mirror of
https://github.com/invoke-ai/InvokeAI
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Merge branch 'main' into tiled-upscaling-graph
This commit is contained in:
skunkworxdark
commit
5816320645
@ -120,7 +120,7 @@ Generate an image with a given prompt, record the seed of the image, and then
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use the `prompt2prompt` syntax to substitute words in the original prompt for
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words in a new prompt. This works for `img2img` as well.
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For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
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For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
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- `a cat playing with a ball in the forest`
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- `a dog playing with a ball in the forest`
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@ -1,104 +1,106 @@
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# List of Default Nodes
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The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
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The table below contains a list of the default nodes shipped with InvokeAI and
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their descriptions.
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| Node <img width=160 align="right"> | Function |
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|: ---------------------------------- | :--------------------------------------------------------------------------------------|
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|Add Integers | Adds two numbers|
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|Boolean Primitive Collection | A collection of boolean primitive values|
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|Boolean Primitive | A boolean primitive value|
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|Canny Processor | Canny edge detection for ControlNet|
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|CLIP Skip | Skip layers in clip text_encoder model.|
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|Collect | Collects values into a collection|
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|Color Correct | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image.|
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|Color Primitive | A color primitive value|
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|Compel Prompt | Parse prompt using compel package to conditioning.|
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|Conditioning Primitive Collection | A collection of conditioning tensor primitive values|
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|Conditioning Primitive | A conditioning tensor primitive value|
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|Content Shuffle Processor | Applies content shuffle processing to image|
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|ControlNet | Collects ControlNet info to pass to other nodes|
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|Denoise Latents | Denoises noisy latents to decodable images|
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|Divide Integers | Divides two numbers|
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|Dynamic Prompt | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
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|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
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|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image|
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|[FaceOff](./detailedNodes/faceTools.md#faceoff) | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
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|Float Math | Perform basic math operations on two floats|
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|Float Primitive Collection | A collection of float primitive values|
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|Float Primitive | A float primitive value|
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|Float Range | Creates a range|
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|HED (softedge) Processor | Applies HED edge detection to image|
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|Blur Image | Blurs an image|
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|Extract Image Channel | Gets a channel from an image.|
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|Image Primitive Collection | A collection of image primitive values|
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|Integer Math | Perform basic math operations on two integers|
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|Convert Image Mode | Converts an image to a different mode.|
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|Crop Image | Crops an image to a specified box. The box can be outside of the image.|
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|Image Hue Adjustment | Adjusts the Hue of an image.|
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|Inverse Lerp Image | Inverse linear interpolation of all pixels of an image|
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|Image Primitive | An image primitive value|
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|Lerp Image | Linear interpolation of all pixels of an image|
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|Offset Image Channel | Add to or subtract from an image color channel by a uniform value.|
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|Multiply Image Channel | Multiply or Invert an image color channel by a scalar value.|
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|Multiply Images | Multiplies two images together using `PIL.ImageChops.multiply()`.|
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|Blur NSFW Image | Add blur to NSFW-flagged images|
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|Paste Image | Pastes an image into another image.|
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|ImageProcessor | Base class for invocations that preprocess images for ControlNet|
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|Resize Image | Resizes an image to specific dimensions|
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|Round Float | Rounds a float to a specified number of decimal places|
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|Float to Integer | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
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|Scale Image | Scales an image by a factor|
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|Image to Latents | Encodes an image into latents.|
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|Add Invisible Watermark | Add an invisible watermark to an image|
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|Solid Color Infill | Infills transparent areas of an image with a solid color|
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|PatchMatch Infill | Infills transparent areas of an image using the PatchMatch algorithm|
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|Tile Infill | Infills transparent areas of an image with tiles of the image|
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|Integer Primitive Collection | A collection of integer primitive values|
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|Integer Primitive | An integer primitive value|
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|Iterate | Iterates over a list of items|
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|Latents Primitive Collection | A collection of latents tensor primitive values|
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|Latents Primitive | A latents tensor primitive value|
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|Latents to Image | Generates an image from latents.|
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|Leres (Depth) Processor | Applies leres processing to image|
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|Lineart Anime Processor | Applies line art anime processing to image|
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|Lineart Processor | Applies line art processing to image|
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|LoRA Loader | Apply selected lora to unet and text_encoder.|
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|Main Model Loader | Loads a main model, outputting its submodels.|
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|Combine Mask | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.|
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|Mask Edge | Applies an edge mask to an image|
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|Mask from Alpha | Extracts the alpha channel of an image as a mask.|
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|Mediapipe Face Processor | Applies mediapipe face processing to image|
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|Midas (Depth) Processor | Applies Midas depth processing to image|
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|MLSD Processor | Applies MLSD processing to image|
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|Multiply Integers | Multiplies two numbers|
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|Noise | Generates latent noise.|
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|Normal BAE Processor | Applies NormalBae processing to image|
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|ONNX Latents to Image | Generates an image from latents.|
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|ONNX Prompt (Raw) | A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
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|ONNX Text to Latents | Generates latents from conditionings.|
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|ONNX Model Loader | Loads a main model, outputting its submodels.|
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|OpenCV Inpaint | Simple inpaint using opencv.|
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|Openpose Processor | Applies Openpose processing to image|
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|PIDI Processor | Applies PIDI processing to image|
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|Prompts from File | Loads prompts from a text file|
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|Random Integer | Outputs a single random integer.|
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|Random Range | Creates a collection of random numbers|
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|Integer Range | Creates a range of numbers from start to stop with step|
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|Integer Range of Size | Creates a range from start to start + size with step|
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|Resize Latents | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.|
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|SDXL Compel Prompt | Parse prompt using compel package to conditioning.|
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|SDXL LoRA Loader | Apply selected lora to unet and text_encoder.|
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|SDXL Main Model Loader | Loads an sdxl base model, outputting its submodels.|
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|SDXL Refiner Compel Prompt | Parse prompt using compel package to conditioning.|
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|SDXL Refiner Model Loader | Loads an sdxl refiner model, outputting its submodels.|
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|Scale Latents | Scales latents by a given factor.|
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|Segment Anything Processor | Applies segment anything processing to image|
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|Show Image | Displays a provided image, and passes it forward in the pipeline.|
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|Step Param Easing | Experimental per-step parameter easing for denoising steps|
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|String Primitive Collection | A collection of string primitive values|
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|String Primitive | A string primitive value|
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|Subtract Integers | Subtracts two numbers|
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|Tile Resample Processor | Tile resampler processor|
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|Upscale (RealESRGAN) | Upscales an image using RealESRGAN.|
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|VAE Loader | Loads a VAE model, outputting a VaeLoaderOutput|
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||||
|Zoe (Depth) Processor | Applies Zoe depth processing to image|
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||||
| Node <img width=160 align="right"> | Function |
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||||
| :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
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||||
| Add Integers | Adds two numbers |
|
||||
| Boolean Primitive Collection | A collection of boolean primitive values |
|
||||
| Boolean Primitive | A boolean primitive value |
|
||||
| Canny Processor | Canny edge detection for ControlNet |
|
||||
| CenterPadCrop | Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image. |
|
||||
| CLIP Skip | Skip layers in clip text_encoder model. |
|
||||
| Collect | Collects values into a collection |
|
||||
| Color Correct | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image. |
|
||||
| Color Primitive | A color primitive value |
|
||||
| Compel Prompt | Parse prompt using compel package to conditioning. |
|
||||
| Conditioning Primitive Collection | A collection of conditioning tensor primitive values |
|
||||
| Conditioning Primitive | A conditioning tensor primitive value |
|
||||
| Content Shuffle Processor | Applies content shuffle processing to image |
|
||||
| ControlNet | Collects ControlNet info to pass to other nodes |
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||||
| Denoise Latents | Denoises noisy latents to decodable images |
|
||||
| Divide Integers | Divides two numbers |
|
||||
| Dynamic Prompt | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator |
|
||||
| [FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting |
|
||||
| [FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image |
|
||||
| [FaceOff](./detailedNodes/faceTools.md#faceoff) | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting |
|
||||
| Float Math | Perform basic math operations on two floats |
|
||||
| Float Primitive Collection | A collection of float primitive values |
|
||||
| Float Primitive | A float primitive value |
|
||||
| Float Range | Creates a range |
|
||||
| HED (softedge) Processor | Applies HED edge detection to image |
|
||||
| Blur Image | Blurs an image |
|
||||
| Extract Image Channel | Gets a channel from an image. |
|
||||
| Image Primitive Collection | A collection of image primitive values |
|
||||
| Integer Math | Perform basic math operations on two integers |
|
||||
| Convert Image Mode | Converts an image to a different mode. |
|
||||
| Crop Image | Crops an image to a specified box. The box can be outside of the image. |
|
||||
| Image Hue Adjustment | Adjusts the Hue of an image. |
|
||||
| Inverse Lerp Image | Inverse linear interpolation of all pixels of an image |
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||||
| Image Primitive | An image primitive value |
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||||
| Lerp Image | Linear interpolation of all pixels of an image |
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||||
| Offset Image Channel | Add to or subtract from an image color channel by a uniform value. |
|
||||
| Multiply Image Channel | Multiply or Invert an image color channel by a scalar value. |
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||||
| Multiply Images | Multiplies two images together using `PIL.ImageChops.multiply()`. |
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||||
| Blur NSFW Image | Add blur to NSFW-flagged images |
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||||
| Paste Image | Pastes an image into another image. |
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| ImageProcessor | Base class for invocations that preprocess images for ControlNet |
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| Resize Image | Resizes an image to specific dimensions |
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||||
| Round Float | Rounds a float to a specified number of decimal places |
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||||
| Float to Integer | Converts a float to an integer. Optionally rounds to an even multiple of a input number. |
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||||
| Scale Image | Scales an image by a factor |
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||||
| Image to Latents | Encodes an image into latents. |
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||||
| Add Invisible Watermark | Add an invisible watermark to an image |
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||||
| Solid Color Infill | Infills transparent areas of an image with a solid color |
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||||
| PatchMatch Infill | Infills transparent areas of an image using the PatchMatch algorithm |
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||||
| Tile Infill | Infills transparent areas of an image with tiles of the image |
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||||
| Integer Primitive Collection | A collection of integer primitive values |
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||||
| Integer Primitive | An integer primitive value |
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| Iterate | Iterates over a list of items |
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||||
| Latents Primitive Collection | A collection of latents tensor primitive values |
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||||
| Latents Primitive | A latents tensor primitive value |
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||||
| Latents to Image | Generates an image from latents. |
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||||
| Leres (Depth) Processor | Applies leres processing to image |
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||||
| Lineart Anime Processor | Applies line art anime processing to image |
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||||
| Lineart Processor | Applies line art processing to image |
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||||
| LoRA Loader | Apply selected lora to unet and text_encoder. |
|
||||
| Main Model Loader | Loads a main model, outputting its submodels. |
|
||||
| Combine Mask | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`. |
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| Mask Edge | Applies an edge mask to an image |
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| Mask from Alpha | Extracts the alpha channel of an image as a mask. |
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||||
| Mediapipe Face Processor | Applies mediapipe face processing to image |
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||||
| Midas (Depth) Processor | Applies Midas depth processing to image |
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||||
| MLSD Processor | Applies MLSD processing to image |
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||||
| Multiply Integers | Multiplies two numbers |
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||||
| Noise | Generates latent noise. |
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| Normal BAE Processor | Applies NormalBae processing to image |
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| ONNX Latents to Image | Generates an image from latents. |
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| ONNX Prompt (Raw) | A node to process inputs and produce outputs. May use dependency injection in **init** to receive providers. |
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||||
| ONNX Text to Latents | Generates latents from conditionings. |
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| ONNX Model Loader | Loads a main model, outputting its submodels. |
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| OpenCV Inpaint | Simple inpaint using opencv. |
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| Openpose Processor | Applies Openpose processing to image |
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| PIDI Processor | Applies PIDI processing to image |
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| Prompts from File | Loads prompts from a text file |
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| Random Integer | Outputs a single random integer. |
|
||||
| Random Range | Creates a collection of random numbers |
|
||||
| Integer Range | Creates a range of numbers from start to stop with step |
|
||||
| Integer Range of Size | Creates a range from start to start + size with step |
|
||||
| Resize Latents | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. |
|
||||
| SDXL Compel Prompt | Parse prompt using compel package to conditioning. |
|
||||
| SDXL LoRA Loader | Apply selected lora to unet and text_encoder. |
|
||||
| SDXL Main Model Loader | Loads an sdxl base model, outputting its submodels. |
|
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| SDXL Refiner Compel Prompt | Parse prompt using compel package to conditioning. |
|
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| SDXL Refiner Model Loader | Loads an sdxl refiner model, outputting its submodels. |
|
||||
| Scale Latents | Scales latents by a given factor. |
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| Segment Anything Processor | Applies segment anything processing to image |
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| Show Image | Displays a provided image, and passes it forward in the pipeline. |
|
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| Step Param Easing | Experimental per-step parameter easing for denoising steps |
|
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| String Primitive Collection | A collection of string primitive values |
|
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| String Primitive | A string primitive value |
|
||||
| Subtract Integers | Subtracts two numbers |
|
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| Tile Resample Processor | Tile resampler processor |
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| Upscale (RealESRGAN) | Upscales an image using RealESRGAN. |
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| VAE Loader | Loads a VAE model, outputting a VaeLoaderOutput |
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| Zoe (Depth) Processor | Applies Zoe depth processing to image |
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@ -1,7 +1,11 @@
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import typing
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from enum import Enum
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from importlib.metadata import PackageNotFoundError, version
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from pathlib import Path
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from platform import python_version
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||||
from typing import Optional
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|
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import torch
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from fastapi import Body
|
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from fastapi.routing import APIRouter
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||||
from pydantic import BaseModel, Field
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@ -40,6 +44,24 @@ class AppVersion(BaseModel):
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||||
version: str = Field(description="App version")
|
||||
|
||||
|
||||
class AppDependencyVersions(BaseModel):
|
||||
"""App depencency Versions Response"""
|
||||
|
||||
accelerate: str = Field(description="accelerate version")
|
||||
compel: str = Field(description="compel version")
|
||||
cuda: Optional[str] = Field(description="CUDA version")
|
||||
diffusers: str = Field(description="diffusers version")
|
||||
numpy: str = Field(description="Numpy version")
|
||||
opencv: str = Field(description="OpenCV version")
|
||||
onnx: str = Field(description="ONNX version")
|
||||
pillow: str = Field(description="Pillow (PIL) version")
|
||||
python: str = Field(description="Python version")
|
||||
torch: str = Field(description="PyTorch version")
|
||||
torchvision: str = Field(description="PyTorch Vision version")
|
||||
transformers: str = Field(description="transformers version")
|
||||
xformers: Optional[str] = Field(description="xformers version")
|
||||
|
||||
|
||||
class AppConfig(BaseModel):
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||||
"""App Config Response"""
|
||||
|
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@ -54,6 +76,29 @@ async def get_version() -> AppVersion:
|
||||
return AppVersion(version=__version__)
|
||||
|
||||
|
||||
@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
|
||||
async def get_app_deps() -> AppDependencyVersions:
|
||||
try:
|
||||
xformers = version("xformers")
|
||||
except PackageNotFoundError:
|
||||
xformers = None
|
||||
return AppDependencyVersions(
|
||||
accelerate=version("accelerate"),
|
||||
compel=version("compel"),
|
||||
cuda=torch.version.cuda,
|
||||
diffusers=version("diffusers"),
|
||||
numpy=version("numpy"),
|
||||
opencv=version("opencv-python"),
|
||||
onnx=version("onnx"),
|
||||
pillow=version("pillow"),
|
||||
python=python_version(),
|
||||
torch=torch.version.__version__,
|
||||
torchvision=version("torchvision"),
|
||||
transformers=version("transformers"),
|
||||
xformers=xformers,
|
||||
)
|
||||
|
||||
|
||||
@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
|
||||
async def get_config() -> AppConfig:
|
||||
infill_methods = ["tile", "lama", "cv2"]
|
||||
|
||||
@ -141,7 +141,7 @@ async def del_model_record(
|
||||
status_code=201,
|
||||
)
|
||||
async def add_model_record(
|
||||
config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")]
|
||||
config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
|
||||
) -> AnyModelConfig:
|
||||
"""
|
||||
Add a model using the configuration information appropriate for its type.
