diff --git a/.gitignore b/.gitignore
index 1dffb108e..4f830e61b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,3 +16,4 @@ __pycache__
/webui-user.bat
/webui-user.sh
/interrogate
+/user.css
diff --git a/README.md b/README.md
index 84a78da58..d97ebc3fb 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ Alternatively, use [Google Colab](https://colab.research.google.com/drive/1Iy-xW
1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
-4. Place `model.ckpt` in the base directory, alongside `webui.py`.
+4. Place `model.ckpt` in the `models` directory.
5. _*(Optional)*_ Place `GFPGANv1.3.pth` in the base directory, alongside `webui.py`.
6. Run `webui-user.bat` from Windows Explorer as normal, non-administrate, user.
@@ -81,6 +81,7 @@ The documentation was moved from this README over to the project's [wiki](https:
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
+- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
diff --git a/models/Put Stable Diffusion checkpoints here.txt b/models/Put Stable Diffusion checkpoints here.txt
new file mode 100644
index 000000000..e69de29bb
diff --git a/modules/devices.py b/modules/devices.py
index e4430e1ad..07bb23397 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -48,3 +48,13 @@ def randn(seed, shape):
torch.manual_seed(seed)
return torch.randn(shape, device=device)
+
+def randn_without_seed(shape):
+ # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
+ if device.type == 'mps':
+ generator = torch.Generator(device=cpu)
+ noise = torch.randn(shape, generator=generator, device=cpu).to(device)
+ return noise
+
+ return torch.randn(shape, device=device)
+
diff --git a/modules/extras.py b/modules/extras.py
index ffae7d671..3d9d9f7a8 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -36,6 +36,7 @@ def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, co
outpath = opts.outdir_samples or opts.outdir_extras_samples
+ outputs = []
for image in imageArr:
existing_pnginfo = image.info or {}
@@ -91,7 +92,9 @@ def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, co
images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo)
- return imageArr, plaintext_to_html(info), ''
+ outputs.append(image)
+
+ return outputs, plaintext_to_html(info), ''
def run_pnginfo(image):
@@ -108,8 +111,9 @@ def run_pnginfo(image):
items['exif comment'] = exif_comment
- for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif']:
- del items[field]
+ for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+ 'loop', 'background', 'timestamp', 'duration']:
+ items.pop(field, None)
info = ''
diff --git a/modules/images.py b/modules/images.py
index f37f5f08f..e287d0df5 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -274,7 +274,7 @@ def apply_filename_pattern(x, p, seed, prompt):
x = x.replace("[height]", str(p.height))
x = x.replace("[sampler]", sd_samplers.samplers[p.sampler_index].name)
- x = x.replace("[model_hash]", shared.sd_model_hash)
+ x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
x = x.replace("[date]", datetime.date.today().isoformat())
if cmd_opts.hide_ui_dir_config:
@@ -353,13 +353,12 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
})
if extension.lower() in ("jpg", "jpeg", "webp"):
- image.save(fullfn, quality=opts.jpeg_quality, exif_bytes=exif_bytes())
+ image.save(fullfn, quality=opts.jpeg_quality)
+ if opts.enable_pnginfo and info is not None:
+ piexif.insert(exif_bytes(), fullfn)
else:
image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
- if extension.lower() == "webp":
- piexif.insert(exif_bytes, 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):
@@ -370,7 +369,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
elif oversize:
image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
- image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality, exif_bytes=exif_bytes())
+ image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality)
+ if opts.enable_pnginfo and info is not None:
+ piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
if opts.save_txt and info is not None:
with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
diff --git a/modules/memmon.py b/modules/memmon.py
new file mode 100644
index 000000000..f2cac841f
--- /dev/null
+++ b/modules/memmon.py
@@ -0,0 +1,77 @@
+import threading
+import time
+from collections import defaultdict
+
+import torch
+
+
+class MemUsageMonitor(threading.Thread):
+ run_flag = None
+ device = None
+ disabled = False
+ opts = None
+ data = None
+
+ def __init__(self, name, device, opts):
+ threading.Thread.__init__(self)
+ self.name = name
+ self.device = device
+ self.opts = opts
+
+ self.daemon = True
+ self.run_flag = threading.Event()
+ self.data = defaultdict(int)
+
+ def run(self):
+ if self.disabled:
+ return
+
+ while True:
+ self.run_flag.wait()
+
+ torch.cuda.reset_peak_memory_stats()
+ self.data.clear()
+
+ if self.opts.memmon_poll_rate <= 0:
+ self.run_flag.clear()
+ continue
+
+ self.data["min_free"] = torch.cuda.mem_get_info()[0]
+
+ while self.run_flag.is_set():
+ free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug?
