Merge branch 'AUTOMATIC1111:master' into img2img-api-scripts
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10
README.md
10
README.md
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@ -1,9 +1,7 @@
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# Stable Diffusion web UI
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A browser interface based on Gradio library for Stable Diffusion.
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Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users.
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## Features
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[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
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@ -97,9 +95,8 @@ Alternatively, use online services (like Google Colab):
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1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
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2. Install [git](https://git-scm.com/download/win).
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3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
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4. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
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5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
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6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
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4. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
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5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
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### Automatic Installation on Linux
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1. Install the dependencies:
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@ -141,6 +138,7 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
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- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
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- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
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- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
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- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
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- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
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- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
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- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
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@ -184,7 +184,7 @@ SOFTWARE.
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</pre>
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<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
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<small>Code added by contirubtors, most likely copied from this repository.</small>
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<small>Code added by contributors, most likely copied from this repository.</small>
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|
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<pre>
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Apache License
|
||||
|
|
@ -390,3 +390,30 @@ SOFTWARE.
|
|||
limitations under the License.
|
||||
</pre>
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<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
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<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
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<pre>
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MIT License
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Copyright (c) 2023 Alex Birch
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Copyright (c) 2023 Amin Rezaei
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
|
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in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
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|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
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</pre>
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|
|
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@ -125,7 +125,7 @@ class ExtrasBaseRequest(BaseModel):
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gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
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codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
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codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
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upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
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upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
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upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
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|
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@ -133,8 +133,26 @@ def numpy_fix(self, *args, **kwargs):
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return orig_tensor_numpy(self, *args, **kwargs)
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# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
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if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
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torch.Tensor.to = tensor_to_fix
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torch.nn.functional.layer_norm = layer_norm_fix
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torch.Tensor.numpy = numpy_fix
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# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
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orig_cumsum = torch.cumsum
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orig_Tensor_cumsum = torch.Tensor.cumsum
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def cumsum_fix(input, cumsum_func, *args, **kwargs):
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if input.device.type == 'mps':
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output_dtype = kwargs.get('dtype', input.dtype)
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if any(output_dtype == broken_dtype for broken_dtype in [torch.bool, torch.int8, torch.int16, torch.int64]):
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return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
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return cumsum_func(input, *args, **kwargs)
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if has_mps():
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if version.parse(torch.__version__) < version.parse("1.13"):
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# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
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torch.Tensor.to = tensor_to_fix
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torch.nn.functional.layer_norm = layer_norm_fix
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torch.Tensor.numpy = numpy_fix
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elif version.parse(torch.__version__) > version.parse("1.13.1"):
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if not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.Tensor([1,1]).to(torch.device("mps")).cumsum(0, dtype=torch.int16)):
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torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
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torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
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orig_narrow = torch.narrow
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torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
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@ -13,7 +13,7 @@ import tqdm
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from einops import rearrange, repeat
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from ldm.util import default
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from modules import devices, processing, sd_models, shared, sd_samplers
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from modules.textual_inversion import textual_inversion
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from modules.textual_inversion import textual_inversion, logging
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from torch import einsum
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from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
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@ -457,7 +457,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
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pin_memory = shared.opts.pin_memory
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
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if shared.opts.save_training_settings_to_txt:
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saved_params = dict(
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model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds),
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**{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
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)
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logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
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latent_sampling_method = ds.latent_sampling_method
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dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
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@ -711,7 +711,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.truncate_x = 0
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self.truncate_y = 0
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def init(self, all_prompts, all_seeds, all_subseeds):
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if self.enable_hr:
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if self.hr_resize_x == 0 and self.hr_resize_y == 0:
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|
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@ -71,6 +71,7 @@ callback_map = dict(
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callbacks_before_component=[],
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callbacks_after_component=[],
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callbacks_image_grid=[],
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callbacks_script_unloaded=[],
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)
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@ -171,6 +172,14 @@ def image_grid_callback(params: ImageGridLoopParams):
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report_exception(c, 'image_grid')
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def script_unloaded_callback():
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for c in reversed(callback_map['callbacks_script_unloaded']):
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try:
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c.callback()
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except Exception:
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report_exception(c, 'script_unloaded')
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def add_callback(callbacks, fun):
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stack = [x for x in inspect.stack() if x.filename != __file__]
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filename = stack[0].filename if len(stack) > 0 else 'unknown file'
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@ -202,7 +211,7 @@ def on_app_started(callback):
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def on_model_loaded(callback):
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"""register a function to be called when the stable diffusion model is created; the model is
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passed as an argument"""
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passed as an argument; this function is also called when the script is reloaded. """
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add_callback(callback_map['callbacks_model_loaded'], callback)
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@ -279,3 +288,10 @@ def on_image_grid(callback):
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- params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
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"""
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add_callback(callback_map['callbacks_image_grid'], callback)
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def on_script_unloaded(callback):
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"""register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
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the script did should be reverted here"""
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add_callback(callback_map['callbacks_script_unloaded'], callback)
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|
|
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@ -290,7 +290,6 @@ class ScriptRunner:
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script.group = group
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dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
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dropdown.save_to_config = True
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inputs[0] = dropdown
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for script in self.selectable_scripts:
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|
|
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|
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@ -7,8 +7,6 @@ from modules.hypernetworks import hypernetwork
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from modules.shared import cmd_opts
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from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
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from modules.sd_hijack_optimizations import invokeAI_mps_available
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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import ldm.modules.diffusionmodules.openaimodel
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@ -43,20 +41,19 @@ def apply_optimizations():
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
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optimization_method = 'xformers'
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elif cmd_opts.opt_sub_quad_attention:
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print("Applying sub-quadratic cross attention optimization.")
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
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optimization_method = 'sub-quadratic'
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elif cmd_opts.opt_split_attention_v1:
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print("Applying v1 cross attention optimization.")
