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Merge branch 'dev' into gradio4
This commit is contained in:
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commit
32281b272e
126
CHANGELOG.md
126
CHANGELOG.md
@ -1,4 +1,126 @@
|
||||
## 1.8.0-RC
|
||||
## 1.9.0
|
||||
|
||||
### Features:
|
||||
* Make refiner switchover based on model timesteps instead of sampling steps ([#14978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14978))
|
||||
* add an option to have old-style directory view instead of tree view; stylistic changes for extra network sorting/search controls
|
||||
* add UI for reordering callbacks, support for specifying callback order in extension metadata ([#15205](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15205))
|
||||
* Sgm uniform scheduler for SDXL-Lightning models ([#15325](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15325))
|
||||
* Scheduler selection in main UI ([#15333](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15333), [#15361](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15361), [#15394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15394))
|
||||
|
||||
### Minor:
|
||||
* "open images directory" button now opens the actual dir ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
|
||||
* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
|
||||
* make extra network card description plaintext by default, with an option to re-enable HTML as it was
|
||||
* resize handle for extra networks ([#15041](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15041))
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||||
* cmd args: `--unix-filenames-sanitization` and `--filenames-max-length` ([#15031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15031))
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* show extra networks parameters in HTML table rather than raw JSON ([#15131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15131))
|
||||
* Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr ([#15160](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15160), [#15283](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15283))
|
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* Add '--no-prompt-history' cmd args for disable last generation prompt history ([#15189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15189))
|
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* update preview on Replace Preview ([#15201](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15201))
|
||||
* only fetch updates for extensions' active git branches ([#15233](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15233))
|
||||
* put upscale postprocessing UI into an accordion ([#15223](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15223))
|
||||
* Support dragdrop for URLs to read infotext ([#15262](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15262))
|
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* use diskcache library for caching ([#15287](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15287), [#15299](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15299))
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* Allow PNG-RGBA for Extras Tab ([#15334](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15334))
|
||||
* Support cover images embedded in safetensors metadata ([#15319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15319))
|
||||
* faster interrupt when using NN upscale ([#15380](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15380))
|
||||
* Extras upscaler: an input field to limit maximul side length for the output image ([#15293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15293), [#15415](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15415), [#15417](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15417), [#15425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15425))
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* add an option to hide postprocessing options in Extras tab
|
||||
|
||||
### Extensions and API:
|
||||
* ResizeHandleRow - allow overriden column scale parametr ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
|
||||
* call script_callbacks.ui_settings_callback earlier; fix extra-options-section built-in extension killing the ui if using a setting that doesn't exist
|
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* make it possible to use zoom.js outside webui context ([#15286](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15286), [#15288](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15288))
|
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* allow variants for extension name in metadata.ini ([#15290](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15290))
|
||||
* make reloading UI scripts optional when doing Reload UI, and off by default
|
||||
* put request: gr.Request at start of img2img function similar to txt2img
|
||||
* open_folder as util ([#15442](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15442))
|
||||
* make it possible to import extensions' script files as `import scripts.<filename>` ([#15423](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15423))
|
||||
|
||||
### Performance:
|
||||
* performance optimization for extra networks HTML pages
|
||||
* optimization for extra networks filtering
|
||||
* optimization for extra networks sorting
|
||||
|
||||
### Bug Fixes:
|
||||
* prevent escape button causing an interrupt when no generation has been made yet
|
||||
* [bug] avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
|
||||
* possible fix for reload button not appearing in some cases for extra networks.
|
||||
* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
|
||||
* Fix resize-handle visability for vertical layout (mobile) ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010))
|
||||
* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
|
||||
* Protect alphas_cumprod during refiner switchover ([#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
|
||||
* Fix EXIF orientation in API image loading ([#15062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15062))
|
||||
* Only override emphasis if actually used in prompt ([#15141](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15141))
|
||||
* Fix emphasis infotext missing from `params.txt` ([#15142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15142))
|
||||
* fix extract_style_text_from_prompt #15132 ([#15135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15135))
|
||||
* Fix Soft Inpaint for AnimateDiff ([#15148](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15148))
|
||||
* edit-attention: deselect surrounding whitespace ([#15178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15178))
|
||||
* chore: fix font not loaded ([#15183](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15183))
|
||||
* use natural sort in extra networks when ordering by path
|
||||
* Fix built-in lora system bugs caused by torch.nn.MultiheadAttention ([#15190](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15190))
|
||||
* Avoid error from None in get_learned_conditioning ([#15191](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15191))
|
||||
* Add entry to MassFileLister after writing metadata ([#15199](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15199))
|
||||
* fix issue with Styles when Hires prompt is used ([#15269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15269), [#15276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15276))
|
||||
* Strip comments from hires fix prompt ([#15263](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15263))
|
||||
* Make imageviewer event listeners browser consistent ([#15261](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15261))
|
||||
* Fix AttributeError in OFT when trying to get MultiheadAttention weight ([#15260](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15260))
|
||||
* Add missing .mean() back ([#15239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15239))
|
||||
* fix "Restore progress" button ([#15221](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15221))
|
||||
* fix ui-config for InputAccordion [custom_script_source] ([#15231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15231))
|
||||
* handle 0 wheel deltaY ([#15268](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15268))
|
||||
* prevent alt menu for firefox ([#15267](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15267))
|
||||
* fix: fix syntax errors ([#15179](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15179))
|
||||
* restore outputs path ([#15307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15307))
|
||||
* Escape btn_copy_path filename ([#15316](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15316))
