 = dict2.get(k,None)
|
||||
new_dict[k] = value2 if value2 is not None else value1
|
||||
return new_dict
|
||||
|
||||
def _create_arg_parser(self):
|
||||
'''
|
||||
This defines all the arguments used on the command line when you launch
|
||||
@ -268,6 +296,17 @@ class Args(object):
|
||||
default='stable-diffusion-1.4',
|
||||
help='Indicates which diffusion model to load. (currently "stable-diffusion-1.4" (default) or "laion400m")',
|
||||
)
|
||||
model_group.add_argument(
|
||||
'--sampler',
|
||||
'-A',
|
||||
'-m',
|
||||
dest='sampler_name',
|
||||
type=str,
|
||||
choices=SAMPLER_CHOICES,
|
||||
metavar='SAMPLER_NAME',
|
||||
help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
|
||||
default='k_lms',
|
||||
)
|
||||
model_group.add_argument(
|
||||
'-F',
|
||||
'--full_precision',
|
||||
@ -294,11 +333,6 @@ class Args(object):
|
||||
action='store_true',
|
||||
help='Place images in subdirectories named after the prompt.',
|
||||
)
|
||||
render_group.add_argument(
|
||||
'--seamless',
|
||||
action='store_true',
|
||||
help='Change the model to seamless tiling (circular) mode',
|
||||
)
|
||||
render_group.add_argument(
|
||||
'--grid',
|
||||
'-g',
|
||||
@ -393,14 +427,12 @@ class Args(object):
|
||||
'--width',
|
||||
type=int,
|
||||
help='Image width, multiple of 64',
|
||||
default=512
|
||||
)
|
||||
render_group.add_argument(
|
||||
'-H',
|
||||
'--height',
|
||||
type=int,
|
||||
help='Image height, multiple of 64',
|
||||
default=512,
|
||||
)
|
||||
render_group.add_argument(
|
||||
'-C',
|
||||
@ -416,8 +448,8 @@ class Args(object):
|
||||
help='generate a grid'
|
||||
)
|
||||
render_group.add_argument(
|
||||
'--individual',
|
||||
'-i',
|
||||
'--individual',
|
||||
action='store_true',
|
||||
help='override command-line --grid setting and generate individual images'
|
||||
)
|
||||
@ -436,7 +468,6 @@ class Args(object):
|
||||
choices=SAMPLER_CHOICES,
|
||||
metavar='SAMPLER_NAME',
|
||||
help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
|
||||
default='k_lms',
|
||||
)
|
||||
render_group.add_argument(
|
||||
'-t',
|
||||
@ -448,7 +479,6 @@ class Args(object):
|
||||
'--outdir',
|
||||
'-o',
|
||||
type=str,
|
||||
default='outputs/img-samples',
|
||||
help='Directory to save generated images and a log of prompts and seeds',
|
||||
)
|
||||
img2img_group.add_argument(
|
||||
@ -535,17 +565,20 @@ class Args(object):
|
||||
)
|
||||
return parser
|
||||
|
||||
# very partial implementation of https://github.com/lstein/stable-diffusion/issues/266
|
||||
# it does not write all the required top-level metadata, writes too much image
|
||||
# data, and doesn't support grids yet. But you gotta start somewhere, no?
|
||||
def format_metadata(opt,
|
||||
seeds=[],
|
||||
weights=None,
|
||||
model_hash=None,
|
||||
postprocessing=None):
|
||||
def format_metadata(**kwargs):
|
||||
print(f'format_metadata() is deprecated. Please use metadata_dumps()')
|
||||
return metadata_dumps(kwargs)
|
||||
|
||||
def metadata_dumps(opt,
|
||||
seeds=[],
|
||||
model_hash=None,
|
||||
postprocessing=None):
|
||||
'''
|
||||
Given an Args object, returns a partial implementation of
|
||||
the stable diffusion metadata standard
|
||||
Given an Args object, returns a dict containing the keys and
|
||||
structure of the proposed stable diffusion metadata standard
|
||||
https://github.com/lstein/stable-diffusion/discussions/392
|
||||
This is intended to be turned into JSON and stored in the
|
||||
"sd
|
||||
'''
|
||||
# add some RFC266 fields that are generated internally, and not as
|
||||
# user args
|
||||
@ -587,12 +620,15 @@ def format_metadata(opt,
|
||||
if opt.init_img:
|
||||
rfc_dict['type'] = 'img2img'
|
||||
rfc_dict['strength_steps'] = rfc_dict.pop('strength')
|
||||
rfc_dict['orig_hash'] = sha256(image_dict['init_img'])
|
||||
rfc_dict['orig_hash'] = calculate_init_img_hash(opt.init_img)
|
||||
rfc_dict['sampler'] = 'ddim' # FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
|
||||
else:
|
||||
rfc_dict['type'] = 'txt2img'
|
||||
|
||||
images = []
|
||||
if len(seeds)==0 and opt.seed:
|
||||
seeds=[seed]
|
||||
|
||||
for seed in seeds:
|
||||
rfc_dict['seed'] = seed
|
||||
images.append(copy.copy(rfc_dict))
|
||||
@ -606,6 +642,44 @@ def format_metadata(opt,
|
||||
'images' : images,
|
||||
}
|
||||
|
||||
def metadata_loads(metadata):
|
||||
'''
|
||||
Takes the dictionary corresponding to RFC266 (https://github.com/lstein/stable-diffusion/issues/266)
|
||||
and returns a series of opt objects for each of the images described in the dictionary.
|
||||
'''
|
||||
results = []
|
||||
try:
|
||||
images = metadata['sd-metadata']['images']
|
||||
for image in images:
|
||||
# repack the prompt and variations
|
||||
image['prompt'] = ','.join([':'.join([x['prompt'], str(x['weight'])]) for x in image['prompt']])
|
||||
image['variations'] = ','.join([':'.join([str(x['seed']),str(x['weight'])]) for x in image['variations']])
|
||||
opt = Args()
|
||||
opt._cmd_switches = Namespace(**image)
|
||||
results.append(opt)
|
||||
except KeyError as e:
|
||||
import sys, traceback
|
||||
print('>> badly-formatted metadata',file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
return results
|
||||
|
||||
# image can either be a file path on disk or a base64-encoded
|
||||
# representation of the file's contents
|
||||
def calculate_init_img_hash(image_string):
|
||||
prefix = 'data:image/png;base64,'
|
||||
hash = None
|
||||
if image_string.startswith(prefix):
|
||||
imagebase64 = image_string[len(prefix):]
|
||||
imagedata = base64.b64decode(imagebase64)
|
||||
with open('outputs/test.png','wb') as file:
|
||||
file.write(imagedata)
|
||||
sha = hashlib.sha256()
|
||||
sha.update(imagedata)
|
||||
hash = sha.hexdigest()
|
||||
else:
|
||||
hash = sha256(image_string)
|
||||
return hash
|
||||
|
||||
# Bah. This should be moved somewhere else...
|
||||
def sha256(path):
|
||||
sha = hashlib.sha256()
|
||||
|
||||
@ -34,7 +34,6 @@ class PngWriter:
|
||||
# saves image named _image_ to outdir/name, writing metadata from prompt
|
||||
# returns full path of output
|
||||
def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None):
|
||||
print(f'self.outdir={self.outdir}, name={name}')
|
||||
path = os.path.join(self.outdir, name)
|
||||
info = PngImagePlugin.PngInfo()
|
||||
info.add_text('Dream', dream_prompt)
|
||||
|
||||
@ -4,7 +4,7 @@ import copy
|
||||
import base64
|
||||
import mimetypes
|
||||
import os
|
||||
from ldm.dream.args import Args, format_metadata
|
||||
from ldm.dream.args import Args, metadata_dumps
|
||||
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
|
||||
from ldm.dream.pngwriter import PngWriter
|
||||
from threading import Event
|
||||
@ -76,7 +76,7 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
self.send_response(200)
|
||||
self.send_header("Content-type", "text/html")
|
||||
self.end_headers()
|
||||
with open("./static/dream_web/index.html", "rb") as content:
|
||||
with open("./static/legacy_web/index.html", "rb") as content:
|
||||
self.wfile.write(content.read())
|
||||
elif self.path == "/config.js":
|
||||
# unfortunately this import can't be at the top level, since that would cause a circular import
|
||||
@ -94,7 +94,7 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
self.end_headers()
|
||||
output = []
|
||||
|
||||
log_file = os.path.join(self.outdir, "dream_web_log.txt")
|
||||
log_file = os.path.join(self.outdir, "legacy_web_log.txt")
|
||||
if os.path.exists(log_file):
|
||||
with open(log_file, "r") as log:
|
||||
for line in log:
|
||||
@ -114,7 +114,7 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
else:
|
||||
path_dir = os.path.dirname(self.path)
|
||||
out_dir = os.path.realpath(self.outdir.rstrip('/'))
|
||||
if self.path.startswith('/static/dream_web/'):
|
||||
if self.path.startswith('/static/legacy_web/'):
|
||||
path = '.' + self.path
|
||||
elif out_dir.replace('\\', '/').endswith(path_dir):
|
||||
file = os.path.basename(self.path)
|
||||
@ -145,7 +145,6 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
opt = build_opt(post_data, self.model.seed, gfpgan_model_exists)
|
||||
|
||||
self.canceled.clear()
|
||||
print(f">> Request to generate with prompt: {opt.prompt}")
|
||||
# In order to handle upscaled images, the PngWriter needs to maintain state
|
||||
# across images generated by each call to prompt2img(), so we define it in
|
||||
# the outer scope of image_done()
|
||||
@ -176,10 +175,9 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
path = pngwriter.save_image_and_prompt_to_png(
|
||||
image,
|
||||
dream_prompt = formatted_prompt,
|
||||
metadata = format_metadata(iter_opt,
|
||||
seeds = [seed],
|
||||
weights = self.model.weights,
|
||||
model_hash = self.model.model_hash
|
||||
metadata = metadata_dumps(iter_opt,
|
||||
seeds = [seed],
|
||||
model_hash = self.model.model_hash
|
||||
),
|
||||
name = name,
|
||||
)
