diff options
Diffstat (limited to 'modules')
61 files changed, 2266 insertions, 757 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 2a4cd8a2..908c4514 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -15,7 +15,7 @@ from fastapi.encoders import jsonable_encoder from secrets import compare_digest import modules.shared as shared -from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart +from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items from modules.api import models from modules.shared import opts from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images @@ -197,6 +197,7 @@ class Api: self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem]) self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) + self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"]) self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse) @@ -333,14 +334,17 @@ class Api: p.outpath_grids = opts.outdir_txt2img_grids p.outpath_samples = opts.outdir_txt2img_samples - shared.state.begin(job="scripts_txt2img") - if selectable_scripts is not None: - p.script_args = script_args - processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + try: + shared.state.begin(job="scripts_txt2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + finally: + shared.state.end() + shared.total_tqdm.clear() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -390,14 +394,17 @@ class Api: p.outpath_grids = opts.outdir_img2img_grids p.outpath_samples = opts.outdir_img2img_samples - shared.state.begin(job="scripts_img2img") - if selectable_scripts is not None: - p.script_args = script_args - processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + try: + shared.state.begin(job="scripts_img2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + finally: + shared.state.end() + shared.total_tqdm.clear() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -604,6 +611,10 @@ class Api: with self.queue_lock: shared.refresh_checkpoints() + def refresh_vae(self): + with self.queue_lock: + shared_items.refresh_vae_list() + def create_embedding(self, args: dict): try: shared.state.begin(job="create_embedding") @@ -720,9 +731,9 @@ class Api: cuda = {'error': f'{err}'} return models.MemoryResponse(ram=ram, cuda=cuda) - def launch(self, server_name, port): + def launch(self, server_name, port, root_path): self.app.include_router(self.router) - uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive) + uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path) def kill_webui(self): restart.stop_program() diff --git a/modules/api/models.py b/modules/api/models.py index b5683071..800c9b93 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,4 +1,5 @@ import inspect + from pydantic import BaseModel, Field, create_model from typing import Any, Optional from typing_extensions import Literal @@ -207,11 +208,10 @@ class PreprocessResponse(BaseModel): fields = {} for key, metadata in opts.data_labels.items(): value = opts.data.get(key) - optType = opts.typemap.get(type(metadata.default), type(value)) + optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any - if (metadata is not None): - fields.update({key: (Optional[optType], Field( - default=metadata.default ,description=metadata.label))}) + if metadata is not None: + fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))}) else: fields.update({key: (Optional[optType], Field())}) diff --git a/modules/cache.py b/modules/cache.py index 28d42a8c..71fe6302 100644 --- a/modules/cache.py +++ b/modules/cache.py @@ -1,6 +1,7 @@ import json
import os.path
import threading
+import time
from modules.paths import data_path, script_path
@@ -8,15 +9,37 @@ cache_filename = os.path.join(data_path, "cache.json") cache_data = None
cache_lock = threading.Lock()
+dump_cache_after = None
+dump_cache_thread = None
+
def dump_cache():
"""
- Saves all cache data to a file.
+ Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written.
"""
+ global dump_cache_after
+ global dump_cache_thread
+
+ def thread_func():
+ global dump_cache_after
+ global dump_cache_thread
+
+ while dump_cache_after is not None and time.time() < dump_cache_after:
+ time.sleep(1)
+
+ with cache_lock:
+ with open(cache_filename, "w", encoding="utf8") as file:
+ json.dump(cache_data, file, indent=4)
+
+ dump_cache_after = None
+ dump_cache_thread = None
+
with cache_lock:
- with open(cache_filename, "w", encoding="utf8") as file:
- json.dump(cache_data, file, indent=4)
+ dump_cache_after = time.time() + 5
+ if dump_cache_thread is None:
+ dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func)
+ dump_cache_thread.start()
def cache(subsection):
@@ -84,7 +107,7 @@ def cached_data_for_file(subsection, title, filename, func): if ondisk_mtime > cached_mtime:
entry = None
- if not entry:
+ if not entry or 'value' not in entry:
value = func()
if value is None:
return None
diff --git a/modules/call_queue.py b/modules/call_queue.py index 61aa240f..f2eb17d6 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -3,7 +3,7 @@ import html import threading
import time
-from modules import shared, progress, errors
+from modules import shared, progress, errors, devices
queue_lock = threading.Lock()
@@ -75,6 +75,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): error_message = f'{type(e).__name__}: {e}'
res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
+ devices.torch_gc()
+
shared.state.skipped = False
shared.state.interrupted = False
shared.state.job_count = 0
diff --git a/modules/cmd_args.py b/modules/cmd_args.py index ae78f469..64f21e01 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -13,8 +13,10 @@ parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup")
parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
@@ -65,6 +67,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -109,3 +112,5 @@ parser.add_argument('--subpath', type=str, help='customize the subpath for gradi parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
+parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
+parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)
diff --git a/modules/devices.py b/modules/devices.py index 57e51da3..00a00b18 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -3,7 +3,7 @@ import contextlib from functools import lru_cache import torch -from modules import errors +from modules import errors, rng_philox if sys.platform == "darwin": from modules import mac_specific @@ -71,14 +71,17 @@ def enable_tf32(): torch.backends.cudnn.allow_tf32 = True - errors.run(enable_tf32, "Enabling TF32") -cpu = torch.device("cpu") -device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None -dtype = torch.float16 -dtype_vae = torch.float16 -dtype_unet = torch.float16 +cpu: torch.device = torch.device("cpu") +device: torch.device = None +device_interrogate: torch.device = None +device_gfpgan: torch.device = None +device_esrgan: torch.device = None +device_codeformer: torch.device = None +dtype: torch.dtype = torch.float16 +dtype_vae: torch.dtype = torch.float16 +dtype_unet: torch.dtype = torch.float16 unet_needs_upcast = False @@ -90,23 +93,87 @@ def cond_cast_float(input): return input.float() if unet_needs_upcast else input +nv_rng = None + + def randn(seed, shape): + """Generate a tensor with random numbers from a normal distribution using seed. + + Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed.""" + from modules.shared import opts - torch.manual_seed(seed) + manual_seed(seed) + + if opts.randn_source == "NV": + return torch.asarray(nv_rng.randn(shape), device=device) + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) + return torch.randn(shape, device=device) +def randn_local(seed, shape): + """Generate a tensor with random numbers from a normal distribution using seed. + + Does not change the global random number generator. You can only generate the seed's first tensor using this function.""" + + from modules.shared import opts + + if opts.randn_source == "NV": + rng = rng_philox.Generator(seed) + return torch.asarray(rng.randn(shape), device=device) + + local_device = cpu if opts.randn_source == "CPU" or device.type == 'mps' else device + local_generator = torch.Generator(local_device).manual_seed(int(seed)) + return torch.randn(shape, device=local_device, generator=local_generator).to(device) + + +def randn_like(x): + """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator. + + Use either randn() or manual_seed() to initialize the generator.""" + + from modules.shared import opts + + if opts.randn_source == "NV": + return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype) + + if opts.randn_source == "CPU" or x.device.type == 'mps': + return torch.randn_like(x, device=cpu).to(x.device) + + return torch.randn_like(x) + + def randn_without_seed(shape): + """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator. + + Use either randn() or manual_seed() to initialize the generator.""" + from modules.shared import opts + if opts.randn_source == "NV": + return torch.asarray(nv_rng.randn(shape), device=device) + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) + return torch.randn(shape, device=device) +def manual_seed(seed): + """Set up a global random number generator using the specified seed.""" + from modules.shared import opts + + if opts.randn_source == "NV": + global nv_rng + nv_rng = rng_philox.Generator(seed) + return + + torch.manual_seed(seed) + + def autocast(disable=False): from modules import shared diff --git a/modules/errors.py b/modules/errors.py index 5271a9fe..192cd8ff 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -14,7 +14,8 @@ def record_exception(): if exception_records and exception_records[-1] == e:
return
- exception_records.append((e, tb))
+ from modules import sysinfo
+ exception_records.append(sysinfo.format_exception(e, tb))
if len(exception_records) > 5:
exception_records.pop(0)
@@ -83,3 +84,53 @@ def run(code, task): code()
except Exception as e:
display(task, e)
+
+
+def check_versions():
+ from packaging import version
+ from modules import shared
+
+ import torch
+ import gradio
+
+ expected_torch_version = "2.0.0"
+ expected_xformers_version = "0.0.20"
+ expected_gradio_version = "3.39.0"
+
+ if version.parse(torch.__version__) < version.parse(expected_torch_version):
+ print_error_explanation(f"""
+You are running torch {torch.__version__}.
+The program is tested to work with torch {expected_torch_version}.
+To reinstall the desired version, run with commandline flag --reinstall-torch.
+Beware that this will cause a lot of large files to be downloaded, as well as
+there are reports of issues with training tab on the latest version.
+
+Use --skip-version-check commandline argument to disable this check.
+ """.strip())
+
+ if shared.xformers_available:
+ import xformers
+
+ if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
+ print_error_explanation(f"""
+You are running xformers {xformers.__version__}.
+The program is tested to work with xformers {expected_xformers_version}.
+To reinstall the desired version, run with commandline flag --reinstall-xformers.
+
+Use --skip-version-check commandline argument to disable this check.
+ """.strip())
+
+ if gradio.__version__ != expected_gradio_version:
+ print_error_explanation(f"""
+You are running gradio {gradio.__version__}.
+The program is designed to work with gradio {expected_gradio_version}.
+Using a different version of gradio is extremely likely to break the program.
+
+Reasons why you have the mismatched gradio version can be:
+ - you use --skip-install flag.
+ - you use webui.py to start the program instead of launch.py.
+ - an extension installs the incompatible gradio version.
+
+Use --skip-version-check commandline argument to disable this check.
+ """.strip())
+
diff --git a/modules/extensions.py b/modules/extensions.py index c561159a..e4633af4 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -11,9 +11,9 @@ os.makedirs(extensions_dir, exist_ok=True) def active():
- if shared.opts.disable_all_extensions == "all":
+ if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
return []
- elif shared.opts.disable_all_extensions == "extra":
+ elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra":
return [x for x in extensions if x.enabled and x.is_builtin]
else:
return [x for x in extensions if x.enabled]
@@ -56,10 +56,12 @@ class Extension: self.do_read_info_from_repo()
return self.to_dict()
-
- d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
- self.from_dict(d)
- self.status = 'unknown'
+ try:
+ d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
+ self.from_dict(d)
+ except FileNotFoundError:
+ pass
+ self.status = 'unknown' if self.status == '' else self.status
def do_read_info_from_repo(self):
repo = None
@@ -139,8 +141,12 @@ def list_extensions(): if not os.path.isdir(extensions_dir):
return
- if shared.opts.disable_all_extensions == "all":
+ if shared.cmd_opts.disable_all_extensions:
+ print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
+ elif shared.opts.disable_all_extensions == "all":
print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+ elif shared.cmd_opts.disable_extra_extensions:
+ print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***")
elif shared.opts.disable_all_extensions == "extra":
print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
diff --git a/modules/extra_networks.py b/modules/extra_networks.py index 41799b0a..fa28ac75 100644 --- a/modules/extra_networks.py +++ b/modules/extra_networks.py @@ -1,19 +1,27 @@ +import json
+import os
import re
from collections import defaultdict
from modules import errors
extra_network_registry = {}
+extra_network_aliases = {}
def initialize():
extra_network_registry.clear()
+ extra_network_aliases.clear()
def register_extra_network(extra_network):
extra_network_registry[extra_network.name] = extra_network
+def register_extra_network_alias(extra_network, alias):
+ extra_network_aliases[alias] = extra_network
+
+
def register_default_extra_networks():
from modules.extra_networks_hypernet import ExtraNetworkHypernet
register_extra_network(ExtraNetworkHypernet())
@@ -82,20 +90,26 @@ def activate(p, extra_network_data): """call activate for extra networks in extra_network_data in specified order, then call
activate for all remaining registered networks with an empty argument list"""
+ activated = []
+
for extra_network_name, extra_network_args in extra_network_data.items():
extra_network = extra_network_registry.get(extra_network_name, None)
+
+ if extra_network is None:
+ extra_network = extra_network_aliases.get(extra_network_name, None)
+
if extra_network is None:
print(f"Skipping unknown extra network: {extra_network_name}")
continue
try:
extra_network.activate(p, extra_network_args)
+ activated.append(extra_network)
except Exception as e:
errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
for extra_network_name, extra_network in extra_network_registry.items():
- args = extra_network_data.get(extra_network_name, None)
- if args is not None:
+ if extra_network in activated:
continue
try:
@@ -165,3 +179,20 @@ def parse_prompts(prompts): return res, extra_data
+
+def get_user_metadata(filename):
+ if filename is None:
+ return {}
+
+ basename, ext = os.path.splitext(filename)
+ metadata_filename = basename + '.json'
+
+ metadata = {}
+ try:
+ if os.path.isfile(metadata_filename):
+ with open(metadata_filename, "r", encoding="utf8") as file:
+ metadata = json.load(file)
+ except Exception as e:
+ errors.display(e, f"reading extra network user metadata from {metadata_filename}")
+
+ return metadata
diff --git a/modules/extras.py b/modules/extras.py index e9c0263e..2a310ae3 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -7,7 +7,7 @@ import json import torch
import tqdm
-from modules import shared, images, sd_models, sd_vae, sd_models_config
+from modules import shared, images, sd_models, sd_vae, sd_models_config, errors
from modules.ui_common import plaintext_to_html
import gradio as gr
import safetensors.torch
@@ -72,7 +72,20 @@ def to_half(tensor, enable): return tensor
-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
+def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name):
+ metadata = {}
+
+ for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]:
+ checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None)
+ if checkpoint_info is None:
+ continue
+
+ metadata.update(checkpoint_info.metadata)
+
+ return json.dumps(metadata, indent=4, ensure_ascii=False)
+
+
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json):
shared.state.begin(job="model-merge")
def fail(message):
@@ -241,11 +254,25 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
- metadata = None
+ metadata = {}
+
+ if save_metadata and copy_metadata_fields:
+ if primary_model_info:
+ metadata.update(primary_model_info.metadata)
+ if secondary_model_info:
+ metadata.update(secondary_model_info.metadata)
+ if tertiary_model_info:
+ metadata.update(tertiary_model_info.metadata)
if save_metadata:
- metadata = {"format": "pt"}
+ try:
+ metadata.update(json.loads(metadata_json))
+ except Exception as e:
+ errors.display(e, "readin metadata from json")
+
+ metadata["format"] = "pt"
+ if save_metadata and add_merge_recipe:
merge_recipe = {
"type": "webui", # indicate this model was merged with webui's built-in merger
"primary_model_hash": primary_model_info.sha256,
@@ -261,7 +288,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ "is_inpainting": result_is_inpainting_model,
"is_instruct_pix2pix": result_is_instruct_pix2pix_model
}
- metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
sd_merge_models = {}
@@ -281,11 +307,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ if tertiary_model_info:
add_model_metadata(tertiary_model_info)
+ metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
metadata["sd_merge_models"] = json.dumps(sd_merge_models)
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
- safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
+ safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None)
else:
torch.save(theta_0, output_modelname)
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index a3448be9..4e286558 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -280,6 +280,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "Hires sampler" not in res:
res["Hires sampler"] = "Use same sampler"
+ if "Hires checkpoint" not in res:
+ res["Hires checkpoint"] = "Use same checkpoint"
+
if "Hires prompt" not in res:
res["Hires prompt"] = ""
diff --git a/modules/gradio_extensons.py b/modules/gradio_extensons.py new file mode 100644 index 00000000..5af7fd8e --- /dev/null +++ b/modules/gradio_extensons.py @@ -0,0 +1,60 @@ +import gradio as gr
+
+from modules import scripts
+
+def add_classes_to_gradio_component(comp):
+ """
+ this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+ """
+
+ comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
+
+ if getattr(comp, 'multiselect', False):
+ comp.elem_classes.append('multiselect')
+
+
+def IOComponent_init(self, *args, **kwargs):
+ self.webui_tooltip = kwargs.pop('tooltip', None)
+
+ if scripts.scripts_current is not None:
+ scripts.scripts_current.before_component(self, **kwargs)
+
+ scripts.script_callbacks.before_component_callback(self, **kwargs)
+
+ res = original_IOComponent_init(self, *args, **kwargs)
+
+ add_classes_to_gradio_component(self)
+
+ scripts.script_callbacks.after_component_callback(self, **kwargs)
+
+ if scripts.scripts_current is not None:
+ scripts.scripts_current.after_component(self, **kwargs)
+
+ return res
+
+
+def Block_get_config(self):
+ config = original_Block_get_config(self)
+
+ webui_tooltip = getattr(self, 'webui_tooltip', None)
+ if webui_tooltip:
+ config["webui_tooltip"] = webui_tooltip
+
+ return config
+
+
+def BlockContext_init(self, *args, **kwargs):
+ res = original_BlockContext_init(self, *args, **kwargs)
+
+ add_classes_to_gradio_component(self)
+
+ return res
+
+
+original_IOComponent_init = gr.components.IOComponent.__init__
+original_Block_get_config = gr.blocks.Block.get_config
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+
+gr.components.IOComponent.__init__ = IOComponent_init
+gr.blocks.Block.get_config = Block_get_config
+gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 79670b87..70f1cbd2 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -10,7 +10,7 @@ import torch import tqdm
from einops import rearrange, repeat
from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
+from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@@ -378,7 +378,7 @@ def apply_hypernetworks(hypernetworks, context, layer=None): return context_k, context_v
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
+def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs):
h = self.heads
q = self.to_q(x)
@@ -469,8 +469,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
- # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
- from modules import images
+ from modules import images, processing
save_hypernetwork_every = save_hypernetwork_every or 0
create_image_every = create_image_every or 0
diff --git a/modules/images.py b/modules/images.py index fb5d2e75..ba3c43a4 100644 --- a/modules/images.py +++ b/modules/images.py @@ -318,7 +318,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None): return res
-invalid_filename_chars = '<>:"/\\|?*\n'
+invalid_filename_chars = '<>:"/\\|?*\n\r\t'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
@@ -363,7 +363,7 @@ class FilenameGenerator: 'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
- 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
+ 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
diff --git a/modules/img2img.py b/modules/img2img.py index a811e7a4..d8e1c534 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -3,14 +3,13 @@ from contextlib import closing from pathlib import Path
import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
import gradio as gr
from modules import sd_samplers, images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
-from modules.images import save_image
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
@@ -18,9 +17,10 @@ import modules.scripts def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
+ output_dir = output_dir.strip()
processing.fix_seed(p)
- images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp")))
+ images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
is_inpaint_batch = False
if inpaint_mask_dir:
@@ -32,11 +32,6 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
- save_normally = output_dir == ''
-
- p.do_not_save_grid = True
- p.do_not_save_samples = not save_normally
-
state.job_count = len(images) * p.n_iter
# extract "default" params to use in case getting png info fails
@@ -111,21 +106,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
- proc = process_images(p)
-
- for n, processed_image in enumerate(proc.images):
- filename = image_path.stem
- infotext = proc.infotext(p, n)
- relpath = os.path.dirname(os.path.relpath(image, input_dir))
-
- if n > 0:
- filename += f"-{n}"
-
- if not save_normally:
- os.makedirs(os.path.join(output_dir, relpath), exist_ok=True)
- if processed_image.mode == 'RGBA':
- processed_image = processed_image.convert("RGB")
- save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False)
+ if output_dir:
+ p.outpath_samples = output_dir
+ p.override_settings['save_to_dirs'] = False
+ if p.n_iter > 1 or p.batch_size > 1:
+ p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+ else:
+ p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+ process_images(p)
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
@@ -141,9 +129,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s mask = None
elif mode == 2: # inpaint
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
- alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
- mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
- mask = ImageChops.lighter(alpha_mask, mask).convert('L')
+ mask = mask.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
image = image.convert("RGB")
elif mode == 3: # inpaint sketch
image = inpaint_color_sketch
diff --git a/modules/launch_utils.py b/modules/launch_utils.py index ff77cbfd..f77b577a 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -1,4 +1,5 @@ # this scripts installs necessary requirements and launches main program in webui.py
+import re
import subprocess
import os
import sys
@@ -9,6 +10,7 @@ from functools import lru_cache from modules import cmd_args, errors
from modules.paths_internal import script_path, extensions_dir
+from modules.timer import startup_timer
args, _ = cmd_args.parser.parse_known_args()
@@ -192,7 +194,7 @@ def run_extension_installer(extension_dir): try:
env = os.environ.copy()
- env['PYTHONPATH'] = os.path.abspath(".")