|
||||
* Fix extra networks buttons when filename contains an apostrophe ([#15331](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15331))
|
||||
* escape brackets in lora random prompt generator ([#15343](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15343))
|
||||
* fix: Python version check for PyTorch installation compatibility ([#15390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15390))
|
||||
* fix typo in call_queue.py ([#15386](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15386))
|
||||
* fix: when find already_loaded model, remove loaded by array index ([#15382](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15382))
|
||||
* minor bug fix of sd model memory management ([#15350](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15350))
|
||||
* Fix CodeFormer weight ([#15414](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15414))
|
||||
* Fix: Remove script callbacks in ordered_callbacks_map ([#15428](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15428))
|
||||
* fix limited file write (thanks, Sylwia)
|
||||
* Fix extra-single-image API not doing upscale failed ([#15465](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15465))
|
||||
* error handling paste_field callables ([#15470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15470))
|
||||
|
||||
|
||||
### Hardware:
|
||||
* Add training support and change lspci for Ascend NPU ([#14981](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14981))
|
||||
* Update to ROCm5.7 and PyTorch ([#14820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14820))
|
||||
* Better workaround for Navi1, removing --pre for Navi3 ([#15224](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15224))
|
||||
* Ascend NPU wiki page ([#15228](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15228))
|
||||
|
||||
### Other:
|
||||
* Update comment for Pad prompt/negative prompt v0 to add a warning about truncation, make it override the v1 implementation
|
||||
* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
|
||||
* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
|
||||
* Use `absolute` path for normalized filepath ([#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
|
||||
* resizeHandle handle double tap ([#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
|
||||
* --dat-models-path cmd flag ([#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
|
||||
* Add a direct link to the binary release ([#15059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15059))
|
||||
* upscaler_utils: Reduce logging ([#15084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15084))
|
||||
* Fix various typos with crate-ci/typos ([#15116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15116))
|
||||
* fix_jpeg_live_preview ([#15102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15102))
|
||||
* [alternative fix] can't load webui if selected wrong extra option in ui ([#15121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15121))
|
||||
* Error handling for unsupported transparency ([#14958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14958))
|
||||
* Add model description to searched terms ([#15198](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15198))
|
||||
* bump action version ([#15272](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15272))
|
||||
* PEP 604 annotations ([#15259](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15259))
|
||||
* Automatically Set the Scale by value when user selects an Upscale Model ([#15244](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15244))
|
||||
* move postprocessing-for-training into builtin extensions ([#15222](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15222))
|
||||
* type hinting in shared.py ([#15211](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15211))
|
||||
* update ruff to 0.3.3
|
||||
* Update pytorch lightning utilities ([#15310](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15310))
|
||||
* Add Size as an XYZ Grid option ([#15354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15354))
|
||||
* Use HF_ENDPOINT variable for HuggingFace domain with default ([#15443](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15443))
|
||||
* re-add update_file_entry ([#15446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15446))
|
||||
* create_infotext allow index and callable, re-work Hires prompt infotext ([#15460](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15460))
|
||||
* update restricted_opts to include more options for --hide-ui-dir-config ([#15492](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15492))
|
||||
|
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|
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## 1.8.0
|
||||
|
||||
### Features:
|
||||
* Update torch to version 2.1.2
|
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@ -61,7 +183,7 @@
|
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* add before_token_counter callback and use it for prompt comments
|
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* ResizeHandleRow - allow overridden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
|
||||
|
||||
### Performance
|
||||
### Performance:
|
||||
* Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528))
|
||||
* Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512))
|
||||
* Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527))
|
||||
|
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@ -1,7 +1,7 @@
|
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from modules import scripts
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from modules.shared import opts
|
||||
|
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xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
|
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xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "scripts.xyz_grid"][0].module
|
||||
|
||||
def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
|
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"""
|
||||
|
||||
@ -568,7 +568,7 @@ function extraNetworksShowMetadata(text) {
|
||||
return;
|
||||
}
|
||||
} catch (error) {
|
||||
console.eror(error);
|
||||
console.error(error);
|
||||
}
|
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|
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var elem = document.createElement('pre');
|
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|
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@ -17,7 +17,7 @@ from fastapi.encoders import jsonable_encoder
|
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from secrets import compare_digest
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|
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import modules.shared as shared
|
||||
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models
|
||||
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models, sd_schedulers
|
||||
from modules.api import models
|
||||
from modules.shared import opts
|
||||
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
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@ -221,6 +221,7 @@ class Api:
|
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self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
|
||||
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
|
||||
self.add_api_route("/sdapi/v1/schedulers", self.get_schedulers, methods=["GET"], response_model=list[models.SchedulerItem])
|
||||
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
|
||||
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
|
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
|
||||
@ -683,6 +684,17 @@ class Api:
|
||||
def get_samplers(self):
|
||||
return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
|
||||
|
||||
def get_schedulers(self):
|
||||
return [
|
||||
{
|
||||
"name": scheduler.name,
|
||||
"label": scheduler.label,
|
||||
"aliases": scheduler.aliases,
|
||||
"default_rho": scheduler.default_rho,
|
||||
"need_inner_model": scheduler.need_inner_model,
|
||||
}
|
||||
for scheduler in sd_schedulers.schedulers]
|
||||
|
||||
def get_upscalers(self):
|
||||
return [
|
||||
{
|
||||
|
||||
@ -145,7 +145,7 @@ class ExtrasBaseRequest(BaseModel):
|
||||
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.")
|
||||
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.")
|
||||
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.")
|
||||
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.")
|
||||
upscaling_resize: float = Field(default=2, title="Upscaling Factor", gt=0, description="By how much to upscale the image, only used when resize_mode=0.")
|
||||
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.")
|
||||
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.")
