rename all modules from ldm.dream to ldm.invoke

- scripts and documentation updated to match
- ran preflight checks on both web and CLI and seems to be working

backend/invoke_ai_web_server.py

@@ -12,9 +12,9 @@ from PIL import Image
 from uuid import uuid4
 from threading import Event
 
-from ldm.dream.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
+from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.dream.pngwriter import PngWriter, retrieve_metadata
+from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
-from ldm.dream.conditioning import split_weighted_subprompts
+from ldm.invoke.conditioning import split_weighted_subprompts
 
 from backend.modules.parameters import parameters_to_command
 
@@ -157,7 +157,7 @@ class InvokeAIWebServer:
         self.init_image_path = os.path.join(self.result_path, 'init-images/')
         self.mask_image_path = os.path.join(self.result_path, 'mask-images/')
         # txt log
-        self.log_path = os.path.join(self.result_path, 'dream_log.txt')
+        self.log_path = os.path.join(self.result_path, 'invoke_log.txt')
         # make all output paths
         [
             os.makedirs(path, exist_ok=True)

backend/modules/create_cmd_parser.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from ldm.dream.args import PRECISION_CHOICES
+from ldm.invoke.args import PRECISION_CHOICES
 
 
 def create_cmd_parser():

backend/modules/parameters.py

@@ -15,7 +15,7 @@ SAMPLER_CHOICES = [
 
 def parameters_to_command(params):
     """
-    Converts dict of parameters into a `dream.py` REPL command.
+    Converts dict of parameters into a `invoke.py` REPL command.
     """
 
     switches = list()

backend/server.py

@@ -30,10 +30,10 @@ from send2trash import send2trash
 
 
 from ldm.generate import Generate
-from ldm.dream.restoration import Restoration
+from ldm.invoke.restoration import Restoration
-from ldm.dream.pngwriter import PngWriter, retrieve_metadata
+from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
-from ldm.dream.args import APP_ID, APP_VERSION, calculate_init_img_hash
+from ldm.invoke.args import APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.dream.conditioning import split_weighted_subprompts
+from ldm.invoke.conditioning import split_weighted_subprompts
 
 from modules.parameters import parameters_to_command
 
@@ -125,7 +125,7 @@ class CanceledException(Exception):
 
 try:
     gfpgan, codeformer, esrgan = None, None, None
-    from ldm.dream.restoration.base import Restoration
+    from ldm.invoke.restoration.base import Restoration
 
     restoration = Restoration()
     gfpgan, codeformer = restoration.load_face_restore_models()
@@ -164,7 +164,7 @@ init_image_path = os.path.join(result_path, "init-images/")
 mask_image_path = os.path.join(result_path, "mask-images/")
 
 # txt log
-log_path = os.path.join(result_path, "dream_log.txt")
+log_path = os.path.join(result_path, "invoke_log.txt")
 
 # make all output paths
 [

ldm/dream/generator/__init__.py

@@ -1,4 +0,0 @@
-'''
-Initialization file for the ldm.dream.generator package
-'''
-from .base import Generator

ldm/dream/restoration/__init__.py

@@ -1,4 +0,0 @@
-'''
-Initialization file for the ldm.dream.restoration package
-'''
-from .base import Restoration

ldm/generate.py

@@ -19,7 +19,7 @@ import cv2
 import skimage
 
 from omegaconf import OmegaConf
-from ldm.dream.generator.base import downsampling
+from ldm.invoke.generator.base import downsampling
 from PIL import Image, ImageOps
 from torch import nn
 from pytorch_lightning import seed_everything, logging
@@ -28,13 +28,11 @@ from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ksampler import KSampler
-from ldm.dream.pngwriter import PngWriter
+from ldm.invoke.pngwriter import PngWriter
-from ldm.dream.args import metadata_from_png
+from ldm.invoke.args import metadata_from_png
-from ldm.dream.image_util import InitImageResizer
+from ldm.invoke.image_util import InitImageResizer
-from ldm.dream.devices import choose_torch_device, choose_precision
+from ldm.invoke.devices import choose_torch_device, choose_precision
-from ldm.dream.conditioning import get_uc_and_c
+from ldm.invoke.conditioning import get_uc_and_c
-
-
 
 def fix_func(orig):
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
@@ -297,9 +295,9 @@ class Generate:
             def process_image(image,seed):
                 image.save(f{'images/seed.png'})
 