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
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optimization_method = 'V1'
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elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
|
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if not invokeAI_mps_available and shared.device.type == 'mps':
|
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print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
|
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print("Applying v1 cross attention optimization.")
|
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
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optimization_method = 'V1'
|
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else:
|
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print("Applying cross attention optimization (InvokeAI).")
|
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
|
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optimization_method = 'InvokeAI'
|
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elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):
|
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print("Applying cross attention optimization (InvokeAI).")
|
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
|
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optimization_method = 'InvokeAI'
|
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elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
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print("Applying cross attention optimization (Doggettx).")
|
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
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|
|
@ -150,10 +147,10 @@ class StableDiffusionModelHijack:
|
|||
def clear_comments(self):
|
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self.comments = []
|
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|
||||
def tokenize(self, text):
|
||||
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
|
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def get_prompt_lengths(self, text):
|
||||
_, token_count = self.clip.process_texts([text])
|
||||
|
||||
return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
|
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return token_count, self.clip.get_target_prompt_token_count(token_count)
|
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|
||||
|
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class EmbeddingsWithFixes(torch.nn.Module):
|
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|
|
|
|||
|
|
@ -1,30 +1,89 @@
|
|||
import math
|
||||
from collections import namedtuple
|
||||
|
||||
import torch
|
||||
|
||||
from modules import prompt_parser, devices
|
||||
from modules import prompt_parser, devices, sd_hijack
|
||||
from modules.shared import opts
|
||||
|
||||
def get_target_prompt_token_count(token_count):
|
||||
return math.ceil(max(token_count, 1) / 75) * 75
|
||||
|
||||
class PromptChunk:
|
||||
"""
|
||||
This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
|
||||
If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
|
||||
Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
|
||||
so just 75 tokens from prompt.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.tokens = []
|
||||
self.multipliers = []
|
||||
self.fixes = []
|
||||
|
||||
|
||||
PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
|
||||
"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
|
||||
chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
|
||||
are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
|
||||
|
||||
|
||||
class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
|
||||
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
|
||||
have unlimited prompt length and assign weights to tokens in prompt.
|
||||
"""
|
||||
|
||||
def __init__(self, wrapped, hijack):
|
||||
super().__init__()
|
||||
|
||||
self.wrapped = wrapped
|
||||
self.hijack = hijack
|
||||
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
|
||||
depending on model."""
|
||||
|
||||
self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
|
||||
self.chunk_length = 75
|
||||
|
||||
def empty_chunk(self):
|
||||
"""creates an empty PromptChunk and returns it"""
|
||||
|
||||
chunk = PromptChunk()
|
||||
chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
|
||||
chunk.multipliers = [1.0] * (self.chunk_length + 2)
|
||||
return chunk
|
||||
|
||||
def get_target_prompt_token_count(self, token_count):
|
||||
"""returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
|
||||
|
||||
return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
|
||||
|
||||
def tokenize(self, texts):
|
||||
"""Converts a batch of texts into a batch of token ids"""
|
||||
|
||||
raise NotImplementedError
|
||||
|
||||
def encode_with_transformers(self, tokens):
|
||||
"""
|
||||
converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
|
||||
All python lists with tokens are assumed to have same length, usually 77.
|
||||
if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
|
||||
model - can be 768 and 1024.
|
||||
Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
|
||||
"""
|
||||
|
||||
raise NotImplementedError
|
||||
|
||||
def encode_embedding_init_text(self, init_text, nvpt):
|
||||
"""Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
|
||||
transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
|
||||
|
||||
raise NotImplementedError
|
||||
|
||||
def tokenize_line(self, line, used_custom_terms, hijack_comments):
|
||||
def tokenize_line(self, line):
|
||||
"""
|
||||
this transforms a single prompt into a list of PromptChunk objects - as many as needed to
|
||||
represent the prompt.
|
||||
Returns the list and the total number of tokens in the prompt.
|
||||
"""
|
||||
|
||||
if opts.enable_emphasis:
|
||||
parsed = prompt_parser.parse_prompt_attention(line)
|
||||
else:
|
||||
|
|
@ -32,205 +91,152 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
|
|||
|
||||
tokenized = self.tokenize([text for text, _ in parsed])
|
||||
|
||||
fixes = []
|
||||
remade_tokens = []
|
||||
multipliers = []
|
||||
chunks = []
|
||||
chunk = PromptChunk()
|
||||
token_count = 0
|
||||
last_comma = -1
|
||||
|
||||
for tokens, (text, weight) in zip(tokenized, parsed):
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
def next_chunk():
|
||||
"""puts current chunk into the list of results and produces the next one - empty"""
|
||||
nonlocal token_count
|
||||
nonlocal last_comma
|
||||
nonlocal chunk
|
||||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
token_count += len(chunk.tokens)
|
||||
to_add = self.chunk_length - len(chunk.tokens)
|
||||
if to_add > 0:
|
||||
chunk.tokens += [self.id_end] * to_add
|
||||
chunk.multipliers += [1.0] * to_add
|
||||
|
||||
chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
|
||||
chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
|
||||
|
||||
last_comma = -1
|
||||
chunks.append(chunk)
|
||||
chunk = PromptChunk()
|
||||
|
||||
for tokens, (text, weight) in zip(tokenized, parsed):
|
||||
position = 0
|
||||
while position < len(tokens):
|
||||
token = tokens[position]
|
||||
|
||||
if token == self.comma_token:
|
||||
last_comma = len(remade_tokens)
|
||||
elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
|
||||
last_comma += 1
|
||||
reloc_tokens = remade_tokens[last_comma:]
|
||||
reloc_mults = multipliers[last_comma:]
|
||||
last_comma = len(chunk.tokens)
|
||||
|
||||
remade_tokens = remade_tokens[:last_comma]
|
||||
length = len(remade_tokens)
|
||||
# this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
|
||||
# is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
|
||||
elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
|
||||
break_location = last_comma + 1
|
||||
|
||||
rem = int(math.ceil(length / 75)) * 75 - length
|
||||
remade_tokens += [self.id_end] * rem + reloc_tokens
|
||||
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
|
||||
reloc_tokens = chunk.tokens[break_location:]
|
||||
reloc_mults = chunk.multipliers[break_location:]
|
||||
|
||||
chunk.tokens = chunk.tokens[:break_location]
|
||||
chunk.multipliers = chunk.multipliers[:break_location]
|
||||
|
||||
next_chunk()
|
||||
chunk.tokens = reloc_tokens
|
||||
chunk.multipliers = reloc_mults
|
||||
|
||||
if len(chunk.tokens) == self.chunk_length:
|
||||
next_chunk()
|
||||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
|
||||
if embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(weight)
|
||||
i += 1
|
||||
else:
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
iteration = len(remade_tokens) // 75
|
||||
if (len(remade_tokens) + emb_len) // 75 != iteration:
|
||||
rem = (75 * (iteration + 1) - len(remade_tokens))
|
||||
remade_tokens += [self.id_end] * rem
|
||||
multipliers += [1.0] * rem
|
||||
iteration += 1
|
||||
fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [weight] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
chunk.tokens.append(token)
|
||||
chunk.multipliers.append(weight)
|
||||
position += 1
|
||||
continue
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
prompt_target_length = get_target_prompt_token_count(token_count)
|
||||
tokens_to_add = prompt_target_length - len(remade_tokens)
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
if len(chunk.tokens) + emb_len > self.chunk_length:
|
||||
next_chunk()
|
||||
|
||||
remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
|
||||
multipliers = multipliers + [1.0] * tokens_to_add
|
||||
chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
|
||||
|
||||
return remade_tokens, fixes, multipliers, token_count
|
||||
chunk.tokens += [0] * emb_len
|
||||
chunk.multipliers += [weight] * emb_len
|
||||
position += embedding_length_in_tokens
|
||||
|
||||
if len(chunk.tokens) > 0 or len(chunks) == 0:
|
||||
next_chunk()
|
||||
|
||||
return chunks, token_count
|
||||
|
||||
def process_texts(self, texts):
|
||||
"""
|
||||
Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
|
||||
length, in tokens, of all texts.