|
||||
upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
|
||||
@ -233,6 +233,13 @@ class SamplerItem(BaseModel):
|
||||
aliases: list[str] = Field(title="Aliases")
|
||||
options: dict[str, str] = Field(title="Options")
|
||||
|
||||
class SchedulerItem(BaseModel):
|
||||
name: str = Field(title="Name")
|
||||
label: str = Field(title="Label")
|
||||
aliases: Optional[list[str]] = Field(title="Aliases")
|
||||
default_rho: Optional[float] = Field(title="Default Rho")
|
||||
need_inner_model: Optional[bool] = Field(title="Needs Inner Model")
|
||||
|
||||
class UpscalerItem(BaseModel):
|
||||
class Config:
|
||||
protected_namespaces = ()
|
||||
|
||||
@ -50,7 +50,7 @@ class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration):
|
||||
|
||||
def restore_face(cropped_face_t):
|
||||
assert self.net is not None
|
||||
return self.net(cropped_face_t, w=w, adain=True)[0]
|
||||
return self.net(cropped_face_t, weight=w, adain=True)[0]
|
||||
|
||||
return self.restore_with_helper(np_image, restore_face)
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ import tqdm
|
||||
from einops import rearrange, repeat
|
||||
from ldm.util import default
|
||||
from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
|
||||
from modules.textual_inversion import textual_inversion, logging
|
||||
from modules.textual_inversion import textual_inversion, saving_settings
|
||||
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
||||
from torch import einsum
|
||||
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
|
||||
@ -533,7 +533,7 @@ def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch
|
||||
model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
|
||||
**{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
|
||||
)
|
||||
logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
|
||||
saving_settings.save_settings_to_file(log_directory, {**saved_params, **locals()})
|
||||
|
||||
latent_sampling_method = ds.latent_sampling_method
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime
|
||||
|
||||
import functools
|
||||
import pytz
|
||||
import io
|
||||
import math
|
||||
@ -13,6 +13,8 @@ import numpy as np
|
||||
import piexif
|
||||
import piexif.helper
|
||||
from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin, ImageOps
|
||||
# pillow_avif needs to be imported somewhere in code for it to work
|
||||
import pillow_avif # noqa: F401
|
||||
import string
|
||||
import json
|
||||
import hashlib
|
||||
@ -347,6 +349,32 @@ def sanitize_filename_part(text, replace_spaces=True):
|
||||
return text
|
||||
|
||||
|
||||
@functools.cache
|
||||
def get_scheduler_str(sampler_name, scheduler_name):
|
||||
"""Returns {Scheduler} if the scheduler is applicable to the sampler"""
|
||||
if scheduler_name == 'Automatic':
|
||||
config = sd_samplers.find_sampler_config(sampler_name)
|
||||
scheduler_name = config.options.get('scheduler', 'Automatic')
|
||||
return scheduler_name.capitalize()
|
||||
|
||||
|
||||
@functools.cache
|
||||
def get_sampler_scheduler_str(sampler_name, scheduler_name):
|
||||
"""Returns the '{Sampler} {Scheduler}' if the scheduler is applicable to the sampler"""
|
||||
return f'{sampler_name} {get_scheduler_str(sampler_name, scheduler_name)}'
|
||||
|
||||
|
||||
def get_sampler_scheduler(p, sampler):
|
||||
"""Returns '{Sampler} {Scheduler}' / '{Scheduler}' / 'NOTHING_AND_SKIP_PREVIOUS_TEXT'"""
|
||||
if hasattr(p, 'scheduler') and hasattr(p, 'sampler_name'):
|
||||
if sampler:
|
||||
sampler_scheduler = get_sampler_scheduler_str(p.sampler_name, p.scheduler)
|
||||
else:
|
||||
sampler_scheduler = get_scheduler_str(p.sampler_name, p.scheduler)
|
||||
return sanitize_filename_part(sampler_scheduler, replace_spaces=False)
|
||||
return NOTHING_AND_SKIP_PREVIOUS_TEXT
|
||||
|
||||
|
||||
class FilenameGenerator:
|
||||
replacements = {
|
||||
'seed': lambda self: self.seed if self.seed is not None else '',
|
||||
@ -358,6 +386,8 @@ class FilenameGenerator:
|
||||
'height': lambda self: self.image.height,
|
||||
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
|
||||
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
|
||||
'sampler_scheduler': lambda self: self.p and get_sampler_scheduler(self.p, True),
|
||||
'scheduler': lambda self: self.p and get_sampler_scheduler(self.p, False),
|
||||
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
|
||||
'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
|
||||
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
|
||||
@ -569,6 +599,16 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
|
||||
})
|
||||
|
||||
piexif.insert(exif_bytes, filename)
|
||||
elif extension.lower() == '.avif':
|
||||
if opts.enable_pnginfo and geninfo is not None:
|
||||
exif_bytes = piexif.dump({
|
||||
"Exif": {
|
||||
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
|
||||
},
|
||||
})
|
||||
|
||||
|
||||
image.save(filename,format=image_format, exif=exif_bytes)
|
||||
elif extension.lower() == ".gif":
|
||||
image.save(filename, format=image_format, comment=geninfo)
|
||||
else:
|
||||
@ -747,7 +787,6 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
|
||||
exif_comment = exif_comment.decode('utf8', errors="ignore")
|
||||
|
||||
if exif_comment:
|
||||
items['exif comment'] = exif_comment
|
||||
geninfo = exif_comment
|
||||
elif "comment" in items: # for gif
|
||||
geninfo = items["comment"].decode('utf8', errors="ignore")
|
||||
|
||||
@ -145,7 +145,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
|
||||
return batch_results
|
||||
|
||||
|
||||
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
|
||||
def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, *args):
|
||||
override_settings = create_override_settings_dict(override_settings_texts)
|
||||
|
||||
is_batch = mode == 5
|
||||
|
||||
@ -8,7 +8,7 @@ import sys
|
||||
|
||||
import gradio as gr
|
||||
from modules.paths import data_path
|
||||
from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser
|
||||
from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser, errors
|
||||
from PIL import Image
|
||||
|
||||
sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__] # alias for old name
|
||||
@ -500,7 +500,11 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
|
||||
|
||||
for output, key in paste_fields:
|
||||
if callable(key):
|
||||
v = key(params)
|
||||
try:
|
||||
v = key(params)
|
||||
except Exception:
|
||||
errors.report(f"Error executing {key}", exc_info=True)
|
||||
v = None
|
||||
else:
|
||||
v = params.get(key, None)
|
||||
|
||||
|
||||
@ -1,17 +1,39 @@
|
||||
from PIL import Image, ImageFilter, ImageOps
|
||||
|
||||
|
||||
def get_crop_region(mask, pad=0):
|
||||
"""finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
|
||||
For example, if a user has painted the top-right part of a 512x512 image, the result may be (256, 0, 512, 256)"""
|
||||
mask_img = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
|
||||
box = mask_img.getbbox()
|
||||
if box:
|
||||
def get_crop_region_v2(mask, pad=0):
|
||||
"""
|
||||
Finds a rectangular region that contains all masked ares in a mask.
