diff options
| -rw-r--r-- | README.md | 14 | ||||
| -rw-r--r-- | extensions-builtin/Lora/extra_networks_lora.py | 20 | ||||
| -rw-r--r-- | extensions-builtin/Lora/lora.py | 198 | ||||
| -rw-r--r-- | extensions-builtin/Lora/scripts/lora_script.py | 30 | ||||
| -rw-r--r-- | extensions-builtin/Lora/ui_extra_networks_lora.py | 35 | ||||
| -rw-r--r-- | modules/extra_networks_hypernet.py | 2 | ||||
| -rw-r--r-- | modules/script_callbacks.py | 15 | ||||
| -rw-r--r-- | modules/ui_extra_networks.py | 2 | ||||
| -rw-r--r-- | webui.py | 2 |
9 files changed, 314 insertions, 4 deletions
@@ -51,6 +51,7 @@ A browser interface based on Gradio library for Stable Diffusion. - Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
+ - Can use a separate neural network to produce previews with almost none VRAM or compute requirement
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
@@ -75,13 +76,22 @@ A browser interface based on Gradio library for Stable Diffusion. - hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
-- Use Hypernetworks
-- Use VAEs
+- Hypernetworks
+- Loras (same as Hypernetworks but more pretty)
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt.
+- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
+- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
+- Now without any bad letters!
+- Load checkpoints in safetensors format
+- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
+- Now with a license!
+- Reorder elements in the UI from settings screen
+-
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py new file mode 100644 index 00000000..8f2e753e --- /dev/null +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -0,0 +1,20 @@ +from modules import extra_networks
+import lora
+
+class ExtraNetworkLora(extra_networks.ExtraNetwork):
+ def __init__(self):
+ super().__init__('lora')
+
+ def activate(self, p, params_list):
+ names = []
+ multipliers = []
+ for params in params_list:
+ assert len(params.items) > 0
+
+ names.append(params.items[0])
+ multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
+
+ lora.load_loras(names, multipliers)
+
+ def deactivate(self, p):
+ pass
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py new file mode 100644 index 00000000..7a3ad9a9 --- /dev/null +++ b/extensions-builtin/Lora/lora.py @@ -0,0 +1,198 @@ +import glob
+import os
+import re
+import torch
+
+from modules import shared, devices, sd_models
+
+re_digits = re.compile(r"\d+")
+re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
+re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
+re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
+re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
+
+
+def convert_diffusers_name_to_compvis(key):
+ def match(match_list, regex):
+ r = re.match(regex, key)
+ if not r:
+ return False
+
+ match_list.clear()
+ match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
+ return True
+
+ m = []
+
+ if match(m, re_unet_down_blocks):
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
+
+ if match(m, re_unet_mid_blocks):
+ return f"diffusion_model_middle_block_1_{m[1]}"
+
+ if match(m, re_unet_up_blocks):
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
+
+ if match(m, re_text_block):
+ return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
+
+ return key
+
+
+class LoraOnDisk:
+ def __init__(self, name, filename):
+ self.name = name
+ self.filename = filename
+
+
+class LoraModule:
+ def __init__(self, name):
+ self.name = name
+ self.multiplier = 1.0
+ self.modules = {}
+ self.mtime = None
+
+
+class LoraUpDownModule:
+ def __init__(self):
+ self.up = None
+ self.down = None
+
+
+def assign_lora_names_to_compvis_modules(sd_model):
+ lora_layer_mapping = {}
+
+ for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
+ lora_name = name.replace(".", "_")
+ lora_layer_mapping[lora_name] = module
+ module.lora_layer_name = lora_name
+
+ for name, module in shared.sd_model.model.named_modules():
+ lora_name = name.replace(".", "_")
+ lora_layer_mapping[lora_name] = module
+ module.lora_layer_name = lora_name
+
+ sd_model.lora_layer_mapping = lora_layer_mapping
+
+
+def load_lora(name, filename):
+ lora = LoraModule(name)
+ lora.mtime = os.path.getmtime(filename)
+
+ sd = sd_models.read_state_dict(filename)
+
+ keys_failed_to_match = []
+
+ for key_diffusers, weight in sd.items():
+ fullkey = convert_diffusers_name_to_compvis(key_diffusers)
+ key, lora_key = fullkey.split(".", 1)
+
+ sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+ if sd_module is None:
+ keys_failed_to_match.append(key_diffusers)
+ continue
+
+ if type(sd_module) == torch.nn.Linear:
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif type(sd_module) == torch.nn.Conv2d:
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+ else:
+ assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
+
+ with torch.no_grad():
+ module.weight.copy_(weight)
+
+ module.to(device=devices.device, dtype=devices.dtype)
+
+ lora_module = lora.modules.get(key, None)
+ if lora_module is None:
+ lora_module = LoraUpDownModule()
+ lora.modules[key] = lora_module
+
+ if lora_key == "lora_up.weight":
+ lora_module.up = module
+ elif lora_key == "lora_down.weight":
+ lora_module.down = module
+ else:
+ assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight or lora_down.weight'
+
+ if len(keys_failed_to_match) > 0:
+ print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
+
+ return lora
+
+
+def load_loras(names, multipliers=None):
+ already_loaded = {}
+
+ for lora in loaded_loras:
+ if lora.name in names:
+ already_loaded[lora.name] = lora
+
+ loaded_loras.clear()
