diff --git a/.gitignore b/.gitignore
index 09734267ff5..ecc1f38edc9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,7 @@ __pycache__
/SwinIR/*
/repositories
/venv
+/venv310
/tmp
/model.ckpt
/models/**/*
@@ -37,3 +38,5 @@ notification.mp3
/node_modules
/package-lock.json
/.coverage*
+/output.log
+/webui-user.sh
\ No newline at end of file
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index ef23968c563..7945d9fab14 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -32,7 +32,7 @@ def before_ui():
script_callbacks.on_infotext_pasted(networks.infotext_pasted)
-shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
+shared.options_templates.update(shared.options_section((None, "Extra Networks"), {
"sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
"lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
"lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
@@ -42,7 +42,7 @@ def before_ui():
}))
-shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
+shared.options_templates.update(shared.options_section((None, "Compatibility"), {
"lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
}))
diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
index 2d8d2d1c014..a90d374002e 100644
--- a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
+++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
@@ -1,7 +1,7 @@
import gradio as gr
from modules import shared
-shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
+shared.options_templates.update(shared.options_section((None, "Canvas Hotkeys"), {
"canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
"canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
"canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
index 983f87ff033..27b38991317 100644
--- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py
+++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -64,7 +64,7 @@ def before_process(self, p, *args):
p.override_settings[name] = value
-shared.options_templates.update(shared.options_section(('ui', "User interface"), {
+shared.options_templates.update(shared.options_section((None, "User interface"), {
"extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
"extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
"extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
diff --git a/modules/api/api.py b/modules/api/api.py
index e6edffe7144..e51b6a77563 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -546,7 +546,9 @@ def unloadapi(self):
return {}
def reloadapi(self):
+ print("start reload api")
reload_model_weights()
+ print("end reload api")
return {}
diff --git a/modules/hashes.py b/modules/hashes.py
index 01b0865ea74..4527dd1114c 100644
--- a/modules/hashes.py
+++ b/modules/hashes.py
@@ -30,6 +30,14 @@ def calculate_remote_sha256(filename):
return hash_sha256.hexdigest()
+# def calculate_remote_sha256(filename):
+# blksize = 1024 * 1024
+
+# buf = read_remote_model(filename, start = 0, size=blksize)
+# hash_object = hashlib.sha256(buf)
+
+# return hash_object.hexdigest()
+
def sha256_from_cache(filename, title, use_addnet_hash=False, remote_model = False):
hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
ondisk_mtime = os.path.getmtime(filename) if not remote_model else get_remote_model_mmtime(filename)
diff --git a/modules/initialize_util.py b/modules/initialize_util.py
index 6b15feeb86b..2b80cef71b6 100644
--- a/modules/initialize_util.py
+++ b/modules/initialize_util.py
@@ -168,7 +168,7 @@ def configure_opts_onchange():
from modules.call_queue import wrap_queued_call
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
- shared.opts.onchange("load_remote_ckpt", wrap_queued_call(lambda: (sd_models.list_models(),sd_models.load_model())), call=False)
+ # shared.opts.onchange("load_remote_ckpt", wrap_queued_call(lambda: (sd_models.list_models(),sd_models.load_model())), call=False)
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_overrides_per_model_preferences", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 6e54d06367c..ab21a419ff3 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -351,7 +351,11 @@ 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 is_installed("cython"):
+ run_pip("install cython", "cython")
+ startup_timer.record("install cython")
+
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; '
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4211131dfa1..5ce5a3257c9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -76,7 +76,7 @@ def read_metadata():
self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name + ('[remote]' if self.remote_model else '' ), filename, read_metadata, remote_model)
except Exception as e:
errors.display(e, f"reading metadata for {filename}")
-
+ print("CheckpointInfo start")
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]
@@ -84,13 +84,18 @@ def read_metadata():
self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name + ('[remote]' if self.remote_model else '' )}", remote_model=remote_model)
self.shorthash = self.sha256[0:10] if self.sha256 else None
-
+ print("sha256: %s" % self.sha256)
+ print("shorthash: %s" % self.shorthash)
+
self.title = name + ('[remote]' if self.remote_model else '' )+ ('' if self.shorthash is None else f'[{self.shorthash}]')
self.short_title = self.name_for_extra + ('[remote]' if self.remote_model else '') + ('' if self.shorthash is None else f'[{self.shorthash}]')
+ print("title: %s" % self.title)
+ print("short_title: %s" % self.short_title)
self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
if self.shorthash:
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
+ print("CheckpointInfo end")
def register(self):
checkpoints_list[self.title] = self
@@ -144,32 +149,34 @@ def list_models():
checkpoints_list.clear()
checkpoint_aliases.clear()
- if not shared.opts.load_remote_ckpt:
- cmd_ckpt = shared.cmd_opts.ckpt
- if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
- model_url = None
- else:
- model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+ # if not shared.opts.load_remote_ckpt:
+ # cmd_ckpt = shared.cmd_opts.ckpt
+ # if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
+ # model_url = None
+ # else:
+ # model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
- model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
+ # model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
- if os.path.exists(cmd_ckpt):
- checkpoint_info = CheckpointInfo(cmd_ckpt)
- checkpoint_info.register()
+ # if os.path.exists(cmd_ckpt):
+ # checkpoint_info = CheckpointInfo(cmd_ckpt)
+ # checkpoint_info.register()
- shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
- 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)
+ # shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
+ # 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 model_list:
- checkpoint_info = CheckpointInfo(filename)
- checkpoint_info.register()
+ # for filename in model_list:
+ # checkpoint_info = CheckpointInfo(filename)
+ # checkpoint_info.register()
- else:
- remote_models = list_remote_models(ext_filter=[".ckpt", ".safetensors"])
- for filename in remote_models:
- checkpoint_info = CheckpointInfo(filename, remote_model=True)
- checkpoint_info.register()
+ # else:
+ remote_models = list_remote_models(ext_filter=[".ckpt", ".safetensors"])
+ for filename in remote_models:
+ checkpoint_info = CheckpointInfo(filename, remote_model=True)
+ checkpoint_info.register()
+ print ("list_model: %s " % filename)
+
re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
diff --git a/modules/sd_remote_models.py b/modules/sd_remote_models.py
index 18147302e1c..673bacf8b10 100644
--- a/modules/sd_remote_models.py
+++ b/modules/sd_remote_models.py
@@ -4,12 +4,18 @@
import threading
from io import BytesIO
from modules import shared
+from osstorchconnector import OssCheckpoint
+import torch
-
+def __check_bucket_opts():
+ if os.environ.get('BUCKET_NAME') and os.environ.get('BUCKET_ENDPOINT'):
+ return True
+ print("Bucket opts not specified.")
