Unverified Commit 6df49457 authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub

Merge branch 'master' into DPM++SDE

parents 45fd7854 b48b7999
......@@ -84,26 +84,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
## Where are Aesthetic Gradients?!?!
Aesthetic Gradients are now an extension. You can install it using git:
```commandline
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
```
After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
## Where is History/Image browser?!?!
Image browser is now an extension. You can install it using git:
```commandline
git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser extensions/images-browser
```
After running this command, make sure that you have `images-browser` dir in webui's `extensions` directory and restart
the UI. The interface for Image browser should appear exactly the same as it was.
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
......
......@@ -134,18 +134,19 @@ def prepare_enviroment():
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "b325595b8a776d483f6935dfa7b45f01c27039e4")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
......@@ -179,6 +180,9 @@ def prepare_enviroment():
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
if not is_installed("open_clip"):
run_pip(f"install {openclip_package}", "open_clip")
if (not is_installed("xformers") or reinstall_xformers) and xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
......@@ -196,7 +200,7 @@ def prepare_enviroment():
os.makedirs(dir_repos, exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
......
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......@@ -524,6 +524,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
else:
image.save(fullfn, quality=opts.jpeg_quality)
image.already_saved_as = fullfn
target_side_length = 4000
oversize = image.width > target_side_length or image.height > target_side_length
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
......
......@@ -51,6 +51,10 @@ def setup_for_low_vram(sd_model, use_medvram):
send_me_to_gpu(first_stage_model, None)
return first_stage_model_decode(z)
# for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
if hasattr(sd_model.cond_stage_model, 'model'):
sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
......@@ -65,6 +69,10 @@ def setup_for_low_vram(sd_model, use_medvram):
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if hasattr(sd_model.cond_stage_model, 'model'):
sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
del sd_model.cond_stage_model.transformer
if use_medvram:
sd_model.model.register_forward_pre_hook(send_me_to_gpu)
else:
......
......@@ -9,7 +9,7 @@ sys.path.insert(0, script_path)
# search for directory of stable diffusion in following places
sd_path = None
possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), '.', os.path.dirname(script_path)]
possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion-stability-ai'), '.', os.path.dirname(script_path)]
for possible_sd_path in possible_sd_paths:
if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
sd_path = os.path.abspath(possible_sd_path)
......
This diff is collapsed.
from torch.utils.checkpoint import checkpoint
def BasicTransformerBlock_forward(self, x, context=None):
return checkpoint(self._forward, x, context)
def AttentionBlock_forward(self, x):
return checkpoint(self._forward, x)
def ResBlock_forward(self, x, emb):
return checkpoint(self._forward, x, emb)
\ No newline at end of file
This diff is collapsed.
......@@ -199,8 +199,8 @@ def sample_plms(self,
@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
b, *_, device = *x.shape, x.device
def get_model_output(x, t):
......@@ -249,6 +249,8 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
if dynamic_threshold is not None:
pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
......@@ -321,12 +323,16 @@ def should_hijack_inpainting(checkpoint_info):
def do_inpainting_hijack():
ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
# most of this stuff seems to no longer be needed because it is already included into SD2.0
# LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
# p_sample_plms is needed because PLMS can't work with dicts as conditionings
# this file should be cleaned up later if weverything tuens out to work fine
# ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
# ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
# ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
# ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
import open_clip.tokenizer
import torch
from modules import sd_hijack_clip, devices
from modules.shared import opts
tokenizer = open_clip.tokenizer._tokenizer
class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
def __init__(self, wrapped, hijack):
super().__init__(wrapped, hijack)
self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
self.id_start = tokenizer.encoder["<start_of_text>"]
self.id_end = tokenizer.encoder["<end_of_text>"]
self.id_pad = 0
def tokenize(self, texts):
assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
tokenized = [tokenizer.encode(text) for text in texts]
return tokenized
def encode_with_transformers(self, tokens):
# set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
z = self.wrapped.encode_with_transformer(tokens)
return z
def encode_embedding_init_text(self, init_text, nvpt):
ids = tokenizer.encode(init_text)
ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
return embedded
......@@ -129,7 +129,8 @@ class InterruptedException(BaseException):
class VanillaStableDiffusionSampler:
def __init__(self, constructor, sd_model):
self.sampler = constructor(sd_model)
self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms
self.is_plms = hasattr(self.sampler, 'p_sample_plms')
self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
self.mask = None
self.nmask = None
self.init_latent = None
......@@ -220,7 +221,6 @@ class VanillaStableDiffusionSampler:
self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
def adjust_steps_if_invalid(self, p, num_steps):
if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
valid_step = 999 / (1000 // num_steps)
......@@ -229,7 +229,6 @@ class VanillaStableDiffusionSampler:
return num_steps
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = setup_img2img_steps(p, steps)
steps = self.adjust_steps_if_invalid(p, steps)
......@@ -262,9 +261,10 @@ class VanillaStableDiffusionSampler:
steps = self.adjust_steps_if_invalid(p, steps or p.steps)
# Wrap the conditioning models with additional image conditioning for inpainting model
# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
if image_conditioning is not None:
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
......@@ -352,7 +352,9 @@ class TorchHijack:
class KDiffusionSampler:
def __init__(self, funcname, sd_model):
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname, [])
......
