Merge branch 'dev' into dev
This commit is contained in:
AUTOMATIC1111
GitHub
commit
88a5001e06
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@ -1,3 +1,8 @@
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## 1.9.4
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### Bug Fixes:
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* pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882))
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## 1.9.3
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### Bug Fixes:
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@ -40,7 +40,7 @@ model:
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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use_checkpoint: False
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legacy: False
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first_stage_config:
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@ -41,7 +41,7 @@ model:
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use_linear_in_transformer: True
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transformer_depth: 1
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context_dim: 1024
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use_checkpoint: True
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use_checkpoint: False
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legacy: False
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first_stage_config:
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@ -45,7 +45,7 @@ model:
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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use_checkpoint: False
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legacy: False
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first_stage_config:
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@ -21,7 +21,7 @@ model:
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params:
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adm_in_channels: 2816
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num_classes: sequential
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use_checkpoint: True
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use_checkpoint: False
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in_channels: 9
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out_channels: 4
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model_channels: 320
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@ -40,7 +40,7 @@ model:
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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use_checkpoint: False
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legacy: False
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first_stage_config:
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@ -40,7 +40,7 @@ model:
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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use_checkpoint: False
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legacy: False
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first_stage_config:
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@ -260,6 +260,16 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
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loaded_networks.clear()
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unavailable_networks = []
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for name in names:
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if name.lower() in forbidden_network_aliases and available_networks.get(name) is None:
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unavailable_networks.append(name)
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elif available_network_aliases.get(name) is None:
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unavailable_networks.append(name)
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if unavailable_networks:
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update_available_networks_by_names(unavailable_networks)
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networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
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if any(x is None for x in networks_on_disk):
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list_available_networks()
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@ -378,13 +388,18 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
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self.network_weights_backup = weights_backup
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bias_backup = getattr(self, "network_bias_backup", None)
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if bias_backup is None:
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if bias_backup is None and wanted_names != ():
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if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
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bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
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elif getattr(self, 'bias', None) is not None:
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bias_backup = self.bias.to(devices.cpu, copy=True)
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else:
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bias_backup = None
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# Unlike weight which always has value, some modules don't have bias.
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# Only report if bias is not None and current bias are not unchanged.
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if bias_backup is not None and current_names != ():
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raise RuntimeError("no backup bias found and current bias are not unchanged")
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self.network_bias_backup = bias_backup
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if current_names != wanted_names:
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@ -566,22 +581,16 @@ def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
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return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
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def list_available_networks():
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available_networks.clear()
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available_network_aliases.clear()
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forbidden_network_aliases.clear()
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available_network_hash_lookup.clear()
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forbidden_network_aliases.update({"none": 1, "Addams": 1})
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os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
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def process_network_files(names: list[str] | None = None):
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candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
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candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
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for filename in candidates:
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if os.path.isdir(filename):
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continue
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name = os.path.splitext(os.path.basename(filename))[0]
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# if names is provided, only load networks with names in the list
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if names and name not in names:
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continue
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try:
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entry = network.NetworkOnDisk(name, filename)
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except OSError: # should catch FileNotFoundError and PermissionError etc.
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@ -597,6 +606,22 @@ def list_available_networks():
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available_network_aliases[entry.alias] = entry
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def update_available_networks_by_names(names: list[str]):
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process_network_files(names)
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def list_available_networks():
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available_networks.clear()
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available_network_aliases.clear()
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forbidden_network_aliases.clear()
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available_network_hash_lookup.clear()
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forbidden_network_aliases.update({"none": 1, "Addams": 1})
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os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
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process_network_files()
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re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
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@ -60,7 +60,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
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else:
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sd_version = lora_on_disk.sd_version
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if shared.opts.lora_show_all or not enable_filter:
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if shared.opts.lora_show_all or not enable_filter or not shared.sd_model:
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pass
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elif sd_version == network.SdVersion.Unknown:
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model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
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@ -1,6 +1,5 @@
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import hypertile
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from modules import scripts, script_callbacks, shared
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from scripts.hypertile_xyz import add_axis_options
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class ScriptHypertile(scripts.Script):
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@ -93,7 +92,6 @@ def on_ui_settings():
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"hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"),
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"hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"),
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"hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"),
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"hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"),
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"hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"),
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"hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"),
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@ -105,5 +103,20 @@ def on_ui_settings():
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shared.opts.add_option(name, opt)
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def add_axis_options():
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xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
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xyz_grid.axis_options.extend([
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xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
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xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet_secondpass', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
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xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_unet"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] Unet Max Depth'), choices=lambda: [str(x) for x in range(4)]),
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xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_unet"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] Unet Max Tile Size')),
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xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_unet"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] Unet Swap Size')),
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xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, xyz_grid.apply_override('hypertile_enable_vae', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
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xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_vae"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] VAE Max Depth'), choices=lambda: [str(x) for x in range(4)]),
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xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_vae"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] VAE Max Tile Size')),
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xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_vae"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] VAE Swap Size')),
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])
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script_callbacks.on_ui_settings(on_ui_settings)
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script_callbacks.on_before_ui(add_axis_options)
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@ -1,51 +0,0 @@
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from modules import scripts
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from modules.shared import opts
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xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
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def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
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"""
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Returns a function that applies the given value to the given value_name in opts.data.
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"""
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def validate(value_name:str, value:str):
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value = int(value)
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# validate value
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if not min_range == -1:
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assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
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if not max_range == -1:
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assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
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def apply_int(p, x, xs):
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validate(value_name, x)
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opts.data[value_name] = int(x)
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return apply_int
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def bool_applier(value_name:str):
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"""
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Returns a function that applies the given value to the given value_name in opts.data.