|
||||
"""
|
||||
|
||||
def process_text(self, texts):
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
hijack_comments = []
|
||||
hijack_fixes = []
|
||||
token_count = 0
|
||||
|
||||
cache = {}
|
||||
batch_multipliers = []
|
||||
batch_chunks = []
|
||||
for line in texts:
|
||||
if line in cache:
|
||||
remade_tokens, fixes, multipliers = cache[line]
|
||||
chunks = cache[line]
|
||||
else:
|
||||
remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
chunks, current_token_count = self.tokenize_line(line)
|
||||
token_count = max(current_token_count, token_count)
|
||||
|
||||
cache[line] = (remade_tokens, fixes, multipliers)
|
||||
cache[line] = chunks
|
||||
|
||||
remade_batch_tokens.append(remade_tokens)
|
||||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
batch_chunks.append(chunks)
|
||||
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
return batch_chunks, token_count
|
||||
|
||||
def process_text_old(self, texts):
|
||||
id_start = self.id_start
|
||||
id_end = self.id_end
|
||||
maxlen = self.wrapped.max_length # you get to stay at 77
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
hijack_comments = []
|
||||
hijack_fixes = []
|
||||
token_count = 0
|
||||
def forward(self, texts):
|
||||
"""
|
||||
Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
|
||||
Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
|
||||
be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
|
||||
An example shape returned by this function can be: (2, 77, 768).
|
||||
Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
|
||||
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
|
||||
"""
|
||||
|
||||
cache = {}
|
||||
batch_tokens = self.tokenize(texts)
|
||||
batch_multipliers = []
|
||||
for tokens in batch_tokens:
|
||||
tuple_tokens = tuple(tokens)
|
||||
if opts.use_old_emphasis_implementation:
|
||||
import modules.sd_hijack_clip_old
|
||||
return modules.sd_hijack_clip_old.forward_old(self, texts)
|
||||
|
||||
if tuple_tokens in cache:
|
||||
remade_tokens, fixes, multipliers = cache[tuple_tokens]
|
||||
else:
|
||||
fixes = []
|
||||
remade_tokens = []
|
||||
multipliers = []
|
||||
mult = 1.0
|
||||
batch_chunks, token_count = self.process_texts(texts)
|
||||
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
used_embeddings = {}
|
||||
chunk_count = max([len(x) for x in batch_chunks])
|
||||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
zs = []
|
||||
for i in range(chunk_count):
|
||||
batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
|
||||
|
||||
mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
|
||||
if mult_change is not None:
|
||||
mult *= mult_change
|
||||
i += 1
|
||||
elif embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(mult)
|
||||
i += 1
|
||||
else:
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
fixes.append((len(remade_tokens), embedding))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [mult] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
tokens = [x.tokens for x in batch_chunk]
|
||||
multipliers = [x.multipliers for x in batch_chunk]
|
||||
self.hijack.fixes = [x.fixes for x in batch_chunk]
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
for fixes in self.hijack.fixes:
|
||||
for position, embedding in fixes:
|
||||
used_embeddings[embedding.name] = embedding
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
||||
z = self.process_tokens(tokens, multipliers)
|
||||
zs.append(z)
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
if len(used_embeddings) > 0:
|
||||
embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()])
|
||||
self.hijack.comments.append(f"Used embeddings: {embeddings_list}")
|
||||
|
||||
remade_batch_tokens.append(remade_tokens)
|
||||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
def forward(self, text):
|
||||
use_old = opts.use_old_emphasis_implementation
|
||||
if use_old:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
|
||||
else:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
if use_old:
|
||||
self.hijack.fixes = hijack_fixes
|
||||
return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
|
||||
z = None
|
||||
i = 0
|
||||
while max(map(len, remade_batch_tokens)) != 0:
|
||||
rem_tokens = [x[75:] for x in remade_batch_tokens]
|
||||
rem_multipliers = [x[75:] for x in batch_multipliers]
|
||||
|
||||
self.hijack.fixes = []
|
||||
for unfiltered in hijack_fixes:
|
||||
fixes = []
|
||||
for fix in unfiltered:
|
||||
if fix[0] == i:
|
||||
fixes.append(fix[1])
|
||||
self.hijack.fixes.append(fixes)
|
||||
|
||||
tokens = []
|
||||
multipliers = []
|
||||
for j in range(len(remade_batch_tokens)):
|
||||
if len(remade_batch_tokens[j]) > 0:
|
||||
tokens.append(remade_batch_tokens[j][:75])
|
||||
multipliers.append(batch_multipliers[j][:75])
|
||||
else:
|
||||
tokens.append([self.id_end] * 75)
|
||||
multipliers.append([1.0] * 75)
|
||||
|
||||
z1 = self.process_tokens(tokens, multipliers)
|
||||
z = z1 if z is None else torch.cat((z, z1), axis=-2)
|
||||
|
||||
remade_batch_tokens = rem_tokens
|
||||
batch_multipliers = rem_multipliers
|
||||
i += 1
|
||||
|
||||
return z
|
||||
return torch.hstack(zs)
|
||||
|
||||
def process_tokens(self, remade_batch_tokens, batch_multipliers):
|
||||
if not opts.use_old_emphasis_implementation:
|
||||
remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]
|
||||
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
|
||||
|
||||
"""
|
||||
sends one single prompt chunk to be encoded by transformers neural network.
