Merge remote-tracking branch 'upstream/development' into mkdocs-updates

README.md

@@ -1,16 +1,36 @@
-<h1 align='center'><b>Stable Diffusion Dream Script</b></h1>
+<div align="center">
 
-<p align='center'>
+# Stable Diffusion Dream Script
-    <img src="docs/assets/logo.png"/>
-</p>
 
-<p align="center">
+![project logo](docs/assets/logo.png)
-    <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"/>
+[![discord badge]][discord link]
-    <br>
+
-    <img src="https://img.shields.io/github/issues/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="issues"/>
+[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
-    <img src="https://img.shields.io/github/issues-pr/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="pull-requests"/>
+
-</p>
+[![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**
+> Note
-
+>
-If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
+> If you have an Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
-full-precision mode as shown below.
+> 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)
+### Latest Changes
-
-- #### [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
 
 - 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).

environment.yaml

@@ -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

ldm/dream/args.py

@@ -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__
+            return self._merge_dict(
-            a.update(cmd_switches.__dict__)
+                arg_switches.__dict__,
-            return a
+                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
+            return not cmd_switches.individual and value_arg  # arg supersedes cmd
-        if name=='individual':
+        return value_cmd if value_cmd is not None else value_arg
-            return value_cmd or value_arg  # cmd supersedes arg
-        if value_cmd is not None:
-            return value_cmd
-        else:
-            return 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
+def format_metadata(**kwargs):
-# it does not write all the required top-level metadata, writes too much image
+    print(f'format_metadata() is deprecated. Please use metadata_dumps()')
-# data, and doesn't support grids yet. But you gotta start somewhere, no?
+    return metadata_dumps(kwargs)
-def format_metadata(opt,
+
-                    seeds=[],
+def metadata_dumps(opt,
-                    weights=None,
+                   seeds=[],
-                    model_hash=None,
+                   model_hash=None,
-                    postprocessing=None):
+                   postprocessing=None):
     '''
-    Given an Args object, returns a partial implementation of
+    Given an Args object, returns a dict containing the keys and
-    the stable diffusion metadata standard
+    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()

ldm/dream/pngwriter.py

@@ -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)

ldm/dream/server.py

@@ -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,
+                metadata = metadata_dumps(iter_opt,
-                                           seeds      = [seed],
+                                          seeds      = [seed],
-                                           weights    = self.model.weights,
+                                          model_hash = self.model.model_hash
-                                           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(

ldm/modules/attention.py

@@ -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):
+        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-        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
 
-    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):
+            return self.einsum_op_slice_1(q, k, v, 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
 
-    def einsum_op_mps_v2(self, q, k, v, r1):
+    def einsum_op_mps_v2(self, q, k, v):
-        if self.mem_total >= 8 and q.shape[1] <= 4096:
+        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
-                r1 = self.einsum_op_compvis(q, k, v, r1)
+            return self.einsum_op_compvis(q, k, v)
         else:
-            slice_size = 1
+            return self.einsum_op_slice_0(q, k, v, 1)
-            for i in range(0, q.shape[0], slice_size):
+
-                end = min(q.shape[0], i + slice_size)
+    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
-                s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
-                s1 *= self.scale
+        if size_mb <= max_tensor_mb:
-                s2 = s1.softmax(dim=-1, dtype=r1.dtype)
+            return self.einsum_op_compvis(q, k, v)
-                del s1
+        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
-                r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+        if div <= q.shape[0]:
-                del s2
+            return self.einsum_op_slice_0(q, k, v, q.shape[0] // div)
-        return r1
+        return self.einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
-    
+
-    def einsum_op_cuda(self, q, k, v, r1):
+    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
+    def einsum_op(self, q, k, v):
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
+        if q.device.type == 'cuda':
-        mem_required = tensor_size * 2.5
+            return self.einsum_op_cuda(q, k, v)
-        steps = 1
 
-        if mem_required > mem_free_total:
+        if q.device.type == 'mps':
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+            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:
+        # Smaller slices are faster due to L2/L3/SLC caches.
-            max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        # Tested on i7 with 8MB L3 cache.
-            raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+        return self.einsum_op_tensor_mem(q, k, v, 32)
-                            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
 