-        The callback used by the prompt2png() can be found in ldm/dream_util.py. It contains code
+        The code used to save images to a directory can be found in ldm/invoke/pngwriter.py. 
-        to create the requested output directory, select a unique informative name for each image, and
+        It contains code to create the requested output directory, select a unique informative
-        write the prompt into the PNG metadata.
+        name for each image, and write the prompt into the PNG metadata.
         """
         # TODO: convert this into a getattr() loop
         steps = steps or self.steps
@@ -524,7 +522,7 @@ class Generate:
             )
 
         elif tool == 'outcrop':
-            from ldm.dream.restoration.outcrop import Outcrop
+            from ldm.invoke.restoration.outcrop import Outcrop
             extend_instructions = {}
             for direction,pixels in _pairwise(opt.outcrop):
                 extend_instructions[direction]=int(pixels)
@@ -561,7 +559,7 @@ class Generate:
                 image_callback = callback,
             )
         elif tool == 'outpaint':
-            from ldm.dream.restoration.outpaint import Outpaint
+            from ldm.invoke.restoration.outpaint import Outpaint
             restorer = Outpaint(image,self)
             return restorer.process(
                 opt,
@@ -620,39 +618,39 @@ class Generate:
 
     def _make_base(self):
         if not self.generators.get('base'):
-            from ldm.dream.generator import Generator
+            from ldm.invoke.generator import Generator
             self.generators['base'] = Generator(self.model, self.precision)
         return self.generators['base']
 
     def _make_img2img(self):
         if not self.generators.get('img2img'):
-            from ldm.dream.generator.img2img import Img2Img
+            from ldm.invoke.generator.img2img import Img2Img
             self.generators['img2img'] = Img2Img(self.model, self.precision)
         return self.generators['img2img']
 
     def _make_embiggen(self):
         if not self.generators.get('embiggen'):
-            from ldm.dream.generator.embiggen import Embiggen
+            from ldm.invoke.generator.embiggen import Embiggen
             self.generators['embiggen'] = Embiggen(self.model, self.precision)
         return self.generators['embiggen']
 
     def _make_txt2img(self):
         if not self.generators.get('txt2img'):
-            from ldm.dream.generator.txt2img import Txt2Img
+            from ldm.invoke.generator.txt2img import Txt2Img
             self.generators['txt2img'] = Txt2Img(self.model, self.precision)
             self.generators['txt2img'].free_gpu_mem = self.free_gpu_mem
         return self.generators['txt2img']
 
     def _make_txt2img2img(self):
         if not self.generators.get('txt2img2'):
-            from ldm.dream.generator.txt2img2img import Txt2Img2Img
+            from ldm.invoke.generator.txt2img2img import Txt2Img2Img
             self.generators['txt2img2'] = Txt2Img2Img(self.model, self.precision)
             self.generators['txt2img2'].free_gpu_mem = self.free_gpu_mem
         return self.generators['txt2img2']
 
     def _make_inpaint(self):
         if not self.generators.get('inpaint'):
-            from ldm.dream.generator.inpaint import Inpaint
+            from ldm.invoke.generator.inpaint import Inpaint
             self.generators['inpaint'] = Inpaint(self.model, self.precision)
         return self.generators['inpaint']
 
@@ -783,7 +781,7 @@ class Generate:
 
         print(msg)
 
-    # Be warned: config is the path to the model config file, not the dream conf file!
+    # Be warned: config is the path to the model config file, not the invoke conf file!
     # Also note that we can get config and weights from self, so why do we need to
     # pass them as args?
     def _load_model_from_config(self, config, weights):

ldm/invoke/args.py

@@ -90,8 +90,8 @@ import re
 import copy
 import base64
 import functools
-import ldm.dream.pngwriter
+import ldm.invoke.pngwriter
-from ldm.dream.conditioning import split_weighted_subprompts
+from ldm.invoke.conditioning import split_weighted_subprompts
 