+ self.data["min_free"] = min(self.data["min_free"], free)
+
+ time.sleep(1 / self.opts.memmon_poll_rate)
+
+ def dump_debug(self):
+ print(self, 'recorded data:')
+ for k, v in self.read().items():
+ print(k, -(v // -(1024 ** 2)))
+
+ print(self, 'raw torch memory stats:')
+ tm = torch.cuda.memory_stats(self.device)
+ for k, v in tm.items():
+ if 'bytes' not in k:
+ continue
+ print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
+
+ print(torch.cuda.memory_summary())
+
+ def monitor(self):
+ self.run_flag.set()
+
+ def read(self):
+ free, total = torch.cuda.mem_get_info()
+ self.data["total"] = total
+
+ torch_stats = torch.cuda.memory_stats(self.device)
+ self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
+ self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
+ self.data["system_peak"] = total - self.data["min_free"]
+
+ return self.data
+
+ def stop(self):
+ self.run_flag.clear()
+ return self.read()
diff --git a/modules/processing.py b/modules/processing.py
index 71a9c6f52..6a99d3837 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -119,8 +119,18 @@ def slerp(val, low, high):
return res
-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
xs = []
+
+ # if we have multiple seeds, this means we are working with batch size>1; this then
+ # enables the generation of additional tensors with noise that the sampler will use during its processing.
+ # Using those pre-genrated tensors instead of siimple torch.randn allows a batch with seeds [100, 101] to
+ # produce the same images as with two batches [100], [101].
+ if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
+ sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
+ else:
+ sampler_noises = None
+
for i, seed in enumerate(seeds):
noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
@@ -155,9 +165,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
noise = x
+ if sampler_noises is not None:
+ cnt = p.sampler.number_of_needed_noises(p)
+ for j in range(cnt):
+ sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
xs.append(noise)
+
+ if sampler_noises is not None:
+ p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
+
x = torch.stack(xs).to(shared.device)
return x
@@ -170,7 +188,11 @@ def fix_seed(p):
def process_images(p: StableDiffusionProcessing) -> Processed:
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
- assert p.prompt is not None
+ if type(p.prompt) == list:
+ assert(len(p.prompt) > 0)
+ else:
+ assert p.prompt is not None
+
devices.torch_gc()
fix_seed(p)
@@ -209,7 +231,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
- "Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model_hash else shared.sd_model_hash),
+ "Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
"Batch size": (None if p.batch_size < 2 else p.batch_size),
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
@@ -247,6 +269,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+ if (len(prompts) == 0):
+ break
+
#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
#c = p.sd_model.get_learned_conditioning(prompts)
uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
@@ -257,7 +282,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
comments[comment] = 1
# we manually generate all input noises because each one should have a specific seed
- x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
+ x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
diff --git a/modules/sd_models.py b/modules/sd_models.py
new file mode 100644
index 000000000..4bd70fc5e
--- /dev/null
+++ b/modules/sd_models.py
@@ -0,0 +1,148 @@
+import glob
+import os.path
+import sys
+from collections import namedtuple
+import torch
+from omegaconf import OmegaConf
+
+
+from ldm.util import instantiate_from_config
+
+from modules import shared
+
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
+checkpoints_list = {}
+
+try:
+ # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
+
+ from transformers import logging
+
+ logging.set_verbosity_error()
+except Exception:
+ pass
+
+
+def list_models():
+ checkpoints_list.clear()
+
+ model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
+
+ def modeltitle(path, h):
+ abspath = os.path.abspath(path)
+
+ if abspath.startswith(model_dir):
+ name = abspath.replace(model_dir, '')
+ else:
+ name = os.path.basename(path)
+
+ if name.startswith("\\") or name.startswith("/"):
+ name = name[1:]
+
+ return f'{name} [{h}]'
+
+ cmd_ckpt = shared.cmd_opts.ckpt
+ if os.path.exists(cmd_ckpt):
+ h = model_hash(cmd_ckpt)
+ title = modeltitle(cmd_ckpt, h)
+ checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
+ elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
+ print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
+
+ if os.path.exists(model_dir):
+ for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
+ h = model_hash(filename)
+ title = modeltitle(filename, h)
+ checkpoints_list[title] = CheckpointInfo(filename, title, h)
+
+
+def model_hash(filename):
+ try:
+ with open(filename, "rb") as file:
+ import hashlib
+ m = hashlib.sha256()
+
+ file.seek(0x100000)
+ m.update(file.read(0x10000))
+ return m.hexdigest()[0:8]
+ except FileNotFoundError:
+ return 'NOFILE'
+
+
+def select_checkpoint():
+ model_checkpoint = shared.opts.sd_model_checkpoint
+ checkpoint_info = checkpoints_list.get(model_checkpoint, None)
+ if checkpoint_info is not None:
+ return checkpoint_info
+
+ if len(checkpoints_list) == 0:
+ print(f"Checkpoint {model_checkpoint} not found and no other checkpoints found", file=sys.stderr)
+ return None
+
+ checkpoint_info = next(iter(checkpoints_list.values()))
+ if model_checkpoint is not None:
+ print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
+
+ return checkpoint_info
+
+
+def load_model_weights(model, checkpoint_file, sd_model_hash):
+ print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+
+ pl_sd = torch.load(checkpoint_file, map_location="cpu")
+ if "global_step" in pl_sd:
+ print(f"Global Step: {pl_sd['global_step']}")
+ sd = pl_sd["state_dict"]
+
+ model.load_state_dict(sd, strict=False)
+
+ if shared.cmd_opts.opt_channelslast:
+ model.to(memory_format=torch.channels_last)
+
+ if not shared.cmd_opts.no_half:
+ model.half()
+
+ model.sd_model_hash = sd_model_hash
+ model.sd_model_checkpint = checkpoint_file
+
+
+def load_model():
+ from modules import lowvram, sd_hijack
+ checkpoint_info = select_checkpoint()
+
+ sd_config = OmegaConf.load(shared.cmd_opts.config)
+ sd_model = instantiate_from_config(sd_config.model)
+ load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
+ else:
+ sd_model.to(shared.device)
+
+ sd_hijack.model_hijack.hijack(sd_model)
+
+ sd_model.eval()
+
+ print(f"Model loaded.")