+ env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
@@ -222,8 +224,51 @@ def run_extensions_installers(settings_file): if not os.path.isdir(extensions_dir):
return
- for dirname_extension in list_extensions(settings_file):
- run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+ with startup_timer.subcategory("run extensions installers"):
+ for dirname_extension in list_extensions(settings_file):
+ path = os.path.join(extensions_dir, dirname_extension)
+
+ if os.path.isdir(path):
+ run_extension_installer(path)
+ startup_timer.record(dirname_extension)
+
+
+re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
+
+
+def requirements_met(requirements_file):
+ """
+ Does a simple parse of a requirements.txt file to determine if all rerqirements in it
+ are already installed. Returns True if so, False if not installed or parsing fails.
+ """
+
+ import importlib.metadata
+ import packaging.version
+
+ with open(requirements_file, "r", encoding="utf8") as file:
+ for line in file:
+ if line.strip() == "":
+ continue
+
+ m = re.match(re_requirement, line)
+ if m is None:
+ return False
+
+ package = m.group(1).strip()
+ version_required = (m.group(2) or "").strip()
+
+ if version_required == "":
+ continue
+
+ try:
+ version_installed = importlib.metadata.version(package)
+ except Exception:
+ return False
+
+ if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
+ return False
+
+ return True
def prepare_environment():
@@ -237,11 +282,13 @@ def prepare_environment(): openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
+ stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
+ stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "5c10deee76adad0032b412294130090932317a87")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
@@ -249,15 +296,18 @@ def prepare_environment(): try:
# the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
os.remove(os.path.join(script_path, "tmp", "restart"))
- os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
+ os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
except OSError:
pass
if not args.skip_python_version_check:
check_python_version()
+ startup_timer.record("checks")
+
commit = commit_hash()
tag = git_tag()
+ startup_timer.record("git version info")
print(f"Python {sys.version}")
print(f"Version: {tag}")
@@ -265,21 +315,27 @@ def prepare_environment(): if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+ startup_timer.record("install torch")
if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
raise RuntimeError(
'Torch is not able to use GPU; '
'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
)
+ startup_timer.record("torch GPU test")
+
if not is_installed("gfpgan"):
run_pip(f"install {gfpgan_package}", "gfpgan")
+ startup_timer.record("install gfpgan")
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
+ startup_timer.record("install clip")
if not is_installed("open_clip"):
run_pip(f"install {openclip_package}", "open_clip")
+ startup_timer.record("install open_clip")
if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
if platform.system() == "Windows":
@@ -293,36 +349,49 @@ def prepare_environment(): elif platform.system() == "Linux":
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+ startup_timer.record("install xformers")
+
if not is_installed("ngrok") and args.ngrok:
run_pip("install ngrok", "ngrok")
+ startup_timer.record("install ngrok")
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
+ git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
+ startup_timer.record("clone repositores")
+
if not is_installed("lpips"):
run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+ startup_timer.record("install CodeFormer requirements")
if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
- run_pip(f"install -r \"{requirements_file}\"", "requirements")
+
+ if not requirements_met(requirements_file):
+ run_pip(f"install -r \"{requirements_file}\"", "requirements")
+ startup_timer.record("install requirements")
run_extensions_installers(settings_file=args.ui_settings_file)
if args.update_check:
version_check(commit)
+ startup_timer.record("check version")
if args.update_all_extensions:
git_pull_recursive(extensions_dir)
+ startup_timer.record("update extensions")
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
exit(0)
+
def configure_for_tests():
if "--api" not in sys.argv:
sys.argv.append("--api")
diff --git a/modules/lowvram.py b/modules/lowvram.py index d95bcfbf..96f52b7b 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -15,6 +15,9 @@ def send_everything_to_cpu(): def setup_for_low_vram(sd_model, use_medvram):
+ if getattr(sd_model, 'lowvram', False):
+ return
+
sd_model.lowvram = True
parents = {}
@@ -53,19 +56,50 @@ def setup_for_low_vram(sd_model, use_medvram): send_me_to_gpu(first_stage_model, None)
return first_stage_model_decode(z)
- # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
- if hasattr(sd_model.cond_stage_model, 'model'):
- sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
+ to_remain_in_cpu = [
+ (sd_model, 'first_stage_model'),
+ (sd_model, 'depth_model'),
+ (sd_model, 'embedder'),
+ (sd_model, 'model'),
+ (sd_model, 'embedder'),
+ ]
+
+ is_sdxl = hasattr(sd_model, 'conditioner')
+ is_sd2 = not is_sdxl and hasattr(sd_model.cond_stage_model, 'model')
+
+ if is_sdxl:
+ to_remain_in_cpu.append((sd_model, 'conditioner'))
+ elif is_sd2:
+ to_remain_in_cpu.append((sd_model.cond_stage_model, 'model'))
+ else:
+ to_remain_in_cpu.append((sd_model.cond_stage_model, 'transformer'))
+
+ # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model
+ stored = []
+ for obj, field in to_remain_in_cpu:
+ module = getattr(obj, field, None)
+ stored.append(module)
+ setattr(obj, field, None)
- # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
- # send the model to GPU. Then put modules back. the modules will be in CPU.
- stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
+ # send the model to GPU.
sd_model.to(devices.device)
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
+
+ # put modules back. the modules will be in CPU.
+ for (obj, field), module in zip(to_remain_in_cpu, stored):
+ setattr(obj, field, module)
# register hooks for those the first three models
- sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+ if is_sdxl:
+ sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
+ elif is_sd2:
+ sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
+ sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
+ parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
+ parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
+ else:
+ sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+ parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
+
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.encode = first_stage_model_encode_wrap
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
@@ -73,11 +107,6 @@ def setup_for_low_vram(sd_model, use_medvram): sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
if sd_model.embedder:
sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
- parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
- if hasattr(sd_model.cond_stage_model, 'model'):
- sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
- del sd_model.cond_stage_model.transformer
if use_medvram:
sd_model.model.register_forward_pre_hook(send_me_to_gpu)
diff --git a/modules/paths.py b/modules/paths.py index bada804e..25052339 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -5,6 +5,21 @@ from modules.paths_internal import models_path, script_path, data_path, extensio import modules.safe # noqa: F401
+def mute_sdxl_imports():
+ """create fake modules that SDXL wants to import but doesn't actually use for our purposes"""
+
+ class Dummy:
+ pass
+
+ module = Dummy()
+ module.LPIPS = None
+ sys.modules['taming.modules.losses.lpips'] = module
+
+ module = Dummy()
+ module.StableDataModuleFromConfig = None
+ sys.modules['sgm.data'] = module
+
+
# data_path = cmd_opts_pre.data
sys.path.insert(0, script_path)
@@ -18,8 +33,11 @@ for possible_sd_path in possible_sd_paths: assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
+mute_sdxl_imports()
+
path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion', []),
+ (os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
@@ -35,6 +53,13 @@ for d, must_exist, what, options in path_dirs: d = os.path.abspath(d)
if "atstart" in options:
sys.path.insert(0, d)
+ elif "sgm" in options:
+ # Stable Diffusion XL repo has scripts dir with __init__.py in it which ruins every extension's scripts dir, so we
+ # import sgm and remove it from sys.path so that when a script imports scripts.something, it doesbn't use sgm's scripts dir.
+
+ sys.path.insert(0, d)
+ import sgm # noqa: F401
+ sys.path.pop(0)
else:
sys.path.append(d)
paths[what] = d
diff --git a/modules/processing.py b/modules/processing.py index 49441e77..ae58b108 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -14,7 +14,7 @@ from skimage import exposure from typing import Any, Dict, List
import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -30,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion from einops import repeat, rearrange
from blendmodes.blend import blendLayers, BlendType
+decode_first_stage = sd_samplers_common.decode_first_stage
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
@@ -330,8 +331,21 @@ class StableDiffusionProcessing: caches is a list with items described above.
"""
+
+ cached_params = (
+ required_prompts,
+ steps,
+ opts.CLIP_stop_at_last_layers,
+ shared.sd_model.sd_checkpoint_info,
+ extra_network_data,
+ opts.sdxl_crop_left,
+ opts.sdxl_crop_top,
+ self.width,
+ self.height,
+ )
+
for cache in caches:
- if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
+ if cache[0] is not None and cached_params == cache[0]:
return cache[1]
cache = caches[0]
@@ -339,14 +353,17 @@ class StableDiffusionProcessing: with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
- cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
+ cache[0] = cached_params
return cache[1]
def setup_conds(self):
+ prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
+ negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
+
sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
- self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
- self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+ self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
+ self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
def parse_extra_network_prompts(self):
self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
@@ -476,7 +493,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see 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)
subnoise = None
- if subseeds is not None:
+ if subseeds is not None and subseed_strength != 0:
subseed = 0 if i >= len(subseeds) else subseeds[i]
subnoise = devices.randn(subseed, noise_shape)
@@ -508,7 +525,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see cnt = p.sampler.number_of_needed_noises(p)
if eta_noise_seed_delta > 0:
- torch.manual_seed(seed + eta_noise_seed_delta)
+ devices.manual_seed(seed + eta_noise_seed_delta)
for j in range(cnt):
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@@ -522,11 +539,42 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x
-def decode_first_stage(model, x):
- with devices.autocast(disable=x.dtype == devices.dtype_vae):
- x = model.decode_first_stage(x)
+class DecodedSamples(list):
+ already_decoded = True
- return x
+
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+ samples = DecodedSamples()
+
+ for i in range(batch.shape[0]):
+ sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+ if check_for_nans:
+ try:
+ devices.test_for_nans(sample, "vae")
+ except devices.NansException as e:
+ if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+ raise e
+
+ errors.print_error_explanation(
+ "A tensor with all NaNs was produced in VAE.\n"
+ "Web UI will now convert VAE into 32-bit float and retry.\n"
+ "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"
+ "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+ )
+
+ devices.dtype_vae = torch.float32
+ model.first_stage_model.to(devices.dtype_vae)
+ batch = batch.to(devices.dtype_vae)
+
+ sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+ if target_device is not None:
+ sample = sample.to(target_device)
+
+ samples.append(sample)
+
+ return samples
def get_fixed_seed(seed):
@@ -551,8 +599,12 @@ def program_version(): return res
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
- index = position_in_batch + iteration * p.batch_size
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+ if index is None:
+ index = position_in_batch + iteration * p.batch_size
+
+ if all_negative_prompts is None:
+ all_negative_prompts = p.all_negative_prompts
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
enable_hr = getattr(p, 'enable_hr', False)
@@ -568,12 +620,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Sampler": p.sampler_name,
"CFG scale": p.cfg_scale,
"Image CFG scale": getattr(p, 'image_cfg_scale', None),
- "Seed": all_seeds[index],
+ "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_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),
- "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
- "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
+ "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
+ "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
@@ -583,7 +635,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
"Init image hash": getattr(p, 'init_img_hash', None),
- "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+ "RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
**p.extra_generation_params,
"Version": program_version() if opts.add_version_to_infotext else None,
@@ -593,7 +645,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
prompt_text = p.prompt if use_main_prompt else all_prompts[index]
- negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
+ negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
@@ -667,9 +719,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
- def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
- return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
-
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -743,9 +792,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
- x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
- for x in x_samples_ddim:
- devices.test_for_nans(x, "vae")
+ if getattr(samples_ddim, 'already_decoded', False):
+ x_samples_ddim = samples_ddim
+ else:
+ x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -760,6 +810,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
+ p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
+ batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+ p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+ x_samples_ddim = batch_params.images
+
+ def infotext(index=0, use_main_prompt=False):
+ return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
+
for i, x_sample in enumerate(x_samples_ddim):
p.batch_index = i
@@ -768,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.restore_faces:
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
- images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+ images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
devices.torch_gc()
@@ -785,15 +845,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.color_corrections is not None and i < len(p.color_corrections):
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
- images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+ images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if opts.samples_save and not p.do_not_save_samples:
- images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+ images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
- text = infotext(n, i)
+ text = infotext(i)
infotexts.append(text)
if opts.enable_pnginfo:
image.info["parameters"] = text
@@ -804,10 +864,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if opts.save_mask:
- images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+ images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.save_mask_composite:
- images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+ images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask:
output_images.append(image_mask)
@@ -848,7 +908,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p,
images_list=output_images,
seed=p.all_seeds[0],
- info=infotext(),
+ info=infotexts[0],
comments="".join(f"{comment}\n" for comment in comments),
subseed=p.all_subseeds[0],
index_of_first_image=index_of_first_image,
@@ -878,7 +938,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): cached_hr_uc = [None, None]
cached_hr_c = [None, None]
- def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
+ def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
@@ -889,11 +949,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_resize_y = hr_resize_y
self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y
+ self.hr_checkpoint_name = hr_checkpoint_name
+ self.hr_checkpoint_info = None
self.hr_sampler_name = hr_sampler_name
self.hr_prompt = hr_prompt
self.hr_negative_prompt = hr_negative_prompt
self.all_hr_prompts = None
self.all_hr_negative_prompts = None
+ self.latent_scale_mode = None
if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width
@@ -916,6 +979,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
+ if self.hr_checkpoint_name:
+ self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
+
+ if self.hr_checkpoint_info is None:
+ raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
+
+ self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+
if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
@@ -925,6 +996,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
+ self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+ if self.enable_hr and self.latent_scale_mode is None:
+ if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+ raise Exception(f"could not find upscaler named {self.hr_upscaler}")
+
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
self.hr_resize_x = self.width
self.hr_resize_y = self.height
@@ -963,14 +1039,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
- # special case: the user has chosen to do nothing
- if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
- self.enable_hr = False
- self.denoising_strength = None
- self.extra_generation_params.pop("Hires upscale", None)
- self.extra_generation_params.pop("Hires resize", None)
- return
-
if not state.processing_has_refined_job_count:
if state.job_count == -1:
state.job_count = self.n_iter
@@ -988,17 +1056,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
- latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
- if self.enable_hr and latent_scale_mode is None:
- if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
- raise Exception(f"could not find upscaler named {self.hr_upscaler}")
-
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+ del x
if not self.enable_hr:
return samples
+ if self.latent_scale_mode is None:
+ decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
+ else:
+ decoded_samples = None
+
+ current = shared.sd_model.sd_checkpoint_info
+ try:
+ if self.hr_checkpoint_info is not None:
+ self.sampler = None
+ sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+ devices.torch_gc()
+
+ return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
+ finally:
+ self.sampler = None
+ sd_models.reload_model_weights(info=current)
+ devices.torch_gc()
+
+ def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
@@ -1014,13 +1097,20 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): image = sd_samplers.sample_to_image(image, index, approximation=0)
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
- images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
+ images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
+
+ img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
+ if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
+ img2img_sampler_name = 'DDIM'
+
+ self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
- if latent_scale_mode is not None:
+ if self.latent_scale_mode is not None:
for i in range(samples.shape[0]):
save_intermediate(samples, i)
- samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
+ samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"])
# Avoid making the inpainting conditioning unless necessary as
# this does need some extra compute to decode / encode the image again.
@@ -1029,7 +1119,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else:
image_conditioning = self.txt2img_image_conditioning(samples)
else:
- decoded_samples = decode_first_stage(self.sd_model, samples)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
batch_images = []
@@ -1048,6 +1137,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(shared.device)
decoded_samples = 2. * decoded_samples - 1.
+ decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
@@ -1055,19 +1145,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob()
- img2img_sampler_name = self.hr_sampler_name or self.sampler_name
-
- if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
- img2img_sampler_name = 'DDIM'
-
- self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
-
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
# GC now before running the next img2img to prevent running out of memory
- x = None
devices.torch_gc()
if not self.disable_extra_networks:
@@ -1086,9 +1168,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
+ decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
self.is_hr_pass = False
- return samples
+ return decoded_samples
def close(self):
super().close()
@@ -1127,8 +1211,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.hr_c is not None:
return
- self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
- self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+ hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
+ hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
+
+ self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+ self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
def setup_conds(self):
super().setup_conds()
@@ -1136,7 +1223,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_uc = None
self.hr_c = None
- if self.enable_hr:
+ if self.enable_hr and self.hr_checkpoint_info is None:
if shared.opts.hires_fix_use_firstpass_conds:
self.calculate_hr_conds()
@@ -1288,9 +1375,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = torch.from_numpy(batch_images)
image = 2. * image - 1.
- image = image.to(shared.device)
+ image = image.to(shared.device, dtype=devices.dtype_vae)
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+ devices.torch_gc()
if self.resize_mode == 3:
self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 0069d8b0..32d214e3 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -1,3 +1,5 @@ +from __future__ import annotations
+
import re
from collections import namedtuple
from typing import List
@@ -17,8 +19,8 @@ prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)* !emphasized: "(" prompt ")"
| "(" prompt ":" prompt ")"
| "[" prompt "]"
-scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
-alternate: "[" prompt ("|" prompt)+ "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]"
+alternate: "[" prompt ("|" [prompt])+ "]"
WHITESPACE: /\s+/
plain: /([^\\\[\]():|]|\\.)+/
%import common.SIGNED_NUMBER -> NUMBER
@@ -51,6 +53,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): [[3, '((a][:b:c '], [10, '((a][:b:c d']]
>>> g("[a|(b:1.1)]")
[[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
+ >>> g("[fe|]male")
+ [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
+ >>> g("[fe|||]male")
+ [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
"""
def collect_steps(steps, tree):
@@ -58,11 +64,11 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): class CollectSteps(lark.Visitor):
def scheduled(self, tree):
- tree.children[-1] = float(tree.children[-1])
- if tree.children[-1] < 1:
- tree.children[-1] *= steps
- tree.children[-1] = min(steps, int(tree.children[-1]))
- res.append(tree.children[-1])
+ tree.children[-2] = float(tree.children[-2])
+ if tree.children[-2] < 1:
+ tree.children[-2] *= steps
+ tree.children[-2] = min(steps, int(tree.children[-2]))
+ res.append(tree.children[-2])
def alternate(self, tree):
res.extend(range(1, steps+1))
@@ -73,10 +79,11 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): def at_step(step, tree):
class AtStep(lark.Transformer):
def scheduled(self, args):
- before, after, _, when = args
+ before, after, _, when, _ = args
yield before or () if step <= when else after
def alternate(self, args):
- yield next(args[(step - 1)%len(args)])
+ args = ["" if not arg else arg for arg in args]
+ yield args[(step - 1) % len(args)]
def start(self, args):
def flatten(x):
if type(x) == str:
@@ -109,7 +116,25 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-def get_learned_conditioning(model, prompts, steps):
+class SdConditioning(list):
+ """
+ A list with prompts for stable diffusion's conditioner model.
+ Can also specify width and height of created image - SDXL needs it.
+ """
+ def __init__(self, prompts, is_negative_prompt=False, width=None, height=None, copy_from=None):
+ super().__init__()
+ self.extend(prompts)
+
+ if copy_from is None:
+ copy_from = prompts
+
+ self.is_negative_prompt = is_negative_prompt or getattr(copy_from, 'is_negative_prompt', False)
+ self.width = width or getattr(copy_from, 'width', None)
+ self.height = height or getattr(copy_from, 'height', None)
+
+
+
+def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
and the sampling step at which this condition is to be replaced by the next one.
@@ -139,12 +164,17 @@ def get_learned_conditioning(model, prompts, steps): res.append(cached)
continue
- texts = [x[1] for x in prompt_schedule]
+ texts = SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts)
conds = model.get_learned_conditioning(texts)
cond_schedule = []
for i, (end_at_step, _) in enumerate(prompt_schedule):
- cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
+ if isinstance(conds, dict):
+ cond = {k: v[i] for k, v in conds.items()}
+ else:
+ cond = conds[i]
+
+ cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond))
cache[prompt] = cond_schedule
res.append(cond_schedule)
@@ -153,13 +183,15 @@ def get_learned_conditioning(model, prompts, steps): re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
-def get_multicond_prompt_list(prompts):
+
+def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
res_indexes = []
- prompt_flat_list = []
prompt_indexes = {}
+ prompt_flat_list = SdConditioning(prompts)
+ prompt_flat_list.clear()
for prompt in prompts:
subprompts = re_AND.split(prompt)
@@ -196,6 +228,7 @@ class MulticondLearnedConditioning: self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+
def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
For each prompt, the list is obtained by splitting the prompt using the AND separator.
@@ -214,20 +247,57 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+class DictWithShape(dict):
+ def __init__(self, x, shape):
+ super().__init__()
+ self.update(x)
+
+ @property
+ def shape(self):
+ return self["crossattn"].shape
+
+
def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond
- res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+ is_dict = isinstance(param, dict)
+
+ if is_dict:
+ dict_cond = param
+ res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()}
+ res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape)
+ else:
+ res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+
for i, cond_schedule in enumerate(c):
target_index = 0
for current, entry in enumerate(cond_schedule):
if current_step <= entry.end_at_step:
target_index = current
break
- res[i] = cond_schedule[target_index].cond
+
+ if is_dict:
+ for k, param in cond_schedule[target_index].cond.items():
+ res[k][i] = param
+ else:
+ res[i] = cond_schedule[target_index].cond
return res
+def stack_conds(tensors):
+ # if prompts have wildly different lengths above the limit we'll get tensors of different shapes
+ # and won't be able to torch.stack them. So this fixes that.
+ token_count = max([x.shape[0] for x in tensors])
+ for i in range(len(tensors)):
+ if tensors[i].shape[0] != token_count:
+ last_vector = tensors[i][-1:]
+ last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+ tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
+ return torch.stack(tensors)
+
+
+
def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
param = c.batch[0][0].schedules[0].cond
@@ -249,16 +319,14 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): conds_list.append(conds_for_batch)
- # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
- # and won't be able to torch.stack them. So this fixes that.
- token_count = max([x.shape[0] for x in tensors])
- for i in range(len(tensors)):
- if tensors[i].shape[0] != token_count:
- last_vector = tensors[i][-1:]
- last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
- tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+ if isinstance(tensors[0], dict):
+ keys = list(tensors[0].keys())
+ stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys}
+ stacked = DictWithShape(stacked, stacked['crossattn'].shape)
+ else:
+ stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype)
- return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+ return conds_list, stacked
re_attention = re.compile(r"""
@@ -270,7 +338,7 @@ re_attention = re.compile(r""" \\|
\(|
\[|
-:([+-]?[.\d]+)\)|
+:\s*([+-]?[.\d]+)\s*\)|
\)|
]|
[^\\()\[\]:]+|
diff --git a/modules/rng_philox.py b/modules/rng_philox.py new file mode 100644 index 00000000..5532cf9d --- /dev/null +++ b/modules/rng_philox.py @@ -0,0 +1,102 @@ +"""RNG imitiating torch cuda randn on CPU. You are welcome.
+
+Usage:
+
+```
+g = Generator(seed=0)
+print(g.randn(shape=(3, 4)))
+```
+
+Expected output:
+```
+[[-0.92466259 -0.42534415 -2.6438457 0.14518388]
+ [-0.12086647 -0.57972564 -0.62285122 -0.32838709]
+ [-1.07454231 -0.36314407 -1.67105067 2.26550497]]
+```
+"""
+
+import numpy as np
+
+philox_m = [0xD2511F53, 0xCD9E8D57]
+philox_w = [0x9E3779B9, 0xBB67AE85]
+
+two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
+two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
+
+
+def uint32(x):
+ """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
+ return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
+
+
+def philox4_round(counter, key):
+ """A single round of the Philox 4x32 random number generator."""
+
+ v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
+ v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
+
+ counter[0] = v2[1] ^ counter[1] ^ key[0]
+ counter[1] = v2[0]
+ counter[2] = v1[1] ^ counter[3] ^ key[1]
+ counter[3] = v1[0]
+
+
+def philox4_32(counter, key, rounds=10):
+ """Generates 32-bit random numbers using the Philox 4x32 random number generator.
+
+ Parameters:
+ counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
+ key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
+ rounds (int): The number of rounds to perform.
+
+ Returns:
+ numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
+ """
+
+ for _ in range(rounds - 1):
+ philox4_round(counter, key)
+
+ key[0] = key[0] + philox_w[0]
+ key[1] = key[1] + philox_w[1]
+
+ philox4_round(counter, key)
+ return counter
+
+
+def box_muller(x, y):
+ """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
+ u = x * two_pow32_inv + two_pow32_inv / 2
+ v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
+
+ s = np.sqrt(-2.0 * np.log(u))
+
+ r1 = s * np.sin(v)
+ return r1.astype(np.float32)
+
+
+class Generator:
+ """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
+
+ def __init__(self, seed):
+ self.seed = seed
+ self.offset = 0
+
+ def randn(self, shape):
+ """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
+
+ n = 1
+ for x in shape:
+ n *= x
+
+ counter = np.zeros((4, n), dtype=np.uint32)
+ counter[0] = self.offset
+ counter[2] = np.arange(n, dtype=np.uint32) # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
+ self.offset += 1
+
+ key = np.empty(n, dtype=np.uint64)
+ key.fill(self.seed)
+ key = uint32(key)
+
+ g = philox4_32(counter, key)
+
+ return box_muller(g[0], g[1]).reshape(shape) # discard g[2] and g[3]
diff --git a/modules/script_loading.py b/modules/script_loading.py index 306a1f35..0d55f193 100644 --- a/modules/script_loading.py +++ b/modules/script_loading.py @@ -12,11 +12,12 @@ def load_module(path): return module
-def preload_extensions(extensions_dir, parser):
+def preload_extensions(extensions_dir, parser, extension_list=None):
if not os.path.isdir(extensions_dir):
return
- for dirname in sorted(os.listdir(extensions_dir)):
+ extensions = extension_list if extension_list is not None else os.listdir(extensions_dir)
+ for dirname in sorted(extensions):
preload_script = os.path.join(extensions_dir, dirname, "preload.py")
if not os.path.isfile(preload_script):
continue
diff --git a/modules/scripts.py b/modules/scripts.py index 7d9dd59f..f7d060aa 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -16,6 +16,11 @@ class PostprocessImageArgs: self.image = image
+class PostprocessBatchListArgs:
+ def __init__(self, images):
+ self.images = images
+
+
class Script:
name = None
"""script's internal name derived from title"""
@@ -119,7 +124,7 @@ class Script: def after_extra_networks_activate(self, p, *args, **kwargs):
"""
- Calledafter extra networks activation, before conds calculation
+ Called after extra networks activation, before conds calculation
allow modification of the network after extra networks activation been applied
won't be call if p.disable_extra_networks
@@ -156,6 +161,25 @@ class Script: pass
+ def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+ """
+ Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+ This is useful when you want to update the entire batch instead of individual images.
+
+ You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+ If the number of images is different from the batch size when returning,
+ then the script has the responsibility to also update the following attributes in the processing object (p):
+ - p.prompts
+ - p.negative_prompts
+ - p.seeds
+ - p.subseeds
+
+ **kwargs will have same items as process_batch, and also:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ """
+
+ pass
+
def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
"""
Called for every image after it has been generated.
@@ -536,6 +560,14 @@ class ScriptRunner: except Exception:
errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
+ def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_batch_list(p, pp, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+
def postprocess_image(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
try:
@@ -599,49 +631,3 @@ def reload_script_body_only(): reload_scripts = load_scripts # compatibility alias
-
-
-def add_classes_to_gradio_component(comp):
- """
- this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
- """
-
- comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
-
- if getattr(comp, 'multiselect', False):
- comp.elem_classes.append('multiselect')
-
-
-
-def IOComponent_init(self, *args, **kwargs):
- if scripts_current is not None:
- scripts_current.before_component(self, **kwargs)
-
- script_callbacks.before_component_callback(self, **kwargs)
-
- res = original_IOComponent_init(self, *args, **kwargs)
-
- add_classes_to_gradio_component(self)
-
- script_callbacks.after_component_callback(self, **kwargs)
-
- if scripts_current is not None:
- scripts_current.after_component(self, **kwargs)
-
- return res
-
-
-original_IOComponent_init = gr.components.IOComponent.__init__
-gr.components.IOComponent.__init__ = IOComponent_init
-
-
-def BlockContext_init(self, *args, **kwargs):
- res = original_BlockContext_init(self, *args, **kwargs)
-
- add_classes_to_gradio_component(self)
-
- return res
-
-
-original_BlockContext_init = gr.blocks.BlockContext.__init__
-gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index 9fc89dc6..695c5736 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -3,8 +3,31 @@ import open_clip import torch
import transformers.utils.hub
+from modules import shared
-class DisableInitialization:
+
+class ReplaceHelper:
+ def __init__(self):
+ self.replaced = []
+
+ def replace(self, obj, field, func):
+ original = getattr(obj, field, None)
+ if original is None:
+ return None
+
+ self.replaced.append((obj, field, original))
+ setattr(obj, field, func)
+
+ return original
+
+ def restore(self):
+ for obj, field, original in self.replaced:
+ setattr(obj, field, original)
+
+ self.replaced.clear()
+
+
+class DisableInitialization(ReplaceHelper):
"""
When an object of this class enters a `with` block, it starts:
- preventing torch's layer initialization functions from working
@@ -21,7 +44,7 @@ class DisableInitialization: """
def __init__(self, disable_clip=True):
- self.replaced = []
+ super().__init__()
self.disable_clip = disable_clip
def replace(self, obj, field, func):
@@ -86,8 +109,81 @@ class DisableInitialization: self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
def __exit__(self, exc_type, exc_val, exc_tb):
- for obj, field, original in self.replaced:
- setattr(obj, field, original)
+ self.restore()
- self.replaced.clear()
+class InitializeOnMeta(ReplaceHelper):
+ """
+ Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
+ which results in those parameters having no values and taking no memory. model.to() will be broken and
+ will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
+
+ Usage:
+ ```
+ with sd_disable_initialization.InitializeOnMeta():
+ sd_model = instantiate_from_config(sd_config.model)
+ ```
+ """
+
+ def __enter__(self):
+ if shared.cmd_opts.disable_model_loading_ram_optimization:
+ return
+
+ def set_device(x):
+ x["device"] = "meta"
+ return x
+
+ linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
+ conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
+ mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
+ self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.restore()
+
+
+class LoadStateDictOnMeta(ReplaceHelper):
+ """
+ Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
+ As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
+ Meant to be used together with InitializeOnMeta above.