+ return False
def __bucket__():
auth = oss2.Auth(os.environ.get('ACCESS_KEY_ID'), os.environ.get('ACCESS_KEY_SECRET'))
- return oss2.Bucket(auth, shared.opts.bucket_endpoint, shared.opts.bucket_name, enable_crc=False)
+ return oss2.Bucket(auth, os.environ.get('BUCKET_ENDPOINT'), os.environ.get('BUCKET_NAME'), enable_crc=False)
def __get_object_size(object_name):
simplifiedmeta = __bucket__().get_object_meta(object_name)
@@ -19,8 +25,10 @@ def get_remote_model_mmtime(model_name):
return __bucket__().head_object(model_name).last_modified
def list_remote_models(ext_filter):
- dir = shared.opts.bucket_model_ckpt_dir if shared.opts.bucket_model_ckpt_dir.endswith('/') else shared.opts.bucket_model_ckpt_dir + '/'
+ if not __check_bucket_opts():
+ return []
output = []
+ dir = os.environ.get('BUCKET_MODEL_DIR') if os.environ.get('BUCKET_MODEL_DIR').endswith('/') else os.environ.get('BUCKET_MODEL_DIR') + '/'
for obj in oss2.ObjectIteratorV2(__bucket__(), prefix = dir, delimiter = '/', start_after=dir, fetch_owner=False):
if obj.is_prefix():
print('directory: ', obj.key)
@@ -69,6 +77,21 @@ def read_remote_model(checkpoint_file, start=0, size=-1):
buffer.seek(0)
return buffer
+
+
+def load_remote_model_ckpt(checkpoint_file, map_location) -> bytes:
+ if not __check_bucket_opts():
+ return bytes()
+
+ checkpoint = OssCheckpoint(endpoint=os.environ.get('BUCKET_ENDPOINT'))
+ CHECKPOINT_URI = "oss://%s/%s" % (os.environ.get('BUCKET_NAME'), checkpoint_file)
+ print("load %s state.." % CHECKPOINT_URI)
+ state_dict = None
+ with checkpoint.reader(CHECKPOINT_URI) as reader:
+ state_dict = torch.load(reader, map_location = map_location, weights_only = True)
+ print("type:", type(state_dict))
+ return state_dict
+
def __range_get(object_name, buffer, offset, start, end, read_chunk_size):
chunk_size = int(read_chunk_size)
with __bucket__().get_object(object_name, byte_range=(start, end))as object_stream:
diff --git a/modules/shared_options.py b/modules/shared_options.py
index 83eee599aed..0779f22877a 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -21,7 +21,7 @@
"outdir_init_images"
}
-options_templates.update(options_section(('saving-images', "Saving images/grids"), {
+options_templates.update(options_section((None, "Saving images/grids"), {
"samples_save": OptionInfo(True, "Always save all generated images"),
"samples_format": OptionInfo('png', 'File format for images'),
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
@@ -64,7 +64,7 @@
"save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
}))
-options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+options_templates.update(options_section((None, "Paths for saving"), {
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
@@ -76,7 +76,7 @@
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
-options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
+options_templates.update(options_section((None, "Saving to a directory"), {
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
@@ -84,21 +84,21 @@
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))
-options_templates.update(options_section(('upscaling', "Upscaling"), {
+options_templates.update(options_section((None, "Upscaling"), {
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
}))
-options_templates.update(options_section(('face-restoration', "Face restoration"), {
+options_templates.update(options_section((None, "Face restoration"), {
"face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
}))
-options_templates.update(options_section(('system', "System"), {
+options_templates.update(options_section((None, "System"), {
"auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
"show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
"show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
@@ -111,13 +111,13 @@
"hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
}))
-options_templates.update(options_section(('API', "API"), {
+options_templates.update(options_section((None, "API"), {
"api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
"api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
"api_useragent": OptionInfo("", "User agent for requests", restrict_api=True),
}))
-options_templates.update(options_section(('training', "Training"), {
+options_templates.update(options_section((None, "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
@@ -132,7 +132,7 @@
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
}))
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
+options_templates.update(options_section((None, "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
"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"),
@@ -149,14 +149,14 @@
"hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
}))
-options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+options_templates.update(options_section((None, "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(('vae', "VAE"), {
+options_templates.update(options_section((None, "VAE"), {
"sd_vae_explanation": OptionHTML("""
VAE is a neural network that transforms a standard RGB
image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
@@ -171,7 +171,7 @@
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
}))
-options_templates.update(options_section(('img2img', "img2img"), {
+options_templates.update(options_section((None, "img2img"), {
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.5, "step": 0.001}, infotext='Noise multiplier'),
"img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img and hires fix", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Extra noise').info("0 = disabled (default); should be lower than denoising strength"),
@@ -186,7 +186,7 @@
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
}))
-options_templates.update(options_section(('optimizations', "Optimizations"), {
+options_templates.update(options_section((None, "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": 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}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
@@ -197,7 +197,7 @@
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
}))
-options_templates.update(options_section(('compatibility', "Compatibility"), {
+options_templates.update(options_section((None, "Compatibility"), {
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
@@ -207,7 +207,7 @@
"use_old_scheduling": OptionInfo(False, "Use old prompt editing timelines.", infotext="Old prompt editing timelines").info("For [red:green:N]; old: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"),
}))
-options_templates.update(options_section(('interrogate', "Interrogate"), {
+options_templates.update(options_section((None, "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}),
@@ -222,7 +222,7 @@
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
}))
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+options_templates.update(options_section((None, "Extra Networks"), {
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
@@ -237,7 +237,8 @@
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
}))
-options_templates.update(options_section(('ui', "User interface"), {
+import os
+options_templates.update(options_section((None, "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_reload_ui(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the gallery.").needs_reload_ui(),
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
@@ -264,14 +265,14 @@
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
- "load_remote_ckpt": OptionInfo(False, "Load ckpt models from remote object storage").needs_reload_ui(),
- 'bucket_name': OptionInfo("", "Bucket name to download ckpt model"),
- 'bucket_endpoint': OptionInfo("", "Bucket endpoint to download ckpt model"),
- 'bucket_model_ckpt_dir': OptionInfo("", "Ckpt model directory in bucket"),
+ # "load_remote_ckpt": OptionInfo(True, "Load ckpt models from remote object storage").needs_reload_ui(),
+ # 'bucket_name': OptionInfo(os.environ.get('BUCKET_NAME'), "Bucket name to download ckpt model"),
+ # 'bucket_endpoint': OptionInfo(os.environ.get('BUCKET_ENDPOINT'), "Bucket endpoint to download ckpt model"),
+ # 'bucket_model_ckpt_dir': OptionInfo(os.environ.get('BUCKET_MODEL_DIR'), "Ckpt model directory in bucket"),
}))
-options_templates.update(options_section(('infotext', "Infotext"), {