     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)
+        k = self.to_k(context) * self.scale
-        v_in = self.to_v(context)
+        v = self.to_v(context)
-        device_type = 'mps' if x.device.type == 'mps' else 'cuda'
         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))
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-        del q_in, k_in, v_in
+        r = self.einsum_op(q, k, v)
-        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
-        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)
 
 
 class BasicTransformerBlock(nn.Module):

ldm/modules/diffusionmodules/model.py

@@ -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)

ldm/modules/diffusionmodules/util.py

@@ -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)

scripts/dream.py

@@ -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:
+        if opt.parse_cmd(command) is None:
-            parser = opt.parse_cmd(command)
-        except SystemExit:
-            parser.print_help()
             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)
+                        first_seed             = first_seed or seed
-                        this_variation = [[seed, opt.variation_amount]]
+                        this_variation         = [[seed, opt.variation_amount]]
-                        if opt.with_variations is None:
+                        opt.with_variations    = prior_variations + this_variation
-                            iter_opt.with_variations = this_variation
+                        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
-                        else:
+                    elif len(prior_variations) > 0:
-                            iter_opt.with_variations = opt.with_variations + this_variation
+                        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
-                        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)
                     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,

scripts/preload_models.py

@@ -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())

static/dream_web/index.css

@@ -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 {
+#initimg > :not(legend) {
-    background-color: #EEEEEE;
+  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;
+    background-color: var(--fields-light);
-}
-.section-header {
-    text-align: left;
-    font-weight: bold;
-    padding: 0 0 0 0;
 }
 #no-results-message:not(:only-child) {
     display: none;
 }
-