|
||||
remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
|
||||
there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
|
||||
Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
|
||||
corresponds to one token.
|
||||
"""
|
||||
tokens = torch.asarray(remade_batch_tokens).to(devices.device)
|
||||
|
||||
# this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
|
||||
if self.id_end != self.id_pad:
|
||||
for batch_pos in range(len(remade_batch_tokens)):
|
||||
index = remade_batch_tokens[batch_pos].index(self.id_end)
|
||||
|
|
@ -239,8 +245,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
|
|||
z = self.encode_with_transformers(tokens)
|
||||
|
||||
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
||||
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
|
||||
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device)
|
||||
batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
|
||||
original_mean = z.mean()
|
||||
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
||||
new_mean = z.mean()
|
||||
|
|
|
|||
81
modules/sd_hijack_clip_old.py
Normal file
81
modules/sd_hijack_clip_old.py
Normal file
|
|
@ -0,0 +1,81 @@
|
|||
from modules import sd_hijack_clip
|
||||
from modules import shared
|
||||
|
||||
|
||||
def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
|
||||
id_start = self.id_start
|
||||
id_end = self.id_end
|
||||
maxlen = self.wrapped.max_length # you get to stay at 77
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
hijack_comments = []
|
||||
hijack_fixes = []
|
||||
token_count = 0
|
||||
|
||||
cache = {}
|
||||
batch_tokens = self.tokenize(texts)
|
||||
batch_multipliers = []
|
||||
for tokens in batch_tokens:
|
||||
tuple_tokens = tuple(tokens)
|
||||
|
||||
if tuple_tokens in cache:
|
||||
remade_tokens, fixes, multipliers = cache[tuple_tokens]
|
||||
else:
|
||||
fixes = []
|
||||
remade_tokens = []
|
||||
multipliers = []
|
||||
mult = 1.0
|
||||
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
|
||||
mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
|
||||
if mult_change is not None:
|
||||
mult *= mult_change
|
||||
i += 1
|
||||
elif embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(mult)
|
||||
i += 1
|
||||
else:
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
fixes.append((len(remade_tokens), embedding))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [mult] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
|
||||
remade_batch_tokens.append(remade_tokens)
|
||||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
|
||||
def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)
|
||||
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
import math
|
||||
import sys
|
||||
import traceback
|
||||
import importlib
|
||||
import psutil
|
||||
|
||||
import torch
|
||||
from torch import einsum
|
||||
|
|
@ -12,6 +12,8 @@ from einops import rearrange
|
|||
from modules import shared
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
from .sub_quadratic_attention import efficient_dot_product_attention
|
||||
|
||||
|
||||
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
|
||||
try:
|
||||
|
|
@ -22,6 +24,19 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
|
|||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
|
||||
def get_available_vram():
|
||||
if shared.device.type == 'cuda':
|
||||
stats = torch.cuda.memory_stats(shared.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
return mem_free_total
|
||||
else:
|
||||
return psutil.virtual_memory().available
|
||||
|
||||
|
||||
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
||||
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
|
@ -76,12 +91,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
mem_free_total = get_available_vram()
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
||||
|
|
@ -118,19 +128,8 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
return self.to_out(r2)
|
||||
|
||||
|
||||
def check_for_psutil():
|
||||
try:
|
||||
spec = importlib.util.find_spec('psutil')
|
||||
return spec is not None
|
||||
except ModuleNotFoundError:
|
||||
return False
|
||||
|
||||
invokeAI_mps_available = check_for_psutil()
|
||||
|
||||
# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
|
||||
if invokeAI_mps_available:
|
||||
import psutil
|
||||
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
|
||||
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
|
||||
|
||||
def einsum_op_compvis(q, k, v):
|
||||
s = einsum('b i d, b j d -> b i j', q, k)
|
||||
|
|
@ -215,6 +214,71 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
|
|||
|
||||
# -- End of code from https://github.com/invoke-ai/InvokeAI --
|
||||
|
||||
|
||||
# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
|
||||
# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
|
||||
def sub_quad_attention_forward(self, x, context=None, mask=None):
|
||||
assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
|
||||
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
|
||||
k = self.to_k(context_k)
|
||||
v = self.to_v(context_v)
|
||||
del context, context_k, context_v, x
|
||||
|
||||
q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
|
||||
x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
|
||||
|
||||
x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
|
||||
|
||||
out_proj, dropout = self.to_out
|
||||
x = out_proj(x)
|
||||
x = dropout(x)
|
||||
|
||||
return x
|
||||
|
||||
def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
|
||||
bytes_per_token = torch.finfo(q.dtype).bits//8
|
||||
batch_x_heads, q_tokens, _ = q.shape
|
||||
_, k_tokens, _ = k.shape
|
||||
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
|
||||
|
||||
if chunk_threshold is None:
|
||||
chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
|
||||
elif chunk_threshold == 0:
|
||||
chunk_threshold_bytes = None
|
||||
else:
|
||||
chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())
|
||||
|
||||
if kv_chunk_size_min is None and chunk_threshold_bytes is not None:
|
||||
kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
|
||||
elif kv_chunk_size_min == 0:
|
||||
kv_chunk_size_min = None
|
||||
|
||||
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
|
||||
# the big matmul fits into our memory limit; do everything in 1 chunk,
|
||||
# i.e. send it down the unchunked fast-path
|
||||
query_chunk_size = q_tokens
|
||||
kv_chunk_size = k_tokens
|
||||
|
||||
return efficient_dot_product_attention(
|
||||
q,
|
||||
k,
|
||||
v,
|
||||
query_chunk_size=q_chunk_size,
|
||||
kv_chunk_size=kv_chunk_size,
|
||||
kv_chunk_size_min = kv_chunk_size_min,
|
||||
use_checkpoint=use_checkpoint,
|
||||
)
|
||||
|
||||
|
||||
def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
q_in = self.to_q(x)
|
||||
|
|
@ -252,12 +316,7 @@ def cross_attention_attnblock_forward(self, x):