+options_templates.update(options_section((None, "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"),
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
@@ -286,7 +287,7 @@
}))
-options_templates.update(options_section(('ui', "Live previews"), {
+options_templates.update(options_section((None, "Live previews"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
@@ -299,7 +300,7 @@
"live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
}))
-options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+options_templates.update(options_section((None, "Sampler parameters"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
"eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
@@ -321,7 +322,7 @@
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
}))
-options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+options_templates.update(options_section((None, "Postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
diff --git a/modules/ui.py b/modules/ui.py
index 579bab9800c..747d2d810c7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,705 +540,705 @@ def create_ui():
extra_tabs.__exit__()
- scripts.scripts_current = scripts.scripts_img2img
- scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
- with gr.Blocks(analytics_enabled=False) as img2img_interface:
- toprow = Toprow(is_img2img=True)
-
- extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
- extra_tabs.__enter__()
-
- with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
- with gr.Column(variant='compact', elem_id="img2img_settings"):
- copy_image_buttons = []
- copy_image_destinations = {}
-
- def add_copy_image_controls(tab_name, elem):
- with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
- gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
-
- for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
- if name == tab_name:
- gr.Button(title, interactive=False)
- copy_image_destinations[name] = elem
- continue
-
- button = gr.Button(title)
- copy_image_buttons.append((button, name, elem))
-
- with gr.Tabs(elem_id="mode_img2img"):
- 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", 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="RGB", 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", 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="RGB", 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)
-
- def update_orig(image, state):
- if image is not None:
- same_size = state is not None and state.size == image.size
- has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
- edited = same_size and has_exact_match
- return image if not edited or state is None else state
-
- inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
-
- with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
- init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
- init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
-
- with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
- hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
- gr.HTML(
- "Process images in a directory on the same machine where the server is running." +
- "
Use an empty output directory to save pictures normally instead of writing to the output directory." +
- f"
Add inpaint batch mask directory to enable inpaint batch processing."
- f"{hidden}
"
- )
- img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
- img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
- img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
- with gr.Accordion("PNG info", open=False):
- img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
- img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
- img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
-
- img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
-
- for i, tab in enumerate(img2img_tabs):
- tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
-
- def copy_image(img):
- if isinstance(img, dict) and 'image' in img:
- return img['image']
-
- return img
-
- for button, name, elem in copy_image_buttons:
- button.click(
- fn=copy_image,
- inputs=[elem],
- outputs=[copy_image_destinations[name]],
- )
- button.click(
- fn=lambda: None,
- _js=f"switch_to_{name.replace(' ', '_')}",
- inputs=[],
- outputs=[],
- )
-
- 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")
-
- scripts.scripts_img2img.prepare_ui()
-
- for category in ordered_ui_categories():
- if category == "sampler":
- steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
-
- elif category == "dimensions":
- with FormRow():
- with gr.Column(elem_id="img2img_column_size", scale=4):
- selected_scale_tab = gr.State(value=0)
-
- with gr.Tabs():
- with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
- with FormRow():
- with gr.Column(elem_id="img2img_column_size", scale=4):
- width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
- height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
- with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
- detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
-
- with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
- scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
-
- with FormRow():
- scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
- gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
- button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
-
- on_change_args = dict(
- fn=resize_from_to_html,
- _js="currentImg2imgSourceResolution",
- inputs=[dummy_component, dummy_component, scale_by],
- outputs=scale_by_html,
- show_progress=False,
- )
-
- scale_by.release(**on_change_args)
- button_update_resize_to.click(**on_change_args)
-
- # the code below is meant to update the resolution label after the image in the image selection UI has changed.
- # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
- # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
- for component in [init_img, sketch]:
- component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
-
- tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
- tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
-
- if opts.dimensions_and_batch_together:
- with gr.Column(elem_id="img2img_column_batch"):
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
- elif category == "denoising":
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-
- elif category == "cfg":
- with gr.Row():
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
- image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
-
- elif category == "checkboxes":
- with FormRow(elem_classes="checkboxes-row", variant="compact"):
- pass
-
- elif category == "accordions":
- with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
- scripts.scripts_img2img.setup_ui_for_section(category)
-
- elif category == "batch":
- if not opts.dimensions_and_batch_together:
- with FormRow(elem_id="img2img_column_batch"):
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
- elif category == "override_settings":
- with FormRow(elem_id="img2img_override_settings_row") as row:
- override_settings = create_override_settings_dropdown('img2img', row)
-
- elif category == "scripts":
- with FormGroup(elem_id="img2img_script_container"):
- custom_inputs = scripts.scripts_img2img.setup_ui()
-
- elif category == "inpaint":
- with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
- with FormRow():
- mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
- mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
-
- with FormRow():
- inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
-
- with FormRow():
- inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
-
- with FormRow():
- with gr.Column():
- inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
-
- with gr.Column(scale=4):
- inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
-
- def select_img2img_tab(tab):
- return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
-
- for i, elem in enumerate(img2img_tabs):
- elem.select(
- fn=lambda tab=i: select_img2img_tab(tab),
- inputs=[],
- outputs=[inpaint_controls, mask_alpha],
- )
-
- if category not in {"accordions"}:
- scripts.scripts_img2img.setup_ui_for_section(category)
-
- img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
-
- img2img_args = dict(
- fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
- _js="submit_img2img",
- inputs=[
- dummy_component,
- dummy_component,
- toprow.prompt,
- toprow.negative_prompt,