+
+ loras_on_disk = [available_loras.get(name, None) for name in names]
+ if any([x is None for x in loras_on_disk]):
+ list_available_loras()
+
+ loras_on_disk = [available_loras.get(name, None) for name in names]
+
+ for i, name in enumerate(names):
+ lora = already_loaded.get(name, None)
+
+ lora_on_disk = loras_on_disk[i]
+ if lora_on_disk is not None:
+ if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
+ lora = load_lora(name, lora_on_disk.filename)
+
+ if lora is None:
+ print(f"Couldn't find Lora with name {name}")
+ continue
+
+ lora.multiplier = multipliers[i] if multipliers else 1.0
+ loaded_loras.append(lora)
+
+
+def lora_forward(module, input, res):
+ if len(loaded_loras) == 0:
+ return res
+
+ lora_layer_name = getattr(module, 'lora_layer_name', None)
+ for lora in loaded_loras:
+ module = lora.modules.get(lora_layer_name, None)
+ if module is not None:
+ res = res + module.up(module.down(input)) * lora.multiplier
+
+ return res
+
+
+def lora_Linear_forward(self, input):
+ return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+
+
+def lora_Conv2d_forward(self, input):
+ return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+
+
+def list_available_loras():
+ available_loras.clear()
+
+ os.makedirs(lora_dir, exist_ok=True)
+
+ candidates = glob.glob(os.path.join(lora_dir, '**/*.pt'), recursive=True) + glob.glob(os.path.join(lora_dir, '**/*.safetensors'), recursive=True)
+
+ for filename in sorted(candidates):
+ if os.path.isdir(filename):
+ continue
+
+ name = os.path.splitext(os.path.basename(filename))[0]
+
+ available_loras[name] = LoraOnDisk(name, filename)
+
+
+lora_dir = os.path.join(shared.models_path, "Lora")
+available_loras = {}
+loaded_loras = []
+
+list_available_loras()
+
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py new file mode 100644 index 00000000..60b9eb64 --- /dev/null +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -0,0 +1,30 @@ +import torch
+
+import lora
+import extra_networks_lora
+import ui_extra_networks_lora
+from modules import script_callbacks, ui_extra_networks, extra_networks
+
+
+def unload():
+ torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+ torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+
+
+def before_ui():
+ ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
+ extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
+
+
+if not hasattr(torch.nn, 'Linear_forward_before_lora'):
+ torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+
+if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
+ torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+
+torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+
+script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
+script_callbacks.on_script_unloaded(unload)
+script_callbacks.on_before_ui(before_ui)
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py new file mode 100644 index 00000000..65397890 --- /dev/null +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -0,0 +1,35 @@ +import os
+import lora
+
+from modules import shared, ui_extra_networks
+
+
+class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Lora')
+
+ def refresh(self):
+ lora.list_available_loras()
+
+ def list_items(self):
+ for name, lora_on_disk in lora.available_loras.items():
+ path, ext = os.path.splitext(lora_on_disk.filename)
+ previews = [path + ".png", path + ".preview.png"]
+
+ preview = None
+ for file in previews:
+ if os.path.isfile(file):
+ preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file))
+ break
+
+ yield {
+ "name": name,
+ "filename": path,
+ "preview": preview,
+ "prompt": f"<lora:{name}:1.0>",
+ "local_preview": path + ".png",
+ }
+
+ def allowed_directories_for_previews(self):
+ return [lora.lora_dir]
+
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index 6a0c4ba8..ff279a1f 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -17,5 +17,5 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): hypernetwork.load_hypernetworks(names, multipliers)
- def deactivate(p, self):
+ def deactivate(self, p):
pass
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index a9e19236..4bb45ec7 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -73,6 +73,7 @@ callback_map = dict( callbacks_image_grid=[],
callbacks_infotext_pasted=[],
callbacks_script_unloaded=[],
+ callbacks_before_ui=[],
)
@@ -189,6 +190,14 @@ def script_unloaded_callback(): report_exception(c, 'script_unloaded')
+def before_ui_callback():
+ for c in reversed(callback_map['callbacks_before_ui']):
+ try:
+ c.callback()
+ except Exception:
+ report_exception(c, 'before_ui')
+
+
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -313,3 +322,9 @@ def on_script_unloaded(callback): the script did should be reverted here"""
add_callback(callback_map['callbacks_script_unloaded'], callback)
+
+
+def on_before_ui(callback):
+ """register a function to be called before the UI is created."""
+
+ add_callback(callback_map['callbacks_before_ui'], callback)
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 253e90f7..796e879c 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -10,7 +10,7 @@ extra_pages = [] def register_page(page):
- """registers extra networks page for the UI; recommend doing it in on_app_started() callback for extensions"""
+ """registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
extra_pages.append(page)
@@ -165,6 +165,8 @@ def webui(): if shared.opts.clean_temp_dir_at_start:
ui_tempdir.cleanup_tmpdr()
+ modules.script_callbacks.before_ui_callback()
+
shared.demo = modules.ui.create_ui()
app, local_url, share_url = shared.demo.launch(
|