|
||||
|
||||
@ -100,6 +100,61 @@ class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
invocation_type="img_pad_crop",
|
||||
title="Center Pad or Crop Image",
|
||||
category="image",
|
||||
tags=["image", "pad", "crop"],
|
||||
version="1.0.0",
|
||||
)
|
||||
class CenterPadCropInvocation(BaseInvocation):
|
||||
"""Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
|
||||
|
||||
image: ImageField = InputField(description="The image to crop")
|
||||
left: int = InputField(
|
||||
default=0,
|
||||
description="Number of pixels to pad/crop from the left (negative values crop inwards, positive values pad outwards)",
|
||||
)
|
||||
right: int = InputField(
|
||||
default=0,
|
||||
description="Number of pixels to pad/crop from the right (negative values crop inwards, positive values pad outwards)",
|
||||
)
|
||||
top: int = InputField(
|
||||
default=0,
|
||||
description="Number of pixels to pad/crop from the top (negative values crop inwards, positive values pad outwards)",
|
||||
)
|
||||
bottom: int = InputField(
|
||||
default=0,
|
||||
description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)",
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
# Calculate and create new image dimensions
|
||||
new_width = image.width + self.right + self.left
|
||||
new_height = image.height + self.top + self.bottom
|
||||
image_crop = Image.new(mode="RGBA", size=(new_width, new_height), color=(0, 0, 0, 0))
|
||||
|
||||
# Paste new image onto input
|
||||
image_crop.paste(image, (self.left, self.top))
|
||||
|
||||
image_dto = context.services.images.create(
|
||||
image=image_crop,
|
||||
image_origin=ResourceOrigin.INTERNAL,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.1.0")
|
||||
class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Pastes an image into another image."""
|
||||
|
||||
@ -221,7 +221,7 @@ def get_scheduler(
|
||||
title="Denoise Latents",
|
||||
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
|
||||
category="latents",
|
||||
version="1.4.0",
|
||||
version="1.5.0",
|
||||
)
|
||||
class DenoiseLatentsInvocation(BaseInvocation):
|
||||
"""Denoises noisy latents to decodable images"""
|
||||
@ -279,6 +279,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
input=Input.Connection,
|
||||
ui_order=7,
|
||||
)
|
||||
cfg_rescale_multiplier: float = InputField(
|
||||
default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
|
||||
)
|
||||
latents: Optional[LatentsField] = InputField(
|
||||
default=None,
|
||||
description=FieldDescriptions.latents,
|
||||
@ -338,6 +341,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
unconditioned_embeddings=uc,
|
||||
text_embeddings=c,
|
||||
guidance_scale=self.cfg_scale,
|
||||
guidance_rescale_multiplier=self.cfg_rescale_multiplier,
|
||||
extra=extra_conditioning_info,
|
||||
postprocessing_settings=PostprocessingSettings(
|
||||
threshold=0.0, # threshold,
|
||||
@ -1190,12 +1194,12 @@ class CropLatentsCoreInvocation(BaseInvocation):
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
x_offset: int = InputField(
|
||||
x: int = InputField(
|
||||
ge=0,
|
||||
multiple_of=LATENT_SCALE_FACTOR,
|
||||
description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
|
||||
)
|
||||
y_offset: int = InputField(
|
||||
y: int = InputField(
|
||||
ge=0,
|
||||
multiple_of=LATENT_SCALE_FACTOR,
|
||||
description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
|
||||
@ -1214,8 +1218,8 @@ class CropLatentsCoreInvocation(BaseInvocation):
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents = context.services.latents.get(self.latents.latents_name)
|
||||
|
||||
x1 = self.x_offset // LATENT_SCALE_FACTOR
|
||||
y1 = self.y_offset // LATENT_SCALE_FACTOR
|
||||
x1 = self.x // LATENT_SCALE_FACTOR
|
||||
y1 = self.y // LATENT_SCALE_FACTOR
|
||||
x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
|
||||
y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
|
||||
|
||||
|
||||
@ -127,6 +127,9 @@ class CoreMetadataInvocation(BaseInvocation):
|
||||
seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
|
||||
rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
|
||||
cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
|
||||
cfg_rescale_multiplier: Optional[float] = InputField(
|
||||
default=None, description=FieldDescriptions.cfg_rescale_multiplier
|
||||
)
|
||||
steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
|
||||
scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
|
||||
seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
|
||||
|
||||
@ -1,5 +1,3 @@
|
||||
from typing import Literal
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from pydantic import BaseModel
|
||||
@ -7,7 +5,6 @@ from pydantic import BaseModel
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
OutputField,
|
||||
@ -18,13 +15,7 @@ from invokeai.app.invocations.baseinvocation import (
|
||||
)
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.backend.tiles.tiles import (
|
||||
calc_tiles_even_split,
|
||||
calc_tiles_min_overlap,
|
||||
calc_tiles_with_overlap,
|
||||
merge_tiles_with_linear_blending,
|
||||
merge_tiles_with_seam_blending,
|
||||
)
|
||||
from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
|
||||
from invokeai.backend.tiles.utils import Tile
|
||||
|
||||
|
||||
@ -65,92 +56,12 @@ class CalculateImageTilesInvocation(BaseInvocation):
|
||||
return CalculateImageTilesOutput(tiles=tiles)
|
||||
|
||||
|
||||
@invocation(
|
||||
"calculate_image_tiles_Even_Split",
|
||||
title="Calculate Image Tiles Even Split",
|
||||
tags=["tiles"],
|
||||
category="tiles",
|
||||
version="1.0.0",
|
||||
)
|
||||
class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
|
||||
"""Calculate the coordinates and overlaps of tiles that cover a target image shape."""
|
||||
|
||||
image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
|
||||
image_height: int = InputField(
|
||||
ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
|
||||
)
|
||||
num_tiles_x: int = InputField(
|
||||
default=2,
|
||||
ge=1,
|
||||
description="Number of tiles to divide image into on the x axis",
|
||||
)
|
||||
num_tiles_y: int = InputField(
|
||||
default=2,
|
||||
ge=1,
|
||||
description="Number of tiles to divide image into on the y axis",
|
||||
)
|
||||
overlap: float = InputField(
|
||||
default=0.25,
|
||||
ge=0,
|
||||
lt=1,
|
||||
description="Overlap amount of tile size (0-1)",
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
|
||||
tiles = calc_tiles_even_split(
|
||||
image_height=self.image_height,
|
||||
image_width=self.image_width,
|
||||
num_tiles_x=self.num_tiles_x,
|
||||
num_tiles_y=self.num_tiles_y,
|
||||
overlap=self.overlap,
|
||||
)
|
||||
return CalculateImageTilesOutput(tiles=tiles)
|
||||
|
||||
|
||||
@invocation(
|
||||
"calculate_image_tiles_min_overlap",
|
||||
title="Calculate Image Tiles Minimum Overlap",
|
||||
tags=["tiles"],
|
||||
category="tiles",
|
||||
version="1.0.0",
|
||||
)
|
||||
class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
|
||||
"""Calculate the coordinates and overlaps of tiles that cover a target image shape."""
|
||||
|
||||
image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
|
||||
image_height: int = InputField(
|
||||
ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
|
||||
)
|
||||
tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
|
||||
tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
|
||||
min_overlap: int = InputField(
|
||||
default=128,
|
||||
ge=0,
|
||||
description="minimum tile overlap size (must be a multiple of 8)",
|
||||
)
|
||||
round_to_8: bool = InputField(
|
||||
default=False,
|
||||
description="Round outputs down to the nearest 8 (for pulling from a large noise field)",
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
|
||||
tiles = calc_tiles_min_overlap(
|
||||
image_height=self.image_height,
|
||||
image_width=self.image_width,
|
||||
tile_height=self.tile_height,
|
||||
tile_width=self.tile_width,
|
||||
min_overlap=self.min_overlap,
|
||||
round_to_8=self.round_to_8,
|
||||
)
|
||||
return CalculateImageTilesOutput(tiles=tiles)
|
||||
|
||||
|
||||
@invocation_output("tile_to_properties_output")
|
||||
class TileToPropertiesOutput(BaseInvocationOutput):
|
||||
coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
|
||||
coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
|
||||
coords_left: int = OutputField(description="Left coordinate of the tile relative to its parent image.")
|
||||
coords_right: int = OutputField(description="Right coordinate of the tile relative to its parent image.")
|
||||
coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
|
||||
coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
|
||||
|
||||
# HACK: The width and height fields are 'meta' fields that can easily be calculated from the other fields on this
|
||||
# object. Including redundant fields that can cheaply/easily be re-calculated goes against conventional API design
|
||||
@ -174,10 +85,10 @@ class TileToPropertiesInvocation(BaseInvocation):
|
||||
|
||||
def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
|
||||
return TileToPropertiesOutput(
|
||||
coords_top=self.tile.coords.top,
|
||||
coords_bottom=self.tile.coords.bottom,
|
||||
coords_left=self.tile.coords.left,
|
||||
coords_right=self.tile.coords.right,
|
||||
coords_top=self.tile.coords.top,
|
||||
coords_bottom=self.tile.coords.bottom,
|
||||
width=self.tile.coords.right - self.tile.coords.left,
|
||||
height=self.tile.coords.bottom - self.tile.coords.top,
|
||||
overlap_top=self.tile.overlap.top,
|
||||
@ -211,22 +122,13 @@ class PairTileImageInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
BLEND_MODES = Literal["Linear", "Seam"]
|
||||
|
||||
|
||||
@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.1.0")
|
||||
@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.0.0")
|
||||
class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Merge multiple tile images into a single image."""
|
||||
|
||||
# Inputs
|
||||
tiles_with_images: list[TileWithImage] = InputField(description="A list of tile images with tile properties.")
|
||||
blend_mode: BLEND_MODES = InputField(
|
||||
default="Seam",
|
||||
description="blending type Linear or Seam",
|
||||
input=Input.Direct,
|
||||
)
|
||||
blend_amount: int = InputField(
|
||||
default=32,
|
||||
ge=0,
|
||||
description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
|
||||
)
|
||||
@ -256,16 +158,10 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
channels = tile_np_images[0].shape[-1]
|
||||
dtype = tile_np_images[0].dtype
|
||||
np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
|
||||
if self.blend_mode == "Linear":
|
||||
merge_tiles_with_linear_blending(
|
||||
dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
|
||||
)
|
||||
else:
|
||||
merge_tiles_with_seam_blending(
|
||||
dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
|
||||
)
|
||||
|
||||
# Convert into a PIL image and save
|
||||
merge_tiles_with_linear_blending(
|
||||
dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
|
||||
)
|
||||
pil_image = Image.fromarray(np_image)
|
||||
|
||||
image_dto = context.services.images.create(
|
||||
|
||||
@ -5,6 +5,8 @@ from typing import Union
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
|
||||
from .latents_storage_base import LatentsStorageBase
|
||||
|
||||
|
||||
@ -17,6 +19,10 @@ class DiskLatentsStorage(LatentsStorageBase):
|
||||
self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
|
||||
self.__output_folder.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def start(self, invoker: Invoker) -> None:
|
||||
self._invoker = invoker
|
||||
self._delete_all_latents()
|
||||
|
||||
def get(self, name: str) -> torch.Tensor:
|
||||
latent_path = self.get_path(name)
|
||||
return torch.load(latent_path)
|
||||
@ -32,3 +38,21 @@ class DiskLatentsStorage(LatentsStorageBase):
|
||||
|
||||
def get_path(self, name: str) -> Path:
|
||||
return self.__output_folder / name
|
||||
|
||||
def _delete_all_latents(self) -> None:
|
||||
"""
|
||||
Deletes all latents from disk.