- toprow.ui_styles.dropdown,
- init_img,
- sketch,
- init_img_with_mask,
- inpaint_color_sketch,
- inpaint_color_sketch_orig,
- init_img_inpaint,
- init_mask_inpaint,
- steps,
- sampler_name,
- mask_blur,
- mask_alpha,
- inpainting_fill,
- batch_count,
- batch_size,
- cfg_scale,
- image_cfg_scale,
- denoising_strength,
- selected_scale_tab,
- height,
- width,
- scale_by,
- resize_mode,
- inpaint_full_res,
- inpaint_full_res_padding,
- inpainting_mask_invert,
- img2img_batch_input_dir,
- img2img_batch_output_dir,
- img2img_batch_inpaint_mask_dir,
- override_settings,
- img2img_batch_use_png_info,
- img2img_batch_png_info_props,
- img2img_batch_png_info_dir,
- ] + custom_inputs,
- outputs=[
- img2img_gallery,
- generation_info,
- html_info,
- html_log,
- ],
- show_progress=False,
- )
-
- interrogate_args = dict(
- _js="get_img2img_tab_index",
- inputs=[
- dummy_component,
- img2img_batch_input_dir,
- img2img_batch_output_dir,
- init_img,
- sketch,
- init_img_with_mask,
- inpaint_color_sketch,
- init_img_inpaint,
- ],
- outputs=[toprow.prompt, dummy_component],
- )
-
- 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)
-
- detect_image_size_btn.click(
- fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
- _js="currentImg2imgSourceResolution",
- inputs=[dummy_component, dummy_component, dummy_component],
- outputs=[width, height],
- show_progress=False,
- )
-
- toprow.restore_progress_button.click(
- fn=progress.restore_progress,
- _js="restoreProgressImg2img",
- inputs=[dummy_component],
- outputs=[
- img2img_gallery,
- generation_info,
- html_info,
- html_log,
- ],
- show_progress=False,
- )
-
- toprow.button_interrogate.click(
- fn=lambda *args: process_interrogate(interrogate, *args),
- **interrogate_args,
- )
-
- toprow.button_deepbooru.click(
- fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
- **interrogate_args,
- )
-
- 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 = [
- (toprow.prompt, "Prompt"),
- (toprow.negative_prompt, "Negative prompt"),
- (steps, "Steps"),
- (sampler_name, "Sampler"),
- (cfg_scale, "CFG scale"),
- (image_cfg_scale, "Image CFG scale"),
- (width, "Size-1"),
- (height, "Size-2"),
- (batch_size, "Batch size"),
- (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"),
- *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=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
- ))
-
- extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
- ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
-
- extra_tabs.__exit__()
-
- scripts.scripts_current = None
+ # scripts.scripts_current = scripts.scripts_img2img
+ # scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+ # with gr.Blocks(analytics_enabled=False) as img2img_interface:
+ # toprow = Toprow(is_img2img=True)
+
+ # extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
+ # extra_tabs.__enter__()
+
+ # with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
+ # with gr.Column(variant='compact', elem_id="img2img_settings"):
+ # copy_image_buttons = []
+ # copy_image_destinations = {}
+
+ # def add_copy_image_controls(tab_name, elem):
+ # with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+ # gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+ # for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+ # if name == tab_name:
+ # gr.Button(title, interactive=False)
+ # copy_image_destinations[name] = elem
+ # continue
+
+ # button = gr.Button(title)
+ # copy_image_buttons.append((button, name, elem))
+
+ # with gr.Tabs(elem_id="mode_img2img"):
+ # 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", 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="RGB", 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", 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="RGB", 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)
+
+ # def update_orig(image, state):
+ # if image is not None:
+ # same_size = state is not None and state.size == image.size
+ # has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+ # edited = same_size and has_exact_match
+ # return image if not edited or state is None else state
+
+ # inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+ # with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+ # init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+ # init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
+
+ # with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+ # hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ # gr.HTML(
+ # "Process images in a directory on the same machine where the server is running." +
+ # "
Use an empty output directory to save pictures normally instead of writing to the output directory." +
+ # f"
Add inpaint batch mask directory to enable inpaint batch processing."
+ # f"{hidden}
"
+ # )
+ # img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+ # img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+ # img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ # with gr.Accordion("PNG info", open=False):
+ # img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+ # img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+ # img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+ # img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+ # for i, tab in enumerate(img2img_tabs):
+ # tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+ # def copy_image(img):
+ # if isinstance(img, dict) and 'image' in img:
+ # return img['image']
+
+ # return img
+
+ # for button, name, elem in copy_image_buttons:
+ # button.click(
+ # fn=copy_image,
+ # inputs=[elem],
+ # outputs=[copy_image_destinations[name]],
+ # )
+ # button.click(
+ # fn=lambda: None,
+ # _js=f"switch_to_{name.replace(' ', '_')}",
+ # inputs=[],
+ # outputs=[],
+ # )
+
+ # 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")
+
+ # scripts.scripts_img2img.prepare_ui()
+
+ # for category in ordered_ui_categories():
+ # if category == "sampler":
+ # steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
+
+ # elif category == "dimensions":
+ # with FormRow():
+ # with gr.Column(elem_id="img2img_column_size", scale=4):
+ # selected_scale_tab = gr.State(value=0)
+
+ # with gr.Tabs():
+ # with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+ # with FormRow():
+ # with gr.Column(elem_id="img2img_column_size", scale=4):
+ # width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+ # height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+ # with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ # res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+ # detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+
+ # with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+ # scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+ # with FormRow():
+ # scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+ # gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+ # button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+ # on_change_args = dict(
+ # fn=resize_from_to_html,
+ # _js="currentImg2imgSourceResolution",
+ # inputs=[dummy_component, dummy_component, scale_by],
+ # outputs=scale_by_html,
+ # show_progress=False,
+ # )
+
+ # scale_by.release(**on_change_args)
+ # button_update_resize_to.click(**on_change_args)
+
+ # # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+ # # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+ # # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+ # for component in [init_img, sketch]:
+ # component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+ # tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+ # tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
+
+ # if opts.dimensions_and_batch_together:
+ # with gr.Column(elem_id="img2img_column_batch"):
+ # batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ # batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ # elif category == "denoising":
+ # denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+ # elif category == "cfg":
+ # with gr.Row():
+ # cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ # image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+
+ # elif category == "checkboxes":
+ # with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ # pass
+
+ # elif category == "accordions":
+ # with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