|
||||
@ -188,7 +186,7 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
config['seed'] = seed
|
||||
# Append post_data to log, but only once!
|
||||
if not upscaled:
|
||||
with open(os.path.join(self.outdir, "dream_web_log.txt"), "a") as log:
|
||||
with open(os.path.join(self.outdir, "legacy_web_log.txt"), "a") as log:
|
||||
log.write(f"{path}: {json.dumps(config)}\n")
|
||||
|
||||
self.wfile.write(bytes(json.dumps(
|
||||
|
||||
@ -90,7 +90,7 @@ class LinearAttention(nn.Module):
|
||||
b, c, h, w = x.shape
|
||||
qkv = self.to_qkv(x)
|
||||
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
|
||||
k = k.softmax(dim=-1)
|
||||
k = k.softmax(dim=-1)
|
||||
context = torch.einsum('bhdn,bhen->bhde', k, v)
|
||||
out = torch.einsum('bhde,bhdn->bhen', context, q)
|
||||
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
|
||||
@ -167,101 +167,85 @@ class CrossAttention(nn.Module):
|
||||
nn.Linear(inner_dim, query_dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
self.einsum_op = self.einsum_op_cuda
|
||||
else:
|
||||
self.mem_total = psutil.virtual_memory().total / (1024**3)
|
||||
self.einsum_op = self.einsum_op_mps_v1 if self.mem_total >= 32 else self.einsum_op_mps_v2
|
||||
|
||||
def einsum_op_compvis(self, q, k, v, r1):
|
||||
s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # faster
|
||||
s2 = s1.softmax(dim=-1, dtype=q.dtype)
|
||||
del s1
|
||||
r1 = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
return r1
|
||||
self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
|
||||
|
||||
def einsum_op_mps_v1(self, q, k, v, r1):
|
||||
def einsum_op_compvis(self, q, k, v):
|
||||
s = einsum('b i d, b j d -> b i j', q, k)
|
||||
s = s.softmax(dim=-1, dtype=s.dtype)
|
||||
return einsum('b i j, b j d -> b i d', s, v)
|
||||
|
||||
def einsum_op_slice_0(self, q, k, v, slice_size):
|
||||
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
for i in range(0, q.shape[0], slice_size):
|
||||
end = i + slice_size
|
||||
r[i:end] = self.einsum_op_compvis(q[i:end], k[i:end], v[i:end])
|
||||
return r
|
||||
|
||||
def einsum_op_slice_1(self, q, k, v, slice_size):
|
||||
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
r[:, i:end] = self.einsum_op_compvis(q[:, i:end], k, v)
|
||||
return r
|
||||
|
||||
def einsum_op_mps_v1(self, q, k, v):
|
||||
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
|
||||
r1 = self.einsum_op_compvis(q, k, v, r1)
|
||||
return self.einsum_op_compvis(q, k, v)
|
||||
else:
|
||||
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
|
||||
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
|
||||
del s1
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
return r1
|
||||
return self.einsum_op_slice_1(q, k, v, slice_size)
|
||||
|
||||
def einsum_op_mps_v2(self, q, k, v, r1):
|
||||
if self.mem_total >= 8 and q.shape[1] <= 4096:
|
||||
r1 = self.einsum_op_compvis(q, k, v, r1)
|
||||
def einsum_op_mps_v2(self, q, k, v):
|
||||
if self.mem_total_gb > 8 and q.shape[1] <= 4096:
|
||||
return self.einsum_op_compvis(q, k, v)
|
||||
else:
|
||||
slice_size = 1
|
||||
for i in range(0, q.shape[0], slice_size):
|
||||
end = min(q.shape[0], i + slice_size)
|
||||
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
|
||||
s1 *= self.scale
|
||||
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
|
||||
del s1
|
||||
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
||||
del s2
|
||||
return r1
|
||||
|
||||
def einsum_op_cuda(self, q, k, v, r1):
|
||||
return self.einsum_op_slice_0(q, k, v, 1)
|
||||
|
||||
def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
|
||||
size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
|
||||
if size_mb <= max_tensor_mb:
|
||||
return self.einsum_op_compvis(q, k, v)
|
||||
div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
|
||||
if div <= q.shape[0]:
|
||||
return self.einsum_op_slice_0(q, k, v, q.shape[0] // div)
|
||||
return self.einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
|
||||
|
||||
def einsum_op_cuda(self, q, k, v):
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
# Divide factor of safety as there's copying and fragmentation
|
||||
return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
|
||||
mem_required = tensor_size * 2.5
|
||||
steps = 1
|
||||
def einsum_op(self, q, k, v):
|
||||
if q.device.type == 'cuda':
|
||||
return self.einsum_op_cuda(q, k, v)
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
if q.device.type == 'mps':
|
||||
if self.mem_total_gb >= 32:
|
||||
return self.einsum_op_mps_v1(q, k, v)
|
||||
return self.einsum_op_mps_v2(q, k, v)
|
||||
|
||||
if steps > 64:
|
||||
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
||||
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
||||
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = min(q.shape[1], i + slice_size)
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
|
||||
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
|
||||
del s1
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
return r1
|
||||
# Smaller slices are faster due to L2/L3/SLC caches.
|
||||
# Tested on i7 with 8MB L3 cache.
|
||||
return self.einsum_op_tensor_mem(q, k, v, 32)
|
||||
|
||||
def forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q_in = self.to_q(x)
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
device_type = 'mps' if x.device.type == 'mps' else 'cuda'
|
||||
k = self.to_k(context) * self.scale
|
||||
v = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
r1 = self.einsum_op(q, k, v, r1)
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
r = self.einsum_op(q, k, v)
|
||||
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
|
||||
|
||||
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
|
||||
@ -3,6 +3,7 @@ import gc
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn.functional import silu
|
||||
import numpy as np
|
||||
from einops import rearrange
|
||||
|
||||
@ -32,11 +33,6 @@ def get_timestep_embedding(timesteps, embedding_dim):
|
||||
return emb
|
||||
|
||||
|
||||
def nonlinearity(x):
|
||||
# swish
|
||||
return x*torch.sigmoid(x)
|
||||
|
||||
|
||||
def Normalize(in_channels, num_groups=32):
|
||||
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
||||
|
||||
@ -122,14 +118,14 @@ class ResnetBlock(nn.Module):
|
||||
|
||||
def forward(self, x, temb):
|
||||
h = self.norm1(x)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
h = self.conv1(h)
|
||||
|
||||
if temb is not None:
|
||||
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
|
||||
h = h + self.temb_proj(silu(temb))[:,:,None,None]
|
||||
|
||||
h = self.norm2(h)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
h = self.dropout(h)
|
||||
h = self.conv2(h)
|
||||
|
||||
@ -368,7 +364,7 @@ class Model(nn.Module):
|
||||
assert t is not None
|
||||
temb = get_timestep_embedding(t, self.ch)
|
||||
temb = self.temb.dense[0](temb)
|
||||
temb = nonlinearity(temb)
|
||||
temb = silu(temb)
|
||||
temb = self.temb.dense[1](temb)
|
||||
else:
|
||||
temb = None
|
||||
@ -402,7 +398,7 @@ class Model(nn.Module):
|
||||
|
||||
# end
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
h = self.conv_out(h)
|
||||
return h
|
||||
|
||||
@ -499,7 +495,7 @@ class Encoder(nn.Module):
|
||||
|
||||
# end
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
h = self.conv_out(h)
|
||||
return h
|
||||
|
||||
@ -611,7 +607,7 @@ class Decoder(nn.Module):
|
||||
return h
|
||||
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
h = self.conv_out(h)
|
||||
if self.tanh_out:
|
||||
h = torch.tanh(h)
|
||||
@ -649,7 +645,7 @@ class SimpleDecoder(nn.Module):
|
||||
x = layer(x)
|
||||
|
||||
h = self.norm_out(x)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
x = self.conv_out(h)
|
||||
return x
|
||||
|
||||
@ -697,7 +693,7 @@ class UpsampleDecoder(nn.Module):
|
||||
if i_level != self.num_resolutions - 1:
|
||||
h = self.upsample_blocks[k](h)
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = silu(h)
|
||||
h = self.conv_out(h)
|
||||
return h
|
||||
|
||||
@ -873,7 +869,7 @@ class FirstStagePostProcessor(nn.Module):
|
||||
z_fs = self.encode_with_pretrained(x)
|
||||
z = self.proj_norm(z_fs)
|
||||
z = self.proj(z)
|
||||
z = nonlinearity(z)
|
||||
z = silu(z)
|
||||
|
||||
for submodel, downmodel in zip(self.model,self.downsampler):
|
||||
z = submodel(z,temb=None)
|
||||
|
||||
@ -252,12 +252,6 @@ def normalization(channels):
|
||||
return GroupNorm32(32, channels)
|
||||
|
||||
|
||||
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
|
||||
class SiLU(nn.Module):
|
||||
def forward(self, x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class GroupNorm32(nn.GroupNorm):
|
||||
def forward(self, x):
|
||||
return super().forward(x.float()).type(x.dtype)
|
||||
|
||||
54
scripts/dream.py
Normal file → Executable file
54
scripts/dream.py
Normal file → Executable file
@ -8,7 +8,7 @@ import copy
|
||||
import warnings
|
||||
import time
|
||||
import ldm.dream.readline
|
||||
from ldm.dream.args import Args, format_metadata
|
||||
from ldm.dream.args import Args, metadata_dumps
|
||||
from ldm.dream.pngwriter import PngWriter
|
||||
from ldm.dream.server import DreamServer, ThreadingDreamServer
|
||||
from ldm.dream.image_util import make_grid
|
||||
@ -100,6 +100,7 @@ def main_loop(gen, opt, infile):
|
||||
done = False
|
||||
path_filter = re.compile(r'[<>:"/\\|?*]')
|
||||
last_results = list()
|
||||
model_config = OmegaConf.load(opt.conf)[opt.model]
|
||||
|
||||
# os.pathconf is not available on Windows
|
||||
if hasattr(os, 'pathconf'):
|
||||
@ -123,7 +124,7 @@ def main_loop(gen, opt, infile):
|
||||
if command.startswith(('#', '//')):
|
||||
continue
|
||||
|
||||
if command.startswith('q '):
|
||||
if len(command.strip()) == 1 and command.startswith('q'):
|
||||
done = True
|
||||
break
|
||||
|
||||
@ -132,15 +133,18 @@ def main_loop(gen, opt, infile):
|
||||
): # in case a stored prompt still contains the !dream command
|
||||
command.replace('!dream','',1)
|
||||
|
||||
try:
|
||||
parser = opt.parse_cmd(command)
|
||||
except SystemExit:
|
||||
parser.print_help()
|
||||
if opt.parse_cmd(command) is None:
|
||||
continue
|
||||
if len(opt.prompt) == 0:
|
||||
print('Try again with a prompt!')
