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

README.md

@@ -1,21 +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)
-    <a href="https://github.com/lstein/stable-diffusion/releases"><img src="https://flat.badgen.net/github/release/lstein/stable-diffusion/development?icon=github" alt="release"/></a>
+
-    <a href="https://github.com/lstein/stable-diffusion/stargazers"><img src="https://flat.badgen.net/github/stars/lstein/stable-diffusion?icon=github" alt="stars"/></a>
+[![discord badge]][discord link]
-    <a href="https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion"><img src="https://flat.badgen.net/github/forks/lstein/stable-diffusion?icon=github" alt="forks"/></a>
+
-    <br />
+[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
-    <a href="https://github.com/lstein/stable-diffusion/actions/workflows/test-dream-conda.yml"><img src="https://flat.badgen.net/github/checks/lstein/stable-diffusion/main?label=CI%20status%20on%20main&cache=900&icon=github" alt="CI status on main"/></a>
+
-    <a href="https://github.com/lstein/stable-diffusion/actions/workflows/test-dream-conda.yml"><img src="https://flat.badgen.net/github/checks/lstein/stable-diffusion/development?label=CI%20status%20on%20dev&cache=900&icon=github" alt="CI status on dev"/></a>
+[![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]
-    <a href="https://github.com/lstein/stable-diffusion/commits/development"><img src="https://flat.badgen.net/github/last-commit/lstein/stable-diffusion/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900" alt="last-dev-commit"/></a>
+
-    <br />
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
-    <a href="https://github.com/lstein/stable-diffusion/issues?q=is%3Aissue+is%3Aopen"><img src="https://flat.badgen.net/github/open-issues/lstein/stable-diffusion?icon=github" alt="open-issues"/></a>
+
-    <a href="https://github.com/lstein/stable-diffusion/pulls?q=is%3Apr+is%3Aopen"><img src="https://flat.badgen.net/github/open-prs/lstein/stable-diffusion?icon=github" alt="open-prs"/></a>
+[CI checks on dev badge]: https://flat.badgen.net/github/checks/lstein/stable-diffusion/development?label=CI%20status%20on%20dev&cache=900&icon=github
-</p>
+[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
@@ -26,7 +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)
@@ -38,38 +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.
 
@@ -79,43 +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)
 
@@ -147,12 +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
@@ -164,13 +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.
@@ -178,7 +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

@@ -21,7 +21,7 @@ dependencies:
     - test-tube>=0.7.5
     - streamlit==1.12.0
     - send2trash==1.8.0
-    - 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 = [
@@ -142,7 +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"]}')
@@ -151,15 +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"]}')
@@ -541,17 +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,
+
+def metadata_dumps(opt,
                    seeds=[],
-                    weights=None,
                    model_hash=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
@@ -593,12 +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))
@@ -612,6 +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/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()
@@ -176,9 +175,8 @@ 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],
-                                           weights    = self.model.weights,
                                           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

@@ -168,100 +168,84 @@ class CrossAttention(nn.Module):
             nn.Dropout(dropout)
         )
 
-        if torch.cuda.is_available():
+        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-            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):
+    def einsum_op_compvis(self, q, k, v):
-        s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # faster
+        s = einsum('b i d, b j d -> b i j', q, k)
-        s2 = s1.softmax(dim=-1, dtype=q.dtype)
+        s = s.softmax(dim=-1, dtype=s.dtype)
-        del s1
+        return einsum('b i j, b j d -> b i d', s, v)
-        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_slice_0(self, q, k, v, slice_size):
-        if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-            r1 = self.einsum_op_compvis(q, k, v, r1)
+        for i in range(0, q.shape[0], slice_size):
-        else:
+            end = i + slice_size
-            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+            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
-                s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
+            r[:, i:end] = self.einsum_op_compvis(q[:, i:end], k, v)
-                s2 = s1.softmax(dim=-1, dtype=r1.dtype)
+        return r
-                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_v1(self, q, k, v):
-        if self.mem_total >= 8 and q.shape[1] <= 4096:
+        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 = 1
+            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
-            for i in range(0, q.shape[0], slice_size):
+            return self.einsum_op_slice_1(q, k, v, slice_size)
-                end = min(q.shape[0], i + slice_size)
-                s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-                s1 *= self.scale
-                s2 = s1.softmax(dim=-1, dtype=r1.dtype)
-                del s1
-                r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-                del s2
-        return r1
 
-    def einsum_op_cuda(self, q, k, v, r1):
+    def einsum_op_mps_v2(self, q, k, v):
+        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
+            return self.einsum_op_compvis(q, k, v)
+        else:
+            return self.einsum_op_slice_0(q, k, v, 1)
+
+    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
+        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
+        if size_mb <= max_tensor_mb:
+            return self.einsum_op_compvis(q, k, v)
+        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
+        if div <= q.shape[0]:
+            return self.einsum_op_slice_0(q, k, v, q.shape[0] // div)
+        return self.einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
+
+    def einsum_op_cuda(self, q, k, v):
         stats = torch.cuda.memory_stats(q.device)
         mem_active = stats['active_bytes.all.current']
         mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+        mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
         mem_free_torch = mem_reserved - mem_active
         mem_free_total = mem_free_cuda + mem_free_torch
+        # Divide factor of safety as there's copying and fragmentation
+        return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
-        gb = 1024 ** 3
+    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
@@ -219,9 +219,13 @@ def main_loop(gen, opt, infile):
             prefix = file_writer.unique_prefix()
             results = []  # list of filename, prompt pairs
             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:
@@ -229,29 +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]]
-                        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,
@@ -275,7 +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,

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()
+};