static/dream_web/index.html

@@ -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>
+<head>
-      <form id="generate-form" method="post" action="#">
+  <title>Stable Diffusion Dream Server</title>
-	<fieldset id="txt2img">
+  <meta charset="utf-8">
-          <div id="search-box">
+  <link rel="icon" type="image/x-icon" href="static/dream_web/favicon.ico" />
-            <textarea rows="3" id="prompt" name="prompt"></textarea>
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-            <input type="submit" id="submit" value="Generate">
+
-	  </div>
+  <script src="config.js"></script>
-        </fieldset>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"
-        <fieldset id="fieldset-config">
+    integrity="sha512-q/dWJ3kcmjBLU4Qc47E4A9kTB4m3wuTY7vkFJDTZKjTs8jhyGQnaUrxa0Ytd0ssMZhbNua9hE+E7Qv1j+DyZwA=="
-	  <div class="section-header">Basic options</div>
+    crossorigin="anonymous"></script>
-          <label for="iterations">Images to generate:</label>
+  <link rel="stylesheet" href="index.css">
-          <input value="1" type="number" id="iterations" name="iterations" size="4">
+  <script src="index.js"></script>
-          <label for="steps">Steps:</label>
+</head>
-          <input value="50" type="number" id="steps" name="steps">
+
-          <label for="cfg_scale">Cfg Scale:</label>
+<body>
-          <input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
+  <header>
-          <label for="sampler_name">Sampler:</label>
+    <h1>Stable Diffusion Dream Server</h1>
-          <select id="sampler_name" name="sampler_name" value="k_lms">
+    <div id="about">
-            <option value="ddim">DDIM</option>
+      For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub
-            <option value="plms">PLMS</option>
+        site</a>
-            <option value="k_lms" selected>KLMS</option>
+    </div>
-            <option value="k_dpm_2">KDPM_2</option>
+  </header>
-            <option value="k_dpm_2_a">KDPM_2A</option>
+
-            <option value="k_euler">KEULER</option>
+  <main>
-	    <option value="k_euler_a">KEULER_A</option>
+    <!--
-            <option value="k_heun">KHEUN</option>
+      <div id="dropper" style="background-color:red;width:200px;height:200px;">
-          </select>
+      </div>
-          <input type="checkbox" name="seamless" id="seamless">
+      -->
-	  <label for="seamless">Seamless circular tiling</label>
+    <form id="generate-form" method="post" action="api/jobs">
-          <br>
+      <fieldset id="txt2img">
-          <label title="Set to multiple of 64" for="width">Width:</label>
+        <legend>
-          <select id="width" name="width" value="512">
+          <input type="checkbox" name="enable_generate" id="enable_generate" checked>
-            <option value="64">64</option> <option value="128">128</option>
+          <label for="enable_generate">Generate</label>
-            <option value="192">192</option> <option value="256">256</option>
+        </legend>
-            <option value="320">320</option> <option value="384">384</option>
+        <div id="search-box">
-            <option value="448">448</option> <option value="512" selected>512</option>
+          <textarea rows="3" id="prompt" name="prompt"></textarea>
-            <option value="576">576</option> <option value="640">640</option>
+        </div>
-            <option value="704">704</option> <option value="768">768</option>
+        <label for="iterations">Images to generate:</label>
-            <option value="832">832</option> <option value="896">896</option>
+        <input value="1" type="number" id="iterations" name="iterations" size="4">
-            <option value="960">960</option> <option value="1024">1024</option>
+        <label for="steps">Steps:</label>
-          </select>
+        <input value="50" type="number" id="steps" name="steps">
-          <label title="Set to multiple of 64" for="height">Height:</label>
+        <label for="cfg_scale">Cfg Scale:</label>
-          <select id="height" name="height" value="512">
+        <input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
-            <option value="64">64</option> <option value="128">128</option>
+        <label for="sampler_name">Sampler:</label>
-            <option value="192">192</option> <option value="256">256</option>
+        <select id="sampler_name" name="sampler_name" value="k_lms">
-            <option value="320">320</option> <option value="384">384</option>
+          <option value="ddim">DDIM</option>
-            <option value="448">448</option> <option value="512" selected>512</option>
+          <option value="plms">PLMS</option>
-            <option value="576">576</option> <option value="640">640</option>
+          <option value="k_lms" selected>KLMS</option>
-            <option value="704">704</option> <option value="768">768</option>
+          <option value="k_dpm_2">KDPM_2</option>
-            <option value="832">832</option> <option value="896">896</option>
+          <option value="k_dpm_2_a">KDPM_2A</option>
-            <option value="960">960</option> <option value="1024">1024</option>
+          <option value="k_euler">KEULER</option>
-          </select>
+          <option value="k_euler_a">KEULER_A</option>
-          <label title="Set to -1 for random seed" for="seed">Seed:</label>
+          <option value="k_heun">KHEUN</option>
-          <input value="-1" type="number" id="seed" name="seed">
+        </select>
-          <button type="button" id="reset-seed">&olarr;</button>
+        <input type="checkbox" name="seamless" id="seamless">
-          <input type="checkbox" name="progress_images" id="progress_images">
+        <label for="seamless">Seamless circular tiling</label>
-	  <label for="progress_images">Display in-progress images (slower)</label>
+        <br>
-	  <button type="button" id="reset-all">Reset to Defaults</button>
+        <label title="Set to multiple of 64" for="width">Width:</label>
-	  <span id="variations">
+        <select id="width" name="width" value="512">
-            <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>
+          <option value="64">64</option>
-            <input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
+          <option value="128">128</option>
-            <label title="list of variations to apply, in the format `seed:weight,seed:weight,..." for="with_variations">With variations (seed:weight,seed:weight,...):</label>
+          <option value="192">192</option>
-            <input value="" type="text" id="with_variations" name="with_variations">
+          <option value="256">256</option>
-	  </span>
+          <option value="320">320</option>
-	</fieldset>
+          <option value="384">384</option>
-	<fieldset id="img2img">
+          <option value="448">448</option>
-	  <div class="section-header">Image-to-image options</div>
+          <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">&olarr;</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>
+        </div>
-	    <label for="strength">Img2Img Strength:</label>
+        <fieldset id="img2img">
-	    <input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
+          <legend>
-	    <input type="checkbox" id="fit" name="fit" checked>
+            <input type="checkbox" name="enable_img2img" id="enable_img2img" checked>
-	    <label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height</label>
+            <label for="enable_img2img">Enable Img2Img</label>
-	</fieldset>
+          </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>
+          <legend>
-          <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
+            <input type="checkbox" name="enable_gfpgan" id="enable_gfpgan">
-          <input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
+            <label for="enable_gfpgan">Enable gfpgan</label>
-          <label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</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>
+      </div>
-      <br>
+      <input type="submit" id="submit" value="Generate">
-      <section id="progress-section">
+    </form>
-        <div id="progress-container">
+    <br>
-          <progress id="progress-bar" value="0" max="1"></progress>
+    <section id="progress-section">
-          <span id="cancel-button" title="Cancel">&#10006;</span>
+      <div id="progress-container">
-          <br>
+        <progress id="progress-bar" value="0" max="1"></progress>
-          <img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
+        <span id="cancel-button" title="Cancel">&#10006;</span>
-          <div id="scaling-inprocess-message">
+        <br>
-            <i><span>Postprocessing...</span><span id="processing_cnt">1/3</span></i>
+        <img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
-          </div>
+        <div id="scaling-inprocess-message">
-        </span>
+          <i><span>Postprocessing...</span><span id="processing_cnt">1</span>/<span id="processing_total">3</span></i>
-      </section>
-
-      <div id="results">
-        <div id="no-results-message">
-          <i><p>No results...</p></i>
         </div>
       </div>
-    </main>
+    </section>
-  </body>
+
+    <div id="results">
+    </div>
+  </main>
+</body>
+
 </html>