|
||||
Must be called after we have access to `self._invoker` (e.g. in `start()`).
|
||||
"""
|
||||
deleted_latents_count = 0
|
||||
freed_space = 0
|
||||
for latents_file in Path(self.__output_folder).glob("*"):
|
||||
if latents_file.is_file():
|
||||
freed_space += latents_file.stat().st_size
|
||||
deleted_latents_count += 1
|
||||
latents_file.unlink()
|
||||
if deleted_latents_count > 0:
|
||||
freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
|
||||
self._invoker.services.logger.info(
|
||||
f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
|
||||
)
|
||||
|
||||
@ -5,6 +5,8 @@ from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
|
||||
from .latents_storage_base import LatentsStorageBase
|
||||
|
||||
|
||||
@ -23,6 +25,18 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
|
||||
self.__cache_ids = Queue()
|
||||
self.__max_cache_size = max_cache_size
|
||||
|
||||
def start(self, invoker: Invoker) -> None:
|
||||
self._invoker = invoker
|
||||
start_op = getattr(self.__underlying_storage, "start", None)
|
||||
if callable(start_op):
|
||||
start_op(invoker)
|
||||
|
||||
def stop(self, invoker: Invoker) -> None:
|
||||
self._invoker = invoker
|
||||
stop_op = getattr(self.__underlying_storage, "stop", None)
|
||||
if callable(stop_op):
|
||||
stop_op(invoker)
|
||||
|
||||
def get(self, name: str) -> torch.Tensor:
|
||||
cache_item = self.__get_cache(name)
|
||||
if cache_item is not None:
|
||||
|
||||
@ -42,7 +42,8 @@ class SqliteSessionQueue(SessionQueueBase):
|
||||
self._set_in_progress_to_canceled()
|
||||
prune_result = self.prune(DEFAULT_QUEUE_ID)
|
||||
local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_session_event)
|
||||
self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
|
||||
if prune_result.deleted > 0:
|
||||
self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
|
||||
|
||||
def __init__(self, db: SqliteDatabase) -> None:
|
||||
super().__init__()
|
||||
|
||||
@ -207,10 +207,12 @@ class IterateInvocationOutput(BaseInvocationOutput):
|
||||
item: Any = OutputField(
|
||||
description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
|
||||
)
|
||||
index: int = OutputField(description="The index of the item", title="Index")
|
||||
total: int = OutputField(description="The total number of items", title="Total")
|
||||
|
||||
|
||||
# TODO: Fill this out and move to invocations
|
||||
@invocation("iterate", version="1.0.0")
|
||||
@invocation("iterate", version="1.1.0")
|
||||
class IterateInvocation(BaseInvocation):
|
||||
"""Iterates over a list of items"""
|
||||
|
||||
@ -221,7 +223,7 @@ class IterateInvocation(BaseInvocation):
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
|
||||
"""Produces the outputs as values"""
|
||||
return IterateInvocationOutput(item=self.collection[self.index])
|
||||
return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
|
||||
|
||||
|
||||
@invocation_output("collect_output")
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import sqlite3
|
||||
import threading
|
||||
from logging import Logger
|
||||
from pathlib import Path
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
@ -8,25 +9,20 @@ sqlite_memory = ":memory:"
|
||||
|
||||
|
||||
class SqliteDatabase:
|
||||
conn: sqlite3.Connection
|
||||
lock: threading.RLock
|
||||
_logger: Logger
|
||||
_config: InvokeAIAppConfig
|
||||
|
||||
def __init__(self, config: InvokeAIAppConfig, logger: Logger):
|
||||
self._logger = logger
|
||||
self._config = config
|
||||
|
||||
if self._config.use_memory_db:
|
||||
location = sqlite_memory
|
||||
self.db_path = sqlite_memory
|
||||
logger.info("Using in-memory database")
|
||||
else:
|
||||
db_path = self._config.db_path
|
||||
db_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
location = str(db_path)
|
||||
self._logger.info(f"Using database at {location}")
|
||||
self.db_path = str(db_path)
|
||||
self._logger.info(f"Using database at {self.db_path}")
|
||||
|
||||
self.conn = sqlite3.connect(location, check_same_thread=False)
|
||||
self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
|
||||
self.lock = threading.RLock()
|
||||
self.conn.row_factory = sqlite3.Row
|
||||
|
||||
@ -37,10 +33,16 @@ class SqliteDatabase:
|
||||
|
||||
def clean(self) -> None:
|
||||
try:
|
||||
if self.db_path == sqlite_memory:
|
||||
return
|
||||
initial_db_size = Path(self.db_path).stat().st_size
|
||||
self.lock.acquire()
|
||||
self.conn.execute("VACUUM;")
|
||||
self.conn.commit()
|
||||
self._logger.info("Cleaned database")
|
||||
final_db_size = Path(self.db_path).stat().st_size
|
||||
freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
|
||||
if freed_space_in_mb > 0:
|
||||
self._logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
|
||||
except Exception as e:
|
||||
self._logger.error(f"Error cleaning database: {e}")
|
||||
raise e
|
||||
|
||||
@ -2,6 +2,7 @@ class FieldDescriptions:
|
||||
denoising_start = "When to start denoising, expressed a percentage of total steps"
|
||||
denoising_end = "When to stop denoising, expressed a percentage of total steps"
|
||||
cfg_scale = "Classifier-Free Guidance scale"
|
||||
cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
|
||||
scheduler = "Scheduler to use during inference"
|
||||
positive_cond = "Positive conditioning tensor"
|
||||
negative_cond = "Negative conditioning tensor"
|
||||
|
||||
@ -54,6 +54,44 @@ class ImageProjModel(torch.nn.Module):
|
||||
return clip_extra_context_tokens
|
||||
|
||||
|
||||
class MLPProjModel(torch.nn.Module):
|
||||
"""SD model with image prompt"""
|
||||
|
||||
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
|
||||
super().__init__()
|
||||
|
||||
self.proj = torch.nn.Sequential(
|
||||
torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
|
||||
torch.nn.GELU(),
|
||||
torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
|
||||
torch.nn.LayerNorm(cross_attention_dim),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_state_dict(cls, state_dict: dict[torch.Tensor]):
|
||||
"""Initialize an MLPProjModel from a state_dict.
|
||||
|
||||
The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
|
||||
|
||||
Args:
|
||||
state_dict (dict[torch.Tensor]): The state_dict of model weights.
|
||||
|
||||
Returns:
|
||||
MLPProjModel
|
||||
"""
|
||||
cross_attention_dim = state_dict["proj.3.weight"].shape[0]
|
||||
clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
|
||||
|
||||
model = cls(cross_attention_dim, clip_embeddings_dim)
|
||||
|
||||
model.load_state_dict(state_dict)
|
||||
return model
|
||||
|
||||
def forward(self, image_embeds):
|
||||
clip_extra_context_tokens = self.proj(image_embeds)
|
||||
return clip_extra_context_tokens
|
||||
|
||||
|
||||
class IPAdapter:
|
||||
"""IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
|
||||
|
||||
@ -130,6 +168,13 @@ class IPAdapterPlus(IPAdapter):
|
||||
return image_prompt_embeds, uncond_image_prompt_embeds
|
||||
|
||||
|
||||
class IPAdapterFull(IPAdapterPlus):
|
||||
"""IP-Adapter Plus with full features."""
|
||||
|
||||
def _init_image_proj_model(self, state_dict: dict[torch.Tensor]):
|
||||
return MLPProjModel.from_state_dict(state_dict).to(self.device, dtype=self.dtype)
|
||||
|
||||
|
||||
class IPAdapterPlusXL(IPAdapterPlus):
|
||||
"""IP-Adapter Plus for SDXL."""
|
||||
|
||||
@ -149,11 +194,9 @@ def build_ip_adapter(
|
||||
) -> Union[IPAdapter, IPAdapterPlus]:
|
||||
state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
|
||||
|
||||
# Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
|
||||
# contains.
|
||||
is_plus = "proj.weight" not in state_dict["image_proj"]
|
||||
|
||||
if is_plus:
|
||||
if "proj.weight" in state_dict["image_proj"]: # IPAdapter (with ImageProjModel).
|
||||
return IPAdapter(state_dict, device=device, dtype=dtype)
|
||||
elif "proj_in.weight" in state_dict["image_proj"]: # IPAdaterPlus or IPAdapterPlusXL (with Resampler).
|
||||
cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
|
||||
if cross_attention_dim == 768:
|
||||
# SD1 IP-Adapter Plus
|
||||
@ -163,5 +206,7 @@ def build_ip_adapter(
|
||||
return IPAdapterPlusXL(state_dict, device=device, dtype=dtype)
|
||||
else:
|
||||
raise Exception(f"Unsupported IP-Adapter Plus cross-attention dimension: {cross_attention_dim}.")
|
||||
elif "proj.0.weight" in state_dict["image_proj"]: # IPAdapterFull (with MLPProjModel).
|
||||
return IPAdapterFull(state_dict, device=device, dtype=dtype)
|
||||
else:
|
||||
return IPAdapter(state_dict, device=device, dtype=dtype)
|
||||
raise ValueError(f"'{ip_adapter_ckpt_path}' has an unrecognized IP-Adapter model architecture.")
|
||||
|
||||
@ -192,20 +192,33 @@ class ModelPatcher:
|
||||
trigger += f"-!pad-{i}"
|
||||
return f"<{trigger}>"
|
||||
|
||||
def _get_ti_embedding(model_embeddings, ti):
|
||||
# for SDXL models, select the embedding that matches the text encoder's dimensions
|
||||
if ti.embedding_2 is not None:
|
||||
return (
|
||||
ti.embedding_2
|
||||
if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0]
|
||||
else ti.embedding
|
||||
)
|
||||
else:
|
||||
return ti.embedding
|
||||
|
||||
# modify tokenizer
|
||||
new_tokens_added = 0
|
||||
for ti_name, ti in ti_list:
|
||||
for i in range(ti.embedding.shape[0]):
|
||||
ti_embedding = _get_ti_embedding(text_encoder.get_input_embeddings(), ti)
|
||||
|
||||
for i in range(ti_embedding.shape[0]):
|
||||
new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
|
||||
|
||||
# modify text_encoder
|
||||
text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
|
||||
model_embeddings = text_encoder.get_input_embeddings()
|
||||
|
||||
for ti_name, ti in ti_list:
|
||||
for ti_name, _ in ti_list:
|
||||
ti_tokens = []
|
||||
for i in range(ti.embedding.shape[0]):
|
||||
embedding = ti.embedding[i]
|
||||
for i in range(ti_embedding.shape[0]):
|
||||
embedding = ti_embedding[i]
|
||||
trigger = _get_trigger(ti_name, i)
|
||||
|
||||
token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
|
||||
@ -273,6 +286,7 @@ class ModelPatcher:
|
||||
|
||||
class TextualInversionModel:
|
||||
embedding: torch.Tensor # [n, 768]|[n, 1280]
|
||||
embedding_2: Optional[torch.Tensor] = None # [n, 768]|[n, 1280] - for SDXL models
|
||||
|
||||
@classmethod
|
||||
def from_checkpoint(
|
||||
@ -296,8 +310,8 @@ class TextualInversionModel:
|
||||
if "string_to_param" in state_dict:
|
||||
if len(state_dict["string_to_param"]) > 1:
|
||||
print(
|
||||
f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first'
|
||||
" token will be used."