......@@ -11,17 +11,18 @@ import tqdm
import modules.artists
import modules.interrogate
import modules.memmon
import modules.sd_models
import modules.styles
import modules.devices as devices
from modules import sd_samplers, sd_models, localization, sd_vae, extensions, script_loading
from modules.hypernetworks import hypernetwork
from modules import localization, sd_vae, extensions, script_loading
from modules.paths import models_path, script_path, sd_path
demo = None
sd_model_file = os.path.join(script_path, 'model.ckpt')
default_sd_model_file = sd_model_file
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
......@@ -121,10 +122,12 @@ xformers_available = False
config_filename = cmd_opts.ui_settings_file
os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
hypernetworks = {}
loaded_hypernetwork = None
def reload_hypernetworks():
from modules.hypernetworks import hypernetwork
global hypernetworks
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
......@@ -206,10 +209,11 @@ class State:
if self.current_latent is None:
return
import modules.sd_samplers
if opts.show_progress_grid:
self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
else:
self.current_image = sd_samplers.sample_to_image(self.current_latent)
self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
self.current_image_sampling_step = self.sampling_step
......@@ -248,6 +252,21 @@ def options_section(section_identifier, options_dict):
return options_dict
def list_checkpoint_tiles():
import modules.sd_models
return modules.sd_models.checkpoint_tiles()
def refresh_checkpoints():
import modules.sd_models
return modules.sd_models.list_models()
def list_samplers():
import modules.sd_samplers
return modules.sd_samplers.all_samplers
hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
options_templates = {}
......@@ -276,6 +295,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
......@@ -322,8 +345,7 @@ options_templates.update(options_section(('system', "System"), {
options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
"tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
......@@ -333,7 +355,7 @@ options_templates.update(options_section(('training', "Training"), {
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
......@@ -385,7 +407,7 @@ options_templates.update(options_section(('ui', "User interface"), {
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
......
......@@ -3,7 +3,7 @@ import numpy as np
import PIL
import torch
from PIL import Image
from torch.utils.data import Dataset
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import random
......@@ -11,25 +11,28 @@ import tqdm
from modules import devices, shared
import re
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
re_numbers_at_start = re.compile(r"^[-\d]+\s*")
class DatasetEntry:
def __init__(self, filename=None, latent=None, filename_text=None):
def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
self.filename = filename
self.latent = latent
self.filename_text = filename_text
self.cond = None
self.cond_text = None
self.latent_dist = latent_dist
self.latent_sample = latent_sample
self.cond = cond
self.cond_text = cond_text
self.pixel_values = pixel_values
class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
self.placeholder_token = placeholder_token
self.batch_size = batch_size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
......@@ -45,11 +48,16 @@ class PersonalizedBase(Dataset):
assert os.path.isdir(data_root), "Dataset directory doesn't exist"
assert os.listdir(data_root), "Dataset directory is empty"
cond_model = shared.sd_model.cond_stage_model
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
if shared.state.interrupted:
raise Exception("inturrupted")
try:
image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
except Exception:
......@@ -71,37 +79,49 @@ class PersonalizedBase(Dataset):
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
torchdata = torch.moveaxis(torchdata, 2, 0)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
init_latent = init_latent.to(devices.cpu)
entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
if include_cond:
torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
latent_sample = None
with torch.autocast("cuda"):
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
latent_sampling_method = "once"
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
elif latent_sampling_method == "deterministic":
# Works only for DiagonalGaussianDistribution
latent_dist.std = 0
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
elif latent_sampling_method == "random":
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
if not (self.tag_drop_out != 0 or self.shuffle_tags):
entry.cond_text = self.create_text(filename_text)
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset.append(entry)
assert len(self.dataset) > 0, "No images have been found in the dataset."