+ return sd_model
+
+
+def reload_model_weights(sd_model, info=None):
+ from modules import lowvram, devices
+ checkpoint_info = info or select_checkpoint()
+
+ if sd_model.sd_model_checkpint == checkpoint_info.filename:
+ return
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ sd_model.to(devices.cpu)
+
+ load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+
+ if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+ sd_model.to(devices.device)
+
+ print(f"Weights loaded.")
+ return sd_model
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index df3a6fe8e..1b3dc3023 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -38,6 +38,17 @@ samplers = [
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
+def setup_img2img_steps(p):
+ if opts.img2img_fix_steps:
+ steps = int(p.steps / min(p.denoising_strength, 0.999))
+ t_enc = p.steps - 1
+ else:
+ steps = p.steps
+ t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+ return steps, t_enc
+
+
def sample_to_image(samples):
x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
@@ -80,8 +91,12 @@ class VanillaStableDiffusionSampler:
self.mask = None
self.nmask = None
self.init_latent = None
+ self.sampler_noises = None
self.step = 0
+ def number_of_needed_noises(self, p):
+ return 0
+
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
@@ -101,13 +116,13 @@ class VanillaStableDiffusionSampler:
return res
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
- t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
+ steps, t_enc = setup_img2img_steps(p)
# existing code fails with cetain step counts, like 9
try:
- self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
+ self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
except Exception:
- self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
+ self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
@@ -115,6 +130,7 @@ class VanillaStableDiffusionSampler:
self.mask = p.mask
self.nmask = p.nmask
self.init_latent = p.init_latent
+ self.step = 0
samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
@@ -127,6 +143,7 @@ class VanillaStableDiffusionSampler:
self.mask = None
self.nmask = None
self.init_latent = None
+ self.step = 0
# existing code fails with cetin step counts, like 9
try:
@@ -183,42 +200,82 @@ def extended_trange(count, *args, **kwargs):
shared.total_tqdm.update()
+class TorchHijack:
+ def __init__(self, kdiff_sampler):
+ self.kdiff_sampler = kdiff_sampler
+
+ def __getattr__(self, item):
+ if item == 'randn_like':
+ return self.kdiff_sampler.randn_like
+
+ if hasattr(torch, item):
+ return getattr(torch, item)
+
+ raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+
class KDiffusionSampler:
def __init__(self, funcname, sd_model):
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+ self.sampler_noises = None
+ self.sampler_noise_index = 0
def callback_state(self, d):
store_latent(d["denoised"])
- def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
- t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
- sigmas = self.model_wrap.get_sigmas(p.steps)
+ def number_of_needed_noises(self, p):
+ return p.steps
- noise = noise * sigmas[p.steps - t_enc - 1]
+ def randn_like(self, x):
+ noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
+
+ if noise is not None and x.shape == noise.shape:
+ res = noise
+ else:
+ res = torch.randn_like(x)
+
+ self.sampler_noise_index += 1
+ return res
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
+ steps, t_enc = setup_img2img_steps(p)
+
+ sigmas = self.model_wrap.get_sigmas(steps)
+
+ noise = noise * sigmas[steps - t_enc - 1]
xi = x + noise
- sigma_sched = sigmas[p.steps - t_enc - 1:]
+ sigma_sched = sigmas[steps - t_enc - 1:]
self.model_wrap_cfg.mask = p.mask
self.model_wrap_cfg.nmask = p.nmask
self.model_wrap_cfg.init_latent = p.init_latent
+ self.model_wrap.step = 0
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
+ if self.sampler_noises is not None:
+ k_diffusion.sampling.torch = TorchHijack(self)
+
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
def sample(self, p, x, conditioning, unconditional_conditioning):
sigmas = self.model_wrap.get_sigmas(p.steps)
x = x * sigmas[0]
+ self.model_wrap_cfg.step = 0
+
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
+ if self.sampler_noises is not None:
+ k_diffusion.sampling.torch = TorchHijack(self)
+
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
return samples_ddim
diff --git a/modules/shared.py b/modules/shared.py
index 78450546e..3c3aa9b62 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -12,14 +12,16 @@ from modules.paths import script_path, sd_path
from modules.devices import get_optimal_device
import modules.styles
import modules.interrogate
+import modules.memmon
+import modules.sd_models
sd_model_file = os.path.join(script_path, 'model.ckpt')
-if not os.path.exists(sd_model_file):
- sd_model_file = "models/ldm/stable-diffusion-v1/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("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
+parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), 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'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@@ -87,13 +89,17 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
face_restorers = []
+modules.sd_models.list_models()
+
+
class Options:
class OptionInfo:
- def __init__(self, default=None, label="", component=None, component_args=None):
+ def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
+ self.onchange = onchange
data = None
hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
@@ -125,9 +131,11 @@ class Options:
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"add_model_hash_to_info": OptionInfo(False, "Add model hash to generation information"),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+ "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normaly you'd do less with less denoising)."),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