 SAMPLER_CHOICES = [
     'ddim',
@@ -811,7 +811,7 @@ def metadata_from_png(png_file_path) -> Args:
     an Args object containing the image metadata. Note that this
     returns a single Args object, not multiple.
     '''
-    meta = ldm.dream.pngwriter.retrieve_metadata(png_file_path)
+    meta = ldm.invoke.pngwriter.retrieve_metadata(png_file_path)
     if 'sd-metadata' in meta and len(meta['sd-metadata'])>0 :
         return metadata_loads(meta)[0]
     else:

ldm/invoke/generator/__init__.py

@@ -0,0 +1,4 @@
+'''
+Initialization file for the ldm.invoke.generator package
+'''
+from .base import Generator

ldm/invoke/generator/base.py

@@ -1,5 +1,5 @@
 '''
-Base class for ldm.dream.generator.*
+Base class for ldm.invoke.generator.*
 including img2img, txt2img, and inpaint
 '''
 import torch
@@ -9,7 +9,7 @@ from tqdm import tqdm, trange
 from PIL               import Image
 from einops import rearrange, repeat
 from pytorch_lightning import seed_everything
-from ldm.dream.devices import choose_autocast
+from ldm.invoke.devices import choose_autocast
 from ldm.util import rand_perlin_2d
 
 downsampling = 8

ldm/invoke/generator/embiggen.py

@@ -1,15 +1,15 @@
 '''
-ldm.dream.generator.embiggen descends from ldm.dream.generator
+ldm.invoke.generator.embiggen descends from ldm.invoke.generator
-and generates with ldm.dream.generator.img2img
+and generates with ldm.invoke.generator.img2img
 '''
 
 import torch
 import numpy as  np
 from tqdm import trange
 from PIL               import Image
-from ldm.dream.generator.base      import Generator
+from ldm.invoke.generator.base      import Generator
-from ldm.dream.generator.img2img   import Img2Img
+from ldm.invoke.generator.img2img   import Img2Img
-from ldm.dream.devices import choose_autocast
+from ldm.invoke.devices import choose_autocast
 from ldm.models.diffusion.ddim     import DDIMSampler
 
 class Embiggen(Generator):
@@ -107,7 +107,7 @@ class Embiggen(Generator):
             initsuperwidth = round(initsuperwidth*embiggen[0])
             initsuperheight = round(initsuperheight*embiggen[0])
             if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
-                from ldm.dream.restoration.realesrgan import ESRGAN
+                from ldm.invoke.restoration.realesrgan import ESRGAN
                 esrgan = ESRGAN()
                 print(
                     f'>> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}')

ldm/invoke/generator/img2img.py

@@ -1,11 +1,11 @@
 '''
-ldm.dream.generator.img2img descends from ldm.dream.generator
+ldm.invoke.generator.img2img descends from ldm.invoke.generator
 '''
 
 import torch
 import numpy as  np
-from ldm.dream.devices             import choose_autocast
+from ldm.invoke.devices             import choose_autocast
-from ldm.dream.generator.base      import Generator
+from ldm.invoke.generator.base      import Generator
 from ldm.models.diffusion.ddim     import DDIMSampler
 
 class Img2Img(Generator):

ldm/invoke/generator/inpaint.py

@@ -1,12 +1,12 @@
 '''
-ldm.dream.generator.inpaint descends from ldm.dream.generator
+ldm.invoke.generator.inpaint descends from ldm.invoke.generator
 '''
 
 import torch
 import numpy as  np
 from einops import rearrange, repeat
-from ldm.dream.devices             import choose_autocast
+from ldm.invoke.devices             import choose_autocast
-from ldm.dream.generator.img2img   import Img2Img
+from ldm.invoke.generator.img2img   import Img2Img
 from ldm.models.diffusion.ddim     import DDIMSampler
 from ldm.models.diffusion.ksampler import KSampler
 

ldm/invoke/generator/txt2img.py

@@ -1,10 +1,10 @@
 '''
-ldm.dream.generator.txt2img inherits from ldm.dream.generator
+ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
 
 import torch
 import numpy as  np
-from ldm.dream.generator.base import Generator
+from ldm.invoke.generator.base import Generator
 
 class Txt2Img(Generator):
     def __init__(self, model, precision):

ldm/invoke/generator/txt2img2img.py

@@ -1,11 +1,11 @@
 '''
-ldm.dream.generator.txt2img inherits from ldm.dream.generator
+ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
 
 import torch
 import numpy as  np
 import math
-from ldm.dream.generator.base  import Generator
+from ldm.invoke.generator.base  import Generator
 from ldm.models.diffusion.ddim import DDIMSampler
 