+
+ Usage:
+ ```
+ with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
+ model.load_state_dict(state_dict, strict=False)
+ ```
+ """
+
+ def __init__(self, state_dict, device):
+ super().__init__()
+ self.state_dict = state_dict
+ self.device = device
+
+ def __enter__(self):
+ if shared.cmd_opts.disable_model_loading_ram_optimization:
+ return
+
+ sd = self.state_dict
+ device = self.device
+
+ def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
+ params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
+
+ for name, param in params:
+ if param.is_meta:
+ self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+
+ original(self, state_dict, prefix, *args, **kwargs)
+
+ for name, _ in params:
+ key = prefix + name
+ if key in sd:
+ del sd[key]
+
+ linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
+ conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
+ mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.restore()
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 6b5aae4b..9ad98199 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -2,11 +2,10 @@ import torch from torch.nn.functional import silu
from types import MethodType
-import modules.textual_inversion.textual_inversion
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, sd_hijack_inpainting
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
@@ -15,6 +14,11 @@ import ldm.models.diffusion.ddim import ldm.models.diffusion.plms
import ldm.modules.encoders.modules
+import sgm.modules.attention
+import sgm.modules.diffusionmodules.model
+import sgm.modules.diffusionmodules.openaimodel
+import sgm.modules.encoders.modules
+
attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
@@ -25,8 +29,12 @@ ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.Cros ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
# silence new console spam from SD2
-ldm.modules.attention.print = lambda *args: None
-ldm.modules.diffusionmodules.model.print = lambda *args: None
+ldm.modules.attention.print = shared.ldm_print
+ldm.modules.diffusionmodules.model.print = shared.ldm_print
+ldm.util.print = shared.ldm_print
+ldm.models.diffusion.ddpm.print = shared.ldm_print
+
+sd_hijack_inpainting.do_inpainting_hijack()
optimizers = []
current_optimizer: sd_hijack_optimizations.SdOptimization = None
@@ -56,6 +64,9 @@ def apply_optimizations(option=None): ldm.modules.diffusionmodules.model.nonlinearity = silu
ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+ sgm.modules.diffusionmodules.model.nonlinearity = silu
+ sgm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+
if current_optimizer is not None:
current_optimizer.undo()
current_optimizer = None
@@ -89,6 +100,10 @@ def undo_optimizations(): ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+ sgm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+ sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
def fix_checkpoint():
"""checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
@@ -152,12 +167,13 @@ class StableDiffusionModelHijack: clip = None
optimization_method = None
- embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
-
def __init__(self):
+ import modules.textual_inversion.textual_inversion
+
self.extra_generation_params = {}
self.comments = []
+ self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
def apply_optimizations(self, option=None):
@@ -168,6 +184,32 @@ class StableDiffusionModelHijack: undo_optimizations()
def hijack(self, m):
+ conditioner = getattr(m, 'conditioner', None)
+ if conditioner:
+ text_cond_models = []
+
+ for i in range(len(conditioner.embedders)):
+ embedder = conditioner.embedders[i]
+ typename = type(embedder).__name__
+ if typename == 'FrozenOpenCLIPEmbedder':
+ embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self)
+ conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(embedder, self)
+ text_cond_models.append(conditioner.embedders[i])
+ if typename == 'FrozenCLIPEmbedder':
+ model_embeddings = embedder.transformer.text_model.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+ conditioner.embedders[i] = sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords(embedder, self)
+ text_cond_models.append(conditioner.embedders[i])
+ if typename == 'FrozenOpenCLIPEmbedder2':
+ embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self, textual_inversion_key='clip_g')
+ conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords(embedder, self)
+ text_cond_models.append(conditioner.embedders[i])
+
+ if len(text_cond_models) == 1:
+ m.cond_stage_model = text_cond_models[0]
+ else:
+ m.cond_stage_model = conditioner
+
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
model_embeddings = m.cond_stage_model.roberta.embeddings
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
@@ -205,7 +247,7 @@ class StableDiffusionModelHijack: ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
def undo_hijack(self, m):
- if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+ if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
m.cond_stage_model = m.cond_stage_model.wrapped
elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
@@ -254,10 +296,11 @@ class StableDiffusionModelHijack: class EmbeddingsWithFixes(torch.nn.Module):
- def __init__(self, wrapped, embeddings):
+ def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
super().__init__()
self.wrapped = wrapped
self.embeddings = embeddings
+ self.textual_inversion_key = textual_inversion_key
def forward(self, input_ids):
batch_fixes = self.embeddings.fixes
@@ -271,7 +314,8 @@ class EmbeddingsWithFixes(torch.nn.Module): vecs = []
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, embedding in fixes:
- emb = devices.cond_cast_unet(embedding.vec)
+ vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
+ emb = devices.cond_cast_unet(vec)
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index c1d780a3..8f29057a 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -42,6 +42,10 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
self.chunk_length = 75
+ self.is_trainable = getattr(wrapped, 'is_trainable', False)
+ self.input_key = getattr(wrapped, 'input_key', 'txt')
+ self.legacy_ucg_val = None
+
def empty_chunk(self):
"""creates an empty PromptChunk and returns it"""
@@ -157,7 +161,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): position += 1
continue
- emb_len = int(embedding.vec.shape[0])
+ emb_len = int(embedding.vectors)
if len(chunk.tokens) + emb_len > self.chunk_length:
next_chunk()
@@ -199,8 +203,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): """
Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
- be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
+ be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
An example shape returned by this function can be: (2, 77, 768).
+ For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
"""
@@ -240,9 +245,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): hashes.append(f"{name}: {shorthash}")
if hashes:
+ if self.hijack.extra_generation_params.get("TI hashes"):
+ hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
- return torch.hstack(zs)
+ if getattr(self.wrapped, 'return_pooled', False):
+ return torch.hstack(zs), zs[0].pooled
+ else:
+ return torch.hstack(zs)
def process_tokens(self, remade_batch_tokens, batch_multipliers):
"""
@@ -262,6 +272,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): z = self.encode_with_transformers(tokens)
+ pooled = getattr(z, 'pooled', None)
+
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
original_mean = z.mean()
@@ -269,6 +281,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): new_mean = z.mean()
z = z * (original_mean / new_mean)
+ if pooled is not None:
+ z.pooled = pooled
+
return z
@@ -324,3 +339,18 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase): embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
return embedded
+
+
+class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ def encode_with_transformers(self, tokens):
+ outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
+
+ if self.wrapped.layer == "last":
+ z = outputs.last_hidden_state
+ else:
+ z = outputs.hidden_states[self.wrapped.layer_idx]
+
+ return z
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index c1977b19..2d44b856 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -92,6 +92,4 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F def do_inpainting_hijack(): - # p_sample_plms is needed because PLMS can't work with dicts as conditionings - ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms diff --git a/modules/sd_hijack_open_clip.py b/modules/sd_hijack_open_clip.py index f733e852..25c5e983 100644 --- a/modules/sd_hijack_open_clip.py +++ b/modules/sd_hijack_open_clip.py @@ -35,3 +35,37 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
return embedded
+
+
+class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
+ self.id_start = tokenizer.encoder["<start_of_text>"]
+ self.id_end = tokenizer.encoder["<end_of_text>"]
+ self.id_pad = 0
+
+ def tokenize(self, texts):
+ assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
+
+ tokenized = [tokenizer.encode(text) for text in texts]
+
+ return tokenized
+
+ def encode_with_transformers(self, tokens):
+ d = self.wrapped.encode_with_transformer(tokens)
+ z = d[self.wrapped.layer]
+
+ pooled = d.get("pooled")
+ if pooled is not None:
+ z.pooled = pooled
+
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ ids = tokenizer.encode(init_text)
+ ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+ embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
+
+ return embedded
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 53e27ade..0e810eec 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -14,7 +14,11 @@ from modules.hypernetworks import hypernetwork import ldm.modules.attention
import ldm.modules.diffusionmodules.model
+import sgm.modules.attention
+import sgm.modules.diffusionmodules.model
+
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+sgm_diffusionmodules_model_AttnBlock_forward = sgm.modules.diffusionmodules.model.AttnBlock.forward
class SdOptimization:
@@ -39,6 +43,9 @@ class SdOptimization: ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+ sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sgm_diffusionmodules_model_AttnBlock_forward
+
class SdOptimizationXformers(SdOptimization):
name = "xformers"
@@ -51,6 +58,8 @@ class SdOptimizationXformers(SdOptimization): def apply(self):
ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = xformers_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
class SdOptimizationSdpNoMem(SdOptimization):
@@ -65,6 +74,8 @@ class SdOptimizationSdpNoMem(SdOptimization): def apply(self):
ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
class SdOptimizationSdp(SdOptimizationSdpNoMem):
@@ -76,6 +87,8 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem): def apply(self):
ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
class SdOptimizationSubQuad(SdOptimization):
@@ -86,6 +99,8 @@ class SdOptimizationSubQuad(SdOptimization): def apply(self):
ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
class SdOptimizationV1(SdOptimization):
@@ -94,9 +109,9 @@ class SdOptimizationV1(SdOptimization): cmd_opt = "opt_split_attention_v1"
priority = 10
-
def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
+ sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
class SdOptimizationInvokeAI(SdOptimization):
@@ -109,6 +124,7 @@ class SdOptimizationInvokeAI(SdOptimization): def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
+ sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
class SdOptimizationDoggettx(SdOptimization):
@@ -119,6 +135,8 @@ class SdOptimizationDoggettx(SdOptimization): def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
def list_optimizers(res):
@@ -155,7 +173,7 @@ def get_available_vram(): # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
-def split_cross_attention_forward_v1(self, x, context=None, mask=None):
+def split_cross_attention_forward_v1(self, x, context=None, mask=None, **kwargs):
h = self.heads
q_in = self.to_q(x)
@@ -196,7 +214,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): # taken from https://github.com/Doggettx/stable-diffusion and modified
-def split_cross_attention_forward(self, x, context=None, mask=None):
+def split_cross_attention_forward(self, x, context=None, mask=None, **kwargs):
h = self.heads
q_in = self.to_q(x)
@@ -238,9 +256,9 @@ def split_cross_attention_forward(self, x, context=None, mask=None): raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
- slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+ slice_size = q.shape[1] // steps
for i in range(0, q.shape[1], slice_size):
- end = i + slice_size
+ end = min(i + slice_size, q.shape[1])
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
s2 = s1.softmax(dim=-1, dtype=q.dtype)
@@ -262,11 +280,13 @@ def split_cross_attention_forward(self, x, context=None, mask=None): # -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+
def einsum_op_compvis(q, k, v):
s = einsum('b i d, b j d -> b i j', q, k)
s = s.softmax(dim=-1, dtype=s.dtype)
return einsum('b i j, b j d -> b i d', s, v)
+
def einsum_op_slice_0(q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[0], slice_size):
@@ -274,6 +294,7 @@ def einsum_op_slice_0(q, k, v, slice_size): r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
return r
+
def einsum_op_slice_1(q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[1], slice_size):
@@ -281,6 +302,7 @@ def einsum_op_slice_1(q, k, v, slice_size): r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
return r
+
def einsum_op_mps_v1(q, k, v):
if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
return einsum_op_compvis(q, k, v)
@@ -290,12 +312,14 @@ def einsum_op_mps_v1(q, k, v): slice_size -= 1
return einsum_op_slice_1(q, k, v, slice_size)
+
def einsum_op_mps_v2(q, k, v):
if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
return einsum_op_compvis(q, k, v)
else:
return einsum_op_slice_0(q, k, v, 1)
+
def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
if size_mb <= max_tensor_mb:
@@ -305,6 +329,7 @@ def einsum_op_tensor_mem(q, k, v, max_tensor_mb): return einsum_op_slice_0(q, k, v, q.shape[0] // div)
return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
+
def einsum_op_cuda(q, k, v):
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
@@ -315,6 +340,7 @@ def einsum_op_cuda(q, k, v): # Divide factor of safety as there's copying and fragmentation
return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+
def einsum_op(q, k, v):
if q.device.type == 'cuda':
return einsum_op_cuda(q, k, v)
@@ -328,7 +354,8 @@ def einsum_op(q, k, v): # Tested on i7 with 8MB L3 cache.