"font": OptionInfo("", "Font for image grids that have text"),
"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+ "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
"ESRGAN_tile": OptionInfo(192, "Tile size for upscaling. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for upscaling. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
@@ -136,6 +144,7 @@ class Options:
"show_progressbar": OptionInfo(True, "Show progressbar"),
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
+ "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step":1}),
"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
@@ -146,6 +155,7 @@ class Options:
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
+ "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
}
def __init__(self):
@@ -176,6 +186,10 @@ class Options:
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
+ def onchange(self, key, func):
+ item = self.data_labels.get(key)
+ item.onchange = func
+
opts = Options()
if os.path.exists(config_filename):
@@ -184,7 +198,6 @@ if os.path.exists(config_filename):
sd_upscalers = []
sd_model = None
-sd_model_hash = ''
progress_print_out = sys.stdout
@@ -215,3 +228,6 @@ class TotalTQDM:
total_tqdm = TotalTQDM()
+
+mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
+mem_mon.start()
diff --git a/modules/ui.py b/modules/ui.py
index b6d5dcd86..2f6eb3070 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -119,6 +119,7 @@ def save_files(js_data, images, index):
def wrap_gradio_call(func):
def f(*args, **kwargs):
+ shared.mem_mon.monitor()
t = time.perf_counter()
try:
@@ -135,8 +136,20 @@ def wrap_gradio_call(func):
elapsed = time.perf_counter() - t
+ mem_stats = {k: -(v//-(1024*1024)) for k,v in shared.mem_mon.stop().items()}
+ active_peak = mem_stats['active_peak']
+ reserved_peak = mem_stats['reserved_peak']
+ sys_peak = '?' if opts.memmon_poll_rate <= 0 else mem_stats['system_peak']
+ sys_total = mem_stats['total']
+ sys_pct = '?' if opts.memmon_poll_rate <= 0 else round(sys_peak/sys_total * 100, 2)
+ vram_tooltip = "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.
" \
+ "Torch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.
" \
+ "Sys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%)."
+
+ vram_html = '' if opts.memmon_poll_rate == 0 else f"Torch active/reserved: {active_peak}/{reserved_peak} MiB, Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)
"
+
# last item is always HTML
- res[-1] = res[-1] + f"Time taken: {elapsed:.2f}s
"
+ res[-1] += f""
shared.state.interrupted = False
@@ -324,6 +337,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
with gr.Column(variant='panel'):
+ progressbar = gr.HTML(elem_id="progressbar")
+
with gr.Group():
txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery').style(grid=4)
@@ -336,8 +351,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
send_to_extras = gr.Button('Send to extras')
interrupt = gr.Button('Interrupt')
- progressbar = gr.HTML(elem_id="progressbar")
-
with gr.Group():
html_info = gr.HTML()
generation_info = gr.Textbox(visible=False)
@@ -461,6 +474,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
with gr.Column(variant='panel'):
+ progressbar = gr.HTML(elem_id="progressbar")
+
with gr.Group():
img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery').style(grid=4)
@@ -474,7 +489,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
interrupt = gr.Button('Interrupt')
img2img_save_style = gr.Button('Save prompt as style')
- progressbar = gr.HTML(elem_id="progressbar")
with gr.Group():
html_info = gr.HTML()
@@ -649,7 +663,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
with gr.TabItem('Batch Process'):
- image_batch = gr.File(label="Batch Process", file_count="multiple", source="upload", interactive=True, type="file")
+ image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
@@ -745,7 +759,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
continue
+ oldval = opts.data.get(key, None)
opts.data[key] = value
+
+ if oldval != value and opts.data_labels[key].onchange is not None:
+ opts.data_labels[key].onchange()
+
up.append(comp.update(value=value))
opts.save(shared.config_filename)
@@ -782,6 +801,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
css = file.read()
+ if os.path.exists(os.path.join(script_path, "user.css")):
+ with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+ usercss = file.read()
+ css += usercss
+
if not cmd_opts.no_progressbar_hiding:
css += css_hide_progressbar
diff --git a/script.js b/script.js
index 0852e421d..113d43351 100644
--- a/script.js
+++ b/script.js
@@ -66,6 +66,8 @@ titles = {
"Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",
"Apply style": "Insert selected styles into prompt fields",
"Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
+
+ "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
}
function gradioApp(){
@@ -74,6 +76,90 @@ function gradioApp(){
global_progressbar = null
+function closeModal() {
+ gradioApp().getElementById("lightboxModal").style.display = "none";
+}
+
+function showModal(event) {
+ var source = event.target || event.srcElement;
+ gradioApp().getElementById("modalImage").src = source.src
+ var lb = gradioApp().getElementById("lightboxModal")
+ lb.style.display = "block";
+ lb.focus()
+ event.stopPropagation()
+}
+
+function negmod(n, m) {
+ return ((n % m) + m) % m;
+}
+
+function modalImageSwitch(offset){
+ var galleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
+
+ if(galleryButtons.length>1){
+ var currentButton = gradioApp().querySelector(".gallery-item.transition-all.\\!ring-2")
+
+ var result = -1
+ galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } })
+
+ if(result != -1){
+ nextButton = galleryButtons[negmod((result+offset),galleryButtons.length)]