 

ldm/invoke/readline.py

@@ -1,17 +1,17 @@
 """
-Readline helper functions for dream.py (linux and mac only).
+Readline helper functions for invoke.py.
 You may import the global singleton `completer` to get access to the
 completer object itself. This is useful when you want to autocomplete
 seeds:
 
- from ldm.dream.readline import completer
+ from ldm.invoke.readline import completer
  completer.add_seed(18247566)
  completer.add_seed(9281839)
 """
 import os
 import re
 import atexit
-from ldm.dream.args import Args
+from ldm.invoke.args import Args
 
 # ---------------readline utilities---------------------
 try:
@@ -20,7 +20,7 @@ try:
 except (ImportError,ModuleNotFoundError):
     readline_available = False
 
-IMG_EXTENSIONS     = ('.png','.jpg','.jpeg')
+IMG_EXTENSIONS     = ('.png','.jpg','.jpeg','.PNG','.JPG','.JPEG','.gif','.GIF')
 COMMANDS = (
     '--steps','-s',
     '--seed','-S',
@@ -74,7 +74,7 @@ class Completer(object):
 
     def complete(self, text, state):
         '''
-        Completes dream command line.
+        Completes invoke command line.
         BUG: it doesn't correctly complete files that have spaces in the name.
         '''
         buffer = readline.get_line_buffer()
@@ -287,7 +287,7 @@ def get_completer(opt:Args)->Completer:
         readline.parse_and_bind('set skip-completed-text on')
         readline.parse_and_bind('set show-all-if-ambiguous on')
 
-        histfile = os.path.join(os.path.expanduser(opt.outdir), '.dream_history')
+        histfile = os.path.join(os.path.expanduser(opt.outdir), '.invoke_history')
         try:
             readline.read_history_file(histfile)
             readline.set_history_length(1000)

ldm/invoke/restoration/__init__.py

@@ -0,0 +1,4 @@
+'''
+Initialization file for the ldm.invoke.restoration package
+'''
+from .base import Restoration

ldm/invoke/restoration/base.py

@@ -23,16 +23,16 @@ class Restoration():
 
     # Face Restore Models
     def load_gfpgan(self, gfpgan_dir, gfpgan_model_path):
-        from ldm.dream.restoration.gfpgan import GFPGAN
+        from ldm.invoke.restoration.gfpgan import GFPGAN
         return GFPGAN(gfpgan_dir, gfpgan_model_path)
 
     def load_codeformer(self):
-        from ldm.dream.restoration.codeformer import CodeFormerRestoration
+        from ldm.invoke.restoration.codeformer import CodeFormerRestoration
         return CodeFormerRestoration()
 
     # Upscale Models
     def load_esrgan(self, esrgan_bg_tile=400):
-        from ldm.dream.restoration.realesrgan import ESRGAN
+        from ldm.invoke.restoration.realesrgan import ESRGAN
         esrgan = ESRGAN(esrgan_bg_tile)
         print('>> ESRGAN Initialized')
         return esrgan;

ldm/invoke/restoration/codeformer.py

@@ -8,7 +8,7 @@ pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v
 
 class CodeFormerRestoration():
     def __init__(self,
-            codeformer_dir='ldm/dream/restoration/codeformer',
+            codeformer_dir='ldm/invoke/restoration/codeformer',
             codeformer_model_path='weights/codeformer.pth') -> None:
         self.model_path = os.path.join(codeformer_dir, codeformer_model_path)
         self.codeformer_model_exists = os.path.isfile(self.model_path)
@@ -27,7 +27,7 @@ class CodeFormerRestoration():
             from basicsr.utils.download_util import load_file_from_url
             from basicsr.utils import img2tensor, tensor2img
             from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-            from ldm.dream.restoration.codeformer_arch import CodeFormer
+            from ldm.invoke.restoration.codeformer_arch import CodeFormer
             from torchvision.transforms.functional import normalize
             from PIL import Image
             
@@ -35,7 +35,7 @@ class CodeFormerRestoration():
             
             cf = cf_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(device)
             