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"""
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def validate(value_name:str, value:str):
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assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false"
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def apply_bool(p, x, xs):
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validate(value_name, x)
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value_boolean = x.lower() == "true"
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opts.data[value_name] = value_boolean
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return apply_bool
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def add_axis_options():
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extra_axis_options = [
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xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)),
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xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)),
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xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]),
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xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)),
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xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)),
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xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)),
|
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xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]),
|
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xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)),
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xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)),
|
||||
]
|
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set_a = {opt.label for opt in xyz_grid.axis_options}
|
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set_b = {opt.label for opt in extra_axis_options}
|
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if set_a.intersection(set_b):
|
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return
|
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|
||||
xyz_grid.axis_options.extend(extra_axis_options)
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|
|
@ -3,6 +3,7 @@ import gradio as gr
|
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import math
|
||||
from modules.ui_components import InputAccordion
|
||||
import modules.scripts as scripts
|
||||
from modules.torch_utils import float64
|
||||
|
||||
|
||||
class SoftInpaintingSettings:
|
||||
|
|
@ -79,13 +80,11 @@ def latent_blend(settings, a, b, t):
|
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|
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# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
|
||||
# 64-bit operations are used here to allow large exponents.
|
||||
current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
|
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current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(float64(image_interp)).add_(0.00001)
|
||||
|
||||
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
|
||||
a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
|
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settings.inpaint_detail_preservation) * one_minus_t3
|
||||
b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
|
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settings.inpaint_detail_preservation) * t3
|
||||
a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(float64(a)).pow_(settings.inpaint_detail_preservation) * one_minus_t3
|
||||
b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(float64(b)).pow_(settings.inpaint_detail_preservation) * t3
|
||||
desired_magnitude = a_magnitude
|
||||
desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
|
||||
del a_magnitude, b_magnitude, t3, one_minus_t3
|
||||
|
|
|
|||
|
|
@ -337,8 +337,8 @@ onOptionsChanged(function() {
|
|||
let txt2img_textarea, img2img_textarea = undefined;
|
||||
|
||||
function restart_reload() {
|
||||
document.body.style.backgroundColor = "var(--background-fill-primary)";
|
||||
document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
|
||||
|
||||
var requestPing = function() {
|
||||
requestGet("./internal/ping", {}, function(data) {
|
||||
location.reload();
|
||||
|
|
|
|||
|
|
@ -438,15 +438,19 @@ class Api:
|
|||
self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
|
||||
|
||||
selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
|
||||
sampler, scheduler = sd_samplers.get_sampler_and_scheduler(txt2imgreq.sampler_name or txt2imgreq.sampler_index, txt2imgreq.scheduler)
|
||||
|
||||
populate = txt2imgreq.copy(update={ # Override __init__ params
|
||||
"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
|
||||
"sampler_name": validate_sampler_name(sampler),
|
||||
"do_not_save_samples": not txt2imgreq.save_images,
|
||||
"do_not_save_grid": not txt2imgreq.save_images,
|
||||
})
|
||||
if populate.sampler_name:
|
||||
populate.sampler_index = None # prevent a warning later on
|
||||
|
||||
if not populate.scheduler and scheduler != "Automatic":
|
||||
populate.scheduler = scheduler
|
||||
|
||||
args = vars(populate)
|
||||
args.pop('script_name', None)
|
||||
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
|
||||
|
|
@ -502,9 +506,10 @@ class Api:
|
|||
self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
|
||||
|
||||
selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
|
||||
sampler, scheduler = sd_samplers.get_sampler_and_scheduler(img2imgreq.sampler_name or img2imgreq.sampler_index, img2imgreq.scheduler)
|
||||
|
||||
populate = img2imgreq.copy(update={ # Override __init__ params
|
||||
"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
|
||||
"sampler_name": validate_sampler_name(sampler),
|
||||
"do_not_save_samples": not img2imgreq.save_images,
|
||||
"do_not_save_grid": not img2imgreq.save_images,
|
||||
"mask": mask,
|
||||
|
|
@ -512,6 +517,9 @@ class Api:
|
|||
if populate.sampler_name:
|
||||
populate.sampler_index = None # prevent a warning later on
|
||||
|
||||
if not populate.scheduler and scheduler != "Automatic":
|
||||
populate.scheduler = scheduler
|
||||
|
||||
args = vars(populate)
|
||||
args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
|
||||
args.pop('script_name', None)
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ parser.add_argument("--lowvram", action='store_true', help="enable stable diffus
|
|||
parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
|
||||
parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
|
||||
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
|
||||
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
|
||||
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "half", "autocast"], default="autocast")
|
||||
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
|
||||
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
|
||||
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
|
||||
|
|
|
|||
|
|
@ -114,6 +114,9 @@ errors.run(enable_tf32, "Enabling TF32")
|
|||
|
||||
cpu: torch.device = torch.device("cpu")