|
||||
print('\nTry again with a prompt!')
|
||||
continue
|
||||
|
||||
# width and height are set by model if not specified
|
||||
if not opt.width:
|
||||
opt.width = model_config.width
|
||||
if not opt.height:
|
||||
opt.height = model_config.height
|
||||
|
||||
# retrieve previous value!
|
||||
if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
|
||||
try:
|
||||
@ -191,14 +195,14 @@ def main_loop(gen, opt, infile):
|
||||
if not os.path.exists(opt.outdir):
|
||||
os.makedirs(opt.outdir)
|
||||
current_outdir = opt.outdir
|
||||
elif prompt_as_dir:
|
||||
elif opt.prompt_as_dir:
|
||||
# sanitize the prompt to a valid folder name
|
||||
subdir = path_filter.sub('_', opt.prompt)[:name_max].rstrip(' .')
|
||||
|
||||
# truncate path to maximum allowed length
|
||||
# 27 is the length of '######.##########.##.png', plus two separators and a NUL
|
||||
subdir = subdir[:(path_max - 27 - len(os.path.abspath(opt.outdir)))]
|
||||
current_outdir = os.path.join(outdir, subdir)
|
||||
current_outdir = os.path.join(opt.outdir, subdir)
|
||||
|
||||
print('Writing files to directory: "' + current_outdir + '"')
|
||||
|
||||
@ -206,7 +210,7 @@ def main_loop(gen, opt, infile):
|
||||
if not os.path.exists(current_outdir):
|
||||
os.makedirs(current_outdir)
|
||||
else:
|
||||
current_outdir = outdir
|
||||
current_outdir = opt.outdir
|
||||
|
||||
# Here is where the images are actually generated!
|
||||
last_results = []
|
||||
@ -214,10 +218,14 @@ def main_loop(gen, opt, infile):
|
||||
file_writer = PngWriter(current_outdir)
|
||||
prefix = file_writer.unique_prefix()
|
||||
results = [] # list of filename, prompt pairs
|
||||
grid_images = dict() # seed -> Image, only used if `opt.grid`
|
||||
grid_images = dict() # seed -> Image, only used if `opt.grid`
|
||||
prior_variations = opt.with_variations or []
|
||||
first_seed = opt.seed
|
||||
|
||||
def image_writer(image, seed, upscaled=False):
|
||||
path = None
|
||||
nonlocal first_seed
|
||||
nonlocal prior_variations
|
||||
if opt.grid:
|
||||
grid_images[seed] = image
|
||||
else:
|
||||
@ -225,29 +233,21 @@ def main_loop(gen, opt, infile):
|
||||
filename = f'{prefix}.{seed}.postprocessed.png'
|
||||
else:
|
||||
filename = f'{prefix}.{seed}.png'
|
||||
# the handling of variations is probably broken
|
||||
# Also, given the ability to add stuff to the dream_prompt_str, it isn't
|
||||
# necessary to make a copy of the opt option just to change its attributes
|
||||
if opt.variation_amount > 0:
|
||||
iter_opt = copy.copy(opt)
|
||||
this_variation = [[seed, opt.variation_amount]]
|
||||
if opt.with_variations is None:
|
||||
iter_opt.with_variations = this_variation
|
||||
else:
|
||||
iter_opt.with_variations = opt.with_variations + this_variation
|
||||
iter_opt.variation_amount = 0
|
||||
formatted_dream_prompt = iter_opt.dream_prompt_str(seed=seed)
|
||||
elif opt.with_variations is not None:
|
||||
formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
|
||||
first_seed = first_seed or seed
|
||||
this_variation = [[seed, opt.variation_amount]]
|
||||
opt.with_variations = prior_variations + this_variation
|
||||
formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
|
||||
elif len(prior_variations) > 0:
|
||||
formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
|
||||
else:
|
||||
formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
|
||||
path = file_writer.save_image_and_prompt_to_png(
|
||||
image = image,
|
||||
dream_prompt = formatted_dream_prompt,
|
||||
metadata = format_metadata(
|
||||
metadata = metadata_dumps(
|
||||
opt,
|
||||
seeds = [seed],
|
||||
weights = gen.weights,
|
||||
model_hash = gen.model_hash,
|
||||
),
|
||||
name = filename,
|
||||
@ -271,7 +271,7 @@ def main_loop(gen, opt, infile):
|
||||
filename = f'{prefix}.{first_seed}.png'
|
||||
formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,grid=True,iterations=len(grid_images))
|
||||
formatted_dream_prompt += f' # {grid_seeds}'
|
||||
metadata = format_metadata(
|
||||
metadata = metadata.dumps(
|
||||
opt,
|
||||
seeds = grid_seeds,
|
||||
weights = gen.weights,
|
||||
|
||||
@ -10,6 +10,7 @@ import sys
|
||||
import transformers
|
||||
import os
|
||||
import warnings
|
||||
import urllib.request
|
||||
|
||||
transformers.logging.set_verbosity_error()
|
||||
|
||||
@ -81,6 +82,16 @@ if gfpgan:
|
||||
print('...success')
|
||||
except Exception:
|
||||
import traceback
|
||||
print('Error loading ESRGAN:')
|
||||
print(traceback.format_exc())
|
||||
|
||||
try:
|
||||
import urllib.request
|
||||
model_path = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'
|
||||
model_dest = 'src/gfpgan/experiments/pretrained_models/GFPGANv1.3.pth'
|
||||
print('downloading gfpgan model file...')
|
||||
urllib.request.urlretrieve(model_path,model_dest)
|
||||
except Exception:
|
||||
import traceback
|
||||
print('Error loading GFPGAN:')
|
||||
print(traceback.format_exc())
|
||||
|
||||
@ -1,3 +1,8 @@
|
||||
:root {
|
||||
--fields-dark:#DCDCDC;
|
||||
--fields-light:#F5F5F5;
|
||||
}
|
||||
|
||||
* {
|
||||
font-family: 'Arial';
|
||||
font-size: 100%;
|
||||
@ -18,15 +23,26 @@ fieldset {
|
||||
border: none;
|
||||
line-height: 2.2em;
|
||||
}
|
||||
fieldset > legend {
|
||||
width: auto;
|
||||
margin-left: 0;
|
||||
margin-right: auto;
|
||||
font-weight:bold;
|
||||
}
|
||||
select, input {
|
||||
margin-right: 10px;
|
||||
padding: 2px;
|
||||
}
|
||||
input:disabled {
|
||||
cursor:auto;
|
||||
}
|
||||
input[type=submit] {
|
||||
cursor: pointer;
|
||||
background-color: #666;
|
||||
color: white;
|
||||
}
|
||||
input[type=checkbox] {
|
||||
cursor: pointer;
|
||||
margin-right: 0px;
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
@ -87,11 +103,11 @@ header h1 {
|
||||
}
|
||||
#results img {
|
||||
border-radius: 5px;
|
||||
object-fit: cover;
|
||||
object-fit: contain;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#fieldset-config {
|
||||
line-height:2em;
|
||||
background-color: #F0F0F0;
|
||||
}
|
||||
input[type="number"] {
|
||||
width: 60px;
|
||||
@ -118,35 +134,46 @@ label {
|
||||
#progress-image {
|
||||
width: 30vh;
|
||||
height: 30vh;
|
||||
object-fit: contain;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#cancel-button {
|
||||
cursor: pointer;
|
||||
color: red;
|
||||
}
|
||||
#basic-parameters {
|
||||
background-color: #EEEEEE;
|
||||
}
|
||||
#txt2img {
|
||||
background-color: #DCDCDC;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#variations {
|
||||
background-color: #EEEEEE;
|
||||
background-color: var(--fields-light);
|
||||
}
|
||||
#initimg {
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#img2img {
|
||||
background-color: #DCDCDC;
|
||||
background-color: var(--fields-light);
|
||||
}
|
||||
#gfpgan {
|
||||
background-color: #EEEEEE;
|
||||
#initimg > :not(legend) {
|
||||
background-color: var(--fields-light);
|
||||
margin: .5em;
|
||||
}
|
||||
|
||||
#postprocess, #initimg {
|
||||
display:flex;
|
||||
flex-wrap:wrap;
|
||||
padding: 0;
|
||||
margin-top: 1em;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#postprocess > fieldset, #initimg > * {
|
||||
flex-grow: 1;
|
||||
}
|
||||
#postprocess > fieldset {
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#progress-section {
|
||||
background-color: #F5F5F5;
|
||||
}
|
||||
.section-header {
|
||||
text-align: left;
|
||||
font-weight: bold;
|
||||
padding: 0 0 0 0;
|
||||
background-color: var(--fields-light);
|
||||
}
|
||||
#no-results-message:not(:only-child) {
|
||||
display: none;
|
||||
}
|
||||
|
||||
|
||||
@ -1,102 +1,152 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<title>Stable Diffusion Dream Server</title>
|
||||
<meta charset="utf-8">
|
||||
<link rel="icon" type="image/x-icon" href="static/dream_web/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<link rel="stylesheet" href="static/dream_web/index.css">
|
||||
<script src="config.js"></script>
|
||||
<script src="static/dream_web/index.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>Stable Diffusion Dream Server</h1>
|
||||
<div id="about">
|
||||
For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<form id="generate-form" method="post" action="#">
|
||||
<fieldset id="txt2img">
|
||||
<div id="search-box">
|
||||
<textarea rows="3" id="prompt" name="prompt"></textarea>