|
||||
f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first',
|
||||
" token will be used.",
|
||||
)
|
||||
|
||||
result.embedding = next(iter(state_dict["string_to_param"].values()))
|
||||
@ -306,6 +320,11 @@ class TextualInversionModel:
|
||||
elif "emb_params" in state_dict:
|
||||
result.embedding = state_dict["emb_params"]
|
||||
|
||||
# v5(sdxl safetensors file)
|
||||
elif "clip_g" in state_dict and "clip_l" in state_dict:
|
||||
result.embedding = state_dict["clip_g"]
|
||||
result.embedding_2 = state_dict["clip_l"]
|
||||
|
||||
# v4(diffusers bin files)
|
||||
else:
|
||||
result.embedding = next(iter(state_dict.values()))
|
||||
@ -342,6 +361,13 @@ class TextualInversionManager(BaseTextualInversionManager):
|
||||
if token_id in self.pad_tokens:
|
||||
new_token_ids.extend(self.pad_tokens[token_id])
|
||||
|
||||
# Do not exceed the max model input size
|
||||
# The -2 here is compensating for compensate compel.embeddings_provider.get_token_ids(),
|
||||
# which first removes and then adds back the start and end tokens.
|
||||
max_length = list(self.tokenizer.max_model_input_sizes.values())[0] - 2
|
||||
if len(new_token_ids) > max_length:
|
||||
new_token_ids = new_token_ids[0:max_length]
|
||||
|
||||
return new_token_ids
|
||||
|
||||
|
||||
@ -490,24 +516,31 @@ class ONNXModelPatcher:
|
||||
trigger += f"-!pad-{i}"
|
||||
return f"<{trigger}>"
|
||||
|
||||
# modify text_encoder
|
||||
orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
|
||||
|
||||
# modify tokenizer
|
||||
new_tokens_added = 0
|
||||
for ti_name, ti in ti_list:
|
||||
for i in range(ti.embedding.shape[0]):
|
||||
new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
|
||||
if ti.embedding_2 is not None:
|
||||
ti_embedding = (
|
||||
ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
|
||||
)
|
||||
else:
|
||||
ti_embedding = ti.embedding
|
||||
|
||||
# modify text_encoder
|
||||
orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
|
||||
for i in range(ti_embedding.shape[0]):
|
||||
new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
|
||||
|
||||
embeddings = np.concatenate(
|
||||
(np.copy(orig_embeddings), np.zeros((new_tokens_added, orig_embeddings.shape[1]))),
|
||||
axis=0,
|
||||
)
|
||||
|
||||
for ti_name, ti in ti_list:
|
||||
for ti_name, _ in ti_list:
|
||||
ti_tokens = []
|
||||
for i in range(ti.embedding.shape[0]):
|
||||
embedding = ti.embedding[i].detach().numpy()
|
||||
for i in range(ti_embedding.shape[0]):
|
||||
embedding = ti_embedding[i].detach().numpy()
|
||||
trigger = _get_trigger(ti_name, i)
|
||||
|
||||
token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
|
||||
|
||||
@ -373,12 +373,16 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
|
||||
token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
|
||||
elif "emb_params" in checkpoint:
|
||||
token_dim = checkpoint["emb_params"].shape[-1]
|
||||
elif "clip_g" in checkpoint:
|
||||
token_dim = checkpoint["clip_g"].shape[-1]
|
||||
else:
|
||||
token_dim = list(checkpoint.values())[0].shape[0]
|
||||
if token_dim == 768:
|
||||
return BaseModelType.StableDiffusion1
|
||||
elif token_dim == 1024:
|
||||
return BaseModelType.StableDiffusion2
|
||||
elif token_dim == 1280:
|
||||
return BaseModelType.StableDiffusionXL
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
@ -607,11 +607,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
if isinstance(guidance_scale, list):
|
||||
guidance_scale = guidance_scale[step_index]
|
||||
|
||||
noise_pred = self.invokeai_diffuser._combine(
|
||||
uc_noise_pred,
|
||||
c_noise_pred,
|
||||
guidance_scale,
|
||||
)
|
||||
noise_pred = self.invokeai_diffuser._combine(uc_noise_pred, c_noise_pred, guidance_scale)
|
||||
guidance_rescale_multiplier = conditioning_data.guidance_rescale_multiplier
|
||||
if guidance_rescale_multiplier > 0:
|
||||
noise_pred = self._rescale_cfg(
|
||||
noise_pred,
|
||||
c_noise_pred,
|
||||
guidance_rescale_multiplier,
|
||||
)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
|
||||
@ -634,6 +637,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
|
||||
return step_output
|
||||
|
||||
@staticmethod
|
||||
def _rescale_cfg(total_noise_pred, pos_noise_pred, multiplier=0.7):
|
||||
"""Implementation of Algorithm 2 from https://arxiv.org/pdf/2305.08891.pdf."""
|
||||
ro_pos = torch.std(pos_noise_pred, dim=(1, 2, 3), keepdim=True)
|
||||
ro_cfg = torch.std(total_noise_pred, dim=(1, 2, 3), keepdim=True)
|
||||
|
||||
x_rescaled = total_noise_pred * (ro_pos / ro_cfg)
|
||||
x_final = multiplier * x_rescaled + (1.0 - multiplier) * total_noise_pred
|
||||
return x_final
|
||||
|
||||
def _unet_forward(
|
||||
self,
|
||||
latents,
|
||||
|
||||
@ -67,13 +67,17 @@ class IPAdapterConditioningInfo:
|
||||
class ConditioningData:
|
||||
unconditioned_embeddings: BasicConditioningInfo
|
||||
text_embeddings: BasicConditioningInfo
|
||||
guidance_scale: Union[float, List[float]]
|
||||
"""
|
||||
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
||||
`guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
|
||||
Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
|
||||
images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
|
||||
"""
|
||||
guidance_scale: Union[float, List[float]]
|
||||
""" for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
|
||||
ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
|
||||
"""
|
||||
guidance_rescale_multiplier: float = 0
|
||||
extra: Optional[ExtraConditioningInfo] = None
|
||||
scheduler_args: dict[str, Any] = field(default_factory=dict)
|
||||
"""
|
||||
|
||||
@ -1,8 +1,9 @@
|
||||
import math
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from invokeai.backend.tiles.utils import TBLR, Tile, calc_overlap, paste, seam_blend
|
||||
from invokeai.backend.tiles.utils import TBLR, Tile, paste
|
||||
|
||||
|
||||
def calc_tiles_with_overlap(
|
||||
@ -62,117 +63,31 @@ def calc_tiles_with_overlap(
|
||||
|
||||
tiles.append(tile)
|
||||
|
||||
return calc_overlap(tiles, num_tiles_x, num_tiles_y)
|
||||
def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
|
||||
if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
|
||||
return None
|
||||
return tiles[idx_y * num_tiles_x + idx_x]
|
||||
|
||||
|
||||
def calc_tiles_even_split(
|
||||
image_height: int, image_width: int, num_tiles_x: int, num_tiles_y: int, overlap: float = 0
|
||||
) -> list[Tile]:
|
||||
"""Calculate the tile coordinates for a given image shape with the number of tiles requested.
|
||||
|
||||
Args:
|
||||
image_height (int): The image height in px.
|
||||
image_width (int): The image width in px.
|
||||
num_x_tiles (int): The number of tile to split the image into on the X-axis.
|
||||
num_y_tiles (int): The number of tile to split the image into on the Y-axis.
|
||||
overlap (int, optional): The target overlap amount of the tiles size. Defaults to 0.
|
||||
|
||||
Returns:
|
||||
list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
|
||||
"""
|
||||
|
||||
# Ensure tile size is divisible by 8
|
||||
if image_width % 8 != 0 or image_height % 8 != 0:
|
||||
raise ValueError(f"image size (({image_width}, {image_height})) must be divisible by 8")
|
||||
|
||||
# Calculate the overlap size based on the percentage and adjust it to be divisible by 8 (rounding up)
|
||||
overlap_x = 8 * math.ceil(int((image_width / num_tiles_x) * overlap) / 8)
|
||||
overlap_y = 8 * math.ceil(int((image_height / num_tiles_y) * overlap) / 8)
|
||||
|
||||
# Calculate the tile size based on the number of tiles and overlap, and ensure it's divisible by 8 (rounding down)
|
||||
tile_size_x = 8 * math.floor(((image_width + overlap_x * (num_tiles_x - 1)) // num_tiles_x) / 8)
|
||||
tile_size_y = 8 * math.floor(((image_height + overlap_y * (num_tiles_y - 1)) // num_tiles_y) / 8)
|
||||
|
||||
# tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
|
||||
tiles: list[Tile] = []
|
||||
|
||||
# Calculate tile coordinates. (Ignore overlap values for now.)
|
||||
# Iterate over tiles again and calculate overlaps.
|
||||
for tile_idx_y in range(num_tiles_y):
|
||||
# Calculate the top and bottom of the row
|
||||
top = tile_idx_y * (tile_size_y - overlap_y)
|
||||
bottom = min(top + tile_size_y, image_height)
|
||||
# For the last row adjust bottom to be the height of the image
|
||||
if tile_idx_y == num_tiles_y - 1:
|
||||
bottom = image_height
|
||||
|
||||
for tile_idx_x in range(num_tiles_x):
|
||||
# Calculate the left & right coordinate of each tile
|
||||
left = tile_idx_x * (tile_size_x - overlap_x)
|
||||
right = min(left + tile_size_x, image_width)
|
||||
# For the last tile in the row adjust right to be the width of the image
|
||||
if tile_idx_x == num_tiles_x - 1:
|
||||
right = image_width
|
||||
cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
|
||||
top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
|
||||
left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
|
||||
|
||||
tile = Tile(
|
||||
coords=TBLR(top=top, bottom=bottom, left=left, right=right),
|
||||
overlap=TBLR(top=0, bottom=0, left=0, right=0),
|
||||
)
|
||||
assert cur_tile is not None
|
||||
|
||||
tiles.append(tile)
|
||||
# Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
|
||||
if top_neighbor_tile is not None:
|
||||
cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
|
||||
top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
|
||||
|
||||
return calc_overlap(tiles, num_tiles_x, num_tiles_y)
|
||||
# Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
|
||||
if left_neighbor_tile is not None:
|
||||
cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
|
||||
left_neighbor_tile.overlap.right = cur_tile.overlap.left
|
||||
|
||||
|
||||
def calc_tiles_min_overlap(
|
||||
image_height: int, image_width: int, tile_height: int, tile_width: int, min_overlap: int, round_to_8: bool
|
||||
) -> list[Tile]:
|
||||
"""Calculate the tile coordinates for a given image shape under a simple tiling scheme with overlaps.
|
||||
|
||||
Args:
|
||||
image_height (int): The image height in px.
|
||||
image_width (int): The image width in px.
|
||||
tile_height (int): The tile height in px. All tiles will have this height.
|
||||
tile_width (int): The tile width in px. All tiles will have this width.
|
||||
min_overlap (int): The target minimum overlap between adjacent tiles. If the tiles do not evenly cover the image
|
||||
shape, then the overlap will be spread between the tiles.
|
||||
|
||||
Returns:
|
||||
list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
|
||||
"""
|
||||
assert image_height >= tile_height
|
||||
assert image_width >= tile_width
|
||||
assert min_overlap < tile_height
|
||||
assert min_overlap < tile_width
|
||||
|
||||
num_tiles_x = math.ceil((image_width - min_overlap) / (tile_width - min_overlap)) if tile_width < image_width else 1
|
||||
num_tiles_y = (
|
||||
math.ceil((image_height - min_overlap) / (tile_height - min_overlap)) if tile_height < image_height else 1
|
||||
)
|
||||
|
||||
# tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
|
||||
tiles: list[Tile] = []
|
||||
|
||||
# Calculate tile coordinates. (Ignore overlap values for now.)
|
||||
for tile_idx_y in range(num_tiles_y):
|
||||
top = (tile_idx_y * (image_height - tile_height)) // (num_tiles_y - 1) if num_tiles_y > 1 else 0
|
||||
if round_to_8:
|
||||
top = 8 * (top // 8)
|
||||
bottom = top + tile_height
|
||||
|
||||
for tile_idx_x in range(num_tiles_x):
|
||||
left = (tile_idx_x * (image_width - tile_width)) // (num_tiles_x - 1) if num_tiles_x > 1 else 0
|
||||
if round_to_8:
|
||||
left = 8 * (left // 8)
|
||||
right = left + tile_width
|
||||
|
||||
tile = Tile(
|
||||
coords=TBLR(top=top, bottom=bottom, left=left, right=right),
|
||||
overlap=TBLR(top=0, bottom=0, left=0, right=0),
|
||||
)
|
||||
|
||||
tiles.append(tile)
|
||||
|
||||
return calc_overlap(tiles, num_tiles_x, num_tiles_y)
|
||||
return tiles
|
||||
|
||||
|
||||
def merge_tiles_with_linear_blending(
|
||||
@ -284,91 +199,3 @@ def merge_tiles_with_linear_blending(
|
||||
),
|
||||
mask=mask,
|
||||
)
|
||||
|
||||
|
||||
def merge_tiles_with_seam_blending(
|
||||
dst_image: np.ndarray, tiles: list[Tile], tile_images: list[np.ndarray], blend_amount: int
|
||||
):
|
||||
"""Merge a set of image tiles into `dst_image` with seam blending between the tiles.
|
||||
|
||||
We expect every tile edge to either:
|
||||
1) have an overlap of 0, because it is aligned with the image edge, or
|
||||
2) have an overlap >= blend_amount.
|
||||
If neither of these conditions are satisfied, we raise an exception.
|
||||
|
||||
The seam blending is centered on a seam of least energy of the overlap between adjacent tiles.
|
||||
|
||||
Args:
|
||||
dst_image (np.ndarray): The destination image. Shape: (H, W, C).
|
||||
tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
|
||||
tile_images (list[np.ndarray]): The tile images to merge into `dst_image`.
|
||||
blend_amount (int): The amount of blending (in px) between adjacent overlapping tiles.