|
||||
fp8: bool = False
|
||||
# Force fp16 for all models in inference. No casting during inference.
|
||||
# This flag is controlled by "--precision half" command line arg.
|
||||
force_fp16: bool = False
|
||||
device: torch.device = None
|
||||
device_interrogate: torch.device = None
|
||||
device_gfpgan: torch.device = None
|
||||
|
|
@ -127,6 +130,8 @@ unet_needs_upcast = False
|
|||
|
||||
|
||||
def cond_cast_unet(input):
|
||||
if force_fp16:
|
||||
return input.to(torch.float16)
|
||||
return input.to(dtype_unet) if unet_needs_upcast else input
|
||||
|
||||
|
||||
|
|
@ -206,6 +211,11 @@ def autocast(disable=False):
|
|||
if disable:
|
||||
return contextlib.nullcontext()
|
||||
|
||||
if force_fp16:
|
||||
# No casting during inference if force_fp16 is enabled.
|
||||
# All tensor dtype conversion happens before inference.
|
||||
return contextlib.nullcontext()
|
||||
|
||||
if fp8 and device==cpu:
|
||||
return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
|
||||
|
||||
|
|
@ -269,3 +279,17 @@ def first_time_calculation():
|
|||
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
|
||||
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
|
||||
conv2d(x)
|
||||
|
||||
|
||||
def force_model_fp16():
|
||||
"""
|
||||
ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which
|
||||
force conversion of input to float32. If force_fp16 is enabled, we need to
|
||||
prevent this casting.
|
||||
"""
|
||||
assert force_fp16
|
||||
import sgm.modules.diffusionmodules.util as sgm_util
|
||||
import ldm.modules.diffusionmodules.util as ldm_util
|
||||
sgm_util.GroupNorm32 = torch.nn.GroupNorm
|
||||
ldm_util.GroupNorm32 = torch.nn.GroupNorm
|
||||
print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.")
|
||||
|
|
|
|||
|
|
@ -191,8 +191,9 @@ class Extension:
|
|||
|
||||
def check_updates(self):
|
||||
repo = Repo(self.path)
|
||||
branch_name = f'{repo.remote().name}/{self.branch}'
|
||||
for fetch in repo.remote().fetch(dry_run=True):
|
||||
if self.branch and fetch.name != f'{repo.remote().name}/{self.branch}':
|
||||
if self.branch and fetch.name != branch_name:
|
||||
continue
|
||||
if fetch.flags != fetch.HEAD_UPTODATE:
|
||||
self.can_update = True
|
||||
|
|
@ -200,7 +201,7 @@ class Extension:
|
|||
return
|
||||
|
||||
try:
|
||||
origin = repo.rev_parse('origin')
|
||||
origin = repo.rev_parse(branch_name)
|
||||
if repo.head.commit != origin:
|
||||
self.can_update = True
|
||||
self.status = "behind HEAD"
|
||||
|
|
@ -213,8 +214,10 @@ class Extension:
|
|||
self.can_update = False
|
||||
self.status = "latest"
|
||||
|
||||
def fetch_and_reset_hard(self, commit='origin'):
|
||||
def fetch_and_reset_hard(self, commit=None):
|
||||
repo = Repo(self.path)
|
||||
if commit is None:
|
||||
commit = f'{repo.remote().name}/{self.branch}'
|
||||
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
|
||||
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
|
||||
repo.git.fetch(all=True)
|
||||
|
|
|
|||
|
|
@ -653,7 +653,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||
# WebP and JPG formats have maximum dimension limits of 16383 and 65535 respectively. switch to PNG which has a much higher limit
|
||||
if (image.height > 65535 or image.width > 65535) and extension.lower() in ("jpg", "jpeg") or (image.height > 16383 or image.width > 16383) and extension.lower() == "webp":
|
||||
print('Image dimensions too large; saving as PNG')
|
||||
extension = ".png"
|
||||
extension = "png"
|
||||
|
||||
if save_to_dirs is None:
|
||||
save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
|
||||
|
|
|
|||
|
|
@ -17,11 +17,14 @@ from modules.ui import plaintext_to_html
|
|||
import modules.scripts
|
||||
|
||||
|
||||
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
|
||||
def process_batch(p, input, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
|
||||
output_dir = output_dir.strip()
|
||||
processing.fix_seed(p)
|
||||
|
||||
batch_images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
|
||||
if isinstance(input, str):
|
||||
batch_images = list(shared.walk_files(input, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
|
||||
else:
|
||||
batch_images = [os.path.abspath(x.name) for x in input]
|
||||
|
||||
is_inpaint_batch = False
|
||||
if inpaint_mask_dir:
|
||||
|
|
@ -146,7 +149,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
|
|||
return batch_results
|
||||
|
||||
|
||||
def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, *args):
|
||||
def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, img2img_batch_source_type: str, img2img_batch_upload: list, *args):
|
||||
override_settings = create_override_settings_dict(override_settings_texts)
|
||||
|
||||
is_batch = mode == 5
|
||||
|
|
@ -221,6 +224,13 @@ def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_
|
|||
|
||||
with closing(p):
|
||||
if is_batch:
|
||||
if img2img_batch_source_type == "upload":
|
||||
assert isinstance(img2img_batch_upload, list) and img2img_batch_upload
|
||||
output_dir = ""
|
||||
inpaint_mask_dir = ""
|
||||
png_info_dir = img2img_batch_png_info_dir if not shared.cmd_opts.hide_ui_dir_config else ""
|
||||
processed = process_batch(p, img2img_batch_upload, output_dir, inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=png_info_dir)
|
||||
else: # "from dir"
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
||||
processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
|
||||
|
||||
|
|
|
|||
|
|
@ -115,10 +115,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
|
|||
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
|
||||
|
||||
else:
|
||||
sd = sd_model.model.state_dict()
|
||||
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
|