|
||||
<input type="submit" id="submit" value="Generate">
|
||||
</div>
|
||||
</fieldset>
|
||||
<fieldset id="fieldset-config">
|
||||
<div class="section-header">Basic options</div>
|
||||
<label for="iterations">Images to generate:</label>
|
||||
<input value="1" type="number" id="iterations" name="iterations" size="4">
|
||||
<label for="steps">Steps:</label>
|
||||
<input value="50" type="number" id="steps" name="steps">
|
||||
<label for="cfg_scale">Cfg Scale:</label>
|
||||
<input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
|
||||
<label for="sampler_name">Sampler:</label>
|
||||
<select id="sampler_name" name="sampler_name" value="k_lms">
|
||||
<option value="ddim">DDIM</option>
|
||||
<option value="plms">PLMS</option>
|
||||
<option value="k_lms" selected>KLMS</option>
|
||||
<option value="k_dpm_2">KDPM_2</option>
|
||||
<option value="k_dpm_2_a">KDPM_2A</option>
|
||||
<option value="k_euler">KEULER</option>
|
||||
<option value="k_euler_a">KEULER_A</option>
|
||||
<option value="k_heun">KHEUN</option>
|
||||
</select>
|
||||
<input type="checkbox" name="seamless" id="seamless">
|
||||
<label for="seamless">Seamless circular tiling</label>
|
||||
<br>
|
||||
<label title="Set to multiple of 64" for="width">Width:</label>
|
||||
<select id="width" name="width" value="512">
|
||||
<option value="64">64</option> <option value="128">128</option>
|
||||
<option value="192">192</option> <option value="256">256</option>
|
||||
<option value="320">320</option> <option value="384">384</option>
|
||||
<option value="448">448</option> <option value="512" selected>512</option>
|
||||
<option value="576">576</option> <option value="640">640</option>
|
||||
<option value="704">704</option> <option value="768">768</option>
|
||||
<option value="832">832</option> <option value="896">896</option>
|
||||
<option value="960">960</option> <option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to multiple of 64" for="height">Height:</label>
|
||||
<select id="height" name="height" value="512">
|
||||
<option value="64">64</option> <option value="128">128</option>
|
||||
<option value="192">192</option> <option value="256">256</option>
|
||||
<option value="320">320</option> <option value="384">384</option>
|
||||
<option value="448">448</option> <option value="512" selected>512</option>
|
||||
<option value="576">576</option> <option value="640">640</option>
|
||||
<option value="704">704</option> <option value="768">768</option>
|
||||
<option value="832">832</option> <option value="896">896</option>
|
||||
<option value="960">960</option> <option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to -1 for random seed" for="seed">Seed:</label>
|
||||
<input value="-1" type="number" id="seed" name="seed">
|
||||
<button type="button" id="reset-seed">↺</button>
|
||||
<input type="checkbox" name="progress_images" id="progress_images">
|
||||
<label for="progress_images">Display in-progress images (slower)</label>
|
||||
<button type="button" id="reset-all">Reset to Defaults</button>
|
||||
<span id="variations">
|
||||
<label title="If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different." for="variation_amount">Variation amount (0 to disable):</label>
|
||||
<input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
|
||||
<label title="list of variations to apply, in the format `seed:weight,seed:weight,..." for="with_variations">With variations (seed:weight,seed:weight,...):</label>
|
||||
<input value="" type="text" id="with_variations" name="with_variations">
|
||||
</span>
|
||||
</fieldset>
|
||||
<fieldset id="img2img">
|
||||
<div class="section-header">Image-to-image options</div>
|
||||
<head>
|
||||
<title>Stable Diffusion Dream Server</title>
|
||||
<meta charset="utf-8">
|
||||
<link rel="icon" type="image/x-icon" href="static/dream_web/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<script src="config.js"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"
|
||||
integrity="sha512-q/dWJ3kcmjBLU4Qc47E4A9kTB4m3wuTY7vkFJDTZKjTs8jhyGQnaUrxa0Ytd0ssMZhbNua9hE+E7Qv1j+DyZwA=="
|
||||
crossorigin="anonymous"></script>
|
||||
<link rel="stylesheet" href="index.css">
|
||||
<script src="index.js"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<header>
|
||||
<h1>Stable Diffusion Dream Server</h1>
|
||||
<div id="about">
|
||||
For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub
|
||||
site</a>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<!--
|
||||
<div id="dropper" style="background-color:red;width:200px;height:200px;">
|
||||
</div>
|
||||
-->
|
||||
<form id="generate-form" method="post" action="api/jobs">
|
||||
<fieldset id="txt2img">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_generate" id="enable_generate" checked>
|
||||
<label for="enable_generate">Generate</label>
|
||||
</legend>
|
||||
<div id="search-box">
|
||||
<textarea rows="3" id="prompt" name="prompt"></textarea>
|
||||
</div>
|
||||
<label for="iterations">Images to generate:</label>
|
||||
<input value="1" type="number" id="iterations" name="iterations" size="4">
|
||||
<label for="steps">Steps:</label>
|
||||
<input value="50" type="number" id="steps" name="steps">
|
||||
<label for="cfg_scale">Cfg Scale:</label>
|
||||
<input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
|
||||
<label for="sampler_name">Sampler:</label>
|
||||
<select id="sampler_name" name="sampler_name" value="k_lms">
|
||||
<option value="ddim">DDIM</option>
|
||||
<option value="plms">PLMS</option>
|
||||
<option value="k_lms" selected>KLMS</option>
|
||||
<option value="k_dpm_2">KDPM_2</option>
|
||||
<option value="k_dpm_2_a">KDPM_2A</option>
|
||||
<option value="k_euler">KEULER</option>
|
||||
<option value="k_euler_a">KEULER_A</option>
|
||||
<option value="k_heun">KHEUN</option>
|
||||
</select>
|
||||
<input type="checkbox" name="seamless" id="seamless">
|
||||
<label for="seamless">Seamless circular tiling</label>
|
||||
<br>
|
||||
<label title="Set to multiple of 64" for="width">Width:</label>
|
||||
<select id="width" name="width" value="512">
|
||||
<option value="64">64</option>
|
||||
<option value="128">128</option>
|
||||
<option value="192">192</option>
|
||||
<option value="256">256</option>
|
||||
<option value="320">320</option>
|
||||
<option value="384">384</option>
|
||||
<option value="448">448</option>
|
||||
<option value="512" selected>512</option>
|
||||
<option value="576">576</option>
|
||||
<option value="640">640</option>
|
||||
<option value="704">704</option>
|
||||
<option value="768">768</option>
|
||||
<option value="832">832</option>
|
||||
<option value="896">896</option>
|
||||
<option value="960">960</option>
|
||||
<option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to multiple of 64" for="height">Height:</label>
|
||||
<select id="height" name="height" value="512">
|
||||
<option value="64">64</option>
|
||||
<option value="128">128</option>
|
||||
<option value="192">192</option>
|
||||
<option value="256">256</option>
|
||||
<option value="320">320</option>
|
||||
<option value="384">384</option>
|
||||
<option value="448">448</option>
|
||||
<option value="512" selected>512</option>
|
||||
<option value="576">576</option>
|
||||
<option value="640">640</option>
|
||||
<option value="704">704</option>
|
||||
<option value="768">768</option>
|
||||
<option value="832">832</option>
|
||||
<option value="896">896</option>
|
||||
<option value="960">960</option>
|
||||
<option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to 0 for random seed" for="seed">Seed:</label>
|
||||
<input value="0" type="number" id="seed" name="seed">
|
||||
<button type="button" id="reset-seed">↺</button>
|
||||
<input type="checkbox" name="progress_images" id="progress_images">
|
||||
<label for="progress_images">Display in-progress images (slower)</label>
|
||||
<button type="button" id="reset-all">Reset to Defaults</button>
|
||||
<div id="variations">
|
||||
<label
|
||||
title="If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different."
|
||||
for="variation_amount">Variation amount (0 to disable):</label>
|
||||
<input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
|
||||
<label title="list of variations to apply, in the format `seed:weight,seed:weight,..."