|
||||
Returns None if mask is completely black mask (all 0)
|
||||
|
||||
Parameters:
|
||||
mask: PIL.Image.Image L mode or numpy 1d array
|
||||
pad: int number of pixels that the region will be extended on all sides
|
||||
Returns: (x1, y1, x2, y2) | None
|
||||
|
||||
Introduced post 1.9.0
|
||||
"""
|
||||
mask = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
|
||||
if box := mask.getbbox():
|
||||
x1, y1, x2, y2 = box
|
||||
else: # when no box is found
|
||||
x1, y1 = mask_img.size
|
||||
x2 = y2 = 0
|
||||
return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask_img.size[0]), min(y2 + pad, mask_img.size[1])
|
||||
return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask.size[0]), min(y2 + pad, mask.size[1]) if pad else box
|
||||
|
||||
|
||||
def get_crop_region(mask, pad=0):
|
||||
"""
|
||||
Same function as get_crop_region_v2 but handles completely black mask (all 0) differently
|
||||
when mask all black still return coordinates but the coordinates may be invalid ie x2>x1 or y2>y1
|
||||
Notes: it is possible for the coordinates to be "valid" again if pad size is sufficiently large
|
||||
(mask_size.x-pad, mask_size.y-pad, pad, pad)
|
||||
|
||||
Extension developer should use get_crop_region_v2 instead unless for compatibility considerations.
|
||||
"""
|
||||
mask = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
|
||||
if box := get_crop_region_v2(mask, pad):
|
||||
return box
|
||||
x1, y1 = mask.size
|
||||
x2 = y2 = 0
|
||||
return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask.size[0]), min(y2 + pad, mask.size[1])
|
||||
|
||||
|
||||
def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
|
||||
|
||||
@ -134,13 +134,15 @@ def run_postprocessing_webui(id_task, *args, **kwargs):
|
||||
return run_postprocessing(*args, **kwargs)
|
||||
|
||||
|
||||
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
|
||||
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True, max_side_length: int = 0):
|
||||
"""old handler for API"""
|
||||
|
||||
args = scripts.scripts_postproc.create_args_for_run({
|
||||
"Upscale": {
|
||||
"upscale_enabled": True,
|
||||
"upscale_mode": resize_mode,
|
||||
"upscale_by": upscaling_resize,
|
||||
"max_side_length": max_side_length,
|
||||
"upscale_to_width": upscaling_resize_w,
|
||||
"upscale_to_height": upscaling_resize_h,
|
||||
"upscale_crop": upscaling_crop,
|
||||
|
||||
@ -608,7 +608,7 @@ class Processed:
|
||||
"version": self.version,
|
||||
}
|
||||
|
||||
return json.dumps(obj)
|
||||
return json.dumps(obj, default=lambda o: None)
|
||||
|
||||
def infotext(self, p: StableDiffusionProcessing, index):
|
||||
return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
|
||||
@ -703,8 +703,54 @@ def program_version():
|
||||
return res
|
||||
|
||||
|
||||
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None, all_hr_prompts=None, all_hr_negative_prompts=None):
|
||||
if index is None:
|
||||
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
|
||||
"""
|
||||
this function is used to generate the infotext that is stored in the generated images, it's contains the parameters that are required to generate the imagee
|
||||
Args:
|
||||
p: StableDiffusionProcessing
|
||||
all_prompts: list[str]
|
||||
all_seeds: list[int]
|
||||
all_subseeds: list[int]
|
||||
comments: list[str]
|
||||
iteration: int
|
||||
position_in_batch: int
|
||||
use_main_prompt: bool
|
||||
index: int
|
||||
all_negative_prompts: list[str]
|
||||
|
||||
Returns: str
|
||||
|
||||
Extra generation params
|
||||
p.extra_generation_params dictionary allows for additional parameters to be added to the infotext
|
||||
this can be use by the base webui or extensions.
|
||||
To add a new entry, add a new key value pair, the dictionary key will be used as the key of the parameter in the infotext
|
||||
the value generation_params can be defined as:
|
||||
- str | None
|
||||
- List[str|None]
|
||||
- callable func(**kwargs) -> str | None
|
||||
|
||||
When defined as a string, it will be used as without extra processing; this is this most common use case.
|
||||
|
||||
Defining as a list allows for parameter that changes across images in the job, for example, the 'Seed' parameter.
|
||||
The list should have the same length as the total number of images in the entire job.
|
||||
|
||||
Defining as a callable function allows parameter cannot be generated earlier or when extra logic is required.
|
||||
For example 'Hires prompt', due to reasons the hr_prompt might be changed by process in the pipeline or extensions
|
||||
and may vary across different images, defining as a static string or list would not work.
|
||||
|
||||
The function takes locals() as **kwargs, as such will have access to variables like 'p' and 'index'.
|
||||
the base signature of the function should be:
|
||||
func(**kwargs) -> str | None
|
||||
optionally it can have additional arguments that will be used in the function:
|
||||
func(p, index, **kwargs) -> str | None
|
||||
note: for better future compatibility even though this function will have access to all variables in the locals(),
|
||||
it is recommended to only use the arguments present in the function signature of create_infotext.
|
||||
For actual implementation examples, see StableDiffusionProcessingTxt2Img.init > get_hr_prompt.