||||
if diffusion_model_input is not None:
|
||||
if diffusion_model_input.shape[1] == 9:
|
||||
if getattr(sd_model.model, "is_sdxl_inpaint", False):
|
||||
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
|
||||
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
|
||||
image_conditioning = images_tensor_to_samples(image_conditioning,
|
||||
|
|
@ -238,11 +235,6 @@ class StableDiffusionProcessing:
|
|||
self.styles = []
|
||||
|
||||
self.sampler_noise_scheduler_override = None
|
||||
self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
|
||||
self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
|
||||
self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
|
||||
self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
|
||||
self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
|
||||
|
||||
self.extra_generation_params = self.extra_generation_params or {}
|
||||
self.override_settings = self.override_settings or {}
|
||||
|
|
@ -259,6 +251,13 @@ class StableDiffusionProcessing:
|
|||
self.cached_uc = StableDiffusionProcessing.cached_uc
|
||||
self.cached_c = StableDiffusionProcessing.cached_c
|
||||
|
||||
def fill_fields_from_opts(self):
|
||||
self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
|
||||
self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
|
||||
self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
|
||||
self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
|
||||
self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
|
||||
|
||||
@property
|
||||
def sd_model(self):
|
||||
return shared.sd_model
|
||||
|
|
@ -390,10 +389,7 @@ class StableDiffusionProcessing:
|
|||
if self.sampler.conditioning_key == "crossattn-adm":
|
||||
return self.unclip_image_conditioning(source_image)
|
||||
|
||||
sd = self.sampler.model_wrap.inner_model.model.state_dict()
|
||||
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
|
||||
if diffusion_model_input is not None:
|
||||
if diffusion_model_input.shape[1] == 9:
|
||||
if getattr(self.sampler.model_wrap.inner_model.model, "is_sdxl_inpaint", False):
|
||||
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
|
||||
|
||||
# Dummy zero conditioning if we're not using inpainting or depth model.
|
||||
|
|
@ -569,7 +565,7 @@ class Processed:
|
|||
self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
|
||||
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
|
||||
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
|
||||
self.infotexts = infotexts or [info]
|
||||
self.infotexts = infotexts or [info] * len(images_list)
|
||||
self.version = program_version()
|
||||
|
||||
def js(self):
|
||||
|
|
@ -794,7 +790,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
|||
"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
|
||||
"Init image hash": getattr(p, 'init_img_hash', None),
|
||||
"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
|
||||
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
|
||||
"Tiling": "True" if p.tiling else None,
|
||||
**p.extra_generation_params,
|
||||
"Version": program_version() if opts.add_version_to_infotext else None,
|
||||
|
|
@ -842,6 +837,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
|
||||
|
||||
# backwards compatibility, fix sampler and scheduler if invalid
|
||||
sd_samplers.fix_p_invalid_sampler_and_scheduler(p)
|
||||
|
||||
res = process_images_inner(p)
|
||||
|
||||
finally:
|
||||
|
|
@ -890,6 +888,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
|
||||
modules.sd_hijack.model_hijack.clear_comments()
|
||||
|
||||
p.fill_fields_from_opts()
|
||||
p.setup_prompts()
|
||||
|
||||
if isinstance(seed, list):
|
||||
|
|
|
|||
|
|
@ -486,7 +486,8 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
|
|||
k_in = self.to_k(context_k)
|
||||
v_in = self.to_v(context_v)
|
||||
|
||||
q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
|
||||
q, k, v = (t.reshape(t.shape[0], t.shape[1], h, -1) for t in (q_in, k_in, v_in))
|
||||
|
||||
del q_in, k_in, v_in
|
||||
|
||||
dtype = q.dtype
|
||||
|
|
@ -497,7 +498,8 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
|
|||
|
||||
out = out.to(dtype)
|
||||
|
||||
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
|
||||
b, n, h, d = out.shape
|
||||
out = out.reshape(b, n, h * d)
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
import torch
|
||||
from packaging import version
|
||||
from einops import repeat
|
||||
import math
|
||||
|
||||
from modules import devices
|
||||
from modules.sd_hijack_utils import CondFunc
|
||||
|
|
@ -36,7 +38,7 @@ th = TorchHijackForUnet()
|
|||
|
||||
# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
|
||||
def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
|
||||
|
||||
"""Always make sure inputs to unet are in correct dtype."""
|
||||
if isinstance(cond, dict):
|
||||
for y in cond.keys():
|
||||
if isinstance(cond[y], list):
|
||||
|
|
@ -45,7 +47,59 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
|
|||
cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
|
||||
|
||||
with devices.autocast():
|
||||
return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
|
||||
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
|
||||
if devices.unet_needs_upcast:
|
||||
return result.float()
|
||||
else:
|
||||
return result
|
||||
|
||||
|
||||
# Monkey patch to create timestep embed tensor on device, avoiding a block.
|
||||
def timestep_embedding(_, timesteps, dim, max_period=10000, repeat_only=False):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param dim: the dimension of the output.
|
||||
:param max_period: controls the minimum frequency of the embeddings.
|
||||
:return: an [N x dim] Tensor of positional embeddings.