|
||||
"""
|
||||
# Sort tiles and images first by left x coordinate, then by top y coordinate. During tile processing, we want to
|
||||
# iterate over tiles left-to-right, top-to-bottom.
|
||||
tiles_and_images = list(zip(tiles, tile_images, strict=True))
|
||||
tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.left)
|
||||
tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.top)
|
||||
|
||||
# Organize tiles into rows.
|
||||
tile_and_image_rows: list[list[tuple[Tile, np.ndarray]]] = []
|
||||
cur_tile_and_image_row: list[tuple[Tile, np.ndarray]] = []
|
||||
first_tile_in_cur_row, _ = tiles_and_images[0]
|
||||
for tile_and_image in tiles_and_images:
|
||||
tile, _ = tile_and_image
|
||||
if not (
|
||||
tile.coords.top == first_tile_in_cur_row.coords.top
|
||||
and tile.coords.bottom == first_tile_in_cur_row.coords.bottom
|
||||
):
|
||||
# Store the previous row, and start a new one.
|
||||
tile_and_image_rows.append(cur_tile_and_image_row)
|
||||
cur_tile_and_image_row = []
|
||||
first_tile_in_cur_row, _ = tile_and_image
|
||||
|
||||
cur_tile_and_image_row.append(tile_and_image)
|
||||
tile_and_image_rows.append(cur_tile_and_image_row)
|
||||
|
||||
for tile_and_image_row in tile_and_image_rows:
|
||||
first_tile_in_row, _ = tile_and_image_row[0]
|
||||
row_height = first_tile_in_row.coords.bottom - first_tile_in_row.coords.top
|
||||
row_image = np.zeros((row_height, dst_image.shape[1], dst_image.shape[2]), dtype=dst_image.dtype)
|
||||
|
||||
# Blend the tiles in the row horizontally.
|
||||
for tile, tile_image in tile_and_image_row:
|
||||
# We expect the tiles to be ordered left-to-right.
|
||||
# For each tile:
|
||||
# - extract the overlap regions and pass to seam_blend()
|
||||
# - apply blended region to the row_image
|
||||
# - apply the un-blended region to the row_image
|
||||
tile_height, tile_width, _ = tile_image.shape
|
||||
overlap_size = tile.overlap.left
|
||||
# Left blending:
|
||||
if overlap_size > 0:
|
||||
assert overlap_size >= blend_amount
|
||||
|
||||
overlap_coord_right = tile.coords.left + overlap_size
|
||||
src_overlap = row_image[:, tile.coords.left : overlap_coord_right]
|
||||
dst_overlap = tile_image[:, :overlap_size]
|
||||
blended_overlap = seam_blend(src_overlap, dst_overlap, blend_amount, x_seam=False)
|
||||
row_image[:, tile.coords.left : overlap_coord_right] = blended_overlap
|
||||
row_image[:, overlap_coord_right : tile.coords.right] = tile_image[:, overlap_size:]
|
||||
else:
|
||||
# no overlap just paste the tile
|
||||
row_image[:, tile.coords.left : tile.coords.right] = tile_image
|
||||
|
||||
# Blend the row into the dst_image
|
||||
# We assume that the entire row has the same vertical overlaps as the first_tile_in_row.
|
||||
# Rows are processed in the same way as tiles (extract overlap, blend, apply)
|
||||
row_overlap_size = first_tile_in_row.overlap.top
|
||||
if row_overlap_size > 0:
|
||||
assert row_overlap_size >= blend_amount
|
||||
|
||||
overlap_coords_bottom = first_tile_in_row.coords.top + row_overlap_size
|
||||
src_overlap = dst_image[first_tile_in_row.coords.top : overlap_coords_bottom, :]
|
||||
dst_overlap = row_image[:row_overlap_size, :]
|
||||
blended_overlap = seam_blend(src_overlap, dst_overlap, blend_amount, x_seam=True)
|
||||
dst_image[first_tile_in_row.coords.top : overlap_coords_bottom, :] = blended_overlap
|
||||
dst_image[overlap_coords_bottom : first_tile_in_row.coords.bottom, :] = row_image[row_overlap_size:, :]
|
||||
else:
|
||||
# no overlap just paste the row
|
||||
row_image[first_tile_in_row.coords.top:first_tile_in_row.coords.bottom, :] = row_image
|
||||
|
||||
@ -1,9 +1,6 @@
|
||||
import math
|
||||
from typing import Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
#from PIL import Image
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
@ -48,130 +45,3 @@ def paste(dst_image: np.ndarray, src_image: np.ndarray, box: TBLR, mask: Optiona
|
||||
mask = np.expand_dims(mask, -1)
|
||||
dst_image_box = dst_image[box.top : box.bottom, box.left : box.right]
|
||||
dst_image[box.top : box.bottom, box.left : box.right] = src_image * mask + dst_image_box * (1.0 - mask)
|
||||
|
||||
|
||||
def calc_overlap(tiles: list[Tile], num_tiles_x, num_tiles_y) -> list[Tile]:
|
||||
"""Calculate and update the overlap of a list of tiles.
|
||||
|
||||
Args:
|
||||
tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
|
||||
num_tiles_x: the number of tiles on the x axis.
|
||||
num_tiles_y: the number of tiles on the y axis.
|
||||
"""
|
||||
def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
|
||||
if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
|
||||
return None
|
||||
return tiles[idx_y * num_tiles_x + idx_x]
|
||||
|
||||
for tile_idx_y in range(num_tiles_y):
|
||||
for tile_idx_x in range(num_tiles_x):
|
||||
cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
|
||||
top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
|
||||
left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
|
||||
|
||||
assert cur_tile is not None
|
||||
|
||||
# Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
|
||||
if top_neighbor_tile is not None:
|
||||
cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
|
||||
top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
|
||||
|
||||
# Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
|
||||
if left_neighbor_tile is not None:
|
||||
cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
|
||||
left_neighbor_tile.overlap.right = cur_tile.overlap.left
|
||||
return tiles
|
||||
|
||||
|
||||
def seam_blend(ia1: np.ndarray, ia2: np.ndarray, blend_amount: int, x_seam: bool,) -> np.ndarray:
|
||||
"""Blend two overlapping tile sections using a seams to find a path.
|
||||
|
||||
It is assumed that input images will be RGB np arrays and are the same size.
|
||||
|
||||
Args:
|
||||
ia1 (torch.Tensor): Image array 1 Shape: (H, W, C).
|
||||
ia2 (torch.Tensor): Image array 2 Shape: (H, W, C).
|
||||
x_seam (bool): If the images should be blended on the x axis or not.
|
||||
blend_amount (int): The size of the blur to use on the seam. Half of this value will be used to avoid the edges of the image.
|
||||
"""
|
||||
|
||||
def shift(arr, num, fill_value=255.0):
|
||||
result = np.full_like(arr, fill_value)
|
||||
if num > 0:
|
||||
result[num:] = arr[:-num]
|
||||
elif num < 0:
|
||||
result[:num] = arr[-num:]
|
||||
else:
|
||||
result[:] = arr
|
||||
return result
|
||||
|
||||
# Assume RGB and convert to grey
|
||||
iag1 = np.dot(ia1, [0.2989, 0.5870, 0.1140])
|
||||
iag2 = np.dot(ia2, [0.2989, 0.5870, 0.1140])
|
||||
|
||||
# Calc Difference between the images
|
||||
ia = iag2 - iag1
|
||||
|
||||
# If the seam is on the X-axis rotate the array so we can treat it like a vertical seam
|
||||
if x_seam:
|
||||
ia = np.rot90(ia, 1)
|
||||
|
||||
# Calc max and min X & Y limits
|
||||
# gutter is used to avoid the blur hitting the edge of the image
|
||||
gutter = math.ceil(blend_amount / 2) if blend_amount > 0 else 0
|
||||
max_y, max_x = ia.shape
|
||||
max_x -= gutter
|
||||
min_x = gutter
|
||||
|
||||
# Calc the energy in the difference
|
||||
energy = np.abs(np.gradient(ia, axis=0)) + np.abs(np.gradient(ia, axis=1))
|
||||
|
||||
#Find the starting position of the seam
|
||||
res = np.copy(energy)
|
||||
for y in range(1, max_y):
|
||||
row = res[y, :]
|
||||
rowl = shift(row, -1)
|
||||
rowr = shift(row, 1)
|
||||
res[y, :] = res[y - 1, :] + np.min([row, rowl, rowr], axis=0)
|
||||
|
||||
# create an array max_y long
|
||||
lowest_energy_line = np.empty([max_y], dtype="uint16")
|
||||
lowest_energy_line[max_y - 1] = np.argmin(res[max_y - 1, min_x : max_x - 1])
|
||||
|
||||
#Calc the path of the seam
|
||||
for ypos in range(max_y - 2, -1, -1):
|
||||
lowest_pos = lowest_energy_line[ypos + 1]
|
||||
lpos = lowest_pos - 1
|
||||
rpos = lowest_pos + 1
|
||||
lpos = np.clip(lpos, min_x, max_x - 1)
|
||||
rpos = np.clip(rpos, min_x, max_x - 1)
|
||||
lowest_energy_line[ypos] = np.argmin(energy[ypos, lpos : rpos + 1]) + lpos
|
||||
|
||||
# Draw the mask
|
||||
mask = np.zeros_like(ia)
|
||||
for ypos in range(0, max_y):
|
||||
to_fill = lowest_energy_line[ypos]
|
||||
mask[ypos, :to_fill] = 1
|
||||
|
||||
# If the seam is on the X-axis rotate the array back
|
||||
if x_seam:
|
||||
mask = np.rot90(mask, 3)
|
||||
|
||||
# blur the seam mask if required
|
||||
if blend_amount > 0:
|
||||
mask = cv2.blur(mask, (blend_amount, blend_amount))
|
||||
|
||||
# copy ia2 over ia1 while applying the seam mask
|
||||
mask = np.expand_dims(mask, -1)
|
||||
blended_image = ia1 * mask + ia2 * (1.0 - mask)
|
||||
|
||||
# for debugging to see the final blended overlap image
|
||||
#image = Image.fromarray((mask * 255.0).astype("uint8"))
|
||||
#i1 = Image.fromarray(ia1.astype("uint8"))
|
||||
#i2 = Image.fromarray(ia2.astype("uint8"))
|
||||
#bimage = Image.fromarray(blended_image.astype("uint8"))
|
||||
|
||||
#print(f"{ia1.shape}, {ia2.shape}, {mask.shape}, {blended_image.shape}")
|
||||
#print(f"{i1.size}, {i2.size}, {image.size}, {bimage.size}")
|
||||
|
||||
return blended_image
|
||||
|
||||
@ -342,14 +342,13 @@ class InvokeAILogger(object): # noqa D102
|
||||
cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
|
||||
) -> logging.Logger: # noqa D102
|
||||
if name in cls.loggers:
|
||||
logger = cls.loggers[name]
|
||||
logger.handlers.clear()
|
||||
else:
|
||||
logger = logging.getLogger(name)
|
||||
return cls.loggers[name]
|
||||
|
||||
logger = logging.getLogger(name)
|
||||
logger.setLevel(config.log_level.upper()) # yes, strings work here
|
||||
for ch in cls.get_loggers(config):
|
||||
logger.addHandler(ch)
|
||||
cls.loggers[name] = logger
|
||||
cls.loggers[name] = logger
|
||||
return cls.loggers[name]
|
||||
|
||||
@classmethod
|
||||
@ -358,7 +357,7 @@ class InvokeAILogger(object): # noqa D102
|
||||
handlers = []
|
||||
for handler in handler_strs:
|
||||
handler_name, *args = handler.split("=", 2)
|
||||
args = args[0] if len(args) > 0 else None
|
||||
arg = args[0] if len(args) > 0 else None
|
||||
|
||||
# console and file get the fancy formatter.
|
||||
# syslog gets a simple one
|
||||
@ -370,16 +369,16 @@ class InvokeAILogger(object): # noqa D102
|
||||
handlers.append(ch)
|
||||
|
||||
elif handler_name == "syslog":
|
||||
ch = cls._parse_syslog_args(args)
|
||||
ch = cls._parse_syslog_args(arg)
|
||||
handlers.append(ch)
|
||||
|
||||
elif handler_name == "file":
|
||||
ch = cls._parse_file_args(args)
|
||||
ch = cls._parse_file_args(arg)
|
||||
ch.setFormatter(formatter())
|
||||
handlers.append(ch)
|
||||
|
||||
elif handler_name == "http":
|
||||
ch = cls._parse_http_args(args)
|
||||
ch = cls._parse_http_args(arg)
|
||||
handlers.append(ch)
|
||||
return handlers
|
||||
|
||||
|
||||
@ -75,6 +75,7 @@
|
||||
"framer-motion": "^10.16.4",
|
||||
"i18next": "^23.6.0",
|
||||
"i18next-http-backend": "^2.3.1",
|
||||
"idb-keyval": "^6.2.1",
|
||||
"konva": "^9.2.3",
|
||||
"lodash-es": "^4.17.21",
|
||||
"nanostores": "^0.9.4",
|
||||
|
||||
@ -803,8 +803,7 @@
|
||||
"canny": "Canny",
|
||||
"hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
|
||||
"scribble": "Scribble",
|
||||
"maxFaces": "Maximal Anzahl Gesichter",
|
||||
"unstarImage": "Markierung aufheben"
|
||||
"maxFaces": "Maximal Anzahl Gesichter"
|
||||
},
|
||||
"queue": {
|
||||
"status": "Status",
|
||||
|
||||
@ -243,7 +243,6 @@
|
||||
"setControlImageDimensions": "Set Control Image Dimensions To W/H",
|
||||
"showAdvanced": "Show Advanced",
|
||||
"toggleControlNet": "Toggle this ControlNet",
|
||||
"unstarImage": "Unstar Image",
|
||||
"w": "W",
|
||||
"weight": "Weight",
|
||||
"enableIPAdapter": "Enable IP Adapter",
|
||||
@ -378,6 +377,8 @@
|
||||
"showGenerations": "Show Generations",
|
||||
"showUploads": "Show Uploads",
|
||||
"singleColumnLayout": "Single Column Layout",
|
||||
"starImage": "Star Image",
|
||||
"unstarImage": "Unstar Image",
|
||||
"unableToLoad": "Unable to load Gallery",
|
||||
"uploads": "Uploads",
|
||||
"deleteSelection": "Delete Selection",
|
||||
@ -599,6 +600,7 @@
|
||||
},
|
||||
"metadata": {
|
||||
"cfgScale": "CFG scale",
|
||||
"cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
|
||||
"createdBy": "Created By",
|
||||
"fit": "Image to image fit",
|
||||
"generationMode": "Generation Mode",
|
||||
@ -977,6 +979,7 @@
|
||||
"unsupportedAnyOfLength": "too many union members ({{count}})",
|
||||
"unsupportedMismatchedUnion": "mismatched CollectionOrScalar type with base types {{firstType}} and {{secondType}}",
|
||||
"unableToParseFieldType": "unable to parse field type",
|
||||
"unableToExtractEnumOptions": "unable to extract enum options",
|
||||
"uNetField": "UNet",
|
||||
"uNetFieldDescription": "UNet submodel.",
|
||||
"unhandledInputProperty": "Unhandled input property",
|
||||
@ -1032,6 +1035,8 @@
|
||||
"setType": "Set cancel type"
|
||||
},
|
||||
"cfgScale": "CFG Scale",
|
||||
"cfgRescaleMultiplier": "CFG Rescale Multiplier",
|
||||
"cfgRescale": "CFG Rescale",
|
||||
"clipSkip": "CLIP Skip",
|
||||
"clipSkipWithLayerCount": "CLIP Skip {{layerCount}}",
|
||||
"closeViewer": "Close Viewer",
|
||||
@ -1470,6 +1475,12 @@
|
||||
"Controls how much your prompt influences the generation process."