+ # scripts.scripts_img2img.setup_ui_for_section(category)
+
+ # elif category == "batch":
+ # if not opts.dimensions_and_batch_together:
+ # with FormRow(elem_id="img2img_column_batch"):
+ # batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ # batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ # elif category == "override_settings":
+ # with FormRow(elem_id="img2img_override_settings_row") as row:
+ # override_settings = create_override_settings_dropdown('img2img', row)
+
+ # elif category == "scripts":
+ # with FormGroup(elem_id="img2img_script_container"):
+ # custom_inputs = scripts.scripts_img2img.setup_ui()
+
+ # elif category == "inpaint":
+ # with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+ # with FormRow():
+ # mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+ # mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+ # with FormRow():
+ # inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+ # with FormRow():
+ # inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+ # with FormRow():
+ # with gr.Column():
+ # inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+ # with gr.Column(scale=4):
+ # inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+ # def select_img2img_tab(tab):
+ # return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+ # for i, elem in enumerate(img2img_tabs):
+ # elem.select(
+ # fn=lambda tab=i: select_img2img_tab(tab),
+ # inputs=[],
+ # outputs=[inpaint_controls, mask_alpha],
+ # )
+
+ # if category not in {"accordions"}:
+ # scripts.scripts_img2img.setup_ui_for_section(category)
+
+ # img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+
+ # img2img_args = dict(
+ # fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+ # _js="submit_img2img",
+ # inputs=[
+ # dummy_component,
+ # dummy_component,
+ # toprow.prompt,
+ # toprow.negative_prompt,
+ # toprow.ui_styles.dropdown,
+ # init_img,
+ # sketch,
+ # init_img_with_mask,
+ # inpaint_color_sketch,
+ # inpaint_color_sketch_orig,
+ # init_img_inpaint,
+ # init_mask_inpaint,
+ # steps,
+ # sampler_name,
+ # mask_blur,
+ # mask_alpha,
+ # inpainting_fill,
+ # batch_count,
+ # batch_size,
+ # cfg_scale,
+ # image_cfg_scale,
+ # denoising_strength,
+ # selected_scale_tab,
+ # height,
+ # width,
+ # scale_by,
+ # resize_mode,
+ # inpaint_full_res,
+ # inpaint_full_res_padding,
+ # inpainting_mask_invert,
+ # img2img_batch_input_dir,
+ # img2img_batch_output_dir,
+ # img2img_batch_inpaint_mask_dir,
+ # override_settings,
+ # img2img_batch_use_png_info,
+ # img2img_batch_png_info_props,
+ # img2img_batch_png_info_dir,
+ # ] + custom_inputs,
+ # outputs=[
+ # img2img_gallery,
+ # generation_info,
+ # html_info,
+ # html_log,
+ # ],
+ # show_progress=False,
+ # )
+
+ # interrogate_args = dict(
+ # _js="get_img2img_tab_index",
+ # inputs=[
+ # dummy_component,
+ # img2img_batch_input_dir,
+ # img2img_batch_output_dir,
+ # init_img,
+ # sketch,
+ # init_img_with_mask,
+ # inpaint_color_sketch,
+ # init_img_inpaint,
+ # ],
+ # outputs=[toprow.prompt, dummy_component],
+ # )
+
+ # 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)
+
+ # detect_image_size_btn.click(
+ # fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+ # _js="currentImg2imgSourceResolution",
+ # inputs=[dummy_component, dummy_component, dummy_component],
+ # outputs=[width, height],
+ # show_progress=False,
+ # )
+
+ # toprow.restore_progress_button.click(
+ # fn=progress.restore_progress,
+ # _js="restoreProgressImg2img",
+ # inputs=[dummy_component],
+ # outputs=[
+ # img2img_gallery,
+ # generation_info,
+ # html_info,
+ # html_log,
+ # ],
+ # show_progress=False,
+ # )
+
+ # toprow.button_interrogate.click(
+ # fn=lambda *args: process_interrogate(interrogate, *args),
+ # **interrogate_args,
+ # )
+
+ # toprow.button_deepbooru.click(
+ # fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+ # **interrogate_args,
+ # )
+
+ # 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 = [
+ # (toprow.prompt, "Prompt"),
+ # (toprow.negative_prompt, "Negative prompt"),
+ # (steps, "Steps"),
+ # (sampler_name, "Sampler"),
+ # (cfg_scale, "CFG scale"),
+ # (image_cfg_scale, "Image CFG scale"),
+ # (width, "Size-1"),
+ # (height, "Size-2"),
+ # (batch_size, "Batch size"),
+ # (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"),
+ # *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=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
+ # ))
+
+ # extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
+ # ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+ # extra_tabs.__exit__()
+
+ # 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(equal_height=False):
- with gr.Column(variant='panel'):
- image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
- with gr.Column(variant='panel'):
- html = gr.HTML()
- generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
- html2 = gr.HTML()
- with gr.Row():
- buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-
- for tabname, button in buttons.items():
- parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
- ))
-
- image.change(
- fn=wrap_gradio_call(modules.extras.run_pnginfo),
- inputs=[image],
- outputs=[html, generation_info, html2],
- )
-
- modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
-
- with gr.Blocks(analytics_enabled=False) as train_interface:
- with gr.Row(equal_height=False):
- gr.HTML(value="See wiki for detailed explanation.
")
-
- with gr.Row(variant="compact", equal_height=False):
- with gr.Tabs(elem_id="train_tabs"):
-
- with gr.Tab(label="Create embedding", id="create_embedding"):
- new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
- initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
- nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
- overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
-
- with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
- 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=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")
- new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
- overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
-
- with gr.Tab(label="Preprocess images", id="preprocess_images"):
- process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
- process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
- process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
- process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
- preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
-
- with gr.Row():
- process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
- process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
- process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
- process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
- process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
- process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
- process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
-
- with gr.Row(visible=False) as process_split_extra_row:
- process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
- process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
-
- with gr.Row(visible=False) as process_focal_crop_row:
- process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
- process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
- process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
- process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-
- with gr.Column(visible=False) as process_multicrop_col:
- gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
- with gr.Row():
- process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
- process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
- with gr.Row():
- process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
- process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
- with gr.Row():
- process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
- process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- with gr.Row():
- interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
- run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
-
- process_split.change(
- fn=lambda show: gr_show(show),
- inputs=[process_split],
- outputs=[process_split_extra_row],
- )
-
- process_focal_crop.change(
- fn=lambda show: gr_show(show),
- inputs=[process_focal_crop],
- outputs=[process_focal_crop_row],
