diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/deepbooru.py | 3 | ||||
| -rw-r--r-- | modules/extras.py | 7 | ||||
| -rw-r--r-- | modules/processing.py | 5 | ||||
| -rw-r--r-- | modules/scripts.py | 15 | ||||
| -rw-r--r-- | modules/sd_hijack_optimizations.py | 14 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 107 | ||||
| -rw-r--r-- | modules/shared.py | 3 | ||||
| -rw-r--r-- | modules/styles.py | 4 | ||||
| -rw-r--r-- | modules/ui.py | 14 |
9 files changed, 103 insertions, 69 deletions
diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 4ad334a1..8914662d 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -157,8 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o # sort by reverse by likelihood and normal for alpha, and format tag text as requested unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) for weight, tag in unsorted_tags_in_theshold: - # note: tag_outformat will still have a colon if include_ranks is True - tag_outformat = tag.replace(':', ' ') + tag_outformat = tag if use_spaces: tag_outformat = tag_outformat.replace('_', ' ') if use_escape: diff --git a/modules/extras.py b/modules/extras.py index c908b43e..b853fa5b 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -216,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam if theta_func1:
for key in tqdm.tqdm(theta_1.keys()):
if 'model' in key:
- t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
- theta_1[key] = theta_func1(theta_1[key], t2)
+ if key in theta_2:
+ t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+ theta_1[key] = theta_func1(theta_1[key], t2)
+ else:
+ theta_1[key] = torch.zeros_like(theta_1[key])
del theta_2, teritary_model
for key in tqdm.tqdm(theta_0.keys()):
diff --git a/modules/processing.py b/modules/processing.py index 6c4bde6e..e965d41c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -419,11 +419,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed: samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
subseed_strength=p.subseed_strength)
- if state.interrupted or state.skipped:
- # if we are interrupted, sample returns just noise
- # use the image collected previously in sampler loop
- samples_ddim = shared.state.current_latent
-
samples_ddim = samples_ddim.to(devices.dtype_vae)
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/scripts.py b/modules/scripts.py index ac66d448..1039fa9c 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs): class ScriptRunner:
def __init__(self):
self.scripts = []
+ self.titles = []
def setup_ui(self, is_img2img):
for script_class, path in scripts_data:
@@ -107,9 +108,10 @@ class ScriptRunner: self.scripts.append(script)
- titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+ self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
- dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+ dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+ dropdown.save_to_config = True
inputs = [dropdown]
for script in self.scripts:
@@ -139,6 +141,15 @@ class ScriptRunner: return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+ def init_field(title):
+ if title == 'None':
+ return
+ script_index = self.titles.index(title)
+ script = self.scripts[script_index]
+ for i in range(script.args_from, script.args_to):
+ inputs[i].visible = True
+
+ dropdown.init_field = init_field
dropdown.change(
fn=select_script,
inputs=[dropdown],
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 79405525..a3345bb9 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -296,10 +296,16 @@ def xformers_attnblock_forward(self, x): try:
h_ = x
h_ = self.norm(h_)
- q1 = self.q(h_).contiguous()
- k1 = self.k(h_).contiguous()
- v = self.v(h_).contiguous()
- out = xformers.ops.memory_efficient_attention(q1, k1, v)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = xformers.ops.memory_efficient_attention(q, k, v)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
out = self.proj_out(out)
return x + out
except NotImplementedError:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 20309e06..b58e810b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -98,25 +98,8 @@ def store_latent(decoded): shared.state.current_image = sample_to_image(decoded)
-
-def extended_tdqm(sequence, *args, desc=None, **kwargs):
- state.sampling_steps = len(sequence)
- state.sampling_step = 0
-
- seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
- for x in seq:
- if state.interrupted or state.skipped:
- break
-
- yield x
-
- state.sampling_step += 1
- shared.total_tqdm.update()
-
-
-ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
-ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
+class InterruptedException(BaseException):
+ pass
class VanillaStableDiffusionSampler:
@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler: self.init_latent = None
self.sampler_noises = None
self.step = 0
+ self.stop_at = None
self.eta = None
self.default_eta = 0.0
self.config = None
+ self.last_latent = None
def number_of_needed_noises(self, p):
return 0
+ def launch_sampling(self, steps, func):
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except InterruptedException:
+ return self.last_latent
+
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+ if state.interrupted or state.skipped:
+ raise InterruptedException
+
+ if self.stop_at is not None and self.step > self.stop_at:
+ raise InterruptedException
+
+
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler: res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
if self.mask is not None:
- store_latent(self.init_latent * self.mask + self.nmask * res[1])
+ self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
else:
- store_latent(res[1])
+ self.last_latent = res[1]
+
+ store_latent(self.last_latent)
self.step += 1
+ state.sampling_step = self.step
+ shared.total_tqdm.update()
+
return res
def initialize(self, p):