|
||||
for="with_variations">With variations (seed:weight,seed:weight,...):</label>
|
||||
<input value="" type="text" id="with_variations" name="with_variations">
|
||||
</div>
|
||||
</fieldset>
|
||||
<fieldset id="initimg">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_init_image" id="enable_init_image" checked>
|
||||
<label for="enable_init_image">Enable init image</label>
|
||||
</legend>
|
||||
<div>
|
||||
<label title="Upload an image to use img2img" for="initimg">Initial image:</label>
|
||||
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
|
||||
<button type="button" id="remove-image">Remove Image</button>
|
||||
<br>
|
||||
<label for="strength">Img2Img Strength:</label>
|
||||
<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
|
||||
<input type="checkbox" id="fit" name="fit" checked>
|
||||
<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height</label>
|
||||
</fieldset>
|
||||
</div>
|
||||
<fieldset id="img2img">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_img2img" id="enable_img2img" checked>
|
||||
<label for="enable_img2img">Enable Img2Img</label>
|
||||
</legend>
|
||||
<label for="strength">Img2Img Strength:</label>
|
||||
<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
|
||||
<input type="checkbox" id="fit" name="fit" checked>
|
||||
<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height:</label>
|
||||
</fieldset>
|
||||
</fieldset>
|
||||
<div id="postprocess">
|
||||
<fieldset id="gfpgan">
|
||||
<div class="section-header">Post-processing options</div>
|
||||
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
|
||||
<input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
|
||||
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_gfpgan" id="enable_gfpgan">
|
||||
<label for="enable_gfpgan">Enable gfpgan</label>
|
||||
</legend>
|
||||
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength:</label>
|
||||
<input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
|
||||
</fieldset>
|
||||
<fieldset id="upscale">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_upscale" id="enable_upscale">
|
||||
<label for="enable_upscale">Enable Upscaling</label>
|
||||
</legend>
|
||||
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level:</label>
|
||||
<select id="upscale_level" name="upscale_level" value="">
|
||||
<option value="" selected>None</option>
|
||||
<option value="2">2x</option>
|
||||
@ -105,25 +155,25 @@
|
||||
<label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
|
||||
<input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
|
||||
</fieldset>
|
||||
</form>
|
||||
<br>
|
||||
<section id="progress-section">
|
||||
<div id="progress-container">
|
||||
<progress id="progress-bar" value="0" max="1"></progress>
|
||||
<span id="cancel-button" title="Cancel">✖</span>
|
||||
<br>
|
||||
<img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
|
||||
<div id="scaling-inprocess-message">
|
||||
<i><span>Postprocessing...</span><span id="processing_cnt">1/3</span></i>
|
||||
</div>
|
||||
</span>
|
||||
</section>
|
||||
|
||||
<div id="results">
|
||||
<div id="no-results-message">
|
||||
<i><p>No results...</p></i>
|
||||
</div>
|
||||
<input type="submit" id="submit" value="Generate">
|
||||
</form>
|
||||
<br>
|
||||
<section id="progress-section">
|
||||
<div id="progress-container">
|
||||
<progress id="progress-bar" value="0" max="1"></progress>
|
||||
<span id="cancel-button" title="Cancel">✖</span>
|
||||
<br>
|
||||
<img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
|
||||
<div id="scaling-inprocess-message">
|
||||
<i><span>Postprocessing...</span><span id="processing_cnt">1</span>/<span id="processing_total">3</span></i>
|
||||
</div>
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
</section>
|
||||
|
||||
<div id="results">
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
|
||||
</html>
|
||||
|
||||
@ -1,3 +1,109 @@
|
||||
const socket = io();
|
||||
|
||||
var priorResultsLoadState = {
|
||||
page: 0,
|
||||
pages: 1,
|
||||
per_page: 10,
|
||||
total: 20,
|
||||
offset: 0, // number of items generated since last load
|
||||
loading: false,
|
||||
initialized: false
|
||||
};
|
||||
|
||||
function loadPriorResults() {
|
||||
// Fix next page by offset
|
||||
let offsetPages = priorResultsLoadState.offset / priorResultsLoadState.per_page;
|
||||
priorResultsLoadState.page += offsetPages;
|
||||
priorResultsLoadState.pages += offsetPages;
|
||||
priorResultsLoadState.total += priorResultsLoadState.offset;
|
||||
priorResultsLoadState.offset = 0;
|
||||
|
||||
if (priorResultsLoadState.loading) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (priorResultsLoadState.page >= priorResultsLoadState.pages) {
|
||||
return; // Nothing more to load
|
||||
}
|
||||
|
||||
// Load
|
||||
priorResultsLoadState.loading = true
|
||||
let url = new URL('/api/images', document.baseURI);
|
||||
url.searchParams.append('page', priorResultsLoadState.initialized ? priorResultsLoadState.page + 1 : priorResultsLoadState.page);
|
||||
url.searchParams.append('per_page', priorResultsLoadState.per_page);
|
||||
fetch(url.href, {
|
||||
method: 'GET',
|
||||
headers: new Headers({'content-type': 'application/json'})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
priorResultsLoadState.page = data.page;
|
||||
priorResultsLoadState.pages = data.pages;
|
||||
priorResultsLoadState.per_page = data.per_page;
|
||||
priorResultsLoadState.total = data.total;
|
||||
|
||||
data.items.forEach(function(dreamId, index) {
|
||||
let src = 'api/images/' + dreamId;
|
||||
fetch('/api/images/' + dreamId + '/metadata', {
|
||||
method: 'GET',
|
||||
headers: new Headers({'content-type': 'application/json'})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(metadata => {
|
||||
let seed = metadata.seed || 0; // TODO: Parse old metadata
|
||||
appendOutput(src, seed, metadata, true);
|
||||
});
|
||||
});
|
||||
|
||||
// Load until page is full
|
||||
if (!priorResultsLoadState.initialized) {
|
||||
if (document.body.scrollHeight <= window.innerHeight) {
|
||||
loadPriorResults();
|
||||
}
|
||||
}
|
||||
})
|
||||
.finally(() => {
|
||||
priorResultsLoadState.loading = false;
|
||||
priorResultsLoadState.initialized = true;
|
||||
});
|
||||
}
|
||||
|
||||
function resetForm() {
|
||||
var form = document.getElementById('generate-form');
|
||||
form.querySelector('fieldset').removeAttribute('disabled');
|
||||
}
|
||||
|
||||
function initProgress(totalSteps, showProgressImages) {
|
||||
// TODO: Progress could theoretically come from multiple jobs at the same time (in the future)
|
||||
let progressSectionEle = document.querySelector('#progress-section');
|
||||
progressSectionEle.style.display = 'initial';
|
||||
let progressEle = document.querySelector('#progress-bar');
|
||||
progressEle.setAttribute('max', totalSteps);
|
||||
|
||||
let progressImageEle = document.querySelector('#progress-image');
|
||||
progressImageEle.src = BLANK_IMAGE_URL;
|
||||
progressImageEle.style.display = showProgressImages ? 'initial': 'none';
|
||||
}
|
||||
|
||||
function setProgress(step, totalSteps, src) {
|
||||
let progressEle = document.querySelector('#progress-bar');
|
||||
progressEle.setAttribute('value', step);
|
||||
|
||||
if (src) {
|
||||
let progressImageEle = document.querySelector('#progress-image');
|
||||
progressImageEle.src = src;
|
||||
}
|
||||
}
|
||||
|
||||
function resetProgress(hide = true) {
|
||||
if (hide) {
|
||||
let progressSectionEle = document.querySelector('#progress-section');
|
||||
progressSectionEle.style.display = 'none';
|
||||
}
|
||||
let progressEle = document.querySelector('#progress-bar');
|
||||
progressEle.setAttribute('value', 0);
|
||||
}
|
||||
|
||||
function toBase64(file) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const r = new FileReader();
|
||||
@ -7,17 +113,41 @@ function toBase64(file) {
|
||||
});
|
||||
}
|
||||
|
||||
function appendOutput(src, seed, config) {
|
||||
let outputNode = document.createElement("figure");
|
||||
|
||||
let variations = config.with_variations;
|
||||
if (config.variation_amount > 0) {
|
||||
variations = (variations ? variations + ',' : '') + seed + ':' + config.variation_amount;
|
||||
function ondragdream(event) {
|
||||
let dream = event.target.dataset.dream;
|
||||
event.dataTransfer.setData("dream", dream);
|
||||
}
|
||||
|
||||
function seedClick(event) {
|
||||
// Get element
|
||||
var image = event.target.closest('figure').querySelector('img');
|
||||
var dream = JSON.parse(decodeURIComponent(image.dataset.dream));
|
||||
|
||||
let form = document.querySelector("#generate-form");
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
if (k == 'initimg') { continue; }
|
||||
let formElem = form.querySelector(`*[name=${k}]`);
|
||||
formElem.value = dream[k] !== undefined ? dream[k] : formElem.defaultValue;
|
||||
}
|
||||
let baseseed = (config.with_variations || config.variation_amount > 0) ? config.seed : seed;
|
||||
let altText = baseseed + ' | ' + (variations ? variations + ' | ' : '') + config.prompt;
|
||||
|
||||
document.querySelector("#seed").value = dream.seed;
|
||||
document.querySelector('#iterations').value = 1; // Reset to 1 iteration since we clicked a single image (not a full job)
|
||||
|
||||
// NOTE: leaving this manual for the user for now - it was very confusing with this behavior
|
||||
// document.querySelector("#with_variations").value = variations || '';
|
||||
// if (document.querySelector("#variation_amount").value <= 0) {
|
||||
// document.querySelector("#variation_amount").value = 0.2;
|
||||
// }
|
||||
|
||||
saveFields(document.querySelector("#generate-form"));
|
||||
}
|
||||
|
||||
function appendOutput(src, seed, config, toEnd=false) {
|
||||
let outputNode = document.createElement("figure");
|
||||
let altText = seed.toString() + " | " + config.prompt;
|
||||
|
||||
// img needs width and height for lazy loading to work
|
||||
// TODO: store the full config in a data attribute on the image?
|
||||
const figureContents = `
|
||||
<a href="${src}" target="_blank">
|
||||
<img src="${src}"
|
||||
@ -25,32 +155,23 @@ function appendOutput(src, seed, config) {
|
||||
title="${altText}"
|
||||
loading="lazy"
|
||||
width="256"
|
||||
height="256">
|
||||
height="256"
|
||||
draggable="true"
|
||||
ondragstart="ondragdream(event, this)"
|
||||
data-dream="${encodeURIComponent(JSON.stringify(config))}"
|
||||
data-dreamId="${encodeURIComponent(config.dreamId)}">
|
||||
</a>
|
||||
<figcaption>${seed}</figcaption>
|
||||
<figcaption onclick="seedClick(event, this)">${seed}</figcaption>
|
||||
`;
|
||||
|
||||
outputNode.innerHTML = figureContents;
|
||||
let figcaption = outputNode.querySelector('figcaption');
|
||||
|
||||
// Reload image config
|
||||
figcaption.addEventListener('click', () => {
|
||||
let form = document.querySelector("#generate-form");
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
if (k == 'initimg') { continue; }
|
||||
form.querySelector(`*[name=${k}]`).value = config[k];
|
||||
}
|
||||
|
||||
document.querySelector("#seed").value = baseseed;
|
||||
document.querySelector("#with_variations").value = variations || '';
|
||||
if (document.querySelector("#variation_amount").value <= 0) {
|
||||
document.querySelector("#variation_amount").value = 0.2;
|
||||
}
|
||||
|
||||
saveFields(document.querySelector("#generate-form"));
|
||||
});
|
||||
|
||||
document.querySelector("#results").prepend(outputNode);
|
||||
if (toEnd) {
|
||||
document.querySelector("#results").append(outputNode);
|
||||
} else {
|
||||
document.querySelector("#results").prepend(outputNode);
|
||||
}
|
||||
document.querySelector("#no-results-message")?.remove();
|
||||
}
|
||||
|
||||
function saveFields(form) {
|
||||
@ -79,93 +200,109 @@ function clearFields(form) {
|
||||
|
||||
const BLANK_IMAGE_URL = 'data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>';
|
||||
async function generateSubmit(form) {
|
||||
const prompt = document.querySelector("#prompt").value;
|
||||
|
||||
// Convert file data to base64
|
||||
// TODO: Should probably uplaod files with formdata or something, and store them in the backend?