+ nextButton.click()
+ gradioApp().getElementById("modalImage").src = nextButton.children[0].src
+ setTimeout( function(){gradioApp().getElementById("lightboxModal").focus()},10)
+ }
+ }
+
+}
+
+function modalNextImage(event){
+ modalImageSwitch(1)
+ event.stopPropagation()
+}
+
+function modalPrevImage(event){
+ modalImageSwitch(-1)
+ event.stopPropagation()
+}
+
+function modalKeyHandler(event){
+ switch (event.key) {
+ case "ArrowLeft":
+ modalPrevImage(event)
+ break;
+ case "ArrowRight":
+ modalNextImage(event)
+ break;
+ }
+}
+
+function showGalleryImage(){
+ setTimeout(function() {
+ fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
+
+ if(fullImg_preview != null){
+ fullImg_preview.forEach(function function_name(e) {
+ if(e && e.parentElement.tagName == 'DIV'){
+
+ e.style.cursor='pointer'
+
+ e.addEventListener('click', function (evt) {
+ showModal(evt)
+
+ },true);
+ }
+ });
+ }
+
+ }, 100);
+}
+
+function galleryImageHandler(e){
+ if(e && e.parentElement.tagName == 'BUTTON'){
+ e.onclick = showGalleryImage;
+ }
+}
+
function addTitles(root){
root.querySelectorAll('span, button, select').forEach(function(span){
tooltip = titles[span.textContent];
@@ -115,13 +201,18 @@ function addTitles(root){
img2img_preview.style.width = img2img_gallery.clientWidth + "px"
img2img_preview.style.height = img2img_gallery.clientHeight + "px"
}
-
-
+
window.setTimeout(requestProgress, 500)
});
mutationObserver.observe( progressbar, { childList:true, subtree:true })
}
+
+ fullImg_preview = gradioApp().querySelectorAll('img.w-full')
+ if(fullImg_preview != null){
+ fullImg_preview.forEach(galleryImageHandler);
+ }
+
}
document.addEventListener("DOMContentLoaded", function() {
@@ -129,6 +220,49 @@ document.addEventListener("DOMContentLoaded", function() {
addTitles(gradioApp());
});
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
+
+ const modalFragment = document.createDocumentFragment();
+ const modal = document.createElement('div')
+ modal.onclick = closeModal;
+
+ const modalClose = document.createElement('span')
+ modalClose.className = 'modalClose cursor';
+ modalClose.innerHTML = '×'
+ modalClose.onclick = closeModal;
+ modal.id = "lightboxModal";
+ modal.tabIndex=0
+ modal.addEventListener('keydown', modalKeyHandler, true)
+ modal.appendChild(modalClose)
+
+ const modalImage = document.createElement('img')
+ modalImage.id = 'modalImage';
+ modalImage.onclick = closeModal;
+ modalImage.tabIndex=0
+ modalImage.addEventListener('keydown', modalKeyHandler, true)
+ modal.appendChild(modalImage)
+
+ const modalPrev = document.createElement('a')
+ modalPrev.className = 'modalPrev';
+ modalPrev.innerHTML = '❮'
+ modalPrev.tabIndex=0
+ modalPrev.addEventListener('click',modalPrevImage,true);
+ modalPrev.addEventListener('keydown', modalKeyHandler, true)
+ modal.appendChild(modalPrev)
+
+ const modalNext = document.createElement('a')
+ modalNext.className = 'modalNext';
+ modalNext.innerHTML = '❯'
+ modalNext.tabIndex=0
+ modalNext.addEventListener('click',modalNextImage,true);
+ modalNext.addEventListener('keydown', modalKeyHandler, true)
+
+ modal.appendChild(modalNext)
+
+
+ gradioApp().getRootNode().appendChild(modal)
+
+ document.body.appendChild(modalFragment);
+
});
function selected_gallery_index(){
@@ -180,6 +314,15 @@ function submit(){
for(var i=0;i 2: # has channels
+ out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+ for c in range(data.shape[2]):
+ c_data = data[:, :, c]
+ out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
+ out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
+ else: # one channel
+ out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+ out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
+ out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
+
+ return out_fft
+
+ def _ifft2(data):
+ if data.ndim > 2: # has channels
+ out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+ for c in range(data.shape[2]):
+ c_data = data[:, :, c]
+ out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
+ out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
+ else: # one channel
+ out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+ out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
+ out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
+
+ return out_ifft
+
+ def _get_gaussian_window(width, height, std=3.14, mode=0):
+ window_scale_x = float(width / min(width, height))
+ window_scale_y = float(height / min(width, height))
+
+ window = np.zeros((width, height))
+ x = (np.arange(width) / width * 2. - 1.) * window_scale_x
+ for y in range(height):
+ fy = (y / height * 2. - 1.) * window_scale_y
+ if mode == 0:
+ window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
+ else:
+ window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian
+
+ return window
+
+ def _get_masked_window_rgb(np_mask_grey, hardness=1.):
+ np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
+ if hardness != 1.:
+ hardened = np_mask_grey[:] ** hardness
+ else:
+ hardened = np_mask_grey[:]
+ for c in range(3):
+ np_mask_rgb[:, :, c] = hardened[:]
+ return np_mask_rgb
+
+ width = _np_src_image.shape[0]
+ height = _np_src_image.shape[1]
+ num_channels = _np_src_image.shape[2]
+
+ np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
+ np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
+ img_mask = np_mask_grey > 1e-6
+ ref_mask = np_mask_grey < 1e-3
+
+ windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
+ windowed_image /= np.max(windowed_image)
+ windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
+
+ src_fft = _fft2(windowed_image) # get feature statistics from masked src img
+ src_dist = np.absolute(src_fft)
+ src_phase = src_fft / src_dist
+
+ noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
+ noise_rgb = np.random.random_sample((width, height, num_channels))
+ noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
+ noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
+ for c in range(num_channels):
+ noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
+
+ noise_fft = _fft2(noise_rgb)
+ for c in range(num_channels):
+ noise_fft[:, :, c] *= noise_window
+ noise_rgb = np.real(_ifft2(noise_fft))
+ shaped_noise_fft = _fft2(noise_rgb)
+ shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
+
+ brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now
+ contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
+
+ # scikit-image is used for histogram matching, very convenient!
+ shaped_noise = np.real(_ifft2(shaped_noise_fft))
+ shaped_noise -= np.min(shaped_noise)
+ shaped_noise /= np.max(shaped_noise)
+ shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
+ shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
+
+ matched_noise = shaped_noise[:]
+
+ return np.clip(matched_noise, 0., 1.)
+
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "Outpainting mk2"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+ if not is_img2img:
+ return None
+
+ info = gr.HTML("Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8
")
+
+ pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
+ direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
+ noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
+ color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)
+
+ return [info, pixels, mask_blur, direction, noise_q, color_variation]
+
+ def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
+ initial_seed_and_info = [None, None]
+
+ process_width = p.width
+ process_height = p.height
+
+ p.mask_blur = mask_blur*4
+ p.inpaint_full_res = False
+ p.inpainting_fill = 1
+ p.do_not_save_samples = True
+ p.do_not_save_grid = True
+
+ left = pixels if "left" in direction else 0
+ right = pixels if "right" in direction else 0
+ up = pixels if "up" in direction else 0
+ down = pixels if "down" in direction else 0
+
+ init_img = p.init_images[0]
+ target_w = math.ceil((init_img.width + left + right) / 64) * 64
+ target_h = math.ceil((init_img.height + up + down) / 64) * 64
+
+ if left > 0:
+ left = left * (target_w - init_img.width) // (left + right)
+ if right > 0:
+ right = target_w - init_img.width - left
+
+ if up > 0:
+ up = up * (target_h - init_img.height) // (up + down)
+
+ if down > 0:
+ down = target_h - init_img.height - up
+
+ init_image = p.init_images[0]
+
+ state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0)+ (1 if up > 0 else 0)+ (1 if down > 0 else 0)
+
+ def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+ is_horiz = is_left or is_right
+ is_vert = is_top or is_bottom
+ pixels_horiz = expand_pixels if is_horiz else 0
+ pixels_vert = expand_pixels if is_vert else 0
+
+ img = Image.new("RGB", (init.width + pixels_horiz, init.height + pixels_vert))
+ img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+ mask = Image.new("RGB", (init.width + pixels_horiz, init.height + pixels_vert), "white")
+ draw = ImageDraw.Draw(mask)
+ draw.rectangle((
+ expand_pixels + mask_blur if is_left else 0,
+ expand_pixels + mask_blur if is_top else 0,
+ mask.width - expand_pixels - mask_blur if is_right else mask.width,
+ mask.height - expand_pixels - mask_blur if is_bottom else mask.height,
+ ), fill="black")
+
+ np_image = (np.asarray(img) / 255.0).astype(np.float64)
+ np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+ noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+ out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
+
+ target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
+ target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
+
+ crop_region = (
+ 0 if is_left else out.width - target_width,
+ 0 if is_top else out.height - target_height,
+ target_width if is_left else out.width,
+ target_height if is_top else out.height,
+ )
+
+ image_to_process = out.crop(crop_region)
+ mask = mask.crop(crop_region)
+
+ p.width = target_width if is_horiz else img.width
+ p.height = target_height if is_vert else img.height
+ p.init_images = [image_to_process]
+ p.image_mask = mask
+
+ latent_mask = Image.new("RGB", (p.width, p.height), "white")
+ draw = ImageDraw.Draw(latent_mask)
+ draw.rectangle((
+ expand_pixels + mask_blur * 2 if is_left else 0,
+ expand_pixels + mask_blur * 2 if is_top else 0,
+ mask.width - expand_pixels - mask_blur * 2 if is_right else mask.width,
+ mask.height - expand_pixels - mask_blur * 2 if is_bottom else mask.height,
+ ), fill="black")
+ p.latent_mask = latent_mask
+
+ proc = process_images(p)
+ proc_img = proc.images[0]
+
+ if initial_seed_and_info[0] is None:
+ initial_seed_and_info[0] = proc.seed
+ initial_seed_and_info[1] = proc.info
+
+ out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
+ return out
+
+ img = init_image
+
+ if left > 0:
+ img = expand(img, left, is_left=True)
+ if right > 0:
+ img = expand(img, right, is_right=True)
+ if up > 0:
+ img = expand(img, up, is_top=True)
+ if down > 0:
+ img = expand(img, down, is_bottom=True)
+
+ res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1])
+
+ if opts.samples_save:
+ images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+
+ return res
+
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index d9b01c81b..513d9a1c5 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -13,28 +13,42 @@ from modules.shared import opts, cmd_opts, state
class Script(scripts.Script):
def title(self):
- return "Prompts from file"
+ return "Prompts from file or textbox"
def ui(self, is_img2img):
+ # This checkbox would look nicer as two tabs, but there are two problems:
+ # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
+ # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
+ # causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
+ # due to the way Script assumes all controls returned can be used as inputs.