return einsum_op_tensor_mem(q, k, v, 32)
-def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
+
+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None, **kwargs):
h = self.heads
q = self.to_q(x)
@@ -356,7 +383,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): # Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
-def sub_quad_attention_forward(self, x, context=None, mask=None):
+def sub_quad_attention_forward(self, x, context=None, mask=None, **kwargs):
assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
h = self.heads
@@ -392,6 +419,7 @@ def sub_quad_attention_forward(self, x, context=None, mask=None): return x
+
def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
bytes_per_token = torch.finfo(q.dtype).bits//8
batch_x_heads, q_tokens, _ = q.shape
@@ -442,7 +470,7 @@ def get_xformers_flash_attention_op(q, k, v): return None
-def xformers_attention_forward(self, x, context=None, mask=None):
+def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
@@ -465,9 +493,10 @@ def xformers_attention_forward(self, x, context=None, mask=None): out = rearrange(out, 'b n h d -> b n (h d)', h=h)
return self.to_out(out)
+
# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
-def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
+def scaled_dot_product_attention_forward(self, x, context=None, mask=None, **kwargs):
batch_size, sequence_length, inner_dim = x.shape
if mask is not None:
@@ -507,10 +536,12 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None): hidden_states = self.to_out[1](hidden_states)
return hidden_states
-def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
+
+def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None, **kwargs):
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
return scaled_dot_product_attention_forward(self, x, context, mask)
+
def cross_attention_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
@@ -569,6 +600,7 @@ def cross_attention_attnblock_forward(self, x): return h3
+
def xformers_attnblock_forward(self, x):
try:
h_ = x
@@ -592,6 +624,7 @@ def xformers_attnblock_forward(self, x): except NotImplementedError:
return cross_attention_attnblock_forward(self, x)
+
def sdp_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
@@ -612,10 +645,12 @@ def sdp_attnblock_forward(self, x): out = self.proj_out(out)
return x + out
+
def sdp_no_mem_attnblock_forward(self, x):
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
return sdp_attnblock_forward(self, x)
+
def sub_quad_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py index ca1daf45..2101f1a0 100644 --- a/modules/sd_hijack_unet.py +++ b/modules/sd_hijack_unet.py @@ -39,7 +39,10 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs): if isinstance(cond, dict):
for y in cond.keys():
- cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+ if isinstance(cond[y], list):
+ cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+ else:
+ cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
with devices.autocast():
return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
@@ -77,3 +80,6 @@ first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devi CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
+
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
+CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
diff --git a/modules/sd_models.py b/modules/sd_models.py index 060e0007..f6051604 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -14,8 +14,7 @@ import ldm.modules.midas as midas from ldm.util import instantiate_from_config
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
-from modules.sd_hijack_inpainting import do_inpainting_hijack
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
from modules.timer import Timer
import tomesd
@@ -33,6 +32,8 @@ class CheckpointInfo: self.filename = filename
abspath = os.path.abspath(filename)
+ self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
elif abspath.startswith(model_path):
@@ -43,6 +44,19 @@ class CheckpointInfo: if name.startswith("\\") or name.startswith("/"):
name = name[1:]
+ def read_metadata():
+ metadata = read_metadata_from_safetensors(filename)
+ self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
+
+ return metadata
+
+ self.metadata = {}
+ if self.is_safetensors:
+ try:
+ self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
+ except Exception as e:
+ errors.display(e, f"reading metadata for {filename}")
+
self.name = name
self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
@@ -52,17 +66,9 @@ class CheckpointInfo: self.shorthash = self.sha256[0:10] if self.sha256 else None
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
+ self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
- self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
-
- self.metadata = {}
-
- _, ext = os.path.splitext(self.filename)
- if ext.lower() == ".safetensors":
- try:
- self.metadata = read_metadata_from_safetensors(filename)
- except Exception as e:
- errors.display(e, f"reading checkpoint metadata: {filename}")
+ self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
def register(self):
checkpoints_list[self.title] = self
@@ -79,8 +85,9 @@ class CheckpointInfo: if self.shorthash not in self.ids:
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
- checkpoints_list.pop(self.title)
+ checkpoints_list.pop(self.title, None)
self.title = f'{self.name} [{self.shorthash}]'
+ self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
self.register()
return self.shorthash
@@ -101,14 +108,8 @@ def setup_model(): enable_midas_autodownload()
-def checkpoint_tiles():
- def convert(name):
- return int(name) if name.isdigit() else name.lower()
-
- def alphanumeric_key(key):
- return [convert(c) for c in re.split('([0-9]+)', key)]
-
- return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
+def checkpoint_tiles(use_short=False):
+ return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
def list_models():
@@ -131,11 +132,14 @@ def list_models(): elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
- for filename in sorted(model_list, key=str.lower):
+ for filename in model_list:
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
+re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
+
+
def get_closet_checkpoint_match(search_string):
checkpoint_info = checkpoint_aliases.get(search_string, None)
if checkpoint_info is not None:
@@ -145,6 +149,11 @@ def get_closet_checkpoint_match(search_string): if found:
return found[0]
+ search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
+ found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
+ if found:
+ return found[0]
+
return None
@@ -289,13 +298,21 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer if state_dict is None:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+ model.is_sdxl = hasattr(model, 'conditioner')
+ model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
+ model.is_sd1 = not model.is_sdxl and not model.is_sd2
+
+ if model.is_sdxl:
+ sd_models_xl.extend_sdxl(model)
+
model.load_state_dict(state_dict, strict=False)
- del state_dict
timer.record("apply weights to model")
if shared.opts.sd_checkpoint_cache > 0:
# cache newly loaded model
- checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+ checkpoints_loaded[checkpoint_info] = state_dict
+
+ del state_dict
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
@@ -319,7 +336,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer timer.record("apply half()")
- devices.dtype_unet = model.model.diffusion_model.dtype
+ devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else model.model.diffusion_model.dtype
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
model.first_stage_model.to(devices.dtype_vae)
@@ -334,7 +351,8 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer model.sd_checkpoint_info = checkpoint_info
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
- model.logvar = model.logvar.to(devices.device) # fix for training
+ if hasattr(model, 'logvar'):
+ model.logvar = model.logvar.to(devices.device) # fix for training
sd_vae.delete_base_vae()
sd_vae.clear_loaded_vae()
@@ -391,10 +409,11 @@ def repair_config(sd_config): if not hasattr(sd_config.model.params, "use_ema"):
sd_config.model.params.use_ema = False
- if shared.cmd_opts.no_half:
- sd_config.model.params.unet_config.params.use_fp16 = False
- elif shared.cmd_opts.upcast_sampling:
- sd_config.model.params.unet_config.params.use_fp16 = True
+ if hasattr(sd_config.model.params, 'unet_config'):
+ if shared.cmd_opts.no_half:
+ sd_config.model.params.unet_config.params.use_fp16 = False
+ elif shared.cmd_opts.upcast_sampling:
+ sd_config.model.params.unet_config.params.use_fp16 = True
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
@@ -407,11 +426,14 @@ def repair_config(sd_config): sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
+sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
+sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
class SdModelData:
def __init__(self):
self.sd_model = None
+ self.loaded_sd_models = []
self.was_loaded_at_least_once = False
self.lock = threading.Lock()
@@ -426,6 +448,7 @@ class SdModelData: try:
load_model()
+
except Exception as e:
errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
@@ -437,23 +460,68 @@ class SdModelData: def set_sd_model(self, v):
self.sd_model = v
+ try:
+ self.loaded_sd_models.remove(v)
+ except ValueError:
+ pass
+
+ if v is not None:
+ self.loaded_sd_models.insert(0, v)
+
model_data = SdModelData()
+def get_empty_cond(sd_model):
+ from modules import extra_networks, processing
+
+ p = processing.StableDiffusionProcessingTxt2Img()
+ extra_networks.activate(p, {})
+
+ if hasattr(sd_model, 'conditioner'):
+ d = sd_model.get_learned_conditioning([""])
+ return d['crossattn']
+ else:
+ return sd_model.cond_stage_model([""])
+
+
+def send_model_to_cpu(m):
+ from modules import lowvram
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ m.to(devices.cpu)
+
+ devices.torch_gc()
+
+
+def send_model_to_device(m):
+ from modules import lowvram
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
+ else:
+ m.to(shared.device)
+
+
+def send_model_to_trash(m):
+ m.to(device="meta")
+ devices.torch_gc()
+
+
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
- from modules import lowvram, sd_hijack
+ from modules import sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
+ timer = Timer()
+
if model_data.sd_model:
- sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+ send_model_to_trash(model_data.sd_model)
model_data.sd_model = None
- gc.collect()
devices.torch_gc()
- do_inpainting_hijack()
-
- timer = Timer()
+ timer.record("unload existing model")
if already_loaded_state_dict is not None:
state_dict = already_loaded_state_dict
@@ -461,7 +529,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
- clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
+ clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
timer.record("find config")
@@ -474,26 +542,28 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): sd_model = None
try:
- with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
- sd_model = instantiate_from_config(sd_config.model)
- except Exception:
- pass
+ with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
+ with sd_disable_initialization.InitializeOnMeta():
+ sd_model = instantiate_from_config(sd_config.model)
+
+ except Exception as e:
+ errors.display(e, "creating model quickly", full_traceback=True)
if sd_model is None:
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
- sd_model = instantiate_from_config(sd_config.model)
+
+ with sd_disable_initialization.InitializeOnMeta():
+ sd_model = instantiate_from_config(sd_config.model)
sd_model.used_config = checkpoint_config
timer.record("create model")
- load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-
- 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)
+ with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
+ load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+ timer.record("load weights from state dict")
+ send_model_to_device(sd_model)
timer.record("move model to device")
sd_hijack.model_hijack.hijack(sd_model)
@@ -501,7 +571,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): timer.record("hijack")
sd_model.eval()
- model_data.sd_model = sd_model
+ model_data.set_sd_model(sd_model)
model_data.was_loaded_at_least_once = True
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
@@ -513,7 +583,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): timer.record("scripts callbacks")
with devices.autocast(), torch.no_grad():
- sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
+ sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
timer.record("calculate empty prompt")
@@ -522,10 +592,61 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): return sd_model
+def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
+ """
+ Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
+ If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
+ If not, returns the model that can be used to load weights from checkpoint_info's file.
+ If no such model exists, returns None.
+ Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
+ """
+
+ already_loaded = None
+ for i in reversed(range(len(model_data.loaded_sd_models))):
+ loaded_model = model_data.loaded_sd_models[i]
+ if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ already_loaded = loaded_model
+ continue
+
+ if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
+ print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
+ model_data.loaded_sd_models.pop()
+ send_model_to_trash(loaded_model)
+ timer.record("send model to trash")
+
+ if shared.opts.sd_checkpoints_keep_in_cpu:
+ send_model_to_cpu(sd_model)
+ timer.record("send model to cpu")
+
+ if already_loaded is not None:
+ send_model_to_device(already_loaded)
+ timer.record("send model to device")
+
+ model_data.set_sd_model(already_loaded)
+ print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
+ return model_data.sd_model
+ elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
+ print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
+
+ model_data.sd_model = None
+ load_model(checkpoint_info)
+ return model_data.sd_model
+ elif len(model_data.loaded_sd_models) > 0:
+ sd_model = model_data.loaded_sd_models.pop()
+ model_data.sd_model = sd_model
+
+ print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
+ return sd_model
+ else:
+ return None
+
+
def reload_model_weights(sd_model=None, info=None):
- from modules import lowvram, devices, sd_hijack
+ from modules import devices, sd_hijack
checkpoint_info = info or select_checkpoint()
+ timer = Timer()
+
if not sd_model:
sd_model = model_data.sd_model
@@ -534,19 +655,17 @@ def reload_model_weights(sd_model=None, info=None): else:
current_checkpoint_info = sd_model.sd_checkpoint_info
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
- return
+ return sd_model
- sd_unet.apply_unet("None")
-
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
- lowvram.send_everything_to_cpu()
- else:
- sd_model.to(devices.cpu)
+ sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
+ if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ return sd_model
+ if sd_model is not None:
+ sd_unet.apply_unet("None")
+ send_model_to_cpu(sd_model)
sd_hijack.model_hijack.undo_hijack(sd_model)
- timer = Timer()
-
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
@@ -554,7 +673,9 @@ def reload_model_weights(sd_model=None, info=None): timer.record("find config")
if sd_model is None or checkpoint_config != sd_model.used_config:
- del sd_model
+ if sd_model is not None:
+ send_model_to_trash(sd_model)
+
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return model_data.sd_model
@@ -577,6 +698,8 @@ def reload_model_weights(sd_model=None, info=None): print(f"Weights loaded in {timer.summary()}.")
+ model_data.set_sd_model(sd_model)
+
return sd_model
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 9bfe1237..8266fa39 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -6,12 +6,15 @@ from modules import shared, paths, sd_disable_initialization sd_configs_path = shared.sd_configs_path
sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
config_default = shared.sd_default_config
config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
+config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
@@ -68,7 +71,11 @@ def guess_model_config_from_state_dict(sd, filename): diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
- if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+ if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
+ return config_sdxl
+ if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
+ return config_sdxl_refiner
+ elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
return config_depth_model
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
return config_unclip
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py new file mode 100644 index 00000000..01123321 --- /dev/null +++ b/modules/sd_models_xl.py @@ -0,0 +1,108 @@ +from __future__ import annotations
+
+import torch
+
+import sgm.models.diffusion
+import sgm.modules.diffusionmodules.denoiser_scaling
+import sgm.modules.diffusionmodules.discretizer
+from modules import devices, shared, prompt_parser
+
+
+def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
+ for embedder in self.conditioner.embedders:
+ embedder.ucg_rate = 0.0
+
+ width = getattr(batch, 'width', 1024)
+ height = getattr(batch, 'height', 1024)
+ is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
+ aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
+
+ devices_args = dict(device=devices.device, dtype=devices.dtype)
+
+ sdxl_conds = {
+ "txt": batch,
+ "original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+ "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
+ "target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+ "aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
+ }
+
+ force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
+ c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
+
+ return c
+
+
+def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
+ return self.model(x, t, cond)
+
+
+def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
+ return x
+
+
+sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
+sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
+sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
+
+
+def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
+ res = []
+
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
+ encoded = embedder.encode_embedding_init_text(init_text, nvpt)
+ res.append(encoded)
+
+ return torch.cat(res, dim=1)
+
+
+def tokenize(self: sgm.modules.GeneralConditioner, texts):
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
+ return embedder.tokenize(texts)
+
+ raise AssertionError('no tokenizer available')
+
+
+
+def process_texts(self, texts):
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
+ return embedder.process_texts(texts)
+
+
+def get_target_prompt_token_count(self, token_count):
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
+ return embedder.get_target_prompt_token_count(token_count)
+
+
+# those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
+sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
+sgm.modules.GeneralConditioner.tokenize = tokenize
+sgm.modules.GeneralConditioner.process_texts = process_texts
+sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
+
+
+def extend_sdxl(model):
+ """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
+
+ dtype = next(model.model.diffusion_model.parameters()).dtype
+ model.model.diffusion_model.dtype = dtype
+ model.model.conditioning_key = 'crossattn'
+ model.cond_stage_key = 'txt'
+ # model.cond_stage_model will be set in sd_hijack
+
+ model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
+
+ discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
+ model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+
+ model.conditioner.wrapped = torch.nn.Module()
+
+
+sgm.modules.attention.print = shared.ldm_print
+sgm.modules.diffusionmodules.model.print = shared.ldm_print
+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
+sgm.modules.encoders.modules.print = shared.ldm_print
+
+# this gets the code to load the vanilla attention that we override
+sgm.modules.attention.SDP_IS_AVAILABLE = True
+sgm.modules.attention.XFORMERS_IS_AVAILABLE = False
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index f22aad8f..bea2684c 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -28,6 +28,9 @@ def create_sampler(name, model): assert config is not None, f'bad sampler name: {name}'
+ if model.is_sdxl and config.options.get("no_sdxl", False):
+ raise Exception(f"Sampler {config.name} is not supported for SDXL")
+
sampler = config.constructor(model)
sampler.config = config
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 763829f1..b3d344e7 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -2,10 +2,8 @@ from collections import namedtuple import numpy as np
import torch
from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
-
+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
from modules.shared import opts, state
-import modules.shared as shared
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -37,7 +35,7 @@ def single_sample_to_image(sample, approximation=None): x_sample = sample * 1.5
x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
else:
- x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
+ x_sample = decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
@@ -46,6 +44,12 @@ def single_sample_to_image(sample, approximation=None): return Image.fromarray(x_sample)
+def decode_first_stage(model, x):
+ x = model.decode_first_stage(x.to(devices.dtype_vae))
+
+ return x
+
+
def sample_to_image(samples, index=0, approximation=None):
return single_sample_to_image(samples[index], approximation)
@@ -85,11 +89,13 @@ class InterruptedException(BaseException): pass
-if opts.randn_source == "CPU":
+def replace_torchsde_browinan():
import torchsde._brownian.brownian_interval
def torchsde_randn(size, dtype, device, seed):
- generator = torch.Generator(devices.cpu).manual_seed(int(seed))
- return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+ return devices.randn_local(seed, size).to(device=device, dtype=dtype)
torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
+replace_torchsde_browinan()
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index bdae8b40..4a8396f9 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -11,9 +11,9 @@ import modules.models.diffusion.uni_pc samplers_data_compvis = [
- sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}),
- sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
- sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
+ sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True, "no_sdxl": True}),
+ sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {"no_sdxl": True}),
+ sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {"no_sdxl": True}),
]
diff --git a/modules/sd_samplers_extra.py b/modules/sd_samplers_extra.py new file mode 100644 index 00000000..1b981ca8 --- /dev/null +++ b/modules/sd_samplers_extra.py @@ -0,0 +1,74 @@ +import torch
+import tqdm
+import k_diffusion.sampling
+
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
+ """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
+ Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
+ If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
+ """
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ step_id = 0
+ from k_diffusion.sampling import to_d, get_sigmas_karras
+
+ def heun_step(x, old_sigma, new_sigma, second_order=True):
+ nonlocal step_id
+ denoised = model(x, old_sigma * s_in, **extra_args)
+ d = to_d(x, old_sigma, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+ dt = new_sigma - old_sigma
+ if new_sigma == 0 or not second_order:
+ # Euler method
+ x = x + d * dt
+ else:
+ # Heun's method
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+ d_2 = to_d(x_2, new_sigma, denoised_2)
+ d_prime = (d + d_2) / 2
+ x = x + d_prime * dt
+ step_id += 1
+ return x
+
+ steps = sigmas.shape[0] - 1
+ if restart_list is None:
+ if steps >= 20:
+ restart_steps = 9
+ restart_times = 1
+ if steps >= 36:
+ restart_steps = steps // 4
+ restart_times = 2
+ sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+ restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+ else:
+ restart_list = {}
+
+ restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
+
+ step_list = []
+ for i in range(len(sigmas) - 1):
+ step_list.append((sigmas[i], sigmas[i + 1]))
+ if i + 1 in restart_list:
+ restart_steps, restart_times, restart_max = restart_list[i + 1]
+ min_idx = i + 1
+ max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+ if max_idx < min_idx:
+ sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+ while restart_times > 0:
+ restart_times -= 1
+ step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+
+ last_sigma = None
+ for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
+ if last_sigma is None:
+ last_sigma = old_sigma
+ elif last_sigma < old_sigma:
+ x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
+ x = heun_step(x, old_sigma, new_sigma)
+ last_sigma = new_sigma
+
+ return x
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 71581b76..8bb639f5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -2,7 +2,7 @@ from collections import deque import torch
import inspect
import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
from modules.shared import opts, state
import modules.shared as shared
@@ -30,12 +30,15 @@ samplers_k_diffusion = [ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+ ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+ ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
]
+
samplers_data_k_diffusion = [
sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
for label, funcname, aliases, options in samplers_k_diffusion
- if hasattr(k_diffusion.sampling, funcname)
+ if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
]
sampler_extra_params = {
@@ -53,6 +56,28 @@ k_diffusion_scheduler = { }
+def catenate_conds(conds):
+ if not isinstance(conds[0], dict):
+ return torch.cat(conds)
+
+ return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+ if not isinstance(cond, dict):
+ return cond[a:b]
+
+ return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+ if not isinstance(tensor, dict):
+ return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+ tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+ return tensor
+
+
class CFGDenoiser(torch.nn.Module):
"""
Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
@@ -105,10 +130,13 @@ class CFGDenoiser(torch.nn.Module): if shared.sd_model.model.conditioning_key == "crossattn-adm":
image_uncond = torch.zeros_like(image_cond)
- make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
else:
image_uncond = image_cond
- make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+ if isinstance(uncond, dict):
+ make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+ else:
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -140,28 +168,28 @@ class CFGDenoiser(torch.nn.Module): num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
- tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+ tensor = pad_cond(tensor, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
- uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+ uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
- cond_in = torch.cat([tensor, uncond, uncond])
+ cond_in = catenate_conds([tensor, uncond, uncond])
elif skip_uncond:
cond_in = tensor
else:
- cond_in = torch.cat([tensor, uncond])
+ cond_in = catenate_conds([tensor, uncond])
if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -170,14 +198,14 @@ class CFGDenoiser(torch.nn.Module): b = min(a + batch_size, tensor.shape[0])
if not is_edit_model:
- c_crossattn = [tensor[a:b]]
+ c_crossattn = subscript_cond(tensor, a, b)
else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
if not skip_uncond:
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
denoised_image_indexes = [x[0][0] for x in conds_list]
if skip_uncond:
@@ -233,10 +261,7 @@ class TorchHijack: if noise.shape == x.shape:
return noise
- if opts.randn_source == "CPU" or x.device.type == 'mps':
- return torch.randn_like(x, device=devices.cpu).to(x.device)
- else:
- return torch.randn_like(x)
+ return devices.randn_like(x)
class KDiffusionSampler:
@@ -245,7 +270,7 @@ class KDiffusionSampler: self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
- self.func = getattr(k_diffusion.sampling, self.funcname)
+ self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname, [])
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None
@@ -351,6 +376,9 @@ class KDiffusionSampler: sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
else:
sigmas = self.model_wrap.get_sigmas(steps)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index e4ff2994..0bd5e19b 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -1,6 +1,6 @@ import os import collections -from modules import paths, shared, devices, script_callbacks, sd_models +from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks import glob from copy import deepcopy @@ -16,6 +16,7 @@ checkpoint_info = None checkpoints_loaded = collections.OrderedDict() + def get_base_vae(model): if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: return base_vae @@ -50,6 +51,7 @@ def get_filename(filepath): def refresh_vae_list(): + global vae_dict vae_dict.clear() paths = [ @@ -83,6 +85,8 @@ def refresh_vae_list(): name = get_filename(filepath) vae_dict[name] = filepath + vae_dict = dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0]))) + def find_vae_near_checkpoint(checkpoint_file): checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0] @@ -97,6 +101,16 @@ def resolve_vae(checkpoint_file): if shared.cmd_opts.vae_path is not None: return shared.cmd_opts.vae_path, 'from commandline argument' + metadata = extra_networks.get_user_metadata(checkpoint_file) + vae_metadata = metadata.get("vae", None) + if vae_metadata is not None and vae_metadata != "Automatic": + if vae_metadata == "None": + return None, None + + vae_from_metadata = vae_dict.get(vae_metadata, None) + if vae_from_metadata is not None: + return vae_from_metadata, "from user metadata" + is_automatic = shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file) diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py index e2f00468..86bd658a 100644 --- a/modules/sd_vae_approx.py +++ b/modules/sd_vae_approx.py @@ -2,9 +2,9 @@ import os import torch
from torch import nn
-from modules import devices, paths
+from modules import devices, paths, shared
-sd_vae_approx_model = None
+sd_vae_approx_models = {}
class VAEApprox(nn.Module):
@@ -31,30 +31,55 @@ class VAEApprox(nn.Module): return x
+def download_model(model_path, model_url):
+ if not os.path.exists(model_path):
+ os.makedirs(os.path.dirname(model_path), exist_ok=True)
+
+ print(f'Downloading VAEApprox model to: {model_path}')
+ torch.hub.download_url_to_file(model_url, model_path)
+
+
def model():
- global sd_vae_approx_model
+ model_name = "vaeapprox-sdxl.pt" if getattr(shared.sd_model, 'is_sdxl', False) else "model.pt"
+ loaded_model = sd_vae_approx_models.get(model_name)
- if sd_vae_approx_model is None:
- model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
- sd_vae_approx_model = VAEApprox()
+ if loaded_model is None:
+ model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
if not os.path.exists(model_path):
- model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
- sd_vae_approx_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
- sd_vae_approx_model.eval()
- sd_vae_approx_model.to(devices.device, devices.dtype)
+ model_path = os.path.join(paths.script_path, "models", "VAE-approx", model_name)
+
+ if not os.path.exists(model_path):
+ model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
+ download_model(model_path, 'https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/download/v1.0.0-pre/' + model_name)
+
+ loaded_model = VAEApprox()
+ loaded_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
+ loaded_model.eval()
+ loaded_model.to(devices.device, devices.dtype)
+ sd_vae_approx_models[model_name] = loaded_model
- return sd_vae_approx_model
+ return loaded_model
def cheap_approximation(sample):
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
- coefs = torch.tensor([
- [0.298, 0.207, 0.208],
- [0.187, 0.286, 0.173],
- [-0.158, 0.189, 0.264],
- [-0.184, -0.271, -0.473],
- ]).to(sample.device)
+ if shared.sd_model.is_sdxl:
+ coeffs = [
+ [ 0.3448, 0.4168, 0.4395],
+ [-0.1953, -0.0290, 0.0250],
+ [ 0.1074, 0.0886, -0.0163],
+ [-0.3730, -0.2499, -0.2088],
+ ]
+ else:
+ coeffs = [
+ [ 0.298, 0.207, 0.208],
+ [ 0.187, 0.286, 0.173],
+ [-0.158, 0.189, 0.264],
+ [-0.184, -0.271, -0.473],
+ ]
+
+ coefs = torch.tensor(coeffs).to(sample.device)
x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py index 5e8496e8..5bf7c76e 100644 --- a/modules/sd_vae_taesd.py +++ b/modules/sd_vae_taesd.py @@ -8,9 +8,9 @@ import os import torch import torch.nn as nn -from modules import devices, paths_internal +from modules import devices, paths_internal, shared -sd_vae_taesd = None +sd_vae_taesd_models = {} def conv(n_in, n_out, **kwargs): @@ -61,9 +61,7 @@ class TAESD(nn.Module): return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) -def download_model(model_path): - model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth' - +def download_model(model_path, model_url): if not os.path.exists(model_path): os.makedirs(os.path.dirname(model_path), exist_ok=True) @@ -72,17 +70,19 @@ def download_model(model_path): def model(): - global sd_vae_taesd + model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth" + loaded_model = sd_vae_taesd_models.get(model_name) - if sd_vae_taesd is None: - model_path = os.path.join(paths_internal.models_path, "VAE-taesd", "taesd_decoder.pth") - download_model(model_path) + if loaded_model is None: + model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name) + download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name) if os.path.exists(model_path): - sd_vae_taesd = TAESD(model_path) - sd_vae_taesd.eval() - sd_vae_taesd.to(devices.device, devices.dtype) + loaded_model = TAESD(model_path) + loaded_model.eval() + loaded_model.to(devices.device, devices.dtype) + sd_vae_taesd_models[model_name] = loaded_model else: raise FileNotFoundError('TAESD model not found') - return sd_vae_taesd.decoder + return loaded_model.decoder diff --git a/modules/shared.py b/modules/shared.py index f6604ef9..8245250a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -11,6 +11,7 @@ import gradio as gr import torch
import tqdm
+import launch
import modules.interrogate
import modules.memmon
import modules.styles
@@ -26,7 +27,7 @@ demo = None parser = cmd_args.parser
-script_loading.preload_extensions(extensions_dir, parser)
+script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
script_loading.preload_extensions(extensions_builtin_dir, parser)
if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
@@ -219,12 +220,19 @@ class State: return
import modules.sd_samplers
- if opts.show_progress_grid:
- self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
- else:
- self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
- self.current_image_sampling_step = self.sampling_step
+ try:
+ if opts.show_progress_grid:
+ self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
+ else:
+ self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
+
+ self.current_image_sampling_step = self.sampling_step
+
+ except Exception:
+ # when switching models during genration, VAE would be on CPU, so creating an image will fail.
+ # we silently ignore this error
+ errors.record_exception()
def assign_current_image(self, image):
self.current_image = image
@@ -384,13 +392,15 @@ options_templates.update(options_section(('face-restoration', "Face restoration" }))
options_templates.update(options_section(('system', "System"), {
- "show_warnings": OptionInfo(False, "Show warnings in console."),
+ "show_warnings": OptionInfo(False, "Show warnings in console.").needs_restart(),
+ "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_restart(),
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
+ "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
}))
options_templates.update(options_section(('training', "Training"), {
@@ -410,28 +420,49 @@ options_templates.update(options_section(('training', "Training"), { options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
- "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+ "sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
+ "sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
+ "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
- "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
- "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.").info("normally you'd do less with less denoising"),
- "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
- "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
+ "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_restart(),
"enable_emphasis": OptionInfo(True, "Enable emphasis").info("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"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
- "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
+ "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
+ "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
+}))
+
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+ "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
+ "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
+ "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
+ "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))
+
+options_templates.update(options_section(('img2img', "img2img"), {
+ "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
+ "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.").info("normally you'd do less with less denoising"),
+ "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
+ "img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
+ "img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_restart(),
+ "img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker, {}).info("brush color of inpaint mask").needs_restart(),
+ "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_restart(),
+ "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+ "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
+}))
+
+
options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
- "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
@@ -448,7 +479,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), { "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
}))
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+options_templates.update(options_section(('interrogate', "Interrogate"), {
"interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
"interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
"interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
@@ -481,10 +512,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), { options_templates.update(options_section(('ui', "User interface"), {
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_restart(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
- "img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
"return_grid": OptionInfo(True, "Show grid in results for web"),
- "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
- "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
@@ -503,11 +531,12 @@ options_templates.update(options_section(('ui', "User interface"), { "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
- "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
+ "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_restart(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
}))
+
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
@@ -880,3 +909,10 @@ def walk_files(path, allowed_extensions=None): continue
yield os.path.join(root, filename)
+
+
+def ldm_print(*args, **kwargs):
+ if opts.hide_ldm_prints:
+ return
+
+ print(*args, **kwargs)
diff --git a/modules/styles.py b/modules/styles.py index ec0e1bc5..0740fe1b 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -106,10 +106,7 @@ class StyleDatabase: if os.path.exists(path):
shutil.copy(path, f"{path}.bak")
- fd = os.open(path, os.O_RDWR | os.O_CREAT)
- 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()
+ with open(path, "w", encoding="utf-8-sig", newline='') as file:
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
writer.writeheader()
writer.writerows(style._asdict() for k, style in self.styles.items())
diff --git a/modules/sysinfo.py b/modules/sysinfo.py index 5f15ac4f..cf24c6dd 100644 --- a/modules/sysinfo.py +++ b/modules/sysinfo.py @@ -109,11 +109,15 @@ def format_traceback(tb): return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
+def format_exception(e, tb):
+ return {"exception": str(e), "traceback": format_traceback(tb)}
+
+
def get_exceptions():
try:
from modules import errors
- return [{"exception": str(e), "traceback": format_traceback(tb)} for e, tb in reversed(errors.exception_records)]
+ return list(reversed(errors.exception_records))
except Exception as e:
return str(e)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 6166c76f..aa79dc09 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -13,7 +13,7 @@ import numpy as np from PIL import Image, PngImagePlugin
from torch.utils.tensorboard import SummaryWriter
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
+from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -181,29 +181,38 @@ class EmbeddingDatabase: else:
return
+
# textual inversion embeddings
if 'string_to_param' in data:
param_dict = data['string_to_param']
param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
- # diffuser concepts
- elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
+ vec = emb.detach().to(devices.device, dtype=torch.float32)
+ shape = vec.shape[-1]
+ vectors = vec.shape[0]
+ elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
+ vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
+ shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
+ vectors = data['clip_g'].shape[0]
+ elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
+ vec = emb.detach().to(devices.device, dtype=torch.float32)
+ shape = vec.shape[-1]
+ vectors = vec.shape[0]
else:
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
- vec = emb.detach().to(devices.device, dtype=torch.float32)
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
embedding.sd_checkpoint = data.get('sd_checkpoint', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
- embedding.vectors = vec.shape[0]
- embedding.shape = vec.shape[-1]
+ embedding.vectors = vectors
+ embedding.shape = shape
embedding.filename = path
embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
@@ -378,6 +387,8 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+ from modules import processing
+
save_embedding_every = save_embedding_every or 0
create_image_every = create_image_every or 0
template_file = textual_inversion_templates.get(template_filename, None)
diff --git a/modules/timer.py b/modules/timer.py index da99e49f..1d38595c 100644 --- a/modules/timer.py +++ b/modules/timer.py @@ -1,4 +1,5 @@ import time
+import argparse
class TimerSubcategory:
@@ -11,20 +12,27 @@ class TimerSubcategory: def __enter__(self):
self.start = time.time()
self.timer.base_category = self.original_base_category + self.category + "/"
+ self.timer.subcategory_level += 1
+
+ if self.timer.print_log:
+ print(f"{' ' * self.timer.subcategory_level}{self.category}:")
def __exit__(self, exc_type, exc_val, exc_tb):
elapsed_for_subcategroy = time.time() - self.start
self.timer.base_category = self.original_base_category
self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
- self.timer.record(self.category)
+ self.timer.subcategory_level -= 1
+ self.timer.record(self.category, disable_log=True)
class Timer:
- def __init__(self):
+ def __init__(self, print_log=False):
self.start = time.time()
self.records = {}
self.total = 0
self.base_category = ''
+ self.print_log = print_log
+ self.subcategory_level = 0
def elapsed(self):
end = time.time()
@@ -38,13 +46,16 @@ class Timer: self.records[category] += amount
- def record(self, category, extra_time=0):
+ def record(self, category, extra_time=0, disable_log=False):
e = self.elapsed()
self.add_time_to_record(self.base_category + category, e + extra_time)
self.total += e + extra_time
+ if self.print_log and not disable_log:
+ print(f"{' ' * self.subcategory_level}{category}: done in {e + extra_time:.3f}s")
+
def subcategory(self, name):
self.elapsed()
@@ -71,6 +82,10 @@ class Timer: self.__init__()
-startup_timer = Timer()
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--log-startup", action='store_true', help="print a detailed log of what's happening at startup")
+args = parser.parse_known_args()[0]
+
+startup_timer = Timer(print_log=args.log_startup)
startup_record = None
diff --git a/modules/txt2img.py b/modules/txt2img.py index 29d94e8c..935ed418 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html import gradio as gr
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
p = processing.StableDiffusionProcessingTxt2Img(
@@ -41,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
+ hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name,
hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None,
hr_prompt=hr_prompt,
hr_negative_prompt=hr_negative_prompt,
diff --git a/modules/ui.py b/modules/ui.py index 085561c1..61a6b4ad 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -12,34 +12,30 @@ import numpy as np from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+from modules import gradio_extensons # noqa: F401
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path
from modules.ui_common import create_refresh_button
from modules.ui_gradio_extensions import reload_javascript
-
from modules.shared import opts, cmd_opts
-import modules.codeformer_model
import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
+import modules.hypernetworks.ui as hypernetworks_ui
+import modules.textual_inversion.ui as textual_inversion_ui
+import modules.textual_inversion.textual_inversion as textual_inversion
import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
+import modules.images
from modules import prompt_parser
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
create_setting_component = ui_settings.create_setting_component
warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
mimetypes.init()
@@ -92,19 +88,6 @@ def send_gradio_gallery_to_image(x): return image_from_url_text(x[0])
-def add_style(name: str, prompt: str, negative_prompt: str):
- if name is None:
- return [gr_show() for x in range(4)]
-
- style = modules.styles.PromptStyle(name, prompt, negative_prompt)
- shared.prompt_styles.styles[style.name] = style
- # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
- # reserialize all styles every time we save them
- shared.prompt_styles.save_styles(shared.styles_filename)
-
- return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
from modules import processing, devices
@@ -129,13 +112,6 @@ def resize_from_to_html(width, height, scale_by): return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
-def apply_styles(prompt, prompt_neg, styles):
- prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
- prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
- return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
if mode in {0, 1, 3, 4}:
return [interrogation_function(ii_singles[mode]), None]
@@ -172,7 +148,6 @@ def interrogate_deepbooru(image): def create_seed_inputs(target_interface):
with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
- seed.style(container=False)
random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
@@ -184,7 +159,6 @@ def create_seed_inputs(target_interface): with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
seed_extras.append(seed_extra_row_1)
subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
- subseed.style(container=False)
random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
@@ -267,70 +241,77 @@ def update_token_counter(text, steps): return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
-def create_toprow(is_img2img):
- id_part = "img2img" if is_img2img else "txt2img"
+class Toprow:
+ """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
- with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
- with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+ def __init__(self, is_img2img):
+ id_part = "img2img" if is_img2img else "txt2img"
+ self.id_part = id_part
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
- button_interrogate = None
- button_deepbooru = None
- if is_img2img:
- with gr.Column(scale=1, elem_classes="interrogate-col"):
- button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
- button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
- with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
- with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
- submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
- skip.click(
- fn=lambda: shared.state.skip(),
- inputs=[],
- outputs=[],
- )
+ with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+ with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+ self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
- interrupt.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+ self.button_interrogate = None
+ self.button_deepbooru = None
+ if is_img2img:
+ with gr.Column(scale=1, elem_classes="interrogate-col"):
+ self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+ self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+ with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+ with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+ self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+ self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+ self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+ self.skip.click(
+ fn=lambda: shared.state.skip(),
+ inputs=[],
+ outputs=[],
+ )
- with gr.Row(elem_id=f"{id_part}_tools"):
- paste = ToolButton(value=paste_symbol, elem_id="paste")
- clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
- prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
- save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
- restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
-
- token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
- token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
- negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
- negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
-
- clear_prompt_button.click(
- fn=lambda *x: x,
- _js="confirm_clear_prompt",
- inputs=[prompt, negative_prompt],
- outputs=[prompt, negative_prompt],
- )
+ self.interrupt.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ with gr.Row(elem_id=f"{id_part}_tools"):
+ self.paste = ToolButton(value=paste_symbol, elem_id="paste")
+ self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+ self.extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+ self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+
+ self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+ self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+ self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+ self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+ self.clear_prompt_button.click(
+ fn=lambda *x: x,
+ _js="confirm_clear_prompt",
+ inputs=[self.prompt, self.negative_prompt],
+ outputs=[self.prompt, self.negative_prompt],
+ )
- with gr.Row(elem_id=f"{id_part}_styles_row"):
- prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
- create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+ self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
- return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, None, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
+ self.prompt_img.change(
+ fn=modules.images.image_data,
+ inputs=[self.prompt_img],
+ outputs=[self.prompt, self.prompt_img],
+ show_progress=False,
+ )
def setup_progressbar(*args, **kwargs):
@@ -414,21 +395,20 @@ def create_ui(): parameters_copypaste.reset()
- modules.scripts.scripts_current = modules.scripts.scripts_txt2img
- modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+ scripts.scripts_current = scripts.scripts_txt2img
+ scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
- txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, _, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
+ toprow = Toprow(is_img2img=False)
dummy_component = gr.Label(visible=False)
- txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
extra_tabs.__enter__()
with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, gr.Row().style(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
- modules.scripts.scripts_txt2img.prepare_ui()
+ scripts.scripts_txt2img.prepare_ui()
for category in ordered_ui_categories():
if category == "sampler":
@@ -474,6 +454,10 @@ def create_ui(): hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+
+ hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
+ create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
+
hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
@@ -496,10 +480,10 @@ def create_ui(): elif category == "scripts":
with FormGroup(elem_id="txt2img_script_container"):
- custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+ custom_inputs = scripts.scripts_txt2img.setup_ui()
else:
- modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+ scripts.scripts_txt2img.setup_ui_for_section(category)
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
@@ -530,9 +514,9 @@ def create_ui(): _js="submit",
inputs=[
dummy_component,
- txt2img_prompt,
- txt2img_negative_prompt,
- txt2img_prompt_styles,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
steps,
sampler_index,
restore_faces,
@@ -551,6 +535,7 @@ def create_ui(): hr_second_pass_steps,
hr_resize_x,
hr_resize_y,
+ hr_checkpoint_name,
hr_sampler_index,
hr_prompt,
hr_negative_prompt,
@@ -567,12 +552,12 @@ def create_ui(): show_progress=False,
)
- txt2img_prompt.submit(**txt2img_args)
- submit.click(**txt2img_args)
+ toprow.prompt.submit(**txt2img_args)
+ toprow.submit.click(**txt2img_args)
res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
- restore_progress_button.click(
+ toprow.restore_progress_button.click(
fn=progress.restore_progress,
_js="restoreProgressTxt2img",
inputs=[dummy_component],
@@ -585,18 +570,6 @@ def create_ui(): show_progress=False,
)
- txt_prompt_img.change(
- fn=modules.images.image_data,
- inputs=[
- txt_prompt_img
- ],
- outputs=[
- txt2img_prompt,
- txt_prompt_img
- ],
- show_progress=False,
- )
-
enable_hr.change(
fn=lambda x: gr_show(x),
inputs=[enable_hr],
@@ -605,8 +578,8 @@ def create_ui(): )
txt2img_paste_fields = [
- (txt2img_prompt, "Prompt"),
- (txt2img_negative_prompt, "Negative prompt"),
+ (toprow.prompt, "Prompt"),
+ (toprow.negative_prompt, "Negative prompt"),
(steps, "Steps"),
(sampler_index, "Sampler"),
(restore_faces, "Face restoration"),
@@ -615,34 +588,36 @@ def create_ui(): (width, "Size-1"),
(height, "Size-2"),
(batch_size, "Batch size"),
+ (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
(subseed, "Variation seed"),
(subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
- (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+ (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
- (enable_hr, lambda d: "Denoising strength" in d),
- (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+ (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
+ (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d))),
(hr_scale, "Hires upscale"),
(hr_upscaler, "Hires upscaler"),
(hr_second_pass_steps, "Hires steps"),
(hr_resize_x, "Hires resize-1"),
(hr_resize_y, "Hires resize-2"),
+ (hr_checkpoint_name, "Hires checkpoint"),
(hr_sampler_index, "Hires sampler"),
- (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+ (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
(hr_prompt, "Hires prompt"),
(hr_negative_prompt, "Hires negative prompt"),
(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
- *modules.scripts.scripts_txt2img.infotext_fields
+ *scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+ paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
))
txt2img_preview_params = [
- txt2img_prompt,
- txt2img_negative_prompt,
+ toprow.prompt,
+ toprow.negative_prompt,
steps,
sampler_index,
cfg_scale,
@@ -651,8 +626,8 @@ def create_ui(): height,
]
- token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
- negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+ toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
from modules import ui_extra_networks
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
@@ -660,13 +635,11 @@ def create_ui(): extra_tabs.__exit__()
- modules.scripts.scripts_current = modules.scripts.scripts_img2img
- modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+ scripts.scripts_current = scripts.scripts_img2img
+ scripts.scripts_img2img.initialize_scripts(is_img2img=True)
with gr.Blocks(analytics_enabled=False) as img2img_interface:
- img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, _, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
-
- img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
+ toprow = Toprow(is_img2img=True)
extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
extra_tabs.__enter__()
@@ -693,19 +666,19 @@ def create_ui(): img2img_selected_tab = gr.State(0)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
- init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
- sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
- init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
- inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
@@ -765,7 +738,7 @@ def create_ui(): with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
- modules.scripts.scripts_img2img.prepare_ui()
+ scripts.scripts_img2img.prepare_ui()
for category in ordered_ui_categories():
if category == "sampler":
@@ -846,7 +819,7 @@ def create_ui(): elif category == "scripts":
with FormGroup(elem_id="img2img_script_container"):
- custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+ custom_inputs = scripts.scripts_img2img.setup_ui()
elif category == "inpaint":
with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
@@ -877,34 +850,22 @@ def create_ui(): outputs=[inpaint_controls, mask_alpha],
)
else:
- modules.scripts.scripts_img2img.setup_ui_for_section(category)
+ scripts.scripts_img2img.setup_ui_for_section(category)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
- img2img_prompt_img.change(
- fn=modules.images.image_data,
- inputs=[
- img2img_prompt_img
- ],
- outputs=[
- img2img_prompt,
- img2img_prompt_img
- ],
- show_progress=False,
- )
-
img2img_args = dict(
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
_js="submit_img2img",
inputs=[
dummy_component,
dummy_component,
- img2img_prompt,
- img2img_negative_prompt,
- img2img_prompt_styles,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
init_img,
sketch,
init_img_with_mask,
@@ -963,11 +924,11 @@ def create_ui(): inpaint_color_sketch,
init_img_inpaint,
],
- outputs=[img2img_prompt, dummy_component],
+ outputs=[toprow.prompt, dummy_component],
)
- img2img_prompt.submit(**img2img_args)
- submit.click(**img2img_args)
+ toprow.prompt.submit(**img2img_args)
+ toprow.submit.click(**img2img_args)
res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
@@ -979,7 +940,7 @@ def create_ui(): show_progress=False,
)
- restore_progress_button.click(
+ toprow.restore_progress_button.click(
fn=progress.restore_progress,
_js="restoreProgressImg2img",
inputs=[dummy_component],
@@ -992,44 +953,22 @@ def create_ui(): show_progress=False,
)
- img2img_interrogate.click(
+ toprow.button_interrogate.click(
fn=lambda *args: process_interrogate(interrogate, *args),
**interrogate_args,
)
- img2img_deepbooru.click(
+ toprow.button_deepbooru.click(
fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
**interrogate_args,
)
- prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
- style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
- style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
- for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
- button.click(
- fn=add_style,
- _js="ask_for_style_name",
- # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
- # the same number of parameters, but we only know the style-name after the JavaScript prompt
- inputs=[dummy_component, prompt, negative_prompt],
- outputs=[txt2img_prompt_styles, img2img_prompt_styles],
- )
-
- for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
- button.click(
- fn=apply_styles,
- _js=js_func,
- inputs=[prompt, negative_prompt, styles],
- outputs=[prompt, negative_prompt, styles],
- )
-
- token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
- negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+ toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
img2img_paste_fields = [
- (img2img_prompt, "Prompt"),
- (img2img_negative_prompt, "Negative prompt"),
+ (toprow.prompt, "Prompt"),
+ (toprow.negative_prompt, "Negative prompt"),
(steps, "Steps"),
(sampler_index, "Sampler"),
(restore_faces, "Face restoration"),
@@ -1039,19 +978,20 @@ def create_ui(): (width, "Size-1"),
(height, "Size-2"),
(batch_size, "Batch size"),
+ (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
(subseed, "Variation seed"),
(subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
- (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+ (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(mask_blur, "Mask blur"),
- *modules.scripts.scripts_img2img.infotext_fields
+ *scripts.scripts_img2img.infotext_fields
]
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+ paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
))
from modules import ui_extra_networks
@@ -1060,13 +1000,13 @@ def create_ui(): extra_tabs.__exit__()
- modules.scripts.scripts_current = None
+ scripts.scripts_current = None
with gr.Blocks(analytics_enabled=False) as extras_interface:
ui_postprocessing.create_ui()
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
- with gr.Row().style(equal_height=False):
+ with gr.Row(equal_height=False):
with gr.Column(variant='panel'):
image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
@@ -1088,64 +1028,13 @@ def create_ui(): outputs=[html, generation_info, html2],
)
- def update_interp_description(value):
- interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
- interp_descriptions = {
- "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
- "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
- "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
- }
- return interp_descriptions[value]
-
- with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='compact'):
- interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
- with FormRow(elem_id="modelmerger_models"):
- primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
- create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
- secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
- create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
- tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
- create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
- custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
- interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
- interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
- interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
- with FormRow():
- checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
- save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
- save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
-
- with FormRow():
- with gr.Column():
- config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
- with gr.Column():
- with FormRow():
- bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
- create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
- with FormRow():
- discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
- with gr.Row():
- modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
- with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
- with gr.Group(elem_id="modelmerger_results_panel"):
- modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+ modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
with gr.Blocks(analytics_enabled=False) as train_interface:
- with gr.Row().style(equal_height=False):
+ with gr.Row(equal_height=False):
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
- with gr.Row(variant="compact").style(equal_height=False):
+ with gr.Row(variant="compact", equal_height=False):
with gr.Tabs(elem_id="train_tabs"):
with gr.Tab(label="Create embedding", id="create_embedding"):
@@ -1165,7 +1054,7 @@ def create_ui(): new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
- new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func")
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
@@ -1305,12 +1194,12 @@ def create_ui(): with gr.Column(elem_id='ti_gallery_container'):
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
- gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+ gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4)
gr.HTML(elem_id="ti_progress", value="")
ti_outcome = gr.HTML(elem_id="ti_error", value="")
create_embedding.click(
- fn=modules.textual_inversion.ui.create_embedding,
+ fn=textual_inversion_ui.create_embedding,
inputs=[
new_embedding_name,
initialization_text,
@@ -1325,7 +1214,7 @@ def create_ui(): )
create_hypernetwork.click(
- fn=modules.hypernetworks.ui.create_hypernetwork,
+ fn=hypernetworks_ui.create_hypernetwork,
inputs=[
new_hypernetwork_name,
new_hypernetwork_sizes,
@@ -1345,7 +1234,7 @@ def create_ui(): )
run_preprocess.click(
- fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+ fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
dummy_component,
@@ -1381,7 +1270,7 @@ def create_ui(): )
train_embedding.click(
- fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+ fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
dummy_component,
@@ -1415,7 +1304,7 @@ def create_ui(): )
train_hypernetwork.click(
- fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
dummy_component,
@@ -1469,7 +1358,7 @@ def create_ui(): (img2img_interface, "img2img", "img2img"),
(extras_interface, "Extras", "extras"),
(pnginfo_interface, "PNG Info", "pnginfo"),
- (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+ (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
(train_interface, "Train", "train"),
]
@@ -1521,49 +1410,11 @@ def create_ui(): settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
- def modelmerger(*args):
- try:
- results = modules.extras.run_modelmerger(*args)
- except Exception as e:
- errors.report("Error loading/saving model file", exc_info=True)
- modules.sd_models.list_models() # to remove the potentially missing models from the list
- return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
- return results
-
- modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
- modelmerger_merge.click(
- fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
- _js='modelmerger',
- inputs=[
- dummy_component,
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- interp_method,
- interp_amount,
- save_as_half,
- custom_name,
- checkpoint_format,
- config_source,
- bake_in_vae,
- discard_weights,
- save_metadata,
- ],
- outputs=[
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- settings.component_dict['sd_model_checkpoint'],
- modelmerger_result,
- ]
- )
+ modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
loadsave.dump_defaults()
demo.ui_loadsave = loadsave
- # Required as a workaround for change() event not triggering when loading values from ui-config.json
- interp_description.value = update_interp_description(interp_method.value)
-
return demo
diff --git a/modules/ui_checkpoint_merger.py b/modules/ui_checkpoint_merger.py new file mode 100644 index 00000000..f9c5dd6b --- /dev/null +++ b/modules/ui_checkpoint_merger.py @@ -0,0 +1,124 @@ +
+import gradio as gr
+
+from modules import sd_models, sd_vae, errors, extras, call_queue
+from modules.ui_components import FormRow
+from modules.ui_common import create_refresh_button
+
+
+def update_interp_description(value):
+ interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+ interp_descriptions = {
+ "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+ "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+ "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+ }
+ return interp_descriptions[value]
+
+
+def modelmerger(*args):
+ try:
+ results = extras.run_modelmerger(*args)
+ except Exception as e:
+ errors.report("Error loading/saving model file", exc_info=True)
+ sd_models.list_models() # to remove the potentially missing models from the list
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+ return results
+
+
+class UiCheckpointMerger:
+ def __init__(self):
+ with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+ with gr.Row(equal_height=False):
+ with gr.Column(variant='compact'):
+ self.interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+ with FormRow(elem_id="modelmerger_models"):
+ self.primary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+ create_refresh_button(self.primary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+ self.secondary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+ create_refresh_button(self.secondary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+ self.tertiary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+ create_refresh_button(self.tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+ self.custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+ self.interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+ self.interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+ self.interp_method.change(fn=update_interp_description, inputs=[self.interp_method], outputs=[self.interp_description])
+
+ with FormRow():
+ self.checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+ self.save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+
+ with FormRow():
+ with gr.Column():
+ self.config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+ with gr.Column():
+ with FormRow():
+ self.bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+ create_refresh_button(self.bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+ with FormRow():
+ self.discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+ with gr.Accordion("Metadata", open=False) as metadata_editor:
+ with FormRow():
+ self.save_metadata = gr.Checkbox(value=True, label="Save metadata", elem_id="modelmerger_save_metadata")
+ self.add_merge_recipe = gr.Checkbox(value=True, label="Add merge recipe metadata", elem_id="modelmerger_add_recipe")
+ self.copy_metadata_fields = gr.Checkbox(value=True, label="Copy metadata from merged models", elem_id="modelmerger_copy_metadata")
+
+ self.metadata_json = gr.TextArea('{}', label="Metadata in JSON format")
+ self.read_metadata = gr.Button("Read metadata from selected checkpoints")
+
+ with FormRow():
+ self.modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+ with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+ with gr.Group(elem_id="modelmerger_results_panel"):
+ self.modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+ self.metadata_editor = metadata_editor
+ self.blocks = modelmerger_interface
+
+ def setup_ui(self, dummy_component, sd_model_checkpoint_component):
+ self.checkpoint_format.change(lambda fmt: gr.update(visible=fmt == 'safetensors'), inputs=[self.checkpoint_format], outputs=[self.metadata_editor], show_progress=False)
+
+ self.read_metadata.click(extras.read_metadata, inputs=[self.primary_model_name, self.secondary_model_name, self.tertiary_model_name], outputs=[self.metadata_json])
+
+ self.modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[self.modelmerger_result])
+ self.modelmerger_merge.click(
+ fn=call_queue.wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+ _js='modelmerger',
+ inputs=[
+ dummy_component,
+ self.primary_model_name,
+ self.secondary_model_name,
+ self.tertiary_model_name,
+ self.interp_method,
+ self.interp_amount,
+ self.save_as_half,
+ self.custom_name,
+ self.checkpoint_format,
+ self.config_source,
+ self.bake_in_vae,
+ self.discard_weights,
+ self.save_metadata,
+ self.add_merge_recipe,
+ self.copy_metadata_fields,
+ self.metadata_json,
+ ],
+ outputs=[
+ self.primary_model_name,
+ self.secondary_model_name,
+ self.tertiary_model_name,
+ sd_model_checkpoint_component,
+ self.modelmerger_result,
+ ]
+ )
+
+ # Required as a workaround for change() event not triggering when loading values from ui-config.json
+ self.interp_description.value = update_interp_description(self.interp_method.value)
+
diff --git a/modules/ui_common.py b/modules/ui_common.py index 11eb2a4b..1dda1627 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -134,7 +134,7 @@ Requested path was: {f} with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
- result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4)
generation_info = None
with gr.Column():
@@ -223,20 +223,44 @@ Requested path was: {f} def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+ refresh_components = refresh_component if isinstance(refresh_component, list) else [refresh_component]
+
+ label = None
+ for comp in refresh_components:
+ label = getattr(comp, 'label', None)
+ if label is not None:
+ break
+
def refresh():
refresh_method()
args = refreshed_args() if callable(refreshed_args) else refreshed_args
for k, v in args.items():
- setattr(refresh_component, k, v)
+ for comp in refresh_components:
+ setattr(comp, k, v)
- return gr.update(**(args or {}))
+ return (gr.update(**(args or {})) for _ in refresh_components) if len(refresh_components) > 1 else gr.update(**(args or {}))
- refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
+ refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id, tooltip=f"{label}: refresh" if label else "Refresh")
refresh_button.click(
fn=refresh,
inputs=[],
- outputs=[refresh_component]
+ outputs=refresh_components
)
return refresh_button
+
+def setup_dialog(button_show, dialog, *, button_close=None):
+ """Sets up the UI so that the dialog (gr.Box) is invisible, and is only shown when buttons_show is clicked, in a fullscreen modal window."""