|
||||
let formData = Object.fromEntries(new FormData(form));
|
||||
if (!formData.enable_generate && !formData.enable_init_image) {
|
||||
gen_label = document.querySelector("label[for=enable_generate]").innerHTML;
|
||||
initimg_label = document.querySelector("label[for=enable_init_image]").innerHTML;
|
||||
alert(`Error: one of "${gen_label}" or "${initimg_label}" must be set`);
|
||||
}
|
||||
|
||||
|
||||
formData.initimg_name = formData.initimg.name
|
||||
formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
|
||||
|
||||
let strength = formData.strength;
|
||||
let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
|
||||
|
||||
let progressSectionEle = document.querySelector('#progress-section');
|
||||
progressSectionEle.style.display = 'initial';
|
||||
let progressEle = document.querySelector('#progress-bar');
|
||||
progressEle.setAttribute('max', totalSteps);
|
||||
let progressImageEle = document.querySelector('#progress-image');
|
||||
progressImageEle.src = BLANK_IMAGE_URL;
|
||||
|
||||
progressImageEle.style.display = {}.hasOwnProperty.call(formData, 'progress_images') ? 'initial': 'none';
|
||||
|
||||
// Post as JSON, using Fetch streaming to get results
|
||||
fetch(form.action, {
|
||||
method: form.method,
|
||||
body: JSON.stringify(formData),
|
||||
}).then(async (response) => {
|
||||
const reader = response.body.getReader();
|
||||
|
||||
let noOutputs = true;
|
||||
while (true) {
|
||||
let {value, done} = await reader.read();
|
||||
value = new TextDecoder().decode(value);
|
||||
if (done) {
|
||||
progressSectionEle.style.display = 'none';
|
||||
break;
|
||||
}
|
||||
|
||||
for (let event of value.split('\n').filter(e => e !== '')) {
|
||||
const data = JSON.parse(event);
|
||||
|
||||
if (data.event === 'result') {
|
||||
noOutputs = false;
|
||||
appendOutput(data.url, data.seed, data.config);
|
||||
progressEle.setAttribute('value', 0);
|
||||
progressEle.setAttribute('max', totalSteps);
|
||||
} else if (data.event === 'upscaling-started') {
|
||||
document.getElementById("processing_cnt").textContent=data.processed_file_cnt;
|
||||
document.getElementById("scaling-inprocess-message").style.display = "block";
|
||||
} else if (data.event === 'upscaling-done') {
|
||||
document.getElementById("scaling-inprocess-message").style.display = "none";
|
||||
} else if (data.event === 'step') {
|
||||
progressEle.setAttribute('value', data.step);
|
||||
if (data.url) {
|
||||
progressImageEle.src = data.url;
|
||||
}
|
||||
} else if (data.event === 'canceled') {
|
||||
// avoid alerting as if this were an error case
|
||||
noOutputs = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Re-enable form, remove no-results-message
|
||||
form.querySelector('fieldset').removeAttribute('disabled');
|
||||
document.querySelector("#prompt").value = prompt;
|
||||
document.querySelector('progress').setAttribute('value', '0');
|
||||
|
||||
if (noOutputs) {
|
||||
alert("Error occurred while generating.");
|
||||
}
|
||||
// Evaluate all checkboxes
|
||||
let checkboxes = form.querySelectorAll('input[type=checkbox]');
|
||||
checkboxes.forEach(function (checkbox) {
|
||||
if (checkbox.checked) {
|
||||
formData[checkbox.name] = 'true';
|
||||
}
|
||||
});
|
||||
|
||||
let strength = formData.strength;
|
||||
let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
|
||||
let showProgressImages = formData.progress_images;
|
||||
|
||||
// Set enabling flags
|
||||
|
||||
|
||||
// Initialize the progress bar
|
||||
initProgress(totalSteps, showProgressImages);
|
||||
|
||||
// POST, use response to listen for events
|
||||
fetch(form.action, {
|
||||
method: form.method,
|
||||
headers: new Headers({'content-type': 'application/json'}),
|
||||
body: JSON.stringify(formData),
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
var jobId = data.jobId;
|
||||
socket.emit('join_room', { 'room': jobId });
|
||||
});
|
||||
|
||||
// Disable form while generating
|
||||
form.querySelector('fieldset').setAttribute('disabled','');
|
||||
document.querySelector("#prompt").value = `Generating: "${prompt}"`;
|
||||
}
|
||||
|
||||
async function fetchRunLog() {
|
||||
try {
|
||||
let response = await fetch('/run_log.json')
|
||||
const data = await response.json();
|
||||
for(let item of data.run_log) {
|
||||
appendOutput(item.url, item.seed, item);
|
||||
}
|
||||
} catch (e) {
|
||||
console.error(e);
|
||||
}
|
||||
function fieldSetEnableChecked(event) {
|
||||
cb = event.target;
|
||||
fields = cb.closest('fieldset');
|
||||
fields.disabled = !cb.checked;
|
||||
}
|
||||
|
||||
// Socket listeners
|
||||
socket.on('job_started', (data) => {})
|
||||
|
||||
socket.on('dream_result', (data) => {
|
||||
var jobId = data.jobId;
|
||||
var dreamId = data.dreamId;
|
||||
var dreamRequest = data.dreamRequest;
|
||||
var src = 'api/images/' + dreamId;
|
||||
|
||||
priorResultsLoadState.offset += 1;
|
||||
appendOutput(src, dreamRequest.seed, dreamRequest);
|
||||
|
||||
resetProgress(false);
|
||||
})
|
||||
|
||||
socket.on('dream_progress', (data) => {
|
||||
// TODO: it'd be nice if we could get a seed reported here, but the generator would need to be updated
|
||||
var step = data.step;
|
||||
var totalSteps = data.totalSteps;
|
||||
var jobId = data.jobId;
|
||||
var dreamId = data.dreamId;
|
||||
|
||||
var progressType = data.progressType
|
||||
if (progressType === 'GENERATION') {
|
||||
var src = data.hasProgressImage ?