|
||||
"""
|
||||
|
||||
if use_main_prompt:
|
||||
index = 0
|
||||
elif index is None:
|
||||
index = position_in_batch + iteration * p.batch_size
|
||||
|
||||
if all_negative_prompts is None:
|
||||
@ -715,6 +761,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
||||
token_merging_ratio = p.get_token_merging_ratio()
|
||||
token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
|
||||
|
||||
prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
|
||||
negative_prompt = p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]
|
||||
|
||||
uses_ensd = opts.eta_noise_seed_delta != 0
|
||||
if uses_ensd:
|
||||
uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
|
||||
@ -747,22 +796,24 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
||||
"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
|
||||
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
|
||||
"Tiling": "True" if p.tiling else None,
|
||||
"Hires prompt": None, # This is set later, insert here to keep order
|
||||
"Hires negative prompt": None, # This is set later, insert here to keep order
|
||||
**p.extra_generation_params,
|
||||
"Version": program_version() if opts.add_version_to_infotext else None,
|
||||
"User": p.user if opts.add_user_name_to_info else None,
|
||||
}
|
||||
|
||||
if all_hr_prompts := all_hr_prompts or getattr(p, 'all_hr_prompts', None):
|
||||
generation_params['Hires prompt'] = all_hr_prompts[index] if all_hr_prompts[index] != all_prompts[index] else None
|
||||
if all_hr_negative_prompts := all_hr_negative_prompts or getattr(p, 'all_hr_negative_prompts', None):
|
||||
generation_params['Hires negative prompt'] = all_hr_negative_prompts[index] if all_hr_negative_prompts[index] != all_negative_prompts[index] else None
|
||||
for key, value in generation_params.items():
|
||||
try:
|
||||
if isinstance(value, list):
|
||||
generation_params[key] = value[index]
|
||||
elif callable(value):
|
||||
generation_params[key] = value(**locals())
|
||||
except Exception:
|
||||
errors.report(f'Error creating infotext for key "{key}"', exc_info=True)
|
||||
generation_params[key] = None
|
||||
|
||||
generation_params_text = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in generation_params.items() if v is not None])
|
||||
|
||||
prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
|
||||
negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
|
||||
negative_prompt_text = f"\nNegative prompt: {negative_prompt}" if negative_prompt else ""
|
||||
|
||||
return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
@ -1204,6 +1255,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
|
||||
self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
|
||||
|
||||
def get_hr_prompt(p, index, prompt_text, **kwargs):
|
||||
hr_prompt = p.all_hr_prompts[index]
|
||||
return hr_prompt if hr_prompt != prompt_text else None
|
||||
|
||||
def get_hr_negative_prompt(p, index, negative_prompt, **kwargs):
|
||||
hr_negative_prompt = p.all_hr_negative_prompts[index]
|
||||
return hr_negative_prompt if hr_negative_prompt != negative_prompt else None
|
||||
|
||||
self.extra_generation_params["Hires prompt"] = get_hr_prompt
|
||||
self.extra_generation_params["Hires negative prompt"] = get_hr_negative_prompt
|
||||
|
||||
self.extra_generation_params["Hires schedule type"] = None # to be set in sd_samplers_kdiffusion.py
|
||||
|
||||
if self.hr_scheduler is None:
|
||||
@ -1549,16 +1611,23 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
if self.inpaint_full_res:
|
||||
self.mask_for_overlay = image_mask
|
||||
mask = image_mask.convert('L')
|
||||
crop_region = masking.get_crop_region(mask, self.inpaint_full_res_padding)
|
||||
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
|
||||
x1, y1, x2, y2 = crop_region
|
||||
|
||||
mask = mask.crop(crop_region)
|
||||
image_mask = images.resize_image(2, mask, self.width, self.height)
|
||||
self.paste_to = (x1, y1, x2-x1, y2-y1)
|
||||
|
||||
self.extra_generation_params["Inpaint area"] = "Only masked"
|
||||
self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
|
||||
crop_region = masking.get_crop_region_v2(mask, self.inpaint_full_res_padding)
|
||||
if crop_region:
|
||||
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
|
||||
x1, y1, x2, y2 = crop_region
|
||||
mask = mask.crop(crop_region)
|
||||
image_mask = images.resize_image(2, mask, self.width, self.height)
|
||||
self.inpaint_full_res = False
|
||||
self.paste_to = (x1, y1, x2-x1, y2-y1)
|
||||
self.extra_generation_params["Inpaint area"] = "Only masked"
|
||||
self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
|
||||
else:
|
||||
crop_region = None
|
||||
image_mask = None
|
||||
self.mask_for_overlay = None
|
||||
massage = 'Unable to perform "Inpaint Only mask" because mask is blank, switch to img2img mode.'
|
||||
model_hijack.comments.append(massage)
|
||||
logging.info(massage)
|
||||
else:
|
||||
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
|
||||
np_mask = np.array(image_mask)
|
||||
@ -1586,6 +1655,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
image = images.resize_image(self.resize_mode, image, self.width, self.height)
|
||||
|
||||
if image_mask is not None:
|
||||
if self.mask_for_overlay.size != (image.width, image.height):
|
||||
self.mask_for_overlay = images.resize_image(self.resize_mode, self.mask_for_overlay, image.width, image.height)
|
||||
image_masked = Image.new('RGBa', (image.width, image.height))
|
||||
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
|
||||
|
||||
|
||||
@ -439,12 +439,18 @@ def remove_current_script_callbacks():
|
||||
for callback_list in callback_map.values():
|
||||
for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
|
||||
callback_list.remove(callback_to_remove)
|
||||
for ordered_callbacks_list in ordered_callbacks_map.values():
|
||||
for callback_to_remove in [cb for cb in ordered_callbacks_list if cb.script == filename]:
|
||||
ordered_callbacks_list.remove(callback_to_remove)
|
||||
|
||||
|
||||
def remove_callbacks_for_function(callback_func):
|
||||
for callback_list in callback_map.values():
|
||||
for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
|
||||
callback_list.remove(callback_to_remove)
|
||||
for ordered_callback_list in ordered_callbacks_map.values():
|
||||
for callback_to_remove in [cb for cb in ordered_callback_list if cb.callback == callback_func]:
|
||||
ordered_callback_list.remove(callback_to_remove)
|
||||
|
||||
|
||||
def on_app_started(callback, *, name=None):
|
||||
|
||||
@ -2,13 +2,20 @@ import os
|
||||
import importlib.util
|
||||
|
||||
from modules import errors
|
||||
import sys
|
||||
|
||||
|
||||
loaded_scripts = {}
|
||||
|
||||
|
||||
def load_module(path):
|
||||
module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
|
||||