|
||||
]
|
||||
},
|
||||
"paramCFGRescaleMultiplier": {
|
||||
"heading": "CFG Rescale Multiplier",
|
||||
"paragraphs": [
|
||||
"Rescale multiplier for CFG guidance, used for models trained using zero-terminal SNR (ztsnr). Suggested value 0.7."
|
||||
]
|
||||
},
|
||||
"paramDenoisingStrength": {
|
||||
"heading": "Denoising Strength",
|
||||
"paragraphs": [
|
||||
|
||||
@ -91,7 +91,19 @@
|
||||
"controlNet": "ControlNet",
|
||||
"auto": "Automatico",
|
||||
"simple": "Semplice",
|
||||
"details": "Dettagli"
|
||||
"details": "Dettagli",
|
||||
"format": "formato",
|
||||
"unknown": "Sconosciuto",
|
||||
"folder": "Cartella",
|
||||
"error": "Errore",
|
||||
"installed": "Installato",
|
||||
"template": "Schema",
|
||||
"outputs": "Uscite",
|
||||
"data": "Dati",
|
||||
"somethingWentWrong": "Qualcosa è andato storto",
|
||||
"copyError": "$t(gallery.copy) Errore",
|
||||
"input": "Ingresso",
|
||||
"notInstalled": "Non $t(common.installed)"
|
||||
},
|
||||
"gallery": {
|
||||
"generations": "Generazioni",
|
||||
@ -122,7 +134,14 @@
|
||||
"preparingDownload": "Preparazione del download",
|
||||
"preparingDownloadFailed": "Problema durante la preparazione del download",
|
||||
"downloadSelection": "Scarica gli elementi selezionati",
|
||||
"noImageSelected": "Nessuna immagine selezionata"
|
||||
"noImageSelected": "Nessuna immagine selezionata",
|
||||
"deleteSelection": "Elimina la selezione",
|
||||
"image": "immagine",
|
||||
"drop": "Rilascia",
|
||||
"unstarImage": "Rimuovi preferenza immagine",
|
||||
"dropOrUpload": "$t(gallery.drop) o carica",
|
||||
"starImage": "Immagine preferita",
|
||||
"dropToUpload": "$t(gallery.drop) per aggiornare"
|
||||
},
|
||||
"hotkeys": {
|
||||
"keyboardShortcuts": "Tasti rapidi",
|
||||
@ -477,7 +496,8 @@
|
||||
"modelType": "Tipo di modello",
|
||||
"customConfigFileLocation": "Posizione del file di configurazione personalizzato",
|
||||
"vaePrecision": "Precisione VAE",
|
||||
"noModelSelected": "Nessun modello selezionato"
|
||||
"noModelSelected": "Nessun modello selezionato",
|
||||
"conversionNotSupported": "Conversione non supportata"
|
||||
},
|
||||
"parameters": {
|
||||
"images": "Immagini",
|
||||
@ -838,7 +858,8 @@
|
||||
"menu": "Menu",
|
||||
"showGalleryPanel": "Mostra il pannello Galleria",
|
||||
"loadMore": "Carica altro",
|
||||
"mode": "Modalità"
|
||||
"mode": "Modalità",
|
||||
"resetUI": "$t(accessibility.reset) l'Interfaccia Utente"
|
||||
},
|
||||
"ui": {
|
||||
"hideProgressImages": "Nascondi avanzamento immagini",
|
||||
@ -1040,7 +1061,15 @@
|
||||
"updateAllNodes": "Aggiorna tutti i nodi",
|
||||
"unableToUpdateNodes_one": "Impossibile aggiornare {{count}} nodo",
|
||||
"unableToUpdateNodes_many": "Impossibile aggiornare {{count}} nodi",
|
||||
"unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi"
|
||||
"unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi",
|
||||
"addLinearView": "Aggiungi alla vista Lineare",
|
||||
"outputFieldInInput": "Campo di uscita in ingresso",
|
||||
"unableToMigrateWorkflow": "Impossibile migrare il flusso di lavoro",
|
||||
"unableToUpdateNode": "Impossibile aggiornare nodo",
|
||||
"unknownErrorValidatingWorkflow": "Errore sconosciuto durante la convalida del flusso di lavoro",
|
||||
"collectionFieldType": "{{name}} Raccolta",
|
||||
"collectionOrScalarFieldType": "{{name}} Raccolta|Scalare",
|
||||
"nodeVersion": "Versione Nodo"
|
||||
},
|
||||
"boards": {
|
||||
"autoAddBoard": "Aggiungi automaticamente bacheca",
|
||||
@ -1062,7 +1091,10 @@
|
||||
"deleteBoardOnly": "Elimina solo la Bacheca",
|
||||
"deleteBoard": "Elimina Bacheca",
|
||||
"deleteBoardAndImages": "Elimina Bacheca e Immagini",
|
||||
"deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate"
|
||||
"deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate",
|
||||
"movingImagesToBoard_one": "Spostare {{count}} immagine nella bacheca:",
|
||||
"movingImagesToBoard_many": "Spostare {{count}} immagini nella bacheca:",
|
||||
"movingImagesToBoard_other": "Spostare {{count}} immagini nella bacheca:"
|
||||
},
|
||||
"controlnet": {
|
||||
"contentShuffleDescription": "Rimescola il contenuto di un'immagine",
|
||||
@ -1136,7 +1168,8 @@
|
||||
"megaControl": "Mega ControlNet",
|
||||
"minConfidence": "Confidenza minima",
|
||||
"scribble": "Scribble",
|
||||
"amult": "Angolo di illuminazione"
|
||||
"amult": "Angolo di illuminazione",
|
||||
"coarse": "Approssimativo"
|
||||
},
|
||||
"queue": {
|
||||
"queueFront": "Aggiungi all'inizio della coda",
|
||||
@ -1204,7 +1237,8 @@
|
||||
"embedding": {
|
||||
"noMatchingEmbedding": "Nessun Incorporamento corrispondente",
|
||||
"addEmbedding": "Aggiungi Incorporamento",
|
||||
"incompatibleModel": "Modello base incompatibile:"
|
||||
"incompatibleModel": "Modello base incompatibile:",
|
||||
"noEmbeddingsLoaded": "Nessun incorporamento caricato"
|
||||
},
|
||||
"models": {
|
||||
"noMatchingModels": "Nessun modello corrispondente",
|
||||
@ -1217,7 +1251,8 @@
|
||||
"noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
|
||||
"noLoRAsInstalled": "Nessun LoRA installato",
|
||||
"esrganModel": "Modello ESRGAN",
|
||||
"addLora": "Aggiungi LoRA"
|
||||
"addLora": "Aggiungi LoRA",
|
||||
"noLoRAsLoaded": "Nessuna LoRA caricata"
|
||||
},
|
||||
"invocationCache": {
|
||||
"disable": "Disabilita",
|
||||
@ -1233,7 +1268,8 @@
|
||||
"enable": "Abilita",
|
||||
"clear": "Svuota",
|
||||
"maxCacheSize": "Dimensione max cache",
|
||||
"cacheSize": "Dimensione cache"
|
||||
"cacheSize": "Dimensione cache",
|
||||
"useCache": "Usa Cache"
|
||||
},
|
||||
"dynamicPrompts": {
|
||||
"seedBehaviour": {
|
||||
|
||||
@ -72,5 +72,13 @@
|
||||
},
|
||||
"unifiedCanvas": {
|
||||
"betaPreserveMasked": "마스크 레이어 유지"
|
||||
},
|
||||
"accessibility": {
|
||||
"previousImage": "이전 이미지",
|
||||
"modifyConfig": "Config 수정",
|
||||
"nextImage": "다음 이미지",
|
||||
"mode": "모드",
|
||||
"menu": "메뉴",
|
||||
"modelSelect": "모델 선택"
|
||||
}
|
||||
}
|
||||
|
||||
@ -99,7 +99,17 @@
|
||||
"data": "数据",
|
||||
"safetensors": "Safetensors",
|
||||
"outpaint": "外扩绘制",
|
||||
"details": "详情"
|
||||
"details": "详情",
|
||||
"format": "格式",
|
||||
"unknown": "未知",
|
||||
"folder": "文件夹",
|
||||
"error": "错误",
|
||||
"installed": "已安装",
|
||||
"file": "文件",
|
||||
"somethingWentWrong": "出了点问题",
|
||||
"copyError": "$t(gallery.copy) 错误",
|
||||
"input": "输入",
|
||||
"notInstalled": "非 $t(common.installed)"
|
||||
},
|
||||
"gallery": {
|
||||
"generations": "生成的图像",
|
||||
@ -130,7 +140,12 @@
|
||||
"preparingDownload": "准备下载",
|
||||
"preparingDownloadFailed": "准备下载时出现问题",
|
||||
"downloadSelection": "下载所选内容",
|
||||
"noImageSelected": "无选中的图像"
|
||||
"noImageSelected": "无选中的图像",
|
||||
"deleteSelection": "删除所选内容",
|
||||
"image": "图像",
|
||||
"drop": "弃用",
|
||||
"dropOrUpload": "$t(gallery.drop) 或上传",
|
||||
"dropToUpload": "$t(gallery.drop) 以上传"
|
||||
},
|
||||
"hotkeys": {
|
||||
"keyboardShortcuts": "键盘快捷键",
|
||||
@ -486,7 +501,8 @@
|
||||
"alpha": "Alpha",
|
||||
"vaePrecision": "VAE 精度",
|
||||
"checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
|
||||
"noModelSelected": "无选中的模型"
|
||||
"noModelSelected": "无选中的模型",
|
||||
"conversionNotSupported": "转换尚未支持"
|
||||
},
|
||||
"parameters": {
|
||||
"images": "图像",
|
||||
@ -615,7 +631,10 @@
|
||||
"seamlessX": "无缝 X",
|
||||
"seamlessY": "无缝 Y",
|
||||
"maskEdge": "遮罩边缘",
|
||||
"unmasked": "取消遮罩"
|
||||
"unmasked": "取消遮罩",
|
||||
"cfgRescaleMultiplier": "CFG 重缩放倍数",
|
||||
"cfgRescale": "CFG 重缩放",
|
||||
"useSize": "使用尺寸"
|
||||
},
|
||||
"settings": {
|
||||
"models": "模型",
|
||||
@ -655,7 +674,8 @@
|
||||
"clearIntermediatesDisabled": "队列为空才能清理中间产物",
|
||||
"enableNSFWChecker": "启用成人内容检测器",
|
||||
"enableInvisibleWatermark": "启用不可见水印",
|
||||
"enableInformationalPopovers": "启用信息弹窗"
|
||||
"enableInformationalPopovers": "启用信息弹窗",
|
||||
"reloadingIn": "重新加载中"
|
||||
},
|
||||
"toast": {
|
||||
"tempFoldersEmptied": "临时文件夹已清空",
|
||||
@ -739,7 +759,8 @@
|
||||
"imageUploadFailed": "图像上传失败",
|
||||
"problemImportingMask": "导入遮罩时出现问题",
|
||||
"baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型",
|
||||
"setAsCanvasInitialImage": "设为画布初始图像"
|
||||
"setAsCanvasInitialImage": "设为画布初始图像",
|
||||
"invalidUpload": "无效的上传"
|
||||
},
|
||||
"unifiedCanvas": {
|
||||
"layer": "图层",
|
||||
@ -748,7 +769,7 @@
|
||||
"maskingOptions": "遮罩选项",
|
||||
"enableMask": "启用遮罩",
|
||||
"preserveMaskedArea": "保留遮罩区域",
|
||||
"clearMask": "清除遮罩",
|
||||
"clearMask": "清除遮罩 (Shift+C)",
|
||||
"brush": "刷子",
|
||||
"eraser": "橡皮擦",
|
||||
"fillBoundingBox": "填充选择区域",
|
||||
@ -801,7 +822,8 @@
|
||||
"betaPreserveMasked": "保留遮罩层",
|
||||
"antialiasing": "抗锯齿",
|
||||
"showResultsOn": "显示结果 (开)",
|
||||
"showResultsOff": "显示结果 (关)"
|
||||
"showResultsOff": "显示结果 (关)",
|
||||
"saveMask": "保存 $t(unifiedCanvas.mask)"
|
||||
},
|
||||
"accessibility": {
|
||||
"modelSelect": "模型选择",
|
||||
@ -826,7 +848,9 @@
|
||||
"menu": "菜单",
|
||||
"showGalleryPanel": "显示图库浮窗",
|
||||
"loadMore": "加载更多",
|
||||
"mode": "模式"
|
||||
"mode": "模式",
|
||||
"resetUI": "$t(accessibility.reset) UI",
|
||||
"createIssue": "创建问题"
|
||||
},
|
||||
"ui": {
|
||||
"showProgressImages": "显示处理中的图片",
|
||||
@ -877,7 +901,7 @@
|
||||
"animatedEdges": "边缘动效",
|
||||
"nodeTemplate": "节点模板",
|
||||
"pickOne": "选择一个",
|
||||
"unableToLoadWorkflow": "无法验证工作流",
|
||||
"unableToLoadWorkflow": "无法加载工作流",
|
||||
"snapToGrid": "对齐网格",
|
||||
"noFieldsLinearview": "线性视图中未添加任何字段",
|
||||
"nodeSearch": "检索节点",
|
||||
@ -929,7 +953,7 @@
|
||||
"skippingUnknownOutputType": "跳过未知类型的输出",
|
||||
"latentsFieldDescription": "Latents 可以在节点间传递。",
|
||||
"denoiseMaskFieldDescription": "去噪遮罩可以在节点间传递",
|
||||
"missingTemplate": "缺失模板",
|
||||
"missingTemplate": "无效的节点：类型为 {{type}} 的节点 {{node}} 缺失模板（无已安装模板？）",
|
||||
"outputSchemaNotFound": "未找到输出模式",
|
||||
"latentsPolymorphicDescription": "Latents 可以在节点间传递。",
|
||||
"colorFieldDescription": "一种 RGBA 颜色。",
|
||||
@ -957,7 +981,7 @@
|
||||
"collectionItem": "项目合集",
|
||||
"controlCollectionDescription": "节点间传递的控制信息。",
|
||||
"skippedReservedInput": "跳过保留的输入",
|
||||
"outputFields": "输出",
|
||||
"outputFields": "输出区域",
|
||||
"edge": "边缘",
|
||||
"inputNode": "输入节点",
|
||||
"enumDescription": "枚举 (Enums) 可能是多个选项的一个数值。",
|
||||
@ -992,7 +1016,7 @@
|
||||
"string": "字符串",
|
||||
"inputFields": "输入",
|
||||
"uNetFieldDescription": "UNet 子模型。",
|
||||
"mismatchedVersion": "不匹配的版本",
|
||||
"mismatchedVersion": "无效的节点：类型为 {{type}} 的节点 {{node}} 版本不匹配（是否尝试更新？）",
|
||||
"vaeFieldDescription": "Vae 子模型。",
|
||||
"imageFieldDescription": "图像可以在节点间传递。",
|
||||
"outputNode": "输出节点",
|
||||
@ -1050,8 +1074,36 @@
|
||||
"latentsPolymorphic": "Latents 多态",
|
||||
"conditioningField": "条件",
|
||||
"latentsField": "Latents",
|
||||
"updateAllNodes": "更新所有节点",
|
||||
"unableToUpdateNodes_other": "{{count}} 个节点无法完成更新"
|
||||
"updateAllNodes": "更新节点",
|
||||
