diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | README.md | 3 | ||||
| -rw-r--r-- | models/Put Stable Diffusion checkpoints here.txt | 0 | ||||
| -rw-r--r-- | modules/devices.py | 10 | ||||
| -rw-r--r-- | modules/extras.py | 10 | ||||
| -rw-r--r-- | modules/images.py | 13 | ||||
| -rw-r--r-- | modules/memmon.py | 77 | ||||
| -rw-r--r-- | modules/processing.py | 33 | ||||
| -rw-r--r-- | modules/sd_models.py | 148 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 71 | ||||
| -rw-r--r-- | modules/shared.py | 26 | ||||
| -rw-r--r-- | modules/ui.py | 34 | ||||
| -rw-r--r-- | script.js | 147 | ||||
| -rw-r--r-- | scripts/img2imgalt.py | 35 | ||||
| -rw-r--r-- | scripts/outpainting_mk_2.py | 290 | ||||
| -rw-r--r-- | scripts/prompts_from_file.py | 36 | ||||
| -rw-r--r-- | scripts/xy_grid.py | 25 | ||||
| -rw-r--r-- | style.css | 106 | ||||
| -rw-r--r-- | webui.py | 61 |
19 files changed, 1011 insertions, 115 deletions
@@ -16,3 +16,4 @@ __pycache__ /webui-user.bat /webui-user.sh /interrogate +/user.css @@ -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 00000000..e69de29b --- /dev/null +++ b/models/Put Stable Diffusion checkpoints here.txt diff --git a/modules/devices.py b/modules/devices.py index e4430e1a..07bb2339 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 ffae7d67..3d9d9f7a 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 f37f5f08..e287d0df 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 00000000..f2cac841 --- /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 71a9c6f5..6a99d383 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 00000000..4bd70fc5 --- /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 df3a6fe8..1b3dc302 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 number_of_needed_noises(self, p):
+ return p.steps
+
+ 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):
- t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
- sigmas = self.model_wrap.get_sigmas(p.steps)
+ steps, t_enc = setup_img2img_steps(p)
+
+ sigmas = self.model_wrap.get_sigmas(steps)
- noise = noise * sigmas[p.steps - t_enc - 1]
+ 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 78450546..3c3aa9b6 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 b6d5dcd8..2f6eb307 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"<p class='vram' title='{vram_tooltip}'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+
# last item is always HTML
- res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
+ res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
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")
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