|
||||
'api/intermediates/' + dreamId + '/' + step
|
||||
: null;
|
||||
setProgress(step, totalSteps, src);
|
||||
} else if (progressType === 'UPSCALING_STARTED') {
|
||||
// step and totalSteps are used for upscale count on this message
|
||||
document.getElementById("processing_cnt").textContent = step;
|
||||
document.getElementById("processing_total").textContent = totalSteps;
|
||||
document.getElementById("scaling-inprocess-message").style.display = "block";
|
||||
} else if (progressType == 'UPSCALING_DONE') {
|
||||
document.getElementById("scaling-inprocess-message").style.display = "none";
|
||||
}
|
||||
})
|
||||
|
||||
socket.on('job_canceled', (data) => {
|
||||
resetForm();
|
||||
resetProgress();
|
||||
})
|
||||
|
||||
socket.on('job_done', (data) => {
|
||||
jobId = data.jobId
|
||||
socket.emit('leave_room', { 'room': jobId });
|
||||
|
||||
resetForm();
|
||||
resetProgress();
|
||||
})
|
||||
|
||||
window.onload = async () => {
|
||||
document.querySelector("#prompt").addEventListener("keydown", (e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
@ -183,7 +320,7 @@ window.onload = async () => {
|
||||
saveFields(e.target.form);
|
||||
});
|
||||
document.querySelector("#reset-seed").addEventListener('click', (e) => {
|
||||
document.querySelector("#seed").value = -1;
|
||||
document.querySelector("#seed").value = 0;
|
||||
saveFields(e.target.form);
|
||||
});
|
||||
document.querySelector("#reset-all").addEventListener('click', (e) => {
|
||||
@ -195,13 +332,13 @@ window.onload = async () => {
|
||||
loadFields(document.querySelector("#generate-form"));
|
||||
|
||||
document.querySelector('#cancel-button').addEventListener('click', () => {
|
||||
fetch('/cancel').catch(e => {
|
||||
fetch('/api/cancel').catch(e => {
|
||||
console.error(e);
|
||||
});
|
||||
});
|
||||
document.documentElement.addEventListener('keydown', (e) => {
|
||||
if (e.key === "Escape")
|
||||
fetch('/cancel').catch(err => {
|
||||
fetch('/api/cancel').catch(err => {
|
||||
console.error(err);
|
||||
});
|
||||
});
|
||||
@ -209,5 +346,51 @@ window.onload = async () => {
|
||||
if (!config.gfpgan_model_exists) {
|
||||
document.querySelector("#gfpgan").style.display = 'none';
|
||||
}
|
||||
await fetchRunLog()
|
||||
|
||||
window.addEventListener("scroll", () => {
|
||||
if ((window.innerHeight + window.pageYOffset) >= document.body.offsetHeight) {
|
||||
loadPriorResults();
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
|
||||
// Enable/disable forms by checkboxes
|
||||
document.querySelectorAll("legend > input[type=checkbox]").forEach(function(cb) {
|
||||
cb.addEventListener('change', fieldSetEnableChecked);
|
||||
fieldSetEnableChecked({ target: cb})
|
||||
});
|
||||
|
||||
|
||||
// Load some of the previous results
|
||||
loadPriorResults();
|
||||
|
||||
// Image drop/upload WIP
|
||||
/*
|
||||
let drop = document.getElementById('dropper');
|
||||
function ondrop(event) {
|
||||
let dreamData = event.dataTransfer.getData('dream');
|
||||
if (dreamData) {
|
||||
var dream = JSON.parse(decodeURIComponent(dreamData));
|
||||
alert(dream.dreamId);
|
||||
}
|
||||
};
|
||||
|
||||
function ondragenter(event) {
|
||||
event.preventDefault();
|
||||
};
|
||||
|
||||
function ondragover(event) {
|
||||
event.preventDefault();
|
||||
};
|
||||
|
||||
function ondragleave(event) {
|
||||
|
||||
}
|
||||
|
||||
drop.addEventListener('drop', ondrop);
|
||||
drop.addEventListener('dragenter', ondragenter);
|
||||
drop.addEventListener('dragover', ondragover);
|
||||
drop.addEventListener('dragleave', ondragleave);
|
||||
*/
|
||||
};
|
||||
|
||||
BIN
static/legacy_web/favicon.ico
Normal file
BIN
static/legacy_web/favicon.ico
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.1 KiB |
152
static/legacy_web/index.css
Normal file
152
static/legacy_web/index.css
Normal file
@ -0,0 +1,152 @@
|
||||
* {
|
||||
font-family: 'Arial';
|
||||
font-size: 100%;
|
||||
}
|
||||
body {
|
||||
font-size: 1em;
|
||||
}
|
||||
textarea {
|
||||
font-size: 0.95em;
|
||||
}
|
||||
header, form, #progress-section {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
max-width: 1024px;
|
||||
text-align: center;
|
||||
}
|
||||
fieldset {
|
||||
border: none;
|
||||
line-height: 2.2em;
|
||||
}
|
||||
select, input {
|
||||
margin-right: 10px;
|
||||
padding: 2px;
|
||||
}
|
||||
input[type=submit] {
|
||||
background-color: #666;
|
||||
color: white;
|
||||
}
|
||||
input[type=checkbox] {
|
||||
margin-right: 0px;
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
vertical-align: middle;
|
||||
}
|
||||
input#seed {
|
||||
margin-right: 0px;
|
||||
}
|
||||
div {
|
||||
padding: 10px 10px 10px 10px;
|
||||
}
|
||||
header {
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
header h1 {
|
||||
margin-bottom: 0;
|
||||
font-size: 2em;
|
||||
}
|
||||
#search-box {
|
||||
display: flex;
|
||||
}
|
||||
#scaling-inprocess-message {
|
||||
font-weight: bold;
|
||||
font-style: italic;
|
||||
display: none;
|
||||
}
|
||||
#prompt {
|
||||
flex-grow: 1;
|
||||
padding: 5px 10px 5px 10px;
|
||||
border: 1px solid #999;
|
||||
outline: none;
|
||||
}
|
||||
#submit {
|
||||
padding: 5px 10px 5px 10px;
|
||||
border: 1px solid #999;
|
||||
}
|
||||
#reset-all, #remove-image {
|
||||
margin-top: 12px;
|
||||
font-size: 0.8em;
|
||||
background-color: pink;
|
||||
border: 1px solid #999;
|
||||
border-radius: 4px;
|
||||
}
|
||||
#results {
|
||||
text-align: center;
|
||||
margin: auto;
|
||||
padding-top: 10px;
|
||||
}
|
||||
#results figure {
|
||||
display: inline-block;
|
||||
margin: 10px;
|
||||
}
|
||||
#results figcaption {
|
||||
font-size: 0.8em;
|
||||
padding: 3px;
|
||||
color: #888;
|
||||
cursor: pointer;
|
||||
}
|
||||
#results img {
|
||||
border-radius: 5px;
|
||||
object-fit: cover;
|
||||
}
|
||||
#fieldset-config {
|
||||
line-height:2em;
|
||||
background-color: #F0F0F0;
|
||||
}
|
||||
input[type="number"] {
|
||||
width: 60px;
|
||||
}
|
||||
#seed {
|
||||
width: 150px;
|
||||
}
|
||||
button#reset-seed {
|
||||
font-size: 1.7em;
|
||||
background: #efefef;
|
||||
border: 1px solid #999;
|
||||
border-radius: 4px;
|
||||
line-height: 0.8;
|
||||
margin: 0 10px 0 0;
|
||||
padding: 0 5px 3px;
|
||||
vertical-align: middle;
|
||||
}
|
||||
label {
|
||||
white-space: nowrap;
|
||||
}
|
||||
#progress-section {
|
||||
display: none;
|
||||
}
|
||||
#progress-image {
|
||||
width: 30vh;
|
||||
height: 30vh;
|
||||
}
|
||||
#cancel-button {
|
||||
cursor: pointer;
|
||||
color: red;
|
||||
}
|
||||
#basic-parameters {
|
||||
background-color: #EEEEEE;
|
||||
}
|
||||
#txt2img {
|
||||
background-color: #DCDCDC;
|
||||
}
|
||||
#variations {
|
||||
background-color: #EEEEEE;
|
||||
}
|
||||
#img2img {
|
||||
background-color: #DCDCDC;
|
||||
}
|
||||
#gfpgan {
|
||||
background-color: #EEEEEE;
|
||||
}
|
||||
#progress-section {
|
||||
background-color: #F5F5F5;
|
||||
}
|
||||
.section-header {
|
||||
text-align: left;
|
||||
font-weight: bold;
|
||||
padding: 0 0 0 0;
|
||||
}
|
||||
#no-results-message:not(:only-child) {
|
||||
display: none;
|
||||
}
|
||||
|
||||
129
static/legacy_web/index.html
Normal file
129
static/legacy_web/index.html
Normal file
@ -0,0 +1,129 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<title>Stable Diffusion Dream Server</title>
|
||||
<meta charset="utf-8">
|
||||
<link rel="icon" type="image/x-icon" href="static/legacy_web/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<link rel="stylesheet" href="static/legacy_web/index.css">
|
||||
<script src="config.js"></script>
|
||||
<script src="static/legacy_web/index.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>Stable Diffusion Dream Server</h1>
|
||||
<div id="about">
|
||||
For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<form id="generate-form" method="post" action="#">
|
||||
<fieldset id="txt2img">
|
||||
<div id="search-box">
|
||||
<textarea rows="3" id="prompt" name="prompt"></textarea>
|
||||
<input type="submit" id="submit" value="Generate">
|
||||
</div>
|
||||
</fieldset>
|
||||
<fieldset id="fieldset-config">
|
||||
<div class="section-header">Basic options</div>
|
||||
<label for="iterations">Images to generate:</label>
|
||||
<input value="1" type="number" id="iterations" name="iterations" size="4">
|
||||
<label for="steps">Steps:</label>
|
||||
<input value="50" type="number" id="steps" name="steps">
|
||||
<label for="cfg_scale">Cfg Scale:</label>
|
||||
<input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
|
||||
<label for="sampler_name">Sampler:</label>
|
||||
<select id="sampler_name" name="sampler_name" value="k_lms">
|
||||
<option value="ddim">DDIM</option>
|
||||
<option value="plms">PLMS</option>
|
||||
<option value="k_lms" selected>KLMS</option>
|
||||
<option value="k_dpm_2">KDPM_2</option>
|
||||
<option value="k_dpm_2_a">KDPM_2A</option>
|
||||
<option value="k_euler">KEULER</option>
|
||||
<option value="k_euler_a">KEULER_A</option>
|
||||
<option value="k_heun">KHEUN</option>
|
||||
</select>
|
||||
<input type="checkbox" name="seamless" id="seamless">
|
||||
<label for="seamless">Seamless circular tiling</label>
|
||||
<br>
|
||||
<label title="Set to multiple of 64" for="width">Width:</label>
|
||||
<select id="width" name="width" value="512">
|
||||
<option value="64">64</option> <option value="128">128</option>
|
||||
<option value="192">192</option> <option value="256">256</option>
|
||||
<option value="320">320</option> <option value="384">384</option>
|
||||
<option value="448">448</option> <option value="512" selected>512</option>
|
||||
<option value="576">576</option> <option value="640">640</option>
|
||||
<option value="704">704</option> <option value="768">768</option>
|
||||
<option value="832">832</option> <option value="896">896</option>
|
||||
<option value="960">960</option> <option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to multiple of 64" for="height">Height:</label>
|
||||
<select id="height" name="height" value="512">
|
||||
<option value="64">64</option> <option value="128">128</option>
|
||||
<option value="192">192</option> <option value="256">256</option>
|
||||
<option value="320">320</option> <option value="384">384</option>
|
||||
<option value="448">448</option> <option value="512" selected>512</option>
|
||||
<option value="576">576</option> <option value="640">640</option>
|
||||
<option value="704">704</option> <option value="768">768</option>
|
||||
<option value="832">832</option> <option value="896">896</option>
|
||||
<option value="960">960</option> <option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to -1 for random seed" for="seed">Seed:</label>