static/dream_web/index.js

@@ -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) {
+function ondragdream(event) {
-    let outputNode = document.createElement("figure");
+  let dream = event.target.dataset.dream;
-    
+  event.dataTransfer.setData("dream", dream);
-    let variations = config.with_variations;
+}
-    if (config.variation_amount > 0) {
+
-        variations = (variations ? variations + ',' : '') + seed + ':' + config.variation_amount;
+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
+    if (toEnd) {
-    figcaption.addEventListener('click', () => {
+      document.querySelector("#results").append(outputNode);
-        let form = document.querySelector("#generate-form");
+    } else {
-        for (const [k, v] of new FormData(form)) {
+      document.querySelector("#results").prepend(outputNode);
-            if (k == 'initimg') { continue; }
+    }
-            form.querySelector(`*[name=${k}]`).value = config[k];
+    document.querySelector("#no-results-message")?.remove();
-        }
-
-        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) {
@@ -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;
+    // Evaluate all checkboxes
-    let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
+    let checkboxes = form.querySelectorAll('input[type=checkbox]');
-
+    checkboxes.forEach(function (checkbox) {
-    let progressSectionEle = document.querySelector('#progress-section');
+      if (checkbox.checked) {
-    progressSectionEle.style.display = 'initial';
+        formData[checkbox.name] = 'true';
-    let progressEle = document.querySelector('#progress-bar');
+      }
-    progressEle.setAttribute('max', totalSteps);
+    });
-    let progressImageEle = document.querySelector('#progress-image');
+
-    progressImageEle.src = BLANK_IMAGE_URL;
+    let strength = formData.strength;
-
+    let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
-    progressImageEle.style.display = {}.hasOwnProperty.call(formData, 'progress_images') ? 'initial': 'none';
+    let showProgressImages = formData.progress_images;
-
+
-    // Post as JSON, using Fetch streaming to get results
+    // Set enabling flags
-    fetch(form.action, {
+
-        method: form.method,
+
-        body: JSON.stringify(formData),
+    // Initialize the progress bar
-    }).then(async (response) => {
+    initProgress(totalSteps, showProgressImages);
-        const reader = response.body.getReader();
+
-
+    // POST, use response to listen for events
-        let noOutputs = true;
+    fetch(form.action, {
-        while (true) {
+        method: form.method,
-            let {value, done} = await reader.read();
+        headers: new Headers({'content-type': 'application/json'}),
-            value = new TextDecoder().decode(value);
+        body: JSON.stringify(formData),
-            if (done) {
+    })
-                progressSectionEle.style.display = 'none';
+    .then(response => response.json())
-                break;
+    .then(data => {
-            }
+        var jobId = data.jobId;
-
+        socket.emit('join_room', { 'room': jobId });
-            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() {
+function fieldSetEnableChecked(event) {
-    try {
+  cb = event.target;
-        let response = await fetch('/run_log.json')
+  fields = cb.closest('fieldset');
-        const data = await response.json();
+  fields.disabled = !cb.checked;
-        for(let item of data.run_log) {
-            appendOutput(item.url, item.seed, item);
-        }
-    } catch (e) {
-        console.error(e);
-    }
 }
 
+// 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);
+    */
 };

static/legacy_web/index.css

@@ -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;
+}
+

static/legacy_web/index.html

@@ -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">&olarr;</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">&#10006;</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>

static/legacy_web/index.js

@@ -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()
+};