+ # Therefore, there's no good way to use grouping components right now,
+ # so we will use a checkbox! :)
+ checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
file = gr.File(label="File with inputs", type='bytes')
+ prompt_txt = gr.TextArea(label="Prompts")
+ checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
+ return [checkbox_txt, file, prompt_txt]
- return [file]
-
- def run(self, p, data: bytes):
- lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
+ def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
+ if (checkbox_txt):
+ lines = [x.strip() for x in prompt_txt.splitlines()]
+ else:
+ lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
lines = [x for x in lines if len(x) > 0]
- batch_count = math.ceil(len(lines) / p.batch_size)
- print(f"Will process {len(lines) * p.n_iter} images in {batch_count * p.n_iter} batches.")
+ img_count = len(lines) * p.n_iter
+ batch_count = math.ceil(img_count / p.batch_size)
+ loop_count = math.ceil(batch_count / p.n_iter)
+ print(f"Will process {img_count} images in {batch_count} batches.")
p.do_not_save_grid = True
state.job_count = batch_count
images = []
- for batch_no in range(batch_count):
- state.job = f"{batch_no + 1} out of {batch_count * p.n_iter}"
- p.prompt = lines[batch_no*p.batch_size:(batch_no+1)*p.batch_size] * p.n_iter
+ for loop_no in range(loop_count):
+ state.job = f"{loop_no + 1} out of {loop_count}"
+ p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
proc = process_images(p)
images += proc.images
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index eccfda877..6a157722a 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -10,7 +10,9 @@ import gradio as gr
from modules import images
from modules.processing import process_images, Processed
from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
import modules.sd_samplers
+import modules.sd_models
import re
@@ -41,6 +43,15 @@ def apply_sampler(p, x, xs):
p.sampler_index = sampler_index
+def apply_checkpoint(p, x, xs):
+ applicable = [info for info in modules.sd_models.checkpoints_list.values() if x in info.title]
+ assert len(applicable) > 0, f'Checkpoint {x} for found'
+
+ info = applicable[0]
+
+ modules.sd_models.reload_model_weights(shared.sd_model, info)
+
+
def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
@@ -74,15 +85,16 @@ axis_options = [
AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
AxisOption("Prompt S/R", str, apply_prompt, format_value),
AxisOption("Sampler", str, apply_sampler, format_value),
+ AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]
-def draw_xy_grid(p, xs, ys, x_label, y_label, cell, draw_legend):
+def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
res = []
- ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
- hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
+ ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
+ hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
first_pocessed = None
@@ -206,8 +218,8 @@ class Script(scripts.Script):
p,
xs=xs,
ys=ys,
- x_label=lambda x: x_opt.format_value(p, x_opt, x),
- y_label=lambda y: y_opt.format_value(p, y_opt, y),
+ x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
+ y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
cell=cell,
draw_legend=draw_legend
)
@@ -215,4 +227,7 @@ class Script(scripts.Script):
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+ # restore checkpoint in case it was changed by axes
+ modules.sd_models.reload_model_weights(shared.sd_model)
+
return processed
diff --git a/style.css b/style.css
index d41c098cb..26c759d76 100644
--- a/style.css
+++ b/style.css
@@ -1,17 +1,48 @@
.output-html p {margin: 0 0.5em;}
-.performance { font-size: 0.85em; color: #444; }
+
+.performance {
+ font-size: 0.85em;
+ color: #444;
+ display: flex;
+ justify-content: space-between;
+ white-space: nowrap;
+}
+
+.performance .time {
+ margin-right: 0;
+}
+
+.performance .vram {
+ margin-left: 0;
+ text-align: right;
+}
#generate{
min-height: 4.5em;
}
-#txt2img_gallery, #img2img_gallery{
- min-height: 768px;
+@media screen and (min-width: 2500px) {
+ #txt2img_gallery, #img2img_gallery {
+ min-height: 768px;