|
||||
"""
|
||||
if not repeat_only:
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
|
||||
)
|
||||
args = timesteps[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||
else:
|
||||
embedding = repeat(timesteps, 'b -> b d', d=dim)
|
||||
return embedding
|
||||
|
||||
|
||||
# Monkey patch to SpatialTransformer removing unnecessary contiguous calls.
|
||||
# Prevents a lot of unnecessary aten::copy_ calls
|
||||
def spatial_transformer_forward(_, self, x: torch.Tensor, context=None):
|
||||
# note: if no context is given, cross-attention defaults to self-attention
|
||||
if not isinstance(context, list):
|
||||
context = [context]
|
||||
b, c, h, w = x.shape
|
||||
x_in = x
|
||||
x = self.norm(x)
|
||||
if not self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
|
||||
if self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
for i, block in enumerate(self.transformer_blocks):
|
||||
x = block(x, context=context[i])
|
||||
if self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
x = x.view(b, h, w, c).permute(0, 3, 1, 2)
|
||||
if not self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
return x + x_in
|
||||
|
||||
|
||||
class GELUHijack(torch.nn.GELU, torch.nn.Module):
|
||||
|
|
@ -64,12 +118,15 @@ def hijack_ddpm_edit():
|
|||
if not ddpm_edit_hijack:
|
||||
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
|
||||
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
|
||||
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
|
||||
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model)
|
||||
|
||||
|
||||
unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
|
||||
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
|
||||
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding)
|
||||
CondFunc('ldm.modules.attention.SpatialTransformer.forward', spatial_transformer_forward)
|
||||
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
|
||||
|
||||
if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
|
||||
CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
|
||||
CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
|
||||
|
|
@ -81,5 +138,17 @@ CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_s
|
|||
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
|
||||
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
|
||||
|
||||
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
|
||||
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
|
||||
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
|
||||
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
|
||||
|
||||
|
||||
def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
|
||||
if devices.unet_needs_upcast and timesteps.dtype == torch.int64:
|
||||
dtype = torch.float32
|
||||
else:
|
||||
dtype = devices.dtype_unet
|
||||
return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
|
||||
|
||||
|
||||
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
|
||||
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,11 @@
|
|||
import importlib
|
||||
|
||||
|
||||
always_true_func = lambda *args, **kwargs: True
|
||||
|
||||
|
||||
class CondFunc:
|
||||
def __new__(cls, orig_func, sub_func, cond_func):
|
||||
def __new__(cls, orig_func, sub_func, cond_func=always_true_func):
|
||||
self = super(CondFunc, cls).__new__(cls)
|
||||
if isinstance(orig_func, str):
|
||||
func_path = orig_func.split('.')
|
||||
|
|
|
|||
|
|
@ -380,6 +380,13 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
|
|||
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
|
||||
model.is_sd1 = not model.is_sdxl and not model.is_sd2
|
||||
model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
|
||||
# Set is_sdxl_inpaint flag.
|
||||
diffusion_model_input = state_dict.get('diffusion_model.input_blocks.0.0.weight', None)
|
||||
model.is_sdxl_inpaint = (
|
||||
model.is_sdxl and
|
||||
diffusion_model_input is not None and
|
||||
diffusion_model_input.shape[1] == 9
|
||||
)
|
||||
if model.is_sdxl:
|
||||
sd_models_xl.extend_sdxl(model)
|
||||
|
||||
|
|
@ -403,6 +410,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
|
|||
model.float()
|
||||
model.alphas_cumprod_original = model.alphas_cumprod
|
||||
devices.dtype_unet = torch.float32
|
||||
assert shared.cmd_opts.precision != "half", "Cannot use --precision half with --no-half"
|
||||
timer.record("apply float()")
|
||||
else:
|
||||
vae = model.first_stage_model
|
||||
|
|
@ -540,7 +548,7 @@ def repair_config(sd_config):
|
|||
if hasattr(sd_config.model.params, 'unet_config'):
|
||||
if shared.cmd_opts.no_half:
|
||||
sd_config.model.params.unet_config.params.use_fp16 = False
|
||||
elif shared.cmd_opts.upcast_sampling:
|
||||
elif shared.cmd_opts.upcast_sampling or shared.cmd_opts.precision == "half":
|
||||
sd_config.model.params.unet_config.params.use_fp16 = True
|
||||
|
||||
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
|
||||
|
|
@ -551,6 +559,14 @@ def repair_config(sd_config):
|
|||
karlo_path = os.path.join(paths.models_path, 'karlo')