|
|||
|
||||
h_ = torch.zeros_like(k, device=q.device)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
mem_free_total = get_available_vram()
|
||||
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
||||
mem_required = tensor_size * 2.5
|
||||
|
|
@ -312,3 +371,19 @@ def xformers_attnblock_forward(self, x):
|
|||
return x + out
|
||||
except NotImplementedError:
|
||||
return cross_attention_attnblock_forward(self, x)
|
||||
|
||||
def sub_quad_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
b, c, h, w = q.shape
|
||||
q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
|
||||
q = q.contiguous()
|
||||
k = k.contiguous()
|
||||
v = v.contiguous()
|
||||
out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
|
||||
out = self.proj_out(out)
|
||||
return x + out
|
||||
|
|
|
|||
|
|
@ -56,6 +56,10 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
|
|||
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
||||
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
|
||||
parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
|
||||
parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
|
||||
parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
|
||||
parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
|
||||
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
|
|
@ -362,6 +366,7 @@ options_templates.update(options_section(('training', "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."),
|
||||
"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
|
||||
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
|
||||
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
|
||||
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
|
||||
|
|
@ -429,7 +434,7 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Witdth/Height and Batch sliders in same row"),
|
||||
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/ing2img UI item order"),
|
||||
'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
}))
|
||||
|
||||
|
|
@ -576,6 +581,7 @@ latent_upscale_modes = {
|
|||
"Latent (bicubic)": {"mode": "bicubic", "antialias": False},
|
||||
"Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
|
||||
"Latent (nearest)": {"mode": "nearest", "antialias": False},
|
||||
"Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
|
||||
}
|
||||
|
||||
sd_upscalers = []
|
||||
|
|
|
|||
205
modules/sub_quadratic_attention.py
Normal file
205
modules/sub_quadratic_attention.py
Normal file
|
|
@ -0,0 +1,205 @@
|
|||
# original source:
|
||||
# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
|
||||
# license:
|
||||
# MIT License (see Memory Efficient Attention under the Licenses section in the web UI interface for the full license)
|
||||
# credit:
|
||||
# Amin Rezaei (original author)
|
||||
# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
|
||||
# brkirch (modified to use torch.narrow instead of dynamic_slice implementation)
|
||||
# implementation of:
|
||||
# Self-attention Does Not Need O(n2) Memory":
|
||||
# https://arxiv.org/abs/2112.05682v2
|
||||
|
||||
from functools import partial
|
||||
import torch
|
||||
from torch import Tensor
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
import math
|
||||
from typing import Optional, NamedTuple, Protocol, List
|
||||
|
||||
def narrow_trunc(
|
||||
input: Tensor,
|
||||
dim: int,
|
||||
start: int,
|
||||
length: int
|
||||
) -> Tensor:
|
||||
return torch.narrow(input, dim, start, length if input.shape[dim] >= start + length else input.shape[dim] - start)
|
||||
|
||||
class AttnChunk(NamedTuple):
|
||||
exp_values: Tensor
|
||||
exp_weights_sum: Tensor
|
||||
max_score: Tensor
|
||||
|
||||
class SummarizeChunk(Protocol):
|
||||
@staticmethod
|
||||
def __call__(
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
) -> AttnChunk: ...
|
||||
|
||||
class ComputeQueryChunkAttn(Protocol):
|
||||
@staticmethod
|
||||
def __call__(
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
) -> Tensor: ...
|
||||
|
||||
def _summarize_chunk(
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
scale: float,
|
||||
) -> AttnChunk:
|
||||
attn_weights = torch.baddbmm(
|
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
|
||||
query,
|
||||
key.transpose(1,2),
|
||||
alpha=scale,
|
||||
beta=0,
|
||||
)
|
||||
max_score, _ = torch.max(attn_weights, -1, keepdim=True)
|
||||
max_score = max_score.detach()
|
||||
exp_weights = torch.exp(attn_weights - max_score)
|
||||
exp_values = torch.bmm(exp_weights, value)
|
||||
max_score = max_score.squeeze(-1)
|
||||
return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
|
||||
|
||||
def _query_chunk_attention(
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
summarize_chunk: SummarizeChunk,
|
||||
kv_chunk_size: int,
|
||||
) -> Tensor:
|
||||
batch_x_heads, k_tokens, k_channels_per_head = key.shape
|
||||
_, _, v_channels_per_head = value.shape
|
||||
|
||||
def chunk_scanner(chunk_idx: int) -> AttnChunk:
|
||||
key_chunk = narrow_trunc(
|
||||
key,
|
||||
1,
|
||||
chunk_idx,
|
||||
kv_chunk_size
|
||||
)
|
||||
value_chunk = narrow_trunc(
|
||||
value,
|
||||
1,
|
||||
chunk_idx,
|
||||
kv_chunk_size
|
||||
)
|
||||
return summarize_chunk(query, key_chunk, value_chunk)
|
||||
|
||||
chunks: List[AttnChunk] = [
|
||||
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
|
||||
]
|
||||
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
|
||||
chunk_values, chunk_weights, chunk_max = acc_chunk
|
||||
|
||||
global_max, _ = torch.max(chunk_max, 0, keepdim=True)
|
||||
max_diffs = torch.exp(chunk_max - global_max)
|
||||
chunk_values *= torch.unsqueeze(max_diffs, -1)
|
||||
chunk_weights *= max_diffs
|
||||
|
||||
all_values = chunk_values.sum(dim=0)
|
||||
all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
|
||||
return all_values / all_weights
|
||||
|
||||
# TODO: refactor CrossAttention#get_attention_scores to share code with this
|
||||
def _get_attention_scores_no_kv_chunking(
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
scale: float,
|
||||
) -> Tensor:
|
||||
attn_scores = torch.baddbmm(
|
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
|
||||
query,
|
||||
key.transpose(1,2),
|
||||
alpha=scale,
|
||||
beta=0,
|
||||
)
|
||||
attn_probs = attn_scores.softmax(dim=-1)
|
||||
del attn_scores
|
||||
hidden_states_slice = torch.bmm(attn_probs, value)
|
||||
return hidden_states_slice
|
||||
|
||||
class ScannedChunk(NamedTuple):
|
||||
chunk_idx: int
|
||||
attn_chunk: AttnChunk
|
||||
|
||||
def efficient_dot_product_attention(
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
query_chunk_size=1024,
|
||||
kv_chunk_size: Optional[int] = None,
|
||||
kv_chunk_size_min: Optional[int] = None,
|
||||
use_checkpoint=True,
|
||||
):
|
||||
"""Computes efficient dot-product attention given query, key, and value.