- )
-
- process_multicrop.change(
- fn=lambda show: gr_show(show),
- inputs=[process_multicrop],
- outputs=[process_multicrop_col],
- )
-
- def get_textual_inversion_template_names():
- return sorted(textual_inversion.textual_inversion_templates)
-
- with gr.Tab(label="Train", id="train"):
- gr.HTML(value="Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]
")
- with FormRow():
- train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
- create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-
- train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
- create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
-
- with FormRow():
- embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
- hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-
- with FormRow():
- clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
- clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
-
- with FormRow():
- batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
- gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
-
- dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
- log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
-
- with FormRow():
- template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
- create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
-
- training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
- training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
- varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
- steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
-
- with FormRow():
- create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
- save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
-
- use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
-
- save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
- preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
-
- shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
- tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
-
- latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
-
- with gr.Row():
- train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
- interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
- train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
-
- params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
- script_callbacks.ui_train_tabs_callback(params)
-
- 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', columns=4)
- gr.HTML(elem_id="ti_progress", value="")
- ti_outcome = gr.HTML(elem_id="ti_error", value="")
-
- create_embedding.click(
- fn=textual_inversion_ui.create_embedding,
- inputs=[
- new_embedding_name,
- initialization_text,
- nvpt,
- overwrite_old_embedding,
- ],
- outputs=[
- train_embedding_name,
- ti_output,
- ti_outcome,
- ]
- )
-
- create_hypernetwork.click(
- fn=hypernetworks_ui.create_hypernetwork,
- inputs=[
- new_hypernetwork_name,
- new_hypernetwork_sizes,
- overwrite_old_hypernetwork,
- new_hypernetwork_layer_structure,
- new_hypernetwork_activation_func,
- new_hypernetwork_initialization_option,
- new_hypernetwork_add_layer_norm,
- new_hypernetwork_use_dropout,
- new_hypernetwork_dropout_structure
- ],
- outputs=[
- train_hypernetwork_name,
- ti_output,
- ti_outcome,
- ]
- )
-
- run_preprocess.click(
- fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- process_src,
- process_dst,
- process_width,
- process_height,
- preprocess_txt_action,
- process_keep_original_size,
- process_flip,
- process_split,
- process_caption,
- process_caption_deepbooru,
- process_split_threshold,
- process_overlap_ratio,
- process_focal_crop,
- process_focal_crop_face_weight,
- process_focal_crop_entropy_weight,
- process_focal_crop_edges_weight,
- process_focal_crop_debug,
- process_multicrop,
- process_multicrop_mindim,
- process_multicrop_maxdim,
- process_multicrop_minarea,
- process_multicrop_maxarea,
- process_multicrop_objective,
- process_multicrop_threshold,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ],
- )
-
- train_embedding.click(
- fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- train_embedding_name,
- embedding_learn_rate,
- batch_size,
- gradient_step,
- dataset_directory,
- 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_file,
- save_image_with_stored_embedding,
- preview_from_txt2img,
- *txt2img_preview_params,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ]
- )
-
- train_hypernetwork.click(
- fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- train_hypernetwork_name,
- hypernetwork_learn_rate,
- batch_size,
- gradient_step,
- dataset_directory,
- 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_file,
- preview_from_txt2img,
- *txt2img_preview_params,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ]
- )
-
- interrupt_training.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
-
- interrupt_preprocessing.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
+ # with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+ # 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")
+
+ # with gr.Column(variant='panel'):
+ # html = gr.HTML()
+ # generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+ # html2 = gr.HTML()
+ # with gr.Row():
+ # buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+ # for tabname, button in buttons.items():
+ # parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ # paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+ # ))
+
+ # image.change(
+ # fn=wrap_gradio_call(modules.extras.run_pnginfo),
+ # inputs=[image],
+ # outputs=[html, generation_info, html2],
+ # )
+
+ # modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
+
+ # with gr.Blocks(analytics_enabled=False) as train_interface:
+ # with gr.Row(equal_height=False):
+ # gr.HTML(value="See wiki for detailed explanation.
")
+
+ # with gr.Row(variant="compact", equal_height=False):
+ # with gr.Tabs(elem_id="train_tabs"):
+
+ # with gr.Tab(label="Create embedding", id="create_embedding"):
+ # new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+ # initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+ # nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+ # overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+ # with gr.Row():
+ # with gr.Column(scale=3):
+ # gr.HTML(value="")
+
+ # with gr.Column():
+ # create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+ # with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
+ # 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=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")
+ # new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+ # overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+ # with gr.Row():
+ # with gr.Column(scale=3):
+ # gr.HTML(value="")
+
+ # with gr.Column():
+ # create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+ # with gr.Tab(label="Preprocess images", id="preprocess_images"):
+ # process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+ # process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+ # process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+ # process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+ # preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
+
+ # with gr.Row():
+ # process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
+ # process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+ # process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+ # process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+ # process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
+ # process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+ # process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
+
+ # with gr.Row(visible=False) as process_split_extra_row:
+ # process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+ # process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
+
+ # with gr.Row(visible=False) as process_focal_crop_row:
+ # process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+ # process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+ # process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+ # process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+
+ # with gr.Column(visible=False) as process_multicrop_col:
+ # gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
+ # with gr.Row():
+ # process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