module_name, _ = os.path.splitext(os.path.basename(path))
|
||||
full_module_name = "scripts." + module_name
|
||||
module_spec = importlib.util.spec_from_file_location(full_module_name, path)
|
||||
module = importlib.util.module_from_spec(module_spec)
|
||||
module_spec.loader.exec_module(module)
|
||||
|
||||
loaded_scripts[path] = module
|
||||
sys.modules[full_module_name] = module
|
||||
return module
|
||||
|
||||
|
||||
|
||||
@ -739,12 +739,17 @@ class ScriptRunner:
|
||||
def onload_script_visibility(params):
|
||||
title = params.get('Script', None)
|
||||
if title:
|
||||
title_index = self.titles.index(title)
|
||||
visibility = title_index == self.script_load_ctr
|
||||
self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
|
||||
return gr.update(visible=visibility)
|
||||
else:
|
||||
return gr.update(visible=False)
|
||||
try:
|
||||
title_index = self.titles.index(title)
|
||||
visibility = title_index == self.script_load_ctr
|
||||
self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
|
||||
return gr.update(visible=visibility)
|
||||
except ValueError:
|
||||
params['Script'] = None
|
||||
massage = f'Cannot find Script: "{title}"'
|
||||
print(massage)
|
||||
gr.Warning(massage)
|
||||
return gr.update(visible=False)
|
||||
|
||||
self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
|
||||
self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
|
||||
|
||||
@ -143,6 +143,7 @@ class ScriptPostprocessingRunner:
|
||||
self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
|
||||
|
||||
scripts_order = shared.opts.postprocessing_operation_order
|
||||
scripts_filter_out = set(shared.opts.postprocessing_disable_in_extras)
|
||||
|
||||
def script_score(name):
|
||||
for i, possible_match in enumerate(scripts_order):
|
||||
@ -151,9 +152,10 @@ class ScriptPostprocessingRunner:
|
||||
|
||||
return len(self.scripts)
|
||||
|
||||
script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
|
||||
filtered_scripts = [script for script in self.scripts if script.name not in scripts_filter_out]
|
||||
script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(filtered_scripts)}
|
||||
|
||||
return sorted(self.scripts, key=lambda x: script_scores[x.name])
|
||||
return sorted(filtered_scripts, key=lambda x: script_scores[x.name])
|
||||
|
||||
def setup_ui(self):
|
||||
inputs = []
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import collections
|
||||
import os.path
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
|
||||
@ -7,7 +7,6 @@ import torch
|
||||
import re
|
||||
import safetensors.torch
|
||||
from omegaconf import OmegaConf, ListConfig
|
||||
from os import mkdir
|
||||
from urllib import request
|
||||
import ldm.modules.midas as midas
|
||||
|
||||
@ -151,7 +150,7 @@ def list_models():
|
||||
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
|
||||
model_url = None
|
||||
else:
|
||||
model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
|
||||
model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
|
||||
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
||||
|
||||
@ -508,7 +507,7 @@ def enable_midas_autodownload():
|
||||
path = midas.api.ISL_PATHS[model_type]
|
||||
if not os.path.exists(path):
|
||||
if not os.path.exists(midas_path):
|
||||
mkdir(midas_path)
|
||||
os.mkdir(midas_path)
|
||||
|
||||
print(f"Downloading midas model weights for {model_type} to {path}")
|
||||
request.urlretrieve(midas_urls[model_type], path)
|
||||
@ -787,6 +786,13 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
|
||||
Additionally deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
|
||||
"""
|
||||
|
||||
if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
|
||||
return sd_model
|
||||
|
||||
if shared.opts.sd_checkpoints_keep_in_cpu:
|
||||
send_model_to_cpu(sd_model)
|
||||
timer.record("send model to cpu")
|
||||
|
||||
already_loaded = None
|
||||
for i in reversed(range(len(model_data.loaded_sd_models))):
|
||||
loaded_model = model_data.loaded_sd_models[i]
|
||||
@ -796,14 +802,10 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
|
||||
|
||||
if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
|
||||
print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
|
||||
model_data.loaded_sd_models.pop()
|
||||
del model_data.loaded_sd_models[i]
|
||||
send_model_to_trash(loaded_model)
|
||||
timer.record("send model to trash")
|
||||
|
||||
if shared.opts.sd_checkpoints_keep_in_cpu:
|
||||
send_model_to_cpu(sd_model)
|
||||
timer.record("send model to cpu")
|
||||
|
||||
if already_loaded is not None:
|
||||
send_model_to_device(already_loaded)
|
||||
timer.record("send model to device")
|
||||
|
||||
@ -90,3 +90,5 @@ list_checkpoint_tiles = shared_items.list_checkpoint_tiles
|
||||
refresh_checkpoints = shared_items.refresh_checkpoints
|
||||
list_samplers = shared_items.list_samplers
|
||||
reload_hypernetworks = shared_items.reload_hypernetworks
|
||||
|
||||
hf_endpoint = os.getenv('HF_ENDPOINT', 'https://huggingface.co')
|
||||
|
||||
@ -19,7 +19,9 @@ restricted_opts = {
|
||||
"outdir_grids",
|
||||
"outdir_txt2img_grids",
|
||||
"outdir_save",
|
||||
"outdir_init_images"
|
||||
"outdir_init_images",
|
||||
"temp_dir",
|
||||
"clean_temp_dir_at_start",
|
||||
}
|
||||
|
||||
categories.register_category("saving", "Saving images")
|
||||
@ -382,6 +384,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
||||
|
||||
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
|
||||
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'postprocessing_disable_in_extras': OptionInfo([], "Disable postprocessing operations in extras tab", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
|
||||
|
||||
@ -1,12 +1,16 @@
|
||||
import base64
|
||||
import json
|
||||
import os.path
|
||||
import warnings
|
||||
import logging
|
||||
|
||||
import numpy as np
|
||||
import zlib
|
||||
from PIL import Image, ImageDraw
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class EmbeddingEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
@ -43,7 +47,7 @@ def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
|
||||
|
||||
def xor_block(block):
|
||||
g = lcg()
|
||||
randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
|
||||
randblock = np.array([next(g) for _ in range(np.prod(block.shape))]).astype(np.uint8).reshape(block.shape)
|
||||
return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
|
||||
|
||||
|
||||
@ -114,7 +118,7 @@ def extract_image_data_embed(image):
|
||||
outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
|
||||
black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
|
||||
if black_cols[0].shape[0] < 2:
|
||||
print('No Image data blocks found.')
|
||||
logger.debug(f'{os.path.basename(getattr(image, "filename", "unknown image file"))}: no embedded information found.')