+ }
}
+
#txt2img_gallery img, #img2img_gallery img{
object-fit: scale-down;
}
+.justify-center.overflow-x-scroll {
+ justify-content: left;
+}
+
+.justify-center.overflow-x-scroll button:first-of-type {
+ margin-left: auto;
+}
+
+.justify-center.overflow-x-scroll button:last-of-type {
+ margin-right: auto;
+}
+
#subseed_show{
min-width: 6em;
max-width: 6em;
@@ -151,6 +182,12 @@ input[type="range"]{
#txt2img_negative_prompt, #img2img_negative_prompt{
}
+#progressbar{
+ position: absolute;
+ z-index: 1000;
+ right: 0;
+}
+
.progressDiv{
width: 100%;
height: 30px;
@@ -174,3 +211,66 @@ input[type="range"]{
border-radius: 8px;
}
+#lightboxModal{
+ display: none;
+ position: fixed;
+ z-index: 900;
+ padding-top: 100px;
+ left: 0;
+ top: 0;
+ width: 100%;
+ height: 100%;
+ overflow: auto;
+ background-color: rgba(20, 20, 20, 0.95);
+}
+
+.modalClose {
+ color: white;
+ position: absolute;
+ top: 10px;
+ right: 25px;
+ font-size: 35px;
+ font-weight: bold;
+}
+
+.modalClose:hover,
+.modalClose:focus {
+ color: #999;
+ text-decoration: none;
+ cursor: pointer;
+}
+
+#modalImage {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+ margin-top: auto;
+ width: auto;
+}
+
+.modalPrev,
+.modalNext {
+ cursor: pointer;
+ position: absolute;
+ top: 50%;
+ width: auto;
+ padding: 16px;
+ margin-top: -50px;
+ color: white;
+ font-weight: bold;
+ font-size: 20px;
+ transition: 0.6s ease;
+ border-radius: 0 3px 3px 0;
+ user-select: none;
+ -webkit-user-select: none;
+}
+
+.modalNext {
+ right: 0;
+ border-radius: 3px 0 0 3px;
+}
+
+.modalPrev:hover,
+.modalNext:hover {
+ background-color: rgba(0, 0, 0, 0.8);
+}
diff --git a/webui.py b/webui.py
index add721233..ff8997dbb 100644
--- a/webui.py
+++ b/webui.py
@@ -3,13 +3,8 @@ import threading
from modules.paths import script_path
-import torch
-from omegaconf import OmegaConf
-
import signal
-from ldm.util import instantiate_from_config
-
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.ui
@@ -24,6 +19,7 @@ import modules.extras
import modules.lowvram
import modules.txt2img
import modules.img2img
+import modules.sd_models
modules.codeformer_model.setup_codeformer()
@@ -33,31 +29,19 @@ shared.face_restorers.append(modules.face_restoration.FaceRestoration())
esrgan.load_models(cmd_opts.esrgan_models_path)
realesrgan.setup_realesrgan()
-
-def load_model_from_config(config, ckpt, verbose=False):
- print(f"Loading model [{shared.sd_model_hash}] from {ckpt}")
- pl_sd = torch.load(ckpt, map_location="cpu")
- if "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
- sd = pl_sd["state_dict"]
-
- model = instantiate_from_config(config.model)
- m, u = model.load_state_dict(sd, strict=False)
- if len(m) > 0 and verbose:
- print("missing keys:")
- print(m)
- if len(u) > 0 and verbose:
- print("unexpected keys:")
- print(u)
- if cmd_opts.opt_channelslast:
- model = model.to(memory_format=torch.channels_last)
- model.eval()
- return model
-
-
queue_lock = threading.Lock()
+def wrap_queued_call(func):
+ def f(*args, **kwargs):
+ with queue_lock:
+ res = func(*args, **kwargs)
+
+ return res
+
+ return f
+
+
def wrap_gradio_gpu_call(func):
def f(*args, **kwargs):
shared.state.sampling_step = 0
@@ -80,33 +64,8 @@ def wrap_gradio_gpu_call(func):
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
-try:
- # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
- from transformers import logging
-
- logging.set_verbosity_error()
-except Exception:
- pass
-
-with open(cmd_opts.ckpt, "rb") as file:
- import hashlib
- m = hashlib.sha256()
-
- file.seek(0x100000)
- m.update(file.read(0x10000))
- shared.sd_model_hash = m.hexdigest()[0:8]
-
-sd_config = OmegaConf.load(cmd_opts.config)
-shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
-shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
-
-if cmd_opts.lowvram or cmd_opts.medvram:
- modules.lowvram.setup_for_low_vram(shared.sd_model, cmd_opts.medvram)
-else:
- shared.sd_model = shared.sd_model.to(shared.device)
-
-modules.sd_hijack.model_hijack.hijack(shared.sd_model)
+shared.sd_model = modules.sd_models.load_model()
+shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
def webui():