|
||||
This is efficient version of attention presented in
|
||||
https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
|
||||
Args:
|
||||
query: queries for calculating attention with shape of
|
||||
`[batch * num_heads, tokens, channels_per_head]`.
|
||||
key: keys for calculating attention with shape of
|
||||
`[batch * num_heads, tokens, channels_per_head]`.
|
||||
value: values to be used in attention with shape of
|
||||
`[batch * num_heads, tokens, channels_per_head]`.
|
||||
query_chunk_size: int: query chunks size
|
||||
kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
|
||||
kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
|
||||
use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
|
||||
Returns:
|
||||
Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
|
||||
"""
|
||||
batch_x_heads, q_tokens, q_channels_per_head = query.shape
|
||||
_, k_tokens, _ = key.shape
|
||||
scale = q_channels_per_head ** -0.5
|
||||
|
||||
kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
|
||||
if kv_chunk_size_min is not None:
|
||||
kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
|
||||
|
||||
def get_query_chunk(chunk_idx: int) -> Tensor:
|
||||
return narrow_trunc(
|
||||
query,
|
||||
1,
|
||||
chunk_idx,
|
||||
min(query_chunk_size, q_tokens)
|
||||
)
|
||||
|
||||
summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
|
||||
summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
|
||||
compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
|
||||
_get_attention_scores_no_kv_chunking,
|
||||
scale=scale
|
||||
) if k_tokens <= kv_chunk_size else (
|
||||
# fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
|
||||
partial(
|
||||
_query_chunk_attention,
|
||||
kv_chunk_size=kv_chunk_size,
|
||||
summarize_chunk=summarize_chunk,
|
||||
)
|
||||
)
|
||||
|
||||
if q_tokens <= query_chunk_size:
|
||||
# fast-path for when there's just 1 query chunk
|
||||
return compute_query_chunk_attn(
|
||||
query=query,
|
||||
key=key,
|
||||
value=value,
|
||||
)
|
||||
|
||||
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
|
||||
# and pass slices to be mutated, instead of torch.cat()ing the returned slices
|
||||
res = torch.cat([
|
||||
compute_query_chunk_attn(
|
||||
query=get_query_chunk(i * query_chunk_size),
|
||||
key=key,
|
||||
value=value,
|
||||
) for i in range(math.ceil(q_tokens / query_chunk_size))
|
||||
], dim=1)
|
||||
return res
|
||||
24
modules/textual_inversion/logging.py
Normal file
24
modules/textual_inversion/logging.py
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
import datetime
|
||||
import json
|
||||
import os
|
||||
|
||||
saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file"}
|
||||
saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
|
||||
saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
|
||||
saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
|
||||
saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
|
||||
|
||||
|
||||
def save_settings_to_file(log_directory, all_params):
|
||||
now = datetime.datetime.now()
|
||||
params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}
|
||||
|
||||
keys = saved_params_all
|
||||
if all_params.get('preview_from_txt2img'):
|
||||
keys = keys | saved_params_previews
|
||||
|
||||
params.update({k: v for k, v in all_params.items() if k in keys})
|
||||
|
||||
filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json'
|
||||
with open(os.path.join(log_directory, filename), "w") as file:
|
||||
json.dump(params, file, indent=4)
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
import os
|
||||
import sys
|
||||
import traceback
|
||||
import inspect
|
||||
|
||||
import torch
|
||||
import tqdm
|
||||
|
|
@ -17,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
|||
from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
|
||||
insert_image_data_embed, extract_image_data_embed,
|
||||
caption_image_overlay)
|
||||
from modules.textual_inversion.logging import save_settings_to_file
|
||||
|
||||
|
||||
class Embedding:
|
||||
def __init__(self, vec, name, step=None):
|
||||
|
|
@ -76,7 +79,6 @@ class EmbeddingDatabase:
|
|||
|
||||
self.word_embeddings[embedding.name] = embedding
|
||||
|
||||
# TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working
|
||||
ids = model.cond_stage_model.tokenize([embedding.name])[0]
|
||||
|
||||
first_id = ids[0]
|
||||
|
|
@ -149,19 +151,20 @@ class EmbeddingDatabase:
|
|||
else:
|
||||
self.skipped_embeddings[name] = embedding
|
||||
|
||||
for fn in os.listdir(self.embeddings_dir):
|
||||
try:
|
||||
fullfn = os.path.join(self.embeddings_dir, fn)
|
||||
for root, dirs, fns in os.walk(self.embeddings_dir):
|
||||
for fn in fns:
|
||||
try:
|
||||
fullfn = os.path.join(root, fn)
|
||||
|
||||
if os.stat(fullfn).st_size == 0:
|
||||
if os.stat(fullfn).st_size == 0:
|
||||
continue
|
||||
|
||||
process_file(fullfn, fn)
|
||||
except Exception:
|
||||
print(f"Error loading embedding {fn}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
process_file(fullfn, fn)
|
||||
except Exception:
|
||||
print(f"Error loading embedding {fn}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
|
||||
if len(self.skipped_embeddings) > 0:
|
||||
print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
|
||||
|
|
@ -229,6 +232,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
|
|||
**values,
|
||||
})
|
||||
|
||||
|
||||
def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
|
||||
assert model_name, f"{name} not selected"
|
||||
assert learn_rate, "Learning rate is empty or 0"
|
||||
|
|
@ -292,8 +296,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
|||
if initial_step >= steps:
|
||||
shared.state.textinfo = "Model has already been trained beyond specified max steps"
|
||||
return embedding, filename
|
||||
|
||||
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
|
||||
|
||||
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
|
||||
torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
|
||||
None
|
||||
|
|
@ -307,6 +311,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
|||
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
|
||||
|
||||
if shared.opts.save_training_settings_to_txt:
|
||||
save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
|
||||
|
||||
latent_sampling_method = ds.latent_sampling_method
|
||||