-            checkpoint_path = load_file_from_url(url=pretrained_model_url, model_dir=os.path.abspath('ldm/dream/restoration/codeformer/weights'), progress=True)
+            checkpoint_path = load_file_from_url(url=pretrained_model_url, model_dir=os.path.abspath('ldm/invoke/restoration/codeformer/weights'), progress=True)
             checkpoint = torch.load(checkpoint_path)['params_ema']
             cf.load_state_dict(checkpoint)
             cf.eval()

ldm/invoke/restoration/codeformer_arch.py

@@ -5,7 +5,7 @@ from torch import nn, Tensor
 import torch.nn.functional as F
 from typing import Optional, List
 
-from ldm.dream.restoration.vqgan_arch import *
+from ldm.invoke.restoration.vqgan_arch import *
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 

ldm/invoke/server.py

@@ -4,9 +4,9 @@ import copy
 import base64
 import mimetypes
 import os
-from ldm.dream.args import Args, metadata_dumps
+from ldm.invoke.args import Args, metadata_dumps
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
-from ldm.dream.pngwriter import PngWriter
+from ldm.invoke.pngwriter import PngWriter
 from threading import Event
 
 def build_opt(post_data, seed, gfpgan_model_exists):

ldm/models/diffusion/ddim.py

@@ -4,7 +4,7 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 from ldm.models.diffusion.sampler import Sampler
 from ldm.modules.diffusionmodules.util import  noise_like
 

ldm/models/diffusion/ksampler.py

@@ -2,7 +2,7 @@
 import k_diffusion as K
 import torch
 import torch.nn as nn
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 from ldm.models.diffusion.sampler import Sampler
 from ldm.util import rand_perlin_2d
 from ldm.modules.diffusionmodules.util import (

ldm/models/diffusion/plms.py

@@ -4,7 +4,7 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 from ldm.models.diffusion.sampler import Sampler
 from ldm.modules.diffusionmodules.util import  noise_like
 

ldm/models/diffusion/sampler.py

@@ -8,7 +8,7 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 
 from ldm.modules.diffusionmodules.util import (
     make_ddim_sampling_parameters,

ldm/modules/encoders/modules.py

@@ -5,7 +5,7 @@ import clip
 from einops import rearrange, repeat
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 
 from ldm.modules.x_transformer import (
     Encoder,

notebooks/notebook_helpers.py

@@ -14,7 +14,7 @@ from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import ismap
 import time
 from omegaconf import OmegaConf
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 
 def download_models(mode):
 

scripts/images2prompt.py

@@ -1,12 +1,12 @@
 #!/usr/bin/env python3
-'''This script reads the "Dream" Stable Diffusion prompt embedded in files generated by dream.py'''
+'''This script reads the "Invoke" Stable Diffusion prompt embedded in files generated by invoke.py'''
 
 import sys
 from PIL import Image,PngImagePlugin
 
 if len(sys.argv) < 2:
     print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
-    print("This script opens up the indicated dream.py-generated PNG file(s) and prints out the prompt used to generate them.")
+    print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out the prompt used to generate them.")
     exit(-1)
 
 filenames = sys.argv[1:]

scripts/invoke.py

@@ -10,11 +10,11 @@ import warnings
 import time
 import traceback
 sys.path.append('.')    # corrects a weird problem on Macs
-from ldm.dream.readline import get_completer
+from ldm.invoke.readline import get_completer
-from ldm.dream.args import Args, metadata_dumps, metadata_from_png, dream_cmd_from_png
+from ldm.invoke.args import Args, metadata_dumps, metadata_from_png, dream_cmd_from_png
-from ldm.dream.pngwriter import PngWriter, retrieve_metadata, write_metadata
+from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
-from ldm.dream.image_util import make_grid
+from ldm.invoke.image_util import make_grid
-from ldm.dream.log import write_log
+from ldm.invoke.log import write_log
 from omegaconf import OmegaConf
 from backend.invoke_ai_web_server import InvokeAIWebServer
 
@@ -45,7 +45,7 @@ def main():
     try:
         gfpgan, codeformer, esrgan = None, None, None
         if opt.restore or opt.esrgan:
-            from ldm.dream.restoration import Restoration
+            from ldm.invoke.restoration import Restoration
             restoration = Restoration()
             if opt.restore:
                 gfpgan, codeformer = restoration.load_face_restore_models(opt.gfpgan_dir, opt.gfpgan_model_path)
@@ -256,7 +256,7 @@ def main_loop(gen, opt, infile):
         if opt.with_variations is not None:
             opt.with_variations = split_variations(opt.with_variations)
 
-        if opt.prompt_as_dir:
+        if opt.prompt_as_dir and operation == 'generate':
             # sanitize the prompt to a valid folder name
             subdir = path_filter.sub('_', opt.prompt)[:name_max].rstrip(' .')
 