@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler: self.init_latent = x
self.step = 0
- samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
+ samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
return samples
@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler: # existing code fails with certain step counts, like 9
try:
- samples_ddim, _ = 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)
+ 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])
except Exception:
- samples_ddim, _ = self.sampler.sample(S=steps+1, 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)
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, 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])
return samples_ddim
@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module): self.step = 0
def forward(self, x, sigma, uncond, cond, cond_scale):
+ if state.interrupted or state.skipped:
+ raise InterruptedException
+
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module): return denoised
-def extended_trange(sampler, count, *args, **kwargs):
- state.sampling_steps = count
- state.sampling_step = 0
-
- seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
- for x in seq:
- if state.interrupted or state.skipped:
- break
-
- if sampler.stop_at is not None and x > sampler.stop_at:
- break
-
- yield x
-
- state.sampling_step += 1
- shared.total_tqdm.update()
-
-
class TorchHijack:
def __init__(self, kdiff_sampler):
self.kdiff_sampler = kdiff_sampler
@@ -314,9 +304,28 @@ class KDiffusionSampler: self.eta = None
self.default_eta = 1.0
self.config = None
+ self.last_latent = None
def callback_state(self, d):
- store_latent(d["denoised"])
+ step = d['i']
+ latent = d["denoised"]
+ store_latent(latent)
+ self.last_latent = latent
+
+ if self.stop_at is not None and step > self.stop_at:
+ raise InterruptedException
+
+ state.sampling_step = step
+ shared.total_tqdm.update()
+
+ def launch_sampling(self, steps, func):
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except InterruptedException:
+ return self.last_latent
def number_of_needed_noises(self, p):
return p.steps
@@ -339,9 +348,6 @@ class KDiffusionSampler: self.sampler_noise_index = 0
self.eta = p.eta or opts.eta_ancestral
- if hasattr(k_diffusion.sampling, 'trange'):
- k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
-
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
@@ -383,8 +389,9 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x
- return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
@@ -406,6 +413,8 @@ class KDiffusionSampler: extra_params_kwargs['n'] = steps
else:
extra_params_kwargs['sigmas'] = sigmas
- samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
return samples
diff --git a/modules/shared.py b/modules/shared.py index 4ed17880..9fce264c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -73,13 +73,13 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-
cmd_opts = parser.parse_args()
restricted_opts = [
"samples_filename_pattern",
@@ -308,6 +308,7 @@ options_templates.update(options_section(('ui', "User interface"), { "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+ "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
"font": OptionInfo("", "Font for image grids that have text"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
diff --git a/modules/styles.py b/modules/styles.py index d44dfc1a..3bf5c5b6 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -45,7 +45,7 @@ class StyleDatabase: if not os.path.exists(path):
return
- with open(path, "r", encoding="utf8", newline='') as file:
+ with open(path, "r", encoding="utf-8-sig", newline='') as file:
reader = csv.DictReader(file)
for row in reader:
# Support loading old CSV format with "name, text"-columns
@@ -79,7 +79,7 @@ class StyleDatabase: def save_styles(self, path: str) -> None:
# Write to temporary file first, so we don't nuke the file if something goes wrong
fd, temp_path = tempfile.mkstemp(".csv")
- with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
+ with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
diff --git a/modules/ui.py b/modules/ui.py index ac30a8a3..9a06b099 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -547,6 +547,10 @@ def apply_setting(key, value): if value is None:
return gr.update()
+ # dont allow model to be swapped when model hash exists in prompt
+ if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+ return gr.update()
+
if key == "sd_model_checkpoint":
ckpt_info = sd_models.get_closet_checkpoint_match(value)
@@ -1809,7 +1813,7 @@ Requested path was: {f} print(traceback.format_exc(), file=sys.stderr)
def loadsave(path, x):
- def apply_field(obj, field, condition=None):
+ def apply_field(obj, field, condition=None, init_field=None):
key = path + "/" + field
if getattr(obj,'custom_script_source',None) is not None:
@@ -1825,6 +1829,8 @@ Requested path was: {f} print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
else:
setattr(obj, field, saved_value)
+ if init_field is not None:
+ init_field(saved_value)
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
apply_field(x, 'visible')
@@ -1850,7 +1856,8 @@ Requested path was: {f} # Since there are many dropdowns that shouldn't be saved,
# we only mark dropdowns that should be saved.
if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
- apply_field(x, 'value', lambda val: val in x.choices)
+ apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+ apply_field(x, 'visible')
visit(txt2img_interface, loadsave, "txt2img")
visit(img2img_interface, loadsave, "img2img")
@@ -1872,6 +1879,9 @@ for filename in sorted(os.listdir(jsdir)): with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
javascript += f"\n<script>{jsfile.read()}</script>"
+if cmd_opts.theme is not None:
+ javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
if 'gradio_routes_templates_response' not in globals():
|