+
+ dialog.visible = False
+
+ button_show.click(
+ fn=lambda: gr.update(visible=True),
+ inputs=[],
+ outputs=[dialog],
+ ).then(fn=None, _js="function(){ popup(gradioApp().getElementById('" + dialog.elem_id + "')); }")
+
+ if button_close:
+ button_close.click(fn=None, _js="closePopup")
+
diff --git a/modules/ui_components.py b/modules/ui_components.py index 64451df7..8f8a7088 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -35,7 +35,7 @@ class FormColumn(FormComponent, gr.Column): class FormGroup(FormComponent, gr.Group):
- """Same as gr.Row but fits inside gradio forms"""
+ """Same as gr.Group but fits inside gradio forms"""
def get_block_name(self):
return "group"
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index f3e4fba7..15a8b0bf 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -164,7 +164,7 @@ def extension_table(): ext_status = ext.status
style = ""
- if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+ if shared.cmd_opts.disable_extra_extensions and not ext.is_builtin or shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
style = STYLE_PRIMARY
version_link = ext.version
@@ -533,16 +533,20 @@ def create_ui(): apply = gr.Button(value=apply_label, variant="primary")
check = gr.Button(value="Check for updates")
extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
- extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
- extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
+ extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False, container=False)
+ extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False, container=False)
html = ""
- if shared.opts.disable_all_extensions != "none":
- html = """
-<span style="color: var(--primary-400);">
- "Disable all extensions" was set, change it to "none" to load all extensions again
-</span>
- """
+
+ if shared.cmd_opts.disable_all_extensions or shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions != "none":
+ if shared.cmd_opts.disable_all_extensions:
+ msg = '"--disable-all-extensions" was used, remove it to load all extensions again'
+ elif shared.opts.disable_all_extensions != "none":
+ msg = '"Disable all extensions" was set, change it to "none" to load all extensions again'
+ elif shared.cmd_opts.disable_extra_extensions:
+ msg = '"--disable-extra-extensions" was used, remove it to load all extensions again'
+ html = f'<span style="color: var(--primary-400);">{msg}</span>'
+
info = gr.HTML(html)
extensions_table = gr.HTML('Loading...')
ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
@@ -565,7 +569,7 @@ def create_ui(): with gr.Row():
refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
extensions_index_url = os.environ.get('WEBUI_EXTENSIONS_INDEX', "https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json")
- available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL").style(container=False)
+ available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL", container=False)
extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
@@ -574,7 +578,7 @@ def create_ui(): sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
with gr.Row():
- search_extensions_text = gr.Text(label="Search").style(container=False)
+ search_extensions_text = gr.Text(label="Search", container=False)
install_result = gr.HTML()
available_extensions_table = gr.HTML()
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 7387d01e..3a73c89e 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -2,7 +2,7 @@ import os.path import urllib.parse
from pathlib import Path
-from modules import shared, ui_extra_networks_user_metadata, errors
+from modules import shared, ui_extra_networks_user_metadata, errors, extra_networks
from modules.images import read_info_from_image, save_image_with_geninfo
from modules.ui import up_down_symbol
import gradio as gr
@@ -62,7 +62,8 @@ def get_single_card(page: str = "", tabname: str = "", name: str = ""): page = next(iter([x for x in extra_pages if x.name == page]), None)
try:
- item = page.create_item(name)
+ item = page.create_item(name, enable_filter=False)
+ page.items[name] = item
except Exception as e:
errors.display(e, "creating item for extra network")
item = page.items.get(name)
@@ -100,16 +101,7 @@ class ExtraNetworksPage: def read_user_metadata(self, item):
filename = item.get("filename", None)
- basename, ext = os.path.splitext(filename)
- metadata_filename = basename + '.json'
-
- metadata = {}
- try:
- if os.path.isfile(metadata_filename):
- with open(metadata_filename, "r", encoding="utf8") as file:
- metadata = json.load(file)
- except Exception as e:
- errors.display(e, f"reading extra network user metadata from {metadata_filename}")
+ metadata = extra_networks.get_user_metadata(filename)
desc = metadata.get("description", None)
if desc is not None:
@@ -252,7 +244,7 @@ class ExtraNetworksPage: "prompt": item.get("prompt", None),
"tabname": quote_js(tabname),
"local_preview": quote_js(item["local_preview"]),
- "name": item["name"],
+ "name": html.escape(item["name"]),
"description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
"card_clicked": onclick,
"save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index 76780cfd..77885022 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -3,6 +3,7 @@ import os from modules import shared, ui_extra_networks, sd_models
from modules.ui_extra_networks import quote_js
+from modules.ui_extra_networks_checkpoints_user_metadata import CheckpointUserMetadataEditor
class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
@@ -12,7 +13,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): def refresh(self):
shared.refresh_checkpoints()
- def create_item(self, name, index=None):
+ def create_item(self, name, index=None, enable_filter=True):
checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
path, ext = os.path.splitext(checkpoint.filename)
return {
@@ -23,6 +24,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
"onclick": '"' + html.escape(f"""return selectCheckpoint({quote_js(name)})""") + '"',
"local_preview": f"{path}.{shared.opts.samples_format}",
+ "metadata": checkpoint.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
}
@@ -33,3 +35,5 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): def allowed_directories_for_previews(self):
return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
+ def create_user_metadata_editor(self, ui, tabname):
+ return CheckpointUserMetadataEditor(ui, tabname, self)
diff --git a/modules/ui_extra_networks_checkpoints_user_metadata.py b/modules/ui_extra_networks_checkpoints_user_metadata.py new file mode 100644 index 00000000..2c69aab8 --- /dev/null +++ b/modules/ui_extra_networks_checkpoints_user_metadata.py @@ -0,0 +1,60 @@ +import gradio as gr
+
+from modules import ui_extra_networks_user_metadata, sd_vae
+from modules.ui_common import create_refresh_button
+
+
+class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
+ def __init__(self, ui, tabname, page):
+ super().__init__(ui, tabname, page)
+
+ self.select_vae = None
+
+ def save_user_metadata(self, name, desc, notes, vae):
+ user_metadata = self.get_user_metadata(name)
+ user_metadata["description"] = desc
+ user_metadata["notes"] = notes
+ user_metadata["vae"] = vae
+
+ self.write_user_metadata(name, user_metadata)
+
+ def put_values_into_components(self, name):
+ user_metadata = self.get_user_metadata(name)
+ values = super().put_values_into_components(name)
+
+ return [
+ *values[0:5],
+ user_metadata.get('vae', ''),
+ ]
+
+ def create_editor(self):
+ self.create_default_editor_elems()
+
+ with gr.Row():
+ self.select_vae = gr.Dropdown(choices=["Automatic", "None"] + list(sd_vae.vae_dict), value="None", label="Preferred VAE", elem_id="checpoint_edit_user_metadata_preferred_vae")
+ create_refresh_button(self.select_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, "checpoint_edit_user_metadata_refresh_preferred_vae")
+
+ self.edit_notes = gr.TextArea(label='Notes', lines=4)
+
+ self.create_default_buttons()
+
+ viewed_components = [
+ self.edit_name,
+ self.edit_description,
+ self.html_filedata,
+ self.html_preview,
+ self.edit_notes,
+ self.select_vae,
+ ]
+
+ self.button_edit\
+ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
+ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
+
+ edited_components = [
+ self.edit_description,
+ self.edit_notes,
+ self.select_vae,
+ ]
+
+ self.setup_save_handler(self.button_save, self.save_user_metadata, edited_components)
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index e53ccb42..514a4562 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -11,7 +11,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): def refresh(self):
shared.reload_hypernetworks()
- def create_item(self, name, index=None):
+ def create_item(self, name, index=None, enable_filter=True):
full_path = shared.hypernetworks[name]
path, ext = os.path.splitext(full_path)
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index d1794e50..73134698 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -12,7 +12,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): def refresh(self):
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
- def create_item(self, name, index=None):
+ def create_item(self, name, index=None, enable_filter=True):
embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
path, ext = os.path.splitext(embedding.filename)
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py index 01ff4e4b..1cb9eb6f 100644 --- a/modules/ui_extra_networks_user_metadata.py +++ b/modules/ui_extra_networks_user_metadata.py @@ -42,6 +42,9 @@ class UserMetadataEditor: return user_metadata
+ def create_extra_default_items_in_left_column(self):
+ pass
+
def create_default_editor_elems(self):
with gr.Row():
with gr.Column(scale=2):
@@ -49,6 +52,8 @@ class UserMetadataEditor: self.edit_description = gr.Textbox(label="Description", lines=4)
self.html_filedata = gr.HTML()
+ self.create_extra_default_items_in_left_column()
+
with gr.Column(scale=1, min_width=0):
self.html_preview = gr.HTML()
@@ -91,6 +96,7 @@ class UserMetadataEditor: stats = os.stat(filename)
params = [
+ ('Filename: ', os.path.basename(filename)),
('File size: ', sysinfo.pretty_bytes(stats.st_size)),
('Modified: ', datetime.datetime.fromtimestamp(stats.st_mtime).strftime('%Y-%m-%d %H:%M')),
]
@@ -111,7 +117,7 @@ class UserMetadataEditor: table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params) + '</table>'
- return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', ''),
+ return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', '')
def write_user_metadata(self, name, metadata):
item = self.page.items.get(name, {})
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index c7dc1154..802e1ce7 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -6,7 +6,7 @@ import modules.generation_parameters_copypaste as parameters_copypaste def create_ui():
tab_index = gr.State(value=0)
- with gr.Row().style(equal_height=False, variant='compact'):
+ with gr.Row(equal_height=False, variant='compact'):
with gr.Column(variant='compact'):
with gr.Tabs(elem_id="mode_extras"):
with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
diff --git a/modules/ui_prompt_styles.py b/modules/ui_prompt_styles.py new file mode 100644 index 00000000..85eb3a64 --- /dev/null +++ b/modules/ui_prompt_styles.py @@ -0,0 +1,110 @@ +import gradio as gr
+
+from modules import shared, ui_common, ui_components, styles
+
+styles_edit_symbol = '\U0001f58c\uFE0F' # 🖌️
+styles_materialize_symbol = '\U0001f4cb' # 📋
+
+
+def select_style(name):
+ style = shared.prompt_styles.styles.get(name)
+ existing = style is not None
+ empty = not name
+
+ prompt = style.prompt if style else gr.update()
+ negative_prompt = style.negative_prompt if style else gr.update()
+
+ return prompt, negative_prompt, gr.update(visible=existing), gr.update(visible=not empty)
+
+
+def save_style(name, prompt, negative_prompt):
+ if not name:
+ return gr.update(visible=False)
+
+ style = styles.PromptStyle(name, prompt, negative_prompt)
+ shared.prompt_styles.styles[style.name] = style
+ shared.prompt_styles.save_styles(shared.styles_filename)
+
+ return gr.update(visible=True)
+
+
+def delete_style(name):
+ if name == "":
+ return
+
+ shared.prompt_styles.styles.pop(name, None)
+ shared.prompt_styles.save_styles(shared.styles_filename)
+
+ return '', '', ''
+
+
+def materialize_styles(prompt, negative_prompt, styles):
+ prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+ negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(negative_prompt, styles)
+
+ return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=negative_prompt), gr.Dropdown.update(value=[])]
+
+
+def refresh_styles():
+ return gr.update(choices=list(shared.prompt_styles.styles)), gr.update(choices=list(shared.prompt_styles.styles))
+
+
+class UiPromptStyles:
+ def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
+ self.tabname = tabname
+
+ with gr.Row(elem_id=f"{tabname}_styles_row"):
+ self.dropdown = gr.Dropdown(label="Styles", show_label=False, elem_id=f"{tabname}_styles", choices=list(shared.prompt_styles.styles), value=[], multiselect=True, tooltip="Styles")
+ edit_button = ui_components.ToolButton(value=styles_edit_symbol, elem_id=f"{tabname}_styles_edit_button", tooltip="Edit styles")
+
+ with gr.Box(elem_id=f"{tabname}_styles_dialog", elem_classes="popup-dialog") as styles_dialog:
+ with gr.Row():
+ self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
+ ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
+ self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
+
+ with gr.Row():
+ self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
+
+ with gr.Row():
+ self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
+
+ with gr.Row():
+ self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
+ self.delete = gr.Button('Delete', variant='primary', elem_id=f'{tabname}_edit_style_delete', visible=False)
+ self.close = gr.Button('Close', variant='secondary', elem_id=f'{tabname}_edit_style_close')
+
+ self.selection.change(
+ fn=select_style,
+ inputs=[self.selection],
+ outputs=[self.prompt, self.neg_prompt, self.delete, self.save],
+ show_progress=False,
+ )
+
+ self.save.click(
+ fn=save_style,
+ inputs=[self.selection, self.prompt, self.neg_prompt],
+ outputs=[self.delete],
+ show_progress=False,
+ ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+ self.delete.click(
+ fn=delete_style,
+ _js='function(name){ if(name == "") return ""; return confirm("Delete style " + name + "?") ? name : ""; }',
+ inputs=[self.selection],
+ outputs=[self.selection, self.prompt, self.neg_prompt],
+ show_progress=False,
+ ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+ self.materialize.click(
+ fn=materialize_styles,
+ inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+ outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+ show_progress=False,
+ ).then(fn=None, _js="function(){update_"+tabname+"_tokens(); closePopup();}", show_progress=False)
+
+ ui_common.setup_dialog(button_show=edit_button, dialog=styles_dialog, button_close=self.close)
+
+
+
+
diff --git a/modules/ui_settings.py b/modules/ui_settings.py index a6076bf3..6dde4b6a 100644 --- a/modules/ui_settings.py +++ b/modules/ui_settings.py @@ -158,7 +158,7 @@ class UiSettings: loadsave.create_ui()
with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
- gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo">(or open as text in a new page)</a>', elem_id="sysinfo_download")
+ gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo" target="_blank">(or open as text in a new page)</a>', elem_id="sysinfo_download")
with gr.Row():
with gr.Column(scale=1):
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