|
||||
dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ from PIL import Image, PngImagePlugin
|
|||
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
|
||||
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
|
||||
from modules.ui_components import FormRow, FormGroup, ToolButton
|
||||
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
|
||||
from modules.paths import script_path
|
||||
|
||||
from modules.shared import opts, cmd_opts, restricted_opts
|
||||
|
|
@ -256,6 +256,20 @@ def add_style(name: str, prompt: str, negative_prompt: str):
|
|||
return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
|
||||
|
||||
|
||||
def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
|
||||
from modules import processing, devices
|
||||
|
||||
if not enable:
|
||||
return ""
|
||||
|
||||
p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
|
||||
|
||||
with devices.autocast():
|
||||
p.init([""], [0], [0])
|
||||
|
||||
return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{p.hr_upscale_to_x}x{p.hr_upscale_to_y}</span>"
|
||||
|
||||
|
||||
def apply_styles(prompt, prompt_neg, style1_name, style2_name):
|
||||
prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
|
||||
prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
|
||||
|
|
@ -368,7 +382,7 @@ def update_token_counter(text, steps):
|
|||
|
||||
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
|
||||
prompts = [prompt_text for step, prompt_text in flat_prompts]
|
||||
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
|
||||
token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
|
||||
style_class = ' class="red"' if (token_count > max_length) else ""
|
||||
return f"<span {style_class}>{token_count}/{max_length}</span>"
|
||||
|
||||
|
|
@ -435,11 +449,9 @@ def create_toprow(is_img2img):
|
|||
with gr.Row():
|
||||
with gr.Column(scale=1, elem_id="style_pos_col"):
|
||||
prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
|
||||
prompt_style.save_to_config = True
|
||||
|
||||
with gr.Column(scale=1, elem_id="style_neg_col"):
|
||||
prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
|
||||
prompt_style2.save_to_config = True
|
||||
|
||||
return prompt, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
|
||||
|
||||
|
|
@ -550,6 +562,8 @@ Requested path was: {f}
|
|||
os.startfile(path)
|
||||
elif platform.system() == "Darwin":
|
||||
sp.Popen(["open", path])
|
||||
elif "microsoft-standard-WSL2" in platform.uname().release:
|
||||
sp.Popen(["wsl-open", path])
|
||||
else:
|
||||
sp.Popen(["xdg-open", path])
|
||||
|
||||
|
|
@ -636,7 +650,6 @@ def create_sampler_and_steps_selection(choices, tabname):
|
|||
if opts.samplers_in_dropdown:
|
||||
with FormRow(elem_id=f"sampler_selection_{tabname}"):
|
||||
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
|
||||
sampler_index.save_to_config = True
|
||||
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
||||
else:
|
||||
with FormGroup(elem_id=f"sampler_selection_{tabname}"):
|
||||
|
|
@ -707,6 +720,7 @@ def create_ui():
|
|||
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
|
||||
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
|
||||
enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
|
||||
hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
|
||||
|
||||
elif category == "hires_fix":
|
||||
with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
|
||||
|
|
@ -730,6 +744,17 @@ def create_ui():
|
|||
with FormGroup(elem_id="txt2img_script_container"):
|
||||
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
|
||||
|
||||
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
|
||||
hr_resolution_preview_args = dict(
|
||||
fn=calc_resolution_hires,
|
||||
inputs=hr_resolution_preview_inputs,
|
||||
outputs=[hr_final_resolution],
|
||||
show_progress=False
|
||||
)
|
||||
|
||||
for input in hr_resolution_preview_inputs:
|
||||
input.change(**hr_resolution_preview_args)
|
||||
|
||||
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
|
||||
parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
|
||||
|
||||
|
|
@ -791,6 +816,7 @@ def create_ui():
|
|||
fn=lambda x: gr_show(x),
|
||||
inputs=[enable_hr],
|
||||
outputs=[hr_options],
|
||||
show_progress = False,
|
||||
)
|
||||
|
||||
txt2img_paste_fields = [
|
||||
|
|
@ -1792,7 +1818,7 @@ def create_ui():
|
|||
if init_field is not None:
|
||||
init_field(saved_value)
|
||||
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
|
||||
apply_field(x, 'visible')
|
||||
|
||||
if type(x) == gr.Slider:
|
||||
|
|
@ -1813,11 +1839,8 @@ def create_ui():
|
|||
if type(x) == gr.Number:
|
||||
apply_field(x, 'value')
|
||||
|
||||
# Since there are many dropdowns that shouldn't be saved,
|
||||
# we only mark dropdowns that should be saved.
|
||||
if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
|
||||
if type(x) == gr.Dropdown:
|
||||
apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
|
||||
apply_field(x, 'visible')
|
||||
|
||||
visit(txt2img_interface, loadsave, "txt2img")
|
||||
visit(img2img_interface, loadsave, "img2img")
|
||||
|
|
|
|||
|
|
@ -23,3 +23,11 @@ class FormGroup(gr.Group, gr.components.FormComponent):
|
|||
|
||||
def get_block_name(self):
|
||||
return "group"
|
||||
|
||||
|
||||
class FormHTML(gr.HTML, gr.components.FormComponent):
|
||||
"""Same as gr.HTML but fits inside gradio forms"""
|
||||
|
||||
def get_block_name(self):
|
||||
return "html"
|
||||
|
||||
|
|
|
|||
|
|
@ -162,15 +162,15 @@ def install_extension_from_url(dirname, url):
|
|||
shutil.rmtree(tmpdir, True)
|
||||
|
||||
|
||||
def install_extension_from_index(url, hide_tags):
|
||||
def install_extension_from_index(url, hide_tags, sort_column):
|
||||
ext_table, message = install_extension_from_url(None, url)
|
||||
|
||||
code, _ = refresh_available_extensions_from_data(hide_tags)
|
||||
code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
|
||||
|
||||
return code, ext_table, message
|
||||
|
||||
|
||||
def refresh_available_extensions(url, hide_tags):
|
||||
def refresh_available_extensions(url, hide_tags, sort_column):
|
||||
global available_extensions
|
||||
|
||||
import urllib.request
|
||||
|
|
@ -179,18 +179,28 @@ def refresh_available_extensions(url, hide_tags):
|
|||
|
||||
available_extensions = json.loads(text)
|
||||
|
||||
code, tags = refresh_available_extensions_from_data(hide_tags)