|
||||
return None
|
||||
|
||||
data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
|
||||
|
||||
@ -17,7 +17,7 @@ import modules.textual_inversion.dataset
|
||||
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
|
||||
from modules.textual_inversion.saving_settings import save_settings_to_file
|
||||
|
||||
|
||||
TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])
|
||||
|
||||
@ -327,8 +327,8 @@ def create_ui():
|
||||
hr_checkpoint_name = gr.Dropdown(label='Checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
|
||||
create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
|
||||
|
||||
hr_sampler_name = gr.Dropdown(label='Sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
|
||||
hr_scheduler = gr.Dropdown(label='Schedule type', elem_id="hr_scheduler", choices=["Use same scheduler"] + [x.label for x in sd_schedulers.schedulers], value="Use same scheduler")
|
||||
hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
|
||||
hr_scheduler = gr.Dropdown(label='Hires schedule type', elem_id="hr_scheduler", choices=["Use same scheduler"] + [x.label for x in sd_schedulers.schedulers], value="Use same scheduler")
|
||||
|
||||
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
|
||||
with gr.Column(scale=80):
|
||||
|
||||
@ -3,14 +3,13 @@ import dataclasses
|
||||
import json
|
||||
import html
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
|
||||
import gradio as gr
|
||||
import subprocess as sp
|
||||
from PIL import Image
|
||||
|
||||
from modules import call_queue, shared, ui_tempdir
|
||||
from modules import call_queue, shared, ui_tempdir, util
|
||||
from modules.infotext_utils import image_from_url_text
|
||||
import modules.images
|
||||
from modules.ui_components import ToolButton
|
||||
import modules.infotext_utils as parameters_copypaste
|
||||
@ -179,31 +178,7 @@ def create_output_panel(tabname, outdir, toprow=None):
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not os.path.exists(f):
|
||||
msg = f'Folder "{f}" does not exist. After you create an image, the folder will be created.'
|
||||
print(msg)
|
||||
gr.Info(msg)
|
||||
return
|
||||
elif not os.path.isdir(f):
|
||||
msg = f"""
|
||||
WARNING
|
||||
An open_folder request was made with an argument that is not a folder.
|
||||
This could be an error or a malicious attempt to run code on your computer.
|
||||
Requested path was: {f}
|
||||
"""
|
||||
print(msg, file=sys.stderr)
|
||||
gr.Warning(msg)
|
||||
return
|
||||
|
||||
path = os.path.normpath(f)
|
||||
if platform.system() == "Windows":
|
||||
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])
|
||||
util.open_folder(f)
|
||||
|
||||
with gr.Column(elem_id=f"{tabname}_results"):
|
||||
if toprow:
|
||||
|
||||
@ -58,8 +58,9 @@ def apply_and_restart(disable_list, update_list, disable_all):
|
||||
|
||||
def save_config_state(name):
|
||||
current_config_state = config_states.get_config()
|
||||
if not name:
|
||||
name = "Config"
|
||||
|
||||
name = os.path.basename(name or "Config")
|
||||
|
||||
current_config_state["name"] = name
|
||||
timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
|
||||
filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
|
||||
|
||||
@ -57,7 +57,10 @@ class Upscaler:
|
||||
dest_h = int((img.height * scale) // 8 * 8)
|
||||
|
||||
for _ in range(3):
|
||||
if img.width >= dest_w and img.height >= dest_h:
|
||||
if img.width >= dest_w and img.height >= dest_h and scale != 1:
|
||||
break
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
shape = (img.width, img.height)
|
||||
|
||||
@ -69,6 +69,8 @@ def upscale_with_model(
|
||||
for y, h, row in grid.tiles:
|
||||
newrow = []
|
||||
for x, w, tile in row:
|
||||
if shared.state.interrupted:
|
||||
return img
|
||||
output = upscale_pil_patch(model, tile)
|
||||
scale_factor = output.width // tile.width
|
||||
newrow.append([x * scale_factor, w * scale_factor, output])
|
||||
|
||||
@ -148,6 +148,11 @@ class MassFileLister:
|
||||
"""Clear the cache of all directories."""
|
||||
self.cached_dirs.clear()
|
||||
|
||||
def update_file_entry(self, path):
|
||||
"""Update the cache for a specific directory."""
|
||||
dirname, filename = os.path.split(path)
|
||||
if cached_dir := self.cached_dirs.get(dirname):
|
||||
cached_dir.update_entry(filename)
|
||||
|
||||
def topological_sort(dependencies):
|
||||
"""Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
|
||||
@ -171,3 +176,38 @@ def topological_sort(dependencies):
|
||||
inner(depname)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def open_folder(path):
|
||||
"""Open a folder in the file manager of the respect OS."""
|
||||
# import at function level to avoid potential issues
|
||||
import gradio as gr
|
||||
import platform
|
||||
import sys
|
||||
import subprocess
|
||||
|
||||
if not os.path.exists(path):
|
||||
msg = f'Folder "{path}" does not exist. after you save an image, the folder will be created.'
|
||||
print(msg)
|
||||
gr.Info(msg)
|
||||
return
|
||||
elif not os.path.isdir(path):
|
||||
msg = f"""
|
||||
WARNING
|
||||
An open_folder request was made with an path that is not a folder.
|
||||
This could be an error or a malicious attempt to run code on your computer.