+ # process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
+ # with gr.Row():
+ # process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
+ # process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
+ # with gr.Row():
+ # process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
+ # process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
+
+ # with gr.Row():
+ # with gr.Column(scale=3):
+ # gr.HTML(value="")
+
+ # with gr.Column():
+ # with gr.Row():
+ # interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+ # run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
+
+ # process_split.change(
+ # fn=lambda show: gr_show(show),
+ # inputs=[process_split],
+ # outputs=[process_split_extra_row],
+ # )
+
+ # process_focal_crop.change(
+ # fn=lambda show: gr_show(show),
+ # inputs=[process_focal_crop],
+ # outputs=[process_focal_crop_row],
+ # )
+
+ # process_multicrop.change(
+ # fn=lambda show: gr_show(show),
+ # inputs=[process_multicrop],
+ # outputs=[process_multicrop_col],
+ # )
+
+ # def get_textual_inversion_template_names():
+ # return sorted(textual_inversion.textual_inversion_templates)
+
+ # with gr.Tab(label="Train", id="train"):
+ # gr.HTML(value="Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]
")
+ # with FormRow():
+ # train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+ # create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+ # train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+ # create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+
+ # with FormRow():
+ # embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+ # hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+ # with FormRow():
+ # clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ # clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+ # with FormRow():
+ # batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+ # gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+ # dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+ # log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+ # with FormRow():
+ # template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+ # create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+ # training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+ # training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+ # varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+ # steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+ # with FormRow():
+ # create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+ # save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+ # use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+ # save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+ # preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+ # shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+ # tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+ # latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+ # with gr.Row():
+ # train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+ # interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+ # train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+ # params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+ # script_callbacks.ui_train_tabs_callback(params)
+
+ # 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', columns=4)
+ # gr.HTML(elem_id="ti_progress", value="")
+ # ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+ # create_embedding.click(
+ # fn=textual_inversion_ui.create_embedding,
+ # inputs=[
+ # new_embedding_name,
+ # initialization_text,
+ # nvpt,
+ # overwrite_old_embedding,
+ # ],
+ # outputs=[
+ # train_embedding_name,
+ # ti_output,
+ # ti_outcome,
+ # ]
+ # )
+
+ # create_hypernetwork.click(
+ # fn=hypernetworks_ui.create_hypernetwork,
+ # inputs=[
+ # new_hypernetwork_name,
+ # new_hypernetwork_sizes,
+ # overwrite_old_hypernetwork,
+ # new_hypernetwork_layer_structure,
+ # new_hypernetwork_activation_func,
+ # new_hypernetwork_initialization_option,
+ # new_hypernetwork_add_layer_norm,
+ # new_hypernetwork_use_dropout,
+ # new_hypernetwork_dropout_structure
+ # ],
+ # outputs=[
+ # train_hypernetwork_name,
+ # ti_output,
+ # ti_outcome,
+ # ]
+ # )
+
+ # run_preprocess.click(
+ # fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
+ # _js="start_training_textual_inversion",
+ # inputs=[
+ # dummy_component,
+ # process_src,
+ # process_dst,
+ # process_width,
+ # process_height,
+ # preprocess_txt_action,
+ # process_keep_original_size,
+ # process_flip,
+ # process_split,
+ # process_caption,
+ # process_caption_deepbooru,
+ # process_split_threshold,
+ # process_overlap_ratio,
+ # process_focal_crop,
+ # process_focal_crop_face_weight,
+ # process_focal_crop_entropy_weight,
+ # process_focal_crop_edges_weight,
+ # process_focal_crop_debug,
+ # process_multicrop,
+ # process_multicrop_mindim,
+ # process_multicrop_maxdim,
+ # process_multicrop_minarea,
+ # process_multicrop_maxarea,
+ # process_multicrop_objective,
+ # process_multicrop_threshold,
+ # ],
+ # outputs=[
+ # ti_output,
+ # ti_outcome,
+ # ],
+ # )
+
+ # train_embedding.click(
+ # fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
+ # _js="start_training_textual_inversion",
+ # inputs=[
+ # dummy_component,
+ # train_embedding_name,
+ # embedding_learn_rate,
+ # batch_size,
+ # gradient_step,
+ # dataset_directory,
+ # 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_file,
+ # save_image_with_stored_embedding,
+ # preview_from_txt2img,
+ # *txt2img_preview_params,
+ # ],
+ # outputs=[
+ # ti_output,
+ # ti_outcome,
+ # ]
+ # )
+
+ # train_hypernetwork.click(
+ # fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ # _js="start_training_textual_inversion",
+ # inputs=[
+ # dummy_component,
+ # train_hypernetwork_name,
+ # hypernetwork_learn_rate,
+ # batch_size,
+ # gradient_step,
+ # dataset_directory,
+ # 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_file,
+ # preview_from_txt2img,
+ # *txt2img_preview_params,
+ # ],
+ # outputs=[
+ # ti_output,
+ # ti_outcome,
+ # ]
+ # )
+
+ # interrupt_training.click(
+ # fn=lambda: shared.state.interrupt(),
+ # inputs=[],
+ # outputs=[],
+ # )
+
+ # interrupt_preprocessing.click(
+ # fn=lambda: shared.state.interrupt(),
+ # inputs=[],
+ # outputs=[],
+ # )
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
@@ -1247,18 +1247,18 @@ def get_textual_inversion_template_names():
interfaces = [
(txt2img_interface, "txt2img", "txt2img"),
- (img2img_interface, "img2img", "img2img"),
- (extras_interface, "Extras", "extras"),
- (pnginfo_interface, "PNG Info", "pnginfo"),
- (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
- (train_interface, "Train", "train"),
+ # (img2img_interface, "img2img", "img2img"),
+ # (extras_interface, "Extras", "extras"),
+ # (pnginfo_interface, "PNG Info", "pnginfo"),
+ # (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
+ # (train_interface, "Train", "train"),
]
interfaces += script_callbacks.ui_tabs_callback()
interfaces += [(settings.interface, "Settings", "settings")]
- extensions_interface = ui_extensions.create_ui()
- interfaces += [(extensions_interface, "Extensions", "extensions")]
+ # extensions_interface = ui_extensions.create_ui()
+ # interfaces += [(extensions_interface, "Extensions", "extensions")]
shared.tab_names = []
for _interface, label, _ifid in interfaces:
@@ -1267,7 +1267,7 @@ def get_textual_inversion_template_names():
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
settings.add_quicksettings()
- parameters_copypaste.connect_paste_params_buttons()
+ # parameters_copypaste.connect_paste_params_buttons()
with gr.Tabs(elem_id="tabs") as tabs:
tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
@@ -1279,12 +1279,12 @@ def get_textual_inversion_template_names():
with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
interface.render()
- if ifid not in ["extensions", "settings"]:
- loadsave.add_block(interface, ifid)
+ # if ifid not in ["extensions", "settings"]:
+ # loadsave.add_block(interface, ifid)
- loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+ # loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
- loadsave.setup_ui()
+ # loadsave.setup_ui()
if os.path.exists(os.path.join(script_path, "notification.mp3")):
gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