self.length = len(self.dataset) * repeats // batch_size
if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
with torch.autocast("cuda"):
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset_length = len(self.dataset)
self.indexes = None
self.shuffle()
self.dataset.append(entry)
del torchdata
del latent_dist
del latent_sample
def shuffle(self):
self.indexes = np.random.permutation(self.dataset_length)
self.length = len(self.dataset)
assert self.length > 0, "No images have been found in the dataset."
self.batch_size = min(batch_size, self.length)
self.gradient_step = min(gradient_step, self.length // self.batch_size)
self.latent_sampling_method = latent_sampling_method
def create_text(self, filename_text):
text = random.choice(self.lines)
text = text.replace("[name]", self.placeholder_token)
tags = filename_text.split(',')
if shared.opts.tag_drop_out != 0:
tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
if shared.opts.shuffle_tags:
if self.tag_drop_out != 0:
tags = [t for t in tags if random.random() > self.tag_drop_out]
if self.shuffle_tags:
random.shuffle(tags)
text = text.replace("[filewords]", ','.join(tags))
return text
......@@ -110,19 +130,43 @@ class PersonalizedBase(Dataset):
return self.length
def __getitem__(self, i):
res = []
for j in range(self.batch_size):
position = i * self.batch_size + j
if position % len(self.indexes) == 0:
self.shuffle()
index = self.indexes[position % len(self.indexes)]
entry = self.dataset[index]
if entry.cond is None:
entry.cond_text = self.create_text(entry.filename_text)
res.append(entry)
return res
entry = self.dataset[i]
if self.tag_drop_out != 0 or self.shuffle_tags:
entry.cond_text = self.create_text(entry.filename_text)
if self.latent_sampling_method == "random":
entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
return entry
class PersonalizedDataLoader(DataLoader):
def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
super(PersonalizedDataLoader, self).__init__(dataset, shuffle=True, drop_last=True, batch_size=batch_size, pin_memory=pin_memory)
if latent_sampling_method == "random":
self.collate_fn = collate_wrapper_random
else:
self.collate_fn = collate_wrapper
class BatchLoader:
def __init__(self, data):
self.cond_text = [entry.cond_text for entry in data]
self.cond = [entry.cond for entry in data]
self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
#self.emb_index = [entry.emb_index for entry in data]
#print(self.latent_sample.device)
def pin_memory(self):
self.latent_sample = self.latent_sample.pin_memory()
return self
def collate_wrapper(batch):
return BatchLoader(batch)
class BatchLoaderRandom(BatchLoader):
def __init__(self, data):
super().__init__(data)
def pin_memory(self):
return self
def collate_wrapper_random(batch):
return BatchLoaderRandom(batch)
\ No newline at end of file
......@@ -157,22 +157,6 @@ def save_files(js_data, images, do_make_zip, index):
return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
def save_pil_to_file(pil_image, dir=None):
use_metadata = False
metadata = PngImagePlugin.PngInfo()
for key, value in pil_image.info.items():
if isinstance(key, str) and isinstance(value, str):
metadata.add_text(key, value)
use_metadata = True
file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
return file_obj
# override save to file function so that it also writes PNG info
gr.processing_utils.save_pil_to_file = save_pil_to_file
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
......@@ -478,9 +462,7 @@ def create_toprow(is_img2img):
if is_img2img:
with gr.Column(scale=1, elem_id="interrogate_col"):
button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
if cmd_opts.deepdanbooru:
button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
with gr.Column(scale=1):
with gr.Row():
......@@ -1004,11 +986,10 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[img2img_prompt],
)
if cmd_opts.deepdanbooru:
img2img_deepbooru.click(
fn=interrogate_deepbooru,
inputs=[init_img],
outputs=[img2img_prompt],
img2img_deepbooru.click(
fn=interrogate_deepbooru,
inputs=[init_img],
outputs=[img2img_prompt],
)
......@@ -1213,7 +1194,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Tab(label="Create hypernetwork"):
new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
......@@ -1259,7 +1240,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Column():
with gr.Row():
interrupt_preprocessing = gr.Button("Interrupt")
run_preprocess = gr.Button(value="Preprocess", variant='primary')
run_preprocess = gr.Button(value="Preprocess", variant='primary')