|
||||
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
|
||||
|
||||
# Do not use checkpoint for inference.
|
||||
# This helps prevent extra performance overhead on checking parameters.
|
||||
# The perf overhead is about 100ms/it on 4090 for SDXL.
|
||||
if hasattr(sd_config.model.params, "network_config"):
|
||||
sd_config.model.params.network_config.params.use_checkpoint = False
|
||||
if hasattr(sd_config.model.params, "unet_config"):
|
||||
sd_config.model.params.unet_config.params.use_checkpoint = False
|
||||
|
||||
|
||||
def rescale_zero_terminal_snr_abar(alphas_cumprod):
|
||||
alphas_bar_sqrt = alphas_cumprod.sqrt()
|
||||
|
|
@ -659,6 +675,7 @@ def get_empty_cond(sd_model):
|
|||
|
||||
|
||||
def send_model_to_cpu(m):
|
||||
if m is not None:
|
||||
if m.lowvram:
|
||||
lowvram.send_everything_to_cpu()
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ def is_using_v_parameterization_for_sd2(state_dict):
|
|||
|
||||
with sd_disable_initialization.DisableInitialization():
|
||||
unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
|
||||
use_checkpoint=True,
|
||||
use_checkpoint=False,
|
||||
use_fp16=False,
|
||||
image_size=32,
|
||||
in_channels=4,
|
||||
|
|
|
|||
|
|
@ -35,10 +35,9 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
|
|||
|
||||
|
||||
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
|
||||
sd = self.model.state_dict()
|
||||
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
|
||||
if diffusion_model_input is not None:
|
||||
if diffusion_model_input.shape[1] == 9:
|
||||
"""WARNING: This function is called once per denoising iteration. DO NOT add
|
||||
expensive functionc calls such as `model.state_dict`. """
|
||||
if self.is_sdxl_inpaint:
|
||||
x = torch.cat([x] + cond['c_concat'], dim=1)
|
||||
|
||||
return self.model(x, t, cond)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import functools
|
||||
|
||||
import logging
|
||||
from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared, sd_samplers_common, sd_schedulers
|
||||
|
||||
# imports for functions that previously were here and are used by other modules
|
||||
|
|
@ -122,4 +122,11 @@ def get_sampler_and_scheduler(sampler_name, scheduler_name):
|
|||
return sampler.name, found_scheduler.label
|
||||
|
||||
|
||||
def fix_p_invalid_sampler_and_scheduler(p):
|
||||
i_sampler_name, i_scheduler = p.sampler_name, p.scheduler
|
||||
p.sampler_name, p.scheduler = get_sampler_and_scheduler(p.sampler_name, p.scheduler)
|
||||
if p.sampler_name != i_sampler_name or i_scheduler != p.scheduler:
|
||||
logging.warning(f'Sampler Scheduler autocorrection: "{i_sampler_name}" -> "{p.sampler_name}", "{i_scheduler}" -> "{p.scheduler}"')
|
||||
|
||||
|
||||
set_samplers()
|
||||
|
|
|
|||
|
|
@ -212,9 +212,16 @@ class CFGDenoiser(torch.nn.Module):
|
|||
uncond = denoiser_params.text_uncond
|
||||
skip_uncond = False
|
||||
|
||||
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
|
||||
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
|
||||
if shared.opts.skip_early_cond != 0. and self.step / self.total_steps <= shared.opts.skip_early_cond:
|
||||
skip_uncond = True
|
||||
self.p.extra_generation_params["Skip Early CFG"] = shared.opts.skip_early_cond
|
||||
elif (self.step % 2 or shared.opts.s_min_uncond_all) and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
|
||||
skip_uncond = True
|
||||
self.p.extra_generation_params["NGMS"] = s_min_uncond
|
||||
if shared.opts.s_min_uncond_all:
|
||||
self.p.extra_generation_params["NGMS all steps"] = shared.opts.s_min_uncond_all
|
||||
|
||||
if skip_uncond:
|
||||
x_in = x_in[:-batch_size]
|
||||
sigma_in = sigma_in[:-batch_size]
|
||||
|
||||
|
|
|
|||
|
|
@ -5,13 +5,14 @@ import numpy as np
|
|||
|
||||
from modules import shared
|
||||
from modules.models.diffusion.uni_pc import uni_pc
|
||||
from modules.torch_utils import float64
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
|
||||
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
||||
alphas = alphas_cumprod[timesteps]
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
|
||||
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
||||
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
|
||||
|
||||
|
|
@ -43,7 +44,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
|
|||
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
|
||||
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
||||
alphas = alphas_cumprod[timesteps]
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
|
||||
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
|
||||
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
||||
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
|
|
|
|||
|
|
@ -4,6 +4,9 @@ import torch
|
|||
|
||||
import k_diffusion
|
||||
|
||||
import numpy as np
|
||||
|
||||
from modules import shared
|
||||
|
||||
@dataclasses.dataclass
|
||||
class Scheduler:
|
||||
|
|
@ -30,6 +33,41 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
|
|||
sigs += [0.0]
|
||||
return torch.FloatTensor(sigs).to(device)
|
||||
|
||||
def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device='cpu'):
|
||||
# https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
|
||||
def loglinear_interp(t_steps, num_steps):
|
||||
"""
|
||||
Performs log-linear interpolation of a given array of decreasing numbers.
|
||||
"""
|
||||
xs = np.linspace(0, 1, len(t_steps))
|
||||
ys = np.log(t_steps[::-1])
|
||||
|
||||
new_xs = np.linspace(0, 1, num_steps)
|
||||
new_ys = np.interp(new_xs, xs, ys)
|
||||
|
||||
interped_ys = np.exp(new_ys)[::-1].copy()
|
||||
return interped_ys
|
||||
|
||||
if shared.sd_model.is_sdxl:
|
||||
sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
|
||||
else:
|
||||
# Default to SD 1.5 sigmas.
|
||||
sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
|
||||
|
||||
if n != len(sigmas):
|
||||
sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
|
||||
else:
|
||||
sigmas.append(0.0)
|
||||
|
||||
return torch.FloatTensor(sigmas).to(device)
|
||||
|
||||
def kl_optimal(n, sigma_min, sigma_max, device):
|
||||
alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
|
||||
alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
|
||||
step_indices = torch.arange(n + 1, device=device)
|
||||
sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
|
||||
return sigmas
|
||||
|
||||
|
||||
schedulers = [
|
||||
Scheduler('automatic', 'Automatic', None),
|
||||
|
|
@ -38,6 +76,8 @@ schedulers = [
|
|||
Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
|
||||
Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
|
||||
Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
|
||||
Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
|
||||
Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
|
||||
]
|
||||
|
||||
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
|
||||
|
|
|
|||
|
|
@ -31,6 +31,14 @@ def initialize():
|
|||
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
|
||||
devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
|
||||
|
||||
if cmd_opts.precision == "half":
|
||||
msg = "--no-half and --no-half-vae conflict with --precision half"
|
||||
assert devices.dtype == torch.float16, msg
|
||||
assert devices.dtype_vae == torch.float16, msg
|
||||
assert devices.dtype_inference == torch.float16, msg
|
||||
devices.force_fp16 = True
|
||||
devices.force_model_fp16()
|
||||
|
||||
shared.device = devices.device
|
||||
shared.weight_load_location = None if cmd_opts.lowram else "cpu"
|
||||
|
||||
|
|
|
|||
|
|
@ -209,7 +209,8 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), {
|
|||
|
||||
options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
|
||||
"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"),
|
||||
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stablediffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
|
||||
"s_min_uncond_all": OptionInfo(False, "Negative Guidance minimum sigma all steps", infotext='NGMS all steps').info("By default, NGMS above skips every other step; this makes it skip all steps"),
|
||||
"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"),
|
||||
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
|
||||
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
|
||||
|
|
@ -380,7 +381,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
|||
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
|
||||
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
|
||||
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
|
||||
'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models")
|
||||
'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
|
||||
'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import torch.nn
|
||||
import torch
|
||||
|
||||
|
||||
def get_param(model) -> torch.nn.Parameter:
|
||||
|
|
@ -15,3 +16,11 @@ def get_param(model) -> torch.nn.Parameter:
|
|||
return param
|
||||
|
||||
raise ValueError(f"No parameters found in model {model!r}")
|
||||
|
||||
|
||||
def float64(t: torch.Tensor):
|
||||
"""return torch.float64 if device is not mps or xpu, else return torch.float32"""
|
||||
match t.device.type:
|
||||
case 'mps', 'xpu':
|
||||
return torch.float32
|
||||
return torch.float64
|
||||
|
|
|
|||
|
|
@ -566,6 +566,11 @@ def create_ui():
|
|||
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:
|
||||
with gr.Tabs(elem_id="img2img_batch_source"):
|
||||
img2img_batch_source_type = gr.Textbox(visible=False, value="upload")
|
||||
with gr.TabItem('Upload', id='batch_upload', elem_id="img2img_batch_upload_tab") as tab_batch_upload:
|
||||
img2img_batch_upload = gr.Files(label="Files", interactive=True, elem_id="img2img_batch_upload")
|
||||
with gr.TabItem('From directory', id='batch_from_dir', elem_id="img2img_batch_from_dir_tab") as tab_batch_from_dir:
|
||||
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
||||
gr.HTML(
|
||||
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
|
||||
|
|
@ -576,8 +581,10 @@ def create_ui():
|
|||
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")
|
||||
tab_batch_upload.select(fn=lambda: "upload", inputs=[], outputs=[img2img_batch_source_type])
|
||||
tab_batch_from_dir.select(fn=lambda: "from dir", inputs=[], outputs=[img2img_batch_source_type])
|
||||
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_use_png_info = gr.Checkbox(label="Append png info to prompts", 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", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
|
||||
|
||||
|
|
@ -759,6 +766,8 @@ def create_ui():
|
|||
img2img_batch_use_png_info,
|
||||
img2img_batch_png_info_props,
|
||||
img2img_batch_png_info_dir,
|
||||
img2img_batch_source_type,
|
||||
img2img_batch_upload,
|
||||
] + custom_inputs,
|
||||
outputs=[
|
||||
output_panel.gallery,
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
setuptools==69.5.1 # temp fix for compatibility with some old packages
|
||||
GitPython==3.1.32
|
||||
Pillow==9.5.0
|
||||
accelerate==0.21.0
|
||||
|
|
|
|||
|
|
@ -95,15 +95,15 @@ def confirm_checkpoints_or_none(p, xs):
|
|||
raise RuntimeError(f"Unknown checkpoint: {x}")
|
||||
|
||||
|
||||
def apply_clip_skip(p, x, xs):
|
||||
opts.data["CLIP_stop_at_last_layers"] = x
|
||||
def confirm_range(min_val, max_val, axis_label):
|
||||
"""Generates a AxisOption.confirm() function that checks all values are within the specified range."""