|
||||
code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)
|
||||
|
||||
return url, code, gr.CheckboxGroup.update(choices=tags), ''
|
||||
|
||||
|
||||
def refresh_available_extensions_for_tags(hide_tags):
|
||||
code, _ = refresh_available_extensions_from_data(hide_tags)
|
||||
def refresh_available_extensions_for_tags(hide_tags, sort_column):
|
||||
code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
|
||||
|
||||
return code, ''
|
||||
|
||||
|
||||
def refresh_available_extensions_from_data(hide_tags):
|
||||
sort_ordering = [
|
||||
# (reverse, order_by_function)
|
||||
(True, lambda x: x.get('added', 'z')),
|
||||
(False, lambda x: x.get('added', 'z')),
|
||||
(False, lambda x: x.get('name', 'z')),
|
||||
(True, lambda x: x.get('name', 'z')),
|
||||
(False, lambda x: 'z'),
|
||||
]
|
||||
|
||||
|
||||
def refresh_available_extensions_from_data(hide_tags, sort_column):
|
||||
extlist = available_extensions["extensions"]
|
||||
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
|
||||
|
||||
|
|
@ -210,8 +220,11 @@ def refresh_available_extensions_from_data(hide_tags):
|
|||
<tbody>
|
||||
"""
|
||||
|
||||
for ext in extlist:
|
||||
sort_reverse, sort_function = sort_ordering[sort_column if 0 <= sort_column < len(sort_ordering) else 0]
|
||||
|
||||
for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):
|
||||
name = ext.get("name", "noname")
|
||||
added = ext.get('added', 'unknown')
|
||||
url = ext.get("url", None)
|
||||
description = ext.get("description", "")
|
||||
extension_tags = ext.get("tags", [])
|
||||
|
|
@ -233,7 +246,7 @@ def refresh_available_extensions_from_data(hide_tags):
|
|||
code += f"""
|
||||
<tr>
|
||||
<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
|
||||
<td>{html.escape(description)}</td>
|
||||
<td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>
|
||||
<td>{install_code}</td>
|
||||
</tr>
|
||||
|
||||
|
|
@ -291,25 +304,32 @@ def create_ui():
|
|||
|
||||
with gr.Row():
|
||||
hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
|
||||
sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
|
||||
|
||||
install_result = gr.HTML()
|
||||
available_extensions_table = gr.HTML()
|
||||
|
||||
refresh_available_extensions_button.click(
|
||||
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
|
||||
inputs=[available_extensions_index, hide_tags],
|
||||
inputs=[available_extensions_index, hide_tags, sort_column],
|
||||
outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
|
||||
)
|
||||
|
||||
install_extension_button.click(
|
||||
fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
|
||||
inputs=[extension_to_install, hide_tags],
|
||||
inputs=[extension_to_install, hide_tags, sort_column],
|
||||
outputs=[available_extensions_table, extensions_table, install_result],
|
||||
)
|
||||
|
||||
hide_tags.change(
|
||||
fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
|
||||
inputs=[hide_tags],
|
||||
inputs=[hide_tags, sort_column],
|
||||
outputs=[available_extensions_table, install_result]
|
||||
)
|
||||
|
||||
sort_column.change(
|
||||
fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
|
||||
inputs=[hide_tags, sort_column],
|
||||
outputs=[available_extensions_table, install_result]
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -30,4 +30,4 @@ inflection
|
|||
GitPython
|
||||
torchsde
|
||||
safetensors
|
||||
psutil; sys_platform == 'darwin'
|
||||
psutil
|
||||
|
|
|
|||
BIN
screenshot.png
BIN
screenshot.png
Binary file not shown.
|
Before Width: | Height: | Size: 513 KiB After Width: | Height: | Size: 411 KiB |
28
style.css
28
style.css
|
|
@ -555,7 +555,7 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
|
|||
|
||||
/* Extensions */
|
||||
|
||||
#tab_extensions table{
|
||||
#tab_extensions table``{
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
|
|
@ -581,6 +581,15 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
|
|||
font-size: 95%;
|
||||
}
|
||||
|
||||
#available_extensions .info{
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
#available_extensions .date_added{
|
||||
opacity: 0.85;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{
|
||||
min-width: auto;
|
||||
padding-left: 0.5em;
|
||||
|
|
@ -633,6 +642,23 @@ footer {
|
|||
opacity: 0.85;
|
||||
}
|
||||
|
||||
#txtimg_hr_finalres{
|
||||
min-height: 0 !important;
|
||||
padding: .625rem .75rem;
|
||||
margin-left: -0.75em
|
||||
|
||||
}
|
||||
|
||||
#txtimg_hr_finalres .resolution{
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
#txt2img_checkboxes > div > div{
|
||||
flex: 0;
|
||||
white-space: nowrap;
|
||||
min-width: auto;
|
||||
}
|
||||
|
||||
/* The following handles localization for right-to-left (RTL) languages like Arabic.
|
||||
The rtl media type will only be activated by the logic in javascript/localization.js.
|
||||
If you change anything above, you need to make sure it is RTL compliant by just running
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 329 KiB |
9
webui.py
9
webui.py
|
|
@ -4,7 +4,7 @@ import threading
|
|||
import time
|
||||
import importlib
|
||||
import signal
|
||||
import threading
|
||||
import re
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
|
|
@ -13,6 +13,11 @@ from modules import import_hook, errors
|
|||
from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
|
||||
from modules.paths import script_path
|
||||
|
||||
import torch
|
||||
# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
|
||||
if ".dev" in torch.__version__ or "+git" in torch.__version__:
|
||||
torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
|
||||
|
||||
from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir
|
||||
import modules.codeformer_model as codeformer
|
||||
import modules.extras
|
||||
|
|
@ -182,12 +187,14 @@ def webui():
|
|||
|
||||
sd_samplers.set_samplers()
|
||||
|
||||
modules.script_callbacks.script_unloaded_callback()
|
||||
extensions.list_extensions()
|
||||
|
||||
localization.list_localizations(cmd_opts.localizations_dir)
|
||||
|
||||
modelloader.forbid_loaded_nonbuiltin_upscalers()
|
||||
modules.scripts.reload_scripts()
|
||||
modules.script_callbacks.model_loaded_callback(shared.sd_model)
|
||||
modelloader.load_upscalers()
|
||||
|
||||
for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
|
||||
|
|
|
|||
Loading…
Reference in a new issue