@@ -1295,11 +1295,11 @@ def get_textual_inversion_template_names():
settings.add_functionality(demo)
- update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
- 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])
+ # update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+ # 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])
- modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
+ # 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
diff --git a/modules/ui_settings.py b/modules/ui_settings.py
index 8ff9c074718..21c5ffaa3a0 100644
--- a/modules/ui_settings.py
+++ b/modules/ui_settings.py
@@ -123,7 +123,7 @@ def create_ui(self, loadsave, dummy_component):
current_row = None
with gr.Tabs(elem_id="settings"):
for i, (k, item) in enumerate(opts.data_labels.items()):
- section_must_be_skipped = item.section[0] is None
+ section_must_be_skipped = item.section[1] != "Licenses"
if previous_section != item.section and not section_must_be_skipped:
elem_id, text = item.section
@@ -154,27 +154,27 @@ def create_ui(self, loadsave, dummy_component):
current_row.__exit__()
current_tab.__exit__()
- with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
- loadsave.create_ui()
+ # with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
+ # loadsave.create_ui()
- with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
- gr.HTML('Download system info
(or open as text in a new page)', elem_id="sysinfo_download")
+ # with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
+ # gr.HTML('Download system info
(or open as text in a new page)', elem_id="sysinfo_download")
- with gr.Row():
- with gr.Column(scale=1):
- sysinfo_check_file = gr.File(label="Check system info for validity", type='binary')
- with gr.Column(scale=1):
- sysinfo_check_output = gr.HTML("", elem_id="sysinfo_validity")
- with gr.Column(scale=100):
- pass
+ # with gr.Row():
+ # with gr.Column(scale=1):
+ # sysinfo_check_file = gr.File(label="Check system info for validity", type='binary')
+ # with gr.Column(scale=1):
+ # sysinfo_check_output = gr.HTML("", elem_id="sysinfo_validity")
+ # with gr.Column(scale=100):
+ # pass
- with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
- request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
- download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
- reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
- with gr.Row():
- unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
- reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
+ # with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
+ # request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ # download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+ # reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ # with gr.Row():
+ # unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+ # reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
@@ -183,48 +183,48 @@ def create_ui(self, loadsave, dummy_component):
self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
- unload_sd_model.click(
- fn=sd_models.unload_model_weights,
- inputs=[],
- outputs=[]
- )
-
- reload_sd_model.click(
- fn=sd_models.reload_model_weights,
- inputs=[],
- outputs=[]
- )
-
- request_notifications.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='function(){}'
- )
-
- download_localization.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='download_localization'
- )
+ # unload_sd_model.click(
+ # fn=sd_models.unload_model_weights,
+ # inputs=[],
+ # outputs=[]
+ # )
+
+ # reload_sd_model.click(
+ # fn=sd_models.reload_model_weights,
+ # inputs=[],
+ # outputs=[]
+ # )
+
+ # request_notifications.click(
+ # fn=lambda: None,
+ # inputs=[],
+ # outputs=[],
+ # _js='function(){}'
+ # )
+
+ # download_localization.click(
+ # fn=lambda: None,
+ # inputs=[],
+ # outputs=[],
+ # _js='download_localization'
+ # )
def reload_scripts():
scripts.reload_script_body_only()
reload_javascript() # need to refresh the html page
- reload_script_bodies.click(
- fn=reload_scripts,
- inputs=[],
- outputs=[]
- )
+ # reload_script_bodies.click(
+ # fn=reload_scripts,
+ # inputs=[],
+ # outputs=[]
+ # )
- restart_gradio.click(
- fn=shared.state.request_restart,
- _js='restart_reload',
- inputs=[],
- outputs=[],
- )
+ # restart_gradio.click(
+ # fn=shared.state.request_restart,
+ # _js='restart_reload',
+ # inputs=[],
+ # outputs=[],
+ # )
def check_file(x):
if x is None:
@@ -235,11 +235,11 @@ def check_file(x):
return 'Invalid'
- sysinfo_check_file.change(
- fn=check_file,
- inputs=[sysinfo_check_file],
- outputs=[sysinfo_check_output],
- )
+ # sysinfo_check_file.change(
+ # fn=check_file,
+ # inputs=[sysinfo_check_file],
+ # outputs=[sysinfo_check_output],
+ # )
self.interface = settings_interface
diff --git a/requirements.txt b/requirements.txt
index 99096afddcd..06e2a193a9c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -33,4 +33,5 @@ torchdiffeq
torchsde
transformers==4.30.2
-oss2
\ No newline at end of file
+oss2
+osstorchconnector
\ No newline at end of file
diff --git a/requirements_versions.txt b/requirements_versions.txt
index ba1af69dfa8..c5e3bb43d9a 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -8,7 +8,7 @@ einops==0.4.1
fastapi==0.94.0
gfpgan==1.3.8
gradio==3.41.2
-httpcore==0.15
+httpcore==1.0.5
inflection==0.5.1
jsonmerge==1.8.0
kornia==0.6.7
@@ -30,4 +30,5 @@ torchdiffeq==0.2.3
torchsde==0.2.5
transformers==4.30.2
oss2==2.18.1
-urllib3==1.26.16
\ No newline at end of file
+urllib3==1.26.16
+osstorchconnector==1.0.0rc1
diff --git a/start.sh b/start.sh
new file mode 100644
index 00000000000..b9910342ce1
--- /dev/null
+++ b/start.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+
+CODE_DIRECTORY="/root/sd-oss"
+
+if [ ! -d "$CODE_DIRECTORY" ]; then
+ mkdir -p "$CODE_DIRECTORY"
+else
+ echo " $CODE_DIRECTORY already exists"
+fi
+
+REQUIRED_VARS=("ACCESS_KEY_ID" "ACCESS_KEY_SECRET" "BUCKET_NAME" "BUCKET_ENDPOINT" "BUCKET_MODEL_DIR")
+
+for VAR in "${REQUIRED_VARS[@]}"; do
+ if [ -z "${!VAR}" ]; then
+ echo "ERROR: env val $VAR needed。"
+ exit 1
+ fi
+done
+
+AK=${ACCESS_KEY_ID}
+SK=${ACCESS_KEY_SECRET}
+
+WEBUI_USER_CONTENT=$(cat < /root/sd-oss/webui-user.sh
+
+
+JSON_CONTENT=$(cat < /root/.alibabacloud/credentials
+
+echo "Credential file:/root/.alibabacloud/credentials"
+
+nohup sudo bash /root/sd-oss/webui.sh -f --listen --port 3389 > output.log 2>&1 &
+echo $!
+rm -f /root/sd-oss/webui-user.sh
diff --git a/webui-user.sh b/webui-user.sh
deleted file mode 100644
index e57d4740a75..00000000000
--- a/webui-user.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/bin/bash
-#########################################################
-# Uncomment and change the variables below to your need:#
-#########################################################
-
-# Install directory without trailing slash
-#install_dir="/home/$(whoami)"
-
-# Name of the subdirectory
-#clone_dir="stable-diffusion-webui"
-
-# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
-#export COMMANDLINE_ARGS=""
-
-# python3 executable
-#python_cmd="python3"
-
-# git executable
-#export GIT="git"
-
-# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-#venv_dir="venv"
-
-# script to launch to start the app
-#export LAUNCH_SCRIPT="launch.py"
-
-# install command for torch
-#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113"
-
-# Requirements file to use for stable-diffusion-webui
-#export REQS_FILE="requirements_versions.txt"
-
-# Fixed git repos
-#export K_DIFFUSION_PACKAGE=""
-#export GFPGAN_PACKAGE=""
-
-# Fixed git commits
-#export STABLE_DIFFUSION_COMMIT_HASH=""
-#export CODEFORMER_COMMIT_HASH=""
-#export BLIP_COMMIT_HASH=""
-
-# Uncomment to enable accelerated launch
-#export ACCELERATE="True"
-
-# Uncomment to disable TCMalloc
-#export NO_TCMALLOC="True"
-
-# Bucket access
-#export ACCESS_KEY_ID=""
-#export ACCESS_KEY_SECRET=""
-
-
-###########################################
diff --git a/webui.sh b/webui.sh
index 3d0f87eed74..d3c3d34b14c 100755
--- a/webui.sh
+++ b/webui.sh
@@ -188,7 +188,7 @@ else
printf "\n%s\n" "${delimiter}"
printf "Clone stable-diffusion-webui"
printf "\n%s\n" "${delimiter}"
- "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
+ "${GIT}" clone https://github.com/shiwk/stable-diffusion-webui.git "-b" demov1 "${clone_dir}"
cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
fi