process_split.change(
fn=lambda show: gr_show(show),
......@@ -1286,6 +1267,7 @@ def create_ui(wrap_gradio_gpu_call):
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
batch_size = gr.Number(label='Batch size', value=1, precision=0)
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
......@@ -1296,6 +1278,11 @@ def create_ui(wrap_gradio_gpu_call):
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
with gr.Row():
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)
with gr.Row():
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
with gr.Row():
interrupt_training = gr.Button(value="Interrupt")
......@@ -1384,11 +1371,15 @@ def create_ui(wrap_gradio_gpu_call):
train_embedding_name,
embedding_learn_rate,
batch_size,
gradient_step,
dataset_directory,
log_directory,
training_width,
training_height,
steps,
shuffle_tags,
tag_drop_out,
latent_sampling_method,
create_image_every,
save_embedding_every,
template_file,
......@@ -1409,11 +1400,15 @@ def create_ui(wrap_gradio_gpu_call):
train_hypernetwork_name,
hypernetwork_learn_rate,
batch_size,
gradient_step,
dataset_directory,
log_directory,
training_width,
training_height,
steps,
shuffle_tags,
tag_drop_out,
latent_sampling_method,
create_image_every,
save_embedding_every,
template_file,
......
import os
import tempfile
from collections import namedtuple
import gradio as gr
from PIL import PngImagePlugin
from modules import shared
Savedfile = namedtuple("Savedfile", ["name"])
def save_pil_to_file(pil_image, dir=None):
already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as:
shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
file_obj = Savedfile(already_saved_as)
return file_obj
if shared.opts.temp_dir != "":
dir = shared.opts.temp_dir
use_metadata = False
metadata = PngImagePlugin.PngInfo()
for key, value in pil_image.info.items():
if isinstance(key, str) and isinstance(value, str):
metadata.add_text(key, value)
use_metadata = True
file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
return file_obj
# override save to file function so that it also writes PNG info
gr.processing_utils.save_pil_to_file = save_pil_to_file
def on_tmpdir_changed():
if shared.opts.temp_dir == "" or shared.demo is None:
return
os.makedirs(shared.opts.temp_dir, exist_ok=True)
shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
def cleanup_tmpdr():
temp_dir = shared.opts.temp_dir
if temp_dir == "" or not os.path.isdir(temp_dir):
return
for root, dirs, files in os.walk(temp_dir, topdown=False):
for name in files:
_, extension = os.path.splitext(name)
if extension != ".png":
continue
filename = os.path.join(root, name)
os.remove(filename)
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
......@@ -10,7 +10,7 @@ from fastapi.middleware.gzip import GZipMiddleware
from modules.paths import script_path
from modules import devices, sd_samplers, upscaler, extensions, localization
from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir
import modules.codeformer_model as codeformer
import modules.extras
import modules.face_restoration
......@@ -23,7 +23,6 @@ import modules.scripts
import modules.sd_hijack
import modules.sd_models
import modules.sd_vae
import modules.shared as shared
import modules.txt2img
import modules.script_callbacks
......@@ -32,12 +31,14 @@ from modules import modelloader
from modules.shared import cmd_opts
import modules.hypernetworks.hypernetwork
queue_lock = threading.Lock()
if cmd_opts.server_name:
server_name = cmd_opts.server_name
else:
server_name = "0.0.0.0" if cmd_opts.listen else None
def wrap_queued_call(func):
def f(*args, **kwargs):
with queue_lock:
......@@ -86,8 +87,9 @@ def initialize():
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: shared.reload_hypernetworks()))
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
......@@ -150,9 +152,12 @@ def webui():
initialize()
while 1:
demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
if shared.opts.clean_temp_dir_at_start:
ui_tempdir.cleanup_tmpdr()
shared.demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
app, local_url, share_url = demo.launch(
app, local_url, share_url = shared.demo.launch(
share=cmd_opts.share,
server_name=server_name,
server_port=cmd_opts.port,
......@@ -179,9 +184,9 @@ def webui():
if launch_api:
create_api(app)
modules.script_callbacks.app_started_callback(demo, app)
modules.script_callbacks.app_started_callback(shared.demo, app)
wait_on_server(demo)
wait_on_server(shared.demo)
sd_samplers.set_samplers()
......
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