@@ -275,6 +275,12 @@ def main_loop(gen, opt, infile):
                 os.makedirs(opt.outdir)
             current_outdir = opt.outdir
 
+        # write out the history at this point
+        if operation == 'postprocess':
+            completer.add_history(f'!fix {command}')
+        else:
+            completer.add_history(command)
+
         # Here is where the images are actually generated!
         last_results = []
         try:
@@ -380,13 +386,9 @@ def main_loop(gen, opt, infile):
             continue
 
         print('Outputs:')
-        log_path = os.path.join(current_outdir, 'dream_log')
+        log_path = os.path.join(current_outdir, 'invoke_log')
         output_cntr = write_log(results, log_path ,('txt', 'md'), output_cntr)
         print()
-        if operation == 'postprocess':
-            completer.add_history(f'!fix {command}')
-        else:
-            completer.add_history(command)
 
     print('goodbye!')
 
@@ -422,6 +424,7 @@ def do_postprocess (gen, opt, callback):
             opt                 = opt,
         )
     except OSError:
+        print(traceback.format_exc(), file=sys.stderr)
         print(f'** {file_path}: file could not be read')
         return
     except (KeyError, AttributeError):

scripts/orig_scripts/img2img.py

@@ -18,7 +18,7 @@ from pytorch_lightning import seed_everything
 from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-from ldm.dream.devices         import choose_torch_device
+from ldm.invoke.devices         import choose_torch_device
 
 
 def chunk(it, size):

scripts/orig_scripts/inpaint.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from main import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.dream.devices import choose_torch_device
+from ldm.invoke.devices import choose_torch_device
 
 def make_batch(image, mask, device):
     image = np.array(Image.open(image).convert("RGB"))

scripts/orig_scripts/txt2img.py

@@ -18,7 +18,7 @@ import torch.nn as nn
 from ldm.util                  import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-from ldm.dream.devices         import choose_torch_device
+from ldm.invoke.devices         import choose_torch_device
 
 def chunk(it, size):
     it = iter(it)

scripts/preload_models.py

@@ -97,7 +97,7 @@ print('preloading CodeFormer model file...',end='')
 try:
         import urllib.request
         model_url  = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-        model_dest = 'ldm/dream/restoration/codeformer/weights/codeformer.pth'
+        model_dest = 'ldm/invoke/restoration/codeformer/weights/codeformer.pth'
         if not os.path.exists(model_dest):
             print('Downloading codeformer model file...')
             os.makedirs(os.path.dirname(model_dest), exist_ok=True)

scripts/sd-metadata.py

@@ -2,11 +2,11 @@
 
 import sys
 import json
-from ldm.dream.pngwriter import retrieve_metadata
+from ldm.invoke.pngwriter import retrieve_metadata
 
 if len(sys.argv) < 2:
     print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
-    print("This script opens up the indicated dream.py-generated PNG file(s) and prints out their metadata.")
+    print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out their metadata.")
     exit(-1)
 
 filenames = sys.argv[1:]

server/application.py

@@ -10,7 +10,7 @@ from flask_cors import CORS
 from flask_socketio import SocketIO
 from omegaconf import OmegaConf
 from dependency_injector.wiring import inject, Provide
-from ldm.dream.args import Args
+from ldm.invoke.args import Args
 from server import views
 from server.containers import Container
 from server.services import GeneratorService, SignalService

server/services.py

@@ -12,12 +12,12 @@ import shlex
 from threading import Thread
 import time
 from flask_socketio import SocketIO, join_room, leave_room
-from ldm.dream.args import Args
+from ldm.invoke.args import Args
-from ldm.dream.generator import embiggen
+from ldm.invoke.generator import embiggen
 from PIL import Image
 
-from ldm.dream.pngwriter import PngWriter
+from ldm.invoke.pngwriter import PngWriter
-from ldm.dream.server import CanceledException
+from ldm.invoke.server import CanceledException
 from ldm.generate import Generate
 from server.models import DreamResult, JobRequest, PaginatedItems, ProgressType, Signal