|
||||
|
||||
def confirm_range_fun(p, xs):
|
||||
for x in xs:
|
||||
if not (max_val >= x >= min_val):
|
||||
raise ValueError(f'{axis_label} value "{x}" out of range [{min_val}, {max_val}]')
|
||||
|
||||
def apply_upscale_latent_space(p, x, xs):
|
||||
if x.lower().strip() != '0':
|
||||
opts.data["use_scale_latent_for_hires_fix"] = True
|
||||
else:
|
||||
opts.data["use_scale_latent_for_hires_fix"] = False
|
||||
return confirm_range_fun
|
||||
|
||||
|
||||
def apply_size(p, x: str, xs) -> None:
|
||||
|
|
@ -118,21 +118,16 @@ def apply_size(p, x: str, xs) -> None:
|
|||
|
||||
|
||||
def find_vae(name: str):
|
||||
if name.lower() in ['auto', 'automatic']:
|
||||
return modules.sd_vae.unspecified
|
||||
if name.lower() == 'none':
|
||||
return None
|
||||
else:
|
||||
choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
|
||||
if len(choices) == 0:
|
||||
print(f"No VAE found for {name}; using automatic")
|
||||
return modules.sd_vae.unspecified
|
||||
else:
|
||||
return modules.sd_vae.vae_dict[choices[0]]
|
||||
match name := name.lower().strip():
|
||||
case 'auto', 'automatic':
|
||||
return 'Automatic'
|
||||
case 'none':
|
||||
return 'None'
|
||||
return next((k for k in modules.sd_vae.vae_dict if k.lower() == name), print(f'No VAE found for {name}; using Automatic') or 'Automatic')
|
||||
|
||||
|
||||
def apply_vae(p, x, xs):
|
||||
modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
|
||||
p.override_settings['sd_vae'] = find_vae(x)
|
||||
|
||||
|
||||
def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
|
||||
|
|
@ -140,7 +135,7 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
|
|||
|
||||
|
||||
def apply_uni_pc_order(p, x, xs):
|
||||
opts.data["uni_pc_order"] = min(x, p.steps - 1)
|
||||
p.override_settings['uni_pc_order'] = min(x, p.steps - 1)
|
||||
|
||||
|
||||
def apply_face_restore(p, opt, x):
|
||||
|
|
@ -162,12 +157,14 @@ def apply_override(field, boolean: bool = False):
|
|||
if boolean:
|
||||
x = True if x.lower() == "true" else False
|
||||
p.override_settings[field] = x
|
||||
|
||||
return fun
|
||||
|
||||
|
||||
def boolean_choice(reverse: bool = False):
|
||||
def choice():
|
||||
return ["False", "True"] if reverse else ["True", "False"]
|
||||
|
||||
return choice
|
||||
|
||||
|
||||
|
|
@ -212,7 +209,7 @@ def list_to_csv_string(data_list):
|
|||
|
||||
|
||||
def csv_string_to_list_strip(data_str):
|
||||
return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str)))))
|
||||
return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str), skipinitialspace=True))))
|
||||
|
||||
|
||||
class AxisOption:
|
||||
|
|
@ -264,13 +261,13 @@ axis_options = [
|
|||
AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
|
||||
AxisOption("Schedule rho", float, apply_override("rho")),
|
||||
AxisOption("Eta", float, apply_field("eta")),
|
||||
AxisOption("Clip skip", int, apply_clip_skip),
|
||||
AxisOption("Clip skip", int, apply_override('CLIP_stop_at_last_layers')),
|
||||
AxisOption("Denoising", float, apply_field("denoising_strength")),
|
||||
AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")),
|
||||
AxisOption("Extra noise", float, apply_override("img2img_extra_noise")),
|
||||
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
|
||||
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['Automatic', 'None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
|
||||
AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
|
||||
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
|
||||
|
|
@ -399,18 +396,12 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
|||
|
||||
class SharedSettingsStackHelper(object):
|
||||
def __enter__(self):
|
||||
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
||||
self.vae = opts.sd_vae
|
||||
self.uni_pc_order = opts.uni_pc_order
|
||||
pass
|
||||
|
||||
def __exit__(self, exc_type, exc_value, tb):
|
||||
opts.data["sd_vae"] = self.vae
|
||||
opts.data["uni_pc_order"] = self.uni_pc_order
|
||||
modules.sd_models.reload_model_weights()
|
||||
modules.sd_vae.reload_vae_weights()
|
||||
|
||||
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
|
||||
|
||||
|
||||
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
||||
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
|
||||
|
|
@ -572,7 +563,7 @@ class Script(scripts.Script):
|
|||
mc = re_range_count.fullmatch(val)
|
||||
if m is not None:
|
||||
start = int(m.group(1))
|
||||
end = int(m.group(2))+1
|
||||
end = int(m.group(2)) + 1
|
||||
step = int(m.group(3)) if m.group(3) is not None else 1
|
||||
|
||||
valslist_ext += list(range(start, end, step))
|
||||
|
|
@ -797,18 +788,18 @@ class Script(scripts.Script):
|
|||
z_count = len(zs)
|
||||
|
||||
# Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
|
||||
processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
|
||||
processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
|
||||
|
||||
if not include_lone_images:
|
||||
# Don't need sub-images anymore, drop from list:
|
||||
processed.images = processed.images[:z_count+1]
|
||||
processed.images = processed.images[:z_count + 1]
|
||||
|
||||
if opts.grid_save:
|
||||
# Auto-save main and sub-grids:
|
||||
grid_count = z_count + 1 if z_count > 1 else 1
|
||||
for g in range(grid_count):
|
||||
# TODO: See previous comment about intentional data misalignment.
|
||||
adj_g = g-1 if g > 0 else g
|
||||
adj_g = g - 1 if g > 0 else g
|
||||
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
|
||||
if not include_sub_grids: # if not include_sub_grids then skip saving after the first grid
|
||||
break
|
||||
|
|
|
|||
|
|
@ -807,6 +807,8 @@ table.popup-table .link{
|
|||
|
||||
#tab_extensions table{
|
||||
border-collapse: collapse;
|
||||
overflow-x: auto;
|
||||
display: block;
|
||||
}
|
||||
|
||||
#tab_extensions table td, #tab_extensions table th{
|
||||
|
|
|
|||
|
|
@ -11,7 +11,12 @@ fi
|
|||
|
||||
export install_dir="$HOME"
|
||||
export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
|
||||
export TORCH_COMMAND="pip install torch==2.1.2 torchvision==0.16.2"
|
||||
export PYTORCH_ENABLE_MPS_FALLBACK=1
|
||||
|
||||
if [[ "$(sysctl -n machdep.cpu.brand_string)" =~ ^.*"Intel".*$ ]]; then
|
||||
export TORCH_COMMAND="pip install torch==2.1.2 torchvision==0.16.2"
|
||||
else
|
||||
export TORCH_COMMAND="pip install torch==2.3.0 torchvision==0.18.0"
|
||||
fi
|
||||
|
||||
####################################################################
|
||||
|
|
|
|||
Loading…
Reference in New Issue