|
||||
<input value="-1" type="number" id="seed" name="seed">
|
||||
<button type="button" id="reset-seed">↺</button>
|
||||
<input type="checkbox" name="progress_images" id="progress_images">
|
||||
<label for="progress_images">Display in-progress images (slower)</label>
|
||||
<button type="button" id="reset-all">Reset to Defaults</button>
|
||||
<span id="variations">
|
||||
<label title="If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different." for="variation_amount">Variation amount (0 to disable):</label>
|
||||
<input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
|
||||
<label title="list of variations to apply, in the format `seed:weight,seed:weight,..." for="with_variations">With variations (seed:weight,seed:weight,...):</label>
|
||||
<input value="" type="text" id="with_variations" name="with_variations">
|
||||
</span>
|
||||
</fieldset>
|
||||
<fieldset id="img2img">
|
||||
<div class="section-header">Image-to-image options</div>
|
||||
<label title="Upload an image to use img2img" for="initimg">Initial image:</label>
|
||||
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
|
||||
<button type="button" id="remove-image">Remove Image</button>
|
||||
<br>
|
||||
<label for="strength">Img2Img Strength:</label>
|
||||
<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
|
||||
<input type="checkbox" id="fit" name="fit" checked>
|
||||
<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height</label>
|
||||
</fieldset>
|
||||
<fieldset id="gfpgan">
|
||||
<div class="section-header">Post-processing options</div>
|
||||
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
|
||||
<input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
|
||||
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
|
||||
<select id="upscale_level" name="upscale_level" value="">
|
||||
<option value="" selected>None</option>
|
||||
<option value="2">2x</option>
|
||||
<option value="4">4x</option>
|
||||
</select>
|
||||
<label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
|
||||
<input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
|
||||
</fieldset>
|
||||
</form>
|
||||
<br>
|
||||
<section id="progress-section">
|
||||
<div id="progress-container">
|
||||
<progress id="progress-bar" value="0" max="1"></progress>
|
||||
<span id="cancel-button" title="Cancel">✖</span>
|
||||
<br>
|
||||
<img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
|
||||
<div id="scaling-inprocess-message">
|
||||
<i><span>Postprocessing...</span><span id="processing_cnt">1/3</span></i>
|
||||
</div>
|
||||
</span>
|
||||
</section>
|
||||
|
||||
<div id="results">
|
||||
<div id="no-results-message">
|
||||
<i><p>No results...</p></i>
|
||||
</div>
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
</html>
|
||||
213
static/legacy_web/index.js
Normal file
213
static/legacy_web/index.js
Normal file
@ -0,0 +1,213 @@
|
||||
function toBase64(file) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const r = new FileReader();
|
||||
r.readAsDataURL(file);
|
||||
r.onload = () => resolve(r.result);
|
||||
r.onerror = (error) => reject(error);
|
||||
});
|
||||
}
|
||||
|
||||
function appendOutput(src, seed, config) {
|
||||
let outputNode = document.createElement("figure");
|
||||
|
||||
let variations = config.with_variations;
|
||||
if (config.variation_amount > 0) {
|
||||
variations = (variations ? variations + ',' : '') + seed + ':' + config.variation_amount;
|
||||
}
|
||||
let baseseed = (config.with_variations || config.variation_amount > 0) ? config.seed : seed;
|
||||
let altText = baseseed + ' | ' + (variations ? variations + ' | ' : '') + config.prompt;
|
||||
|
||||
// img needs width and height for lazy loading to work
|
||||
const figureContents = `
|
||||
<a href="${src}" target="_blank">
|
||||
<img src="${src}"
|
||||
alt="${altText}"
|
||||
title="${altText}"
|
||||
loading="lazy"
|
||||
width="256"
|
||||
height="256">
|
||||
</a>
|
||||
<figcaption>${seed}</figcaption>
|
||||
`;
|
||||
|
||||
outputNode.innerHTML = figureContents;
|
||||
let figcaption = outputNode.querySelector('figcaption');
|
||||
|
||||
// Reload image config
|
||||
figcaption.addEventListener('click', () => {
|
||||
let form = document.querySelector("#generate-form");
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
if (k == 'initimg') { continue; }
|
||||
form.querySelector(`*[name=${k}]`).value = config[k];
|
||||
}
|
||||
|
||||
document.querySelector("#seed").value = baseseed;
|
||||
document.querySelector("#with_variations").value = variations || '';
|
||||
if (document.querySelector("#variation_amount").value <= 0) {
|
||||
document.querySelector("#variation_amount").value = 0.2;
|
||||
}
|
||||
|
||||
saveFields(document.querySelector("#generate-form"));
|
||||
});
|
||||
|
||||
document.querySelector("#results").prepend(outputNode);
|
||||
}
|
||||
|
||||
function saveFields(form) {
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
if (typeof v !== 'object') { // Don't save 'file' type
|
||||
localStorage.setItem(k, v);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function loadFields(form) {
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
const item = localStorage.getItem(k);
|
||||
if (item != null) {
|
||||
form.querySelector(`*[name=${k}]`).value = item;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function clearFields(form) {
|
||||
localStorage.clear();
|
||||
let prompt = form.prompt.value;
|
||||
form.reset();
|
||||
form.prompt.value = prompt;
|
||||
}
|
||||
|
||||
const BLANK_IMAGE_URL = 'data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>';
|
||||
async function generateSubmit(form) {
|
||||
const prompt = document.querySelector("#prompt").value;
|
||||
|
||||
// Convert file data to base64
|
||||
let formData = Object.fromEntries(new FormData(form));
|
||||
formData.initimg_name = formData.initimg.name
|
||||
formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
|
||||
|
||||
let strength = formData.strength;
|
||||
let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
|
||||
|
||||
let progressSectionEle = document.querySelector('#progress-section');
|
||||
progressSectionEle.style.display = 'initial';
|
||||
let progressEle = document.querySelector('#progress-bar');
|
||||
progressEle.setAttribute('max', totalSteps);
|
||||
let progressImageEle = document.querySelector('#progress-image');
|
||||
progressImageEle.src = BLANK_IMAGE_URL;
|
||||
|
||||
progressImageEle.style.display = {}.hasOwnProperty.call(formData, 'progress_images') ? 'initial': 'none';
|
||||
|
||||
// Post as JSON, using Fetch streaming to get results
|
||||
fetch(form.action, {
|
||||
method: form.method,
|
||||
body: JSON.stringify(formData),
|
||||
}).then(async (response) => {
|
||||
const reader = response.body.getReader();
|
||||
|
||||
let noOutputs = true;
|
||||
while (true) {
|
||||
let {value, done} = await reader.read();
|
||||
value = new TextDecoder().decode(value);
|
||||
if (done) {
|
||||
progressSectionEle.style.display = 'none';
|
||||
break;
|
||||
}
|
||||
|
||||
for (let event of value.split('\n').filter(e => e !== '')) {
|
||||
const data = JSON.parse(event);
|
||||
|
||||
if (data.event === 'result') {
|
||||
noOutputs = false;
|
||||
appendOutput(data.url, data.seed, data.config);
|
||||
progressEle.setAttribute('value', 0);
|
||||
progressEle.setAttribute('max', totalSteps);
|
||||
} else if (data.event === 'upscaling-started') {
|
||||
document.getElementById("processing_cnt").textContent=data.processed_file_cnt;
|
||||
document.getElementById("scaling-inprocess-message").style.display = "block";
|
||||
} else if (data.event === 'upscaling-done') {
|
||||
document.getElementById("scaling-inprocess-message").style.display = "none";
|
||||
} else if (data.event === 'step') {
|
||||
progressEle.setAttribute('value', data.step);
|
||||
if (data.url) {
|
||||
progressImageEle.src = data.url;
|
||||
}
|
||||
} else if (data.event === 'canceled') {
|
||||
// avoid alerting as if this were an error case
|
||||
noOutputs = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Re-enable form, remove no-results-message
|
||||
form.querySelector('fieldset').removeAttribute('disabled');
|
||||
document.querySelector("#prompt").value = prompt;
|
||||
document.querySelector('progress').setAttribute('value', '0');
|
||||
|
||||
if (noOutputs) {
|
||||
alert("Error occurred while generating.");
|
||||
}
|
||||
});
|
||||
|
||||
// Disable form while generating
|
||||
form.querySelector('fieldset').setAttribute('disabled','');
|
||||
document.querySelector("#prompt").value = `Generating: "${prompt}"`;
|
||||
}
|
||||
|
||||
async function fetchRunLog() {
|
||||
try {
|
||||
let response = await fetch('/run_log.json')
|
||||
const data = await response.json();
|
||||
for(let item of data.run_log) {
|
||||
appendOutput(item.url, item.seed, item);
|
||||
}
|
||||
} catch (e) {
|
||||
console.error(e);
|
||||
}
|
||||
}
|
||||
|
||||
window.onload = async () => {
|
||||
document.querySelector("#prompt").addEventListener("keydown", (e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
const form = e.target.form;
|
||||
generateSubmit(form);
|
||||
}
|
||||
});
|
||||
document.querySelector("#generate-form").addEventListener('submit', (e) => {
|
||||
e.preventDefault();
|
||||
const form = e.target;
|
||||
|
||||
generateSubmit(form);
|
||||
});
|
||||
document.querySelector("#generate-form").addEventListener('change', (e) => {
|
||||
saveFields(e.target.form);
|
||||
});
|
||||
document.querySelector("#reset-seed").addEventListener('click', (e) => {
|
||||
document.querySelector("#seed").value = -1;
|
||||
saveFields(e.target.form);
|
||||
});
|
||||
document.querySelector("#reset-all").addEventListener('click', (e) => {
|
||||
clearFields(e.target.form);
|
||||
});
|
||||
document.querySelector("#remove-image").addEventListener('click', (e) => {
|
||||
initimg.value=null;
|
||||
});
|
||||
loadFields(document.querySelector("#generate-form"));
|
||||
|
||||
document.querySelector('#cancel-button').addEventListener('click', () => {
|
||||
fetch('/cancel').catch(e => {
|
||||
console.error(e);
|
||||
});
|
||||
});
|
||||
document.documentElement.addEventListener('keydown', (e) => {
|
||||
if (e.key === "Escape")
|
||||
fetch('/cancel').catch(err => {
|
||||
console.error(err);
|
||||
});
|
||||
});
|
||||
|
||||
if (!config.gfpgan_model_exists) {
|
||||
document.querySelector("#gfpgan").style.display = 'none';
|
||||
}
|
||||
await fetchRunLog()
|
||||
};
|
||||
Loading…
Reference in New Issue
Block a user