"unableToUpdateNodes_other": "{{count}} 个节点无法完成更新",
|
||||
"inputFieldTypeParseError": "无法解析 {{node}} 的输入类型 {{field}}。({{message}})",
|
||||
"unsupportedArrayItemType": "不支持的数组类型 \"{{type}}\"",
|
||||
"addLinearView": "添加到线性视图",
|
||||
"targetNodeFieldDoesNotExist": "无效的边缘：{{node}} 的目标/输入区域 {{field}} 不存在",
|
||||
"unsupportedMismatchedUnion": "合集或标量类型与基类 {{firstType}} 和 {{secondType}} 不匹配",
|
||||
"allNodesUpdated": "已更新所有节点",
|
||||
"sourceNodeDoesNotExist": "无效的边缘：{{node}} 的源/输出节点不存在",
|
||||
"unableToExtractEnumOptions": "无法提取枚举选项",
|
||||
"unableToParseFieldType": "无法解析类型",
|
||||
"outputFieldInInput": "输入中的输出区域",
|
||||
"unrecognizedWorkflowVersion": "无法识别的工作流架构版本：{{version}}",
|
||||
"outputFieldTypeParseError": "无法解析 {{node}} 的输出类型 {{field}}。({{message}})",
|
||||
"sourceNodeFieldDoesNotExist": "无效的边缘：{{node}} 的源/输出区域 {{field}} 不存在",
|
||||
"unableToGetWorkflowVersion": "无法获取工作流架构版本",
|
||||
"nodePack": "节点包",
|
||||
"unableToExtractSchemaNameFromRef": "无法从参考中提取架构名",
|
||||
"unableToMigrateWorkflow": "无法迁移工作流",
|
||||
"unknownOutput": "未知输出：{{name}}",
|
||||
"unableToUpdateNode": "无法更新节点",
|
||||
"unknownErrorValidatingWorkflow": "验证工作流时出现未知错误",
|
||||
"collectionFieldType": "{{name}} 合集",
|
||||
"unknownNodeType": "未知节点类型",
|
||||
"targetNodeDoesNotExist": "无效的边缘：{{node}} 的目标/输入节点不存在",
|
||||
"unknownFieldType": "$t(nodes.unknownField) 类型：{{type}}",
|
||||
"collectionOrScalarFieldType": "{{name}} 合集 | 标量",
|
||||
"nodeVersion": "节点版本",
|
||||
"deletedInvalidEdge": "已删除无效的边缘 {{source}} -> {{target}}",
|
||||
"unknownInput": "未知输入：{{name}}"
|
||||
},
|
||||
"controlnet": {
|
||||
"resize": "直接缩放",
|
||||
@ -1137,8 +1189,7 @@
|
||||
"openPose": "Openpose",
|
||||
"controlAdapter_other": "Control Adapters",
|
||||
"lineartAnime": "Lineart Anime",
|
||||
"canny": "Canny",
|
||||
"unstarImage": "取消收藏图像"
|
||||
"canny": "Canny"
|
||||
},
|
||||
"queue": {
|
||||
"status": "状态",
|
||||
@ -1246,7 +1297,8 @@
|
||||
"fit": "图生图匹配",
|
||||
"recallParameters": "召回参数",
|
||||
"noRecallParameters": "未找到要召回的参数",
|
||||
"vae": "VAE"
|
||||
"vae": "VAE",
|
||||
"cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
|
||||
},
|
||||
"models": {
|
||||
"noMatchingModels": "无相匹配的模型",
|
||||
@ -1259,7 +1311,8 @@
|
||||
"noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
|
||||
"noLoRAsInstalled": "无已安装的 LoRA",
|
||||
"esrganModel": "ESRGAN 模型",
|
||||
"addLora": "添加 LoRA"
|
||||
"addLora": "添加 LoRA",
|
||||
"noLoRAsLoaded": "无已加载的 LoRA"
|
||||
},
|
||||
"boards": {
|
||||
"autoAddBoard": "自动添加面板",
|
||||
@ -1281,12 +1334,14 @@
|
||||
"deleteBoardOnly": "仅删除面板",
|
||||
"deleteBoard": "删除面板",
|
||||
"deleteBoardAndImages": "删除面板和图像",
|
||||
"deletedBoardsCannotbeRestored": "已删除的面板无法被恢复"
|
||||
"deletedBoardsCannotbeRestored": "已删除的面板无法被恢复",
|
||||
"movingImagesToBoard_other": "移动 {{count}} 张图像到面板："
|
||||
},
|
||||
"embedding": {
|
||||
"noMatchingEmbedding": "不匹配的 Embedding",
|
||||
"addEmbedding": "添加 Embedding",
|
||||
"incompatibleModel": "不兼容的基础模型："
|
||||
"incompatibleModel": "不兼容的基础模型：",
|
||||
"noEmbeddingsLoaded": "无已加载的 Embedding"
|
||||
},
|
||||
"dynamicPrompts": {
|
||||
"seedBehaviour": {
|
||||
@ -1515,6 +1570,12 @@
|
||||
"ControlNet 为生成过程提供引导，为生成具有受控构图、结构、样式的图像提供帮助，具体的功能由所选的模型决定。"
|
||||
],
|
||||
"heading": "ControlNet"
|
||||
},
|
||||
"paramCFGRescaleMultiplier": {
|
||||
"heading": "CFG 重缩放倍数",
|
||||
"paragraphs": [
|
||||
"CFG 引导的重缩放倍率，用于通过 zero-terminal SNR (ztsnr) 训练的模型。推荐设为 0.7。"
|
||||
]
|
||||
}
|
||||
},
|
||||
"invocationCache": {
|
||||
@ -1531,7 +1592,8 @@
|
||||
"enable": "启用",
|
||||
"clear": "清除",
|
||||
"maxCacheSize": "最大缓存大小",
|
||||
"cacheSize": "缓存大小"
|
||||
"cacheSize": "缓存大小",
|
||||
"useCache": "使用缓存"
|
||||
},
|
||||
"hrf": {
|
||||
"enableHrf": "启用高分辨率修复",
|
||||
|
||||
@ -21,6 +21,7 @@ import GlobalHotkeys from './GlobalHotkeys';
|
||||
import PreselectedImage from './PreselectedImage';
|
||||
import Toaster from './Toaster';
|
||||
import { useSocketIO } from 'app/hooks/useSocketIO';
|
||||
import { useClearStorage } from 'common/hooks/useClearStorage';
|
||||
|
||||
const DEFAULT_CONFIG = {};
|
||||
|
||||
@ -36,15 +37,16 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
|
||||
const language = useAppSelector(languageSelector);
|
||||
const logger = useLogger('system');
|
||||
const dispatch = useAppDispatch();
|
||||
const clearStorage = useClearStorage();
|
||||
|
||||
// singleton!
|
||||
useSocketIO();
|
||||
|
||||
const handleReset = useCallback(() => {
|
||||
localStorage.clear();
|
||||
clearStorage();
|
||||
location.reload();
|
||||
return false;
|
||||
}, []);
|
||||
}, [clearStorage]);
|
||||
|
||||
useEffect(() => {
|
||||
i18n.changeLanguage(language);
|
||||
|
||||
@ -7,21 +7,23 @@ import { $headerComponent } from 'app/store/nanostores/headerComponent';
|
||||
import { $isDebugging } from 'app/store/nanostores/isDebugging';
|
||||
import { $projectId } from 'app/store/nanostores/projectId';
|
||||
import { $queueId, DEFAULT_QUEUE_ID } from 'app/store/nanostores/queueId';
|
||||
import { store } from 'app/store/store';
|
||||
import { $store } from 'app/store/nanostores/store';
|
||||
import { createStore } from 'app/store/store';
|
||||
import { PartialAppConfig } from 'app/types/invokeai';
|
||||
import Loading from 'common/components/Loading/Loading';
|
||||
import AppDndContext from 'features/dnd/components/AppDndContext';
|
||||
import 'i18n';
|
||||
import React, {
|
||||
PropsWithChildren,
|
||||
ReactNode,
|
||||
lazy,
|
||||
memo,
|
||||
useEffect,
|
||||
useMemo,
|
||||
} from 'react';
|
||||
import { Provider } from 'react-redux';
|
||||
import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
|
||||
import { ManagerOptions, SocketOptions } from 'socket.io-client';
|
||||
import Loading from 'common/components/Loading/Loading';
|
||||
import AppDndContext from 'features/dnd/components/AppDndContext';
|
||||
import 'i18n';
|
||||
|
||||
const App = lazy(() => import('./App'));
|
||||
const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
|
||||
@ -137,6 +139,14 @@ const InvokeAIUI = ({
|
||||
};
|
||||
}, [isDebugging]);
|
||||
|
||||
const store = useMemo(() => {
|
||||
return createStore(projectId);
|
||||
}, [projectId]);
|
||||
|
||||
useEffect(() => {
|
||||
$store.set(store);
|
||||
}, [store]);
|
||||
|
||||
return (
|
||||
<React.StrictMode>
|
||||
<Provider store={store}>
|
||||
|
||||
@ -9,9 +9,9 @@ import { TOAST_OPTIONS, theme as invokeAITheme } from 'theme/theme';
|
||||
|
||||
import '@fontsource-variable/inter';
|
||||
import { MantineProvider } from '@mantine/core';
|
||||
import { useMantineTheme } from 'mantine-theme/theme';
|
||||
import 'overlayscrollbars/overlayscrollbars.css';
|
||||
import 'theme/css/overlayscrollbars.css';
|
||||
import { useMantineTheme } from 'mantine-theme/theme';
|
||||
|
||||
type ThemeLocaleProviderProps = {
|
||||
children: ReactNode;
|
||||
|
||||
@ -3,8 +3,8 @@ import { $authToken } from 'app/store/nanostores/authToken';
|
||||
import { $baseUrl } from 'app/store/nanostores/baseUrl';
|
||||
import { $isDebugging } from 'app/store/nanostores/isDebugging';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import { MapStore, WritableAtom, atom, map } from 'nanostores';
|
||||
import { useEffect } from 'react';
|
||||
import { MapStore, atom, map } from 'nanostores';
|
||||
import { useEffect, useMemo } from 'react';
|
||||
import {
|
||||
ClientToServerEvents,
|
||||
ServerToClientEvents,
|
||||
@ -16,57 +16,10 @@ import { ManagerOptions, Socket, SocketOptions, io } from 'socket.io-client';
|
||||
declare global {
|
||||
interface Window {
|
||||
$socketOptions?: MapStore<Partial<ManagerOptions & SocketOptions>>;
|
||||
$socketUrl?: WritableAtom<string>;
|
||||
}
|
||||
}
|
||||
|
||||
const makeSocketOptions = (): Partial<ManagerOptions & SocketOptions> => {
|
||||
const socketOptions: Parameters<typeof io>[0] = {
|
||||
timeout: 60000,
|
||||
path: '/ws/socket.io',
|
||||
autoConnect: false, // achtung! removing this breaks the dynamic middleware
|
||||
forceNew: true,
|
||||
};
|
||||
|
||||
// if building in package mode, replace socket url with open api base url minus the http protocol
|
||||
if (['nodes', 'package'].includes(import.meta.env.MODE)) {
|
||||
const authToken = $authToken.get();
|
||||
if (authToken) {
|
||||
// TODO: handle providing jwt to socket.io
|
||||
socketOptions.auth = { token: authToken };
|
||||
}
|
||||
|
||||
socketOptions.transports = ['websocket', 'polling'];
|
||||
}
|
||||
|
||||
return socketOptions;
|
||||
};
|
||||
|
||||
const makeSocketUrl = (): string => {
|
||||
const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws';
|
||||
let socketUrl = `${wsProtocol}://${window.location.host}`;
|
||||
if (['nodes', 'package'].includes(import.meta.env.MODE)) {
|
||||
const baseUrl = $baseUrl.get();
|
||||
if (baseUrl) {
|
||||
//eslint-disable-next-line
|
||||
socketUrl = baseUrl.replace(/^https?\:\/\//i, '');
|
||||
}
|
||||
}
|
||||
return socketUrl;
|
||||
};
|
||||
|
||||
const makeSocket = (): Socket<ServerToClientEvents, ClientToServerEvents> => {
|
||||
const socketOptions = makeSocketOptions();
|
||||
const socketUrl = $socketUrl.get();
|
||||
const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(
|
||||
socketUrl,
|
||||
{ ...socketOptions, ...$socketOptions.get() }
|
||||
);
|
||||
return socket;
|
||||
};
|
||||
|
||||
export const $socketOptions = map<Partial<ManagerOptions & SocketOptions>>({});
|
||||
export const $socketUrl = atom<string>(makeSocketUrl());
|
||||
export const $isSocketInitialized = atom<boolean>(false);
|
||||
|
||||
/**
|
||||
@ -74,23 +27,50 @@ export const $isSocketInitialized = atom<boolean>(false);
|
||||
*/
|
||||
export const useSocketIO = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const socketOptions = useStore($socketOptions);
|
||||
const socketUrl = useStore($socketUrl);
|
||||
const baseUrl = useStore($baseUrl);
|
||||
const authToken = useStore($authToken);
|
||||
const addlSocketOptions = useStore($socketOptions);
|
||||
|
||||
const socketUrl = useMemo(() => {
|
||||
const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws';
|
||||
if (baseUrl) {
|
||||
return baseUrl.replace(/^https?:\/\//i, '');
|
||||
}
|
||||
|
||||
return `${wsProtocol}://${window.location.host}`;
|
||||
}, [baseUrl]);
|
||||
|
||||
const socketOptions = useMemo(() => {
|
||||
const options: Parameters<typeof io>[0] = {
|
||||
timeout: 60000,
|
||||
path: '/ws/socket.io',
|
||||
autoConnect: false, // achtung! removing this breaks the dynamic middleware
|
||||
forceNew: true,
|
||||
};
|
||||
|
||||
if (authToken) {
|
||||
options.auth = { token: authToken };
|
||||
options.transports = ['websocket', 'polling'];
|
||||
}
|
||||
|
||||
return { ...options, ...addlSocketOptions };
|
||||
}, [authToken, addlSocketOptions]);
|
||||
|
||||
useEffect(() => {
|
||||
if ($isSocketInitialized.get()) {
|
||||
// Singleton!