|
||||
Requested path was: {path}
|
||||
"""
|
||||
print(msg, file=sys.stderr)
|
||||
gr.Warning(msg)
|
||||
return
|
||||
|
||||
path = os.path.normpath(path)
|
||||
if platform.system() == "Windows":
|
||||
os.startfile(path)
|
||||
elif platform.system() == "Darwin":
|
||||
subprocess.Popen(["open", path])
|
||||
elif "microsoft-standard-WSL2" in platform.uname().release:
|
||||
subprocess.Popen(["wsl-open", path])
|
||||
else:
|
||||
subprocess.Popen(["xdg-open", path])
|
||||
|
||||
@ -30,3 +30,4 @@ torch
|
||||
torchdiffeq
|
||||
torchsde
|
||||
transformers==4.30.2
|
||||
pillow-avif-plugin==1.4.3
|
||||
@ -29,3 +29,4 @@ torchdiffeq==0.2.3
|
||||
torchsde==0.2.6
|
||||
transformers==4.30.2
|
||||
httpx==0.24.1
|
||||
pillow-avif-plugin==1.4.3
|
||||
|
||||
@ -12,6 +12,17 @@ from modules.ui import switch_values_symbol
|
||||
upscale_cache = {}
|
||||
|
||||
|
||||
def limit_size_by_one_dimention(w, h, limit):
|
||||
if h > w and h > limit:
|
||||
w = limit * w // h
|
||||
h = limit
|
||||
elif w > limit:
|
||||
h = limit * h // w
|
||||
w = limit
|
||||
|
||||
return int(w), int(h)
|
||||
|
||||
|
||||
class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Upscale"
|
||||
order = 1000
|
||||
@ -30,7 +41,11 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
with FormRow():
|
||||
with gr.Tabs(elem_id="extras_resize_mode"):
|
||||
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
|
||||
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
|
||||
with gr.Row():
|
||||
with gr.Column(scale=4):
|
||||
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
|
||||
with gr.Column(scale=1, min_width=160):
|
||||
max_side_length = gr.Number(label="Max side length", value=0, elem_id="extras_upscale_max_side_length", tooltip="If any of two sides of the image ends up larger than specified, will downscale it to fit. 0 = no limit.", min_width=160, step=8, minimum=0)
|
||||
|
||||
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
|
||||
with FormRow():
|
||||
@ -61,6 +76,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
"upscale_enabled": upscale_enabled,
|
||||
"upscale_mode": selected_tab,
|
||||
"upscale_by": upscaling_resize,
|
||||
"max_side_length": max_side_length,
|
||||
"upscale_to_width": upscaling_resize_w,
|
||||
"upscale_to_height": upscaling_resize_h,
|
||||
"upscale_crop": upscaling_crop,
|
||||
@ -69,12 +85,18 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
"upscaler_2_visibility": extras_upscaler_2_visibility,
|
||||
}
|
||||
|
||||
def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):
|
||||
def upscale(self, image, info, upscaler, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop):
|
||||
if upscale_mode == 1:
|
||||
upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
|
||||
info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
|
||||
else:
|
||||
info["Postprocess upscale by"] = upscale_by
|
||||
if max_side_length != 0 and max(*image.size)*upscale_by > max_side_length:
|
||||
upscale_mode = 1
|
||||
upscale_crop = False
|
||||
upscale_to_width, upscale_to_height = limit_size_by_one_dimention(image.width*upscale_by, image.height*upscale_by, max_side_length)
|
||||
upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
|
||||
info["Max side length"] = max_side_length
|
||||
|
||||
cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
cached_image = upscale_cache.pop(cache_key, None)
|
||||
@ -96,7 +118,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
|
||||
return image
|
||||
|
||||
def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, upscale_mode=1, upscale_by=2.0, max_side_length=0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
if upscale_mode == 1:
|
||||
pp.shared.target_width = upscale_to_width
|
||||
pp.shared.target_height = upscale_to_height
|
||||
@ -104,10 +126,13 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
pp.shared.target_width = int(pp.image.width * upscale_by)
|
||||
pp.shared.target_height = int(pp.image.height * upscale_by)
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
pp.shared.target_width, pp.shared.target_height = limit_size_by_one_dimention(pp.shared.target_width, pp.shared.target_height, max_side_length)
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_enabled=True, upscale_mode=1, upscale_by=2.0, max_side_length=0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
if not upscale_enabled:
|
||||
return
|
||||
|
||||
upscaler_1_name = upscaler_1_name
|
||||
if upscaler_1_name == "None":
|
||||
upscaler_1_name = None
|
||||
|
||||
@ -117,17 +142,20 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
if not upscaler1:
|
||||
return
|
||||
|
||||
upscaler_2_name = upscaler_2_name
|
||||
if upscaler_2_name == "None":
|
||||
upscaler_2_name = None
|
||||
|
||||
upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
|
||||
assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
|
||||
|
||||
upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
pp.info["Postprocess upscaler"] = upscaler1.name
|
||||
|
||||
if upscaler2 and upscaler_2_visibility > 0:
|
||||
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
if upscaled_image.mode != second_upscale.mode:
|
||||
second_upscale = second_upscale.convert(upscaled_image.mode)
|
||||
upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
|
||||
|
||||
pp.info["Postprocess upscaler 2"] = upscaler2.name
|
||||
@ -163,5 +191,5 @@ class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
|
||||
upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None)
|
||||
assert upscaler1, f'could not find upscaler named {upscaler_name}'
|
||||
|
||||
pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)
|
||||
pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, 0, False)
|
||||
pp.info["Postprocess upscaler"] = upscaler1.name
|
||||
|
||||
10
webui.sh
10
webui.sh
@ -113,13 +113,13 @@ then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [[ -d .git ]]
|
||||
if [[ -d "$SCRIPT_DIR/.git" ]]
|
||||
then
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Repo already cloned, using it as install directory"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
install_dir="${PWD}/../"
|
||||
clone_dir="${PWD##*/}"
|
||||
install_dir="${SCRIPT_DIR}/../"
|
||||
clone_dir="${SCRIPT_DIR##*/}"
|
||||
fi
|
||||
|
||||
# Check prerequisites
|
||||
@ -129,7 +129,7 @@ case "$gpu_info" in
|
||||
export HSA_OVERRIDE_GFX_VERSION=10.3.0
|
||||
if [[ -z "${TORCH_COMMAND}" ]]
|
||||
then
|
||||
pyv="$(${python_cmd} -c 'import sys; print(".".join(map(str, sys.version_info[0:2])))')"
|
||||
pyv="$(${python_cmd} -c 'import sys; print(f"{sys.version_info[0]}.{sys.version_info[1]:02d}")')"
|
||||
# Using an old nightly compiled against rocm 5.2 for Navi1, see https://github.com/pytorch/pytorch/issues/106728#issuecomment-1749511711
|
||||
if [[ $pyv == "3.8" ]]
|
||||
then
|
||||
@ -243,7 +243,7 @@ prepare_tcmalloc() {
|
||||
for lib in "${TCMALLOC_LIBS[@]}"
|
||||
do
|
||||
# Determine which type of tcmalloc library the library supports
|
||||
TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -P $lib | head -n 1)"
|
||||
TCMALLOC="$(PATH=/sbin:/usr/sbin:$PATH ldconfig -p | grep -P $lib | head -n 1)"
|
||||
TC_INFO=(${TCMALLOC//=>/})
|
||||
if [[ ! -z "${TC_INFO}" ]]; then
|
||||
echo "Check TCMalloc: ${TC_INFO}"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user