|
||||
return;
|
||||
}
|
||||
const socket = makeSocket();
|
||||
|
||||
const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(
|
||||
socketUrl,
|
||||
socketOptions
|
||||
);
|
||||
setEventListeners({ dispatch, socket });
|
||||
socket.connect();
|
||||
|
||||
if ($isDebugging.get()) {
|
||||
window.$socketOptions = $socketOptions;
|
||||
window.$socketUrl = $socketUrl;
|
||||
console.log('Socket initialized', socket);
|
||||
}
|
||||
|
||||
@ -99,11 +79,10 @@ export const useSocketIO = () => {
|
||||
return () => {
|
||||
if ($isDebugging.get()) {
|
||||
window.$socketOptions = undefined;
|
||||
window.$socketUrl = undefined;
|
||||
console.log('Socket teardown', socket);
|
||||
}
|
||||
socket.disconnect();
|
||||
$isSocketInitialized.set(false);
|
||||
};
|
||||
}, [dispatch, socketOptions, socketUrl, baseUrl, authToken]);
|
||||
}, [dispatch, socketOptions, socketUrl]);
|
||||
};
|
||||
|
||||
@ -1,8 +1 @@
|
||||
export const LOCALSTORAGE_KEYS = [
|
||||
'chakra-ui-color-mode',
|
||||
'i18nextLng',
|
||||
'ROARR_FILTER',
|
||||
'ROARR_LOG',
|
||||
];
|
||||
|
||||
export const LOCALSTORAGE_PREFIX = '@@invokeai-';
|
||||
export const STORAGE_PREFIX = '@@invokeai-';
|
||||
|
||||
@ -23,16 +23,16 @@ import systemReducer from 'features/system/store/systemSlice';
|
||||
import hotkeysReducer from 'features/ui/store/hotkeysSlice';
|
||||
import uiReducer from 'features/ui/store/uiSlice';
|
||||
import dynamicMiddlewares from 'redux-dynamic-middlewares';
|
||||
import { rememberEnhancer, rememberReducer } from 'redux-remember';
|
||||
import { Driver, rememberEnhancer, rememberReducer } from 'redux-remember';
|
||||
import { api } from 'services/api';
|
||||
import { LOCALSTORAGE_PREFIX } from './constants';
|
||||
import { STORAGE_PREFIX } from './constants';
|
||||
import { serialize } from './enhancers/reduxRemember/serialize';
|
||||
import { unserialize } from './enhancers/reduxRemember/unserialize';
|
||||
import { actionSanitizer } from './middleware/devtools/actionSanitizer';
|
||||
import { actionsDenylist } from './middleware/devtools/actionsDenylist';
|
||||
import { stateSanitizer } from './middleware/devtools/stateSanitizer';
|
||||
import { listenerMiddleware } from './middleware/listenerMiddleware';
|
||||
import { $store } from './nanostores/store';
|
||||
import { createStore as createIDBKeyValStore, get, set } from 'idb-keyval';
|
||||
|
||||
const allReducers = {
|
||||
canvas: canvasReducer,
|
||||
@ -74,57 +74,70 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
|
||||
'modelmanager',
|
||||
];
|
||||
|
||||
export const store = configureStore({
|
||||
reducer: rememberedRootReducer,
|
||||
enhancers: (existingEnhancers) => {
|
||||
return existingEnhancers
|
||||
.concat(
|
||||
rememberEnhancer(window.localStorage, rememberedKeys, {
|
||||
persistDebounce: 300,
|
||||
serialize,
|
||||
unserialize,
|
||||
prefix: LOCALSTORAGE_PREFIX,
|
||||
})
|
||||
)
|
||||
.concat(autoBatchEnhancer());
|
||||
},
|
||||
middleware: (getDefaultMiddleware) =>
|
||||
getDefaultMiddleware({
|
||||
serializableCheck: false,
|
||||
immutableCheck: false,
|
||||
})
|
||||
.concat(api.middleware)
|
||||
.concat(dynamicMiddlewares)
|
||||
.prepend(listenerMiddleware.middleware),
|
||||
devTools: {
|
||||
actionSanitizer,
|
||||
stateSanitizer,
|
||||
trace: true,
|
||||
predicate: (state, action) => {
|
||||
// TODO: hook up to the log level param in system slice
|
||||
// manually type state, cannot type the arg
|
||||
// const typedState = state as ReturnType<typeof rootReducer>;
|
||||
// Create a custom idb-keyval store (just needed to customize the name)
|
||||
export const idbKeyValStore = createIDBKeyValStore('invoke', 'invoke-store');
|
||||
|
||||
// TODO: doing this breaks the rtk query devtools, commenting out for now
|
||||
// if (action.type.startsWith('api/')) {
|
||||
// // don't log api actions, with manual cache updates they are extremely noisy
|
||||
// return false;
|
||||
// }
|
||||
// Create redux-remember driver, wrapping idb-keyval
|
||||
const idbKeyValDriver: Driver = {
|
||||
getItem: (key) => get(key, idbKeyValStore),
|
||||
setItem: (key, value) => set(key, value, idbKeyValStore),
|
||||
};
|
||||
|
||||
if (actionsDenylist.includes(action.type)) {
|
||||
// don't log other noisy actions
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
export const createStore = (uniqueStoreKey?: string) =>
|
||||
configureStore({
|
||||
reducer: rememberedRootReducer,
|
||||
enhancers: (existingEnhancers) => {
|
||||
return existingEnhancers
|
||||
.concat(
|
||||
rememberEnhancer(idbKeyValDriver, rememberedKeys, {
|
||||
persistDebounce: 300,
|
||||
serialize,
|
||||
unserialize,
|
||||
prefix: uniqueStoreKey
|
||||
? `${STORAGE_PREFIX}${uniqueStoreKey}-`
|
||||
: STORAGE_PREFIX,
|
||||
})
|
||||
)
|
||||
.concat(autoBatchEnhancer());
|
||||
},
|
||||
},
|
||||
});
|
||||
middleware: (getDefaultMiddleware) =>
|
||||
getDefaultMiddleware({
|
||||
serializableCheck: false,
|
||||
immutableCheck: false,
|
||||
})
|
||||
.concat(api.middleware)
|
||||
.concat(dynamicMiddlewares)
|
||||
.prepend(listenerMiddleware.middleware),
|
||||
devTools: {
|
||||
actionSanitizer,
|
||||
stateSanitizer,
|
||||
trace: true,
|
||||
predicate: (state, action) => {
|
||||
// TODO: hook up to the log level param in system slice
|
||||
// manually type state, cannot type the arg
|
||||
// const typedState = state as ReturnType<typeof rootReducer>;
|
||||
|
||||
export type AppGetState = typeof store.getState;
|
||||
export type RootState = ReturnType<typeof store.getState>;
|
||||
// TODO: doing this breaks the rtk query devtools, commenting out for now
|
||||
// if (action.type.startsWith('api/')) {
|
||||
// // don't log api actions, with manual cache updates they are extremely noisy
|
||||
// return false;
|
||||
// }
|
||||
|
||||
if (actionsDenylist.includes(action.type)) {
|
||||
// don't log other noisy actions
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
},
|
||||
},
|
||||
});
|
||||
|
||||
export type AppGetState = ReturnType<
|
||||
ReturnType<typeof createStore>['getState']
|
||||
>;
|
||||
export type RootState = ReturnType<ReturnType<typeof createStore>['getState']>;
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
export type AppThunkDispatch = ThunkDispatch<RootState, any, AnyAction>;
|
||||
export type AppDispatch = typeof store.dispatch;
|
||||
export type AppDispatch = ReturnType<typeof createStore>['dispatch'];
|
||||
export const stateSelector = (state: RootState) => state;
|
||||
$store.set(store);
|
||||
|
||||
@ -25,6 +25,7 @@ export type Feature =
|
||||
| 'lora'
|
||||
| 'noiseUseCPU'
|
||||
| 'paramCFGScale'
|
||||
| 'paramCFGRescaleMultiplier'
|
||||
| 'paramDenoisingStrength'
|
||||
| 'paramIterations'
|
||||
| 'paramModel'
|
||||
|
||||
12
invokeai/frontend/web/src/common/hooks/useClearStorage.ts
Normal file
12
invokeai/frontend/web/src/common/hooks/useClearStorage.ts
Normal file
@ -0,0 +1,12 @@
|
||||
import { idbKeyValStore } from 'app/store/store';
|
||||
import { clear } from 'idb-keyval';
|
||||
import { useCallback } from 'react';
|
||||
|
||||
export const useClearStorage = () => {
|
||||
const clearStorage = useCallback(() => {
|
||||
clear(idbKeyValStore);
|
||||
localStorage.clear();
|
||||
}, []);
|
||||
|
||||
return clearStorage;
|
||||
};
|
||||
@ -5,14 +5,19 @@ import { stateSelector } from 'app/store/store';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
import IAIDndImage from 'common/components/IAIDndImage';
|
||||
import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
|
||||
import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
|
||||
import { useControlAdapterControlImage } from 'features/controlAdapters/hooks/useControlAdapterControlImage';
|
||||
import { useControlAdapterProcessedControlImage } from 'features/controlAdapters/hooks/useControlAdapterProcessedControlImage';
|
||||
import { useControlAdapterProcessorType } from 'features/controlAdapters/hooks/useControlAdapterProcessorType';
|
||||
import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
|
||||
import {
|
||||
TypesafeDraggableData,
|
||||
TypesafeDroppableData,
|
||||
} from 'features/dnd/types';
|
||||
import { setHeight, setWidth } from 'features/parameters/store/generationSlice';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import { memo, useCallback, useMemo, useState } from 'react';
|
||||
import { memo, useCallback, useEffect, useMemo, useState } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { FaRulerVertical, FaSave, FaUndo } from 'react-icons/fa';
|
||||
import {
|
||||
@ -22,11 +27,6 @@ import {
|
||||
useRemoveImageFromBoardMutation,
|
||||
} from 'services/api/endpoints/images';
|
||||
import { PostUploadAction } from 'services/api/types';
|
||||
import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
|
||||
import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
|
||||
import { useControlAdapterControlImage } from 'features/controlAdapters/hooks/useControlAdapterControlImage';
|
||||
import { useControlAdapterProcessedControlImage } from 'features/controlAdapters/hooks/useControlAdapterProcessedControlImage';
|
||||
import { useControlAdapterProcessorType } from 'features/controlAdapters/hooks/useControlAdapterProcessorType';
|
||||
|
||||
type Props = {
|
||||
id: string;
|
||||
@ -35,13 +35,15 @@ type Props = {
|
||||
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
({ controlAdapters, gallery }) => {
|
||||
({ controlAdapters, gallery, system }) => {
|
||||
const { pendingControlImages } = controlAdapters;
|
||||
const { autoAddBoardId } = gallery;
|
||||
const { isConnected } = system;
|
||||
|
||||
return {
|
||||
pendingControlImages,
|
||||
autoAddBoardId,
|
||||
isConnected,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
@ -55,18 +57,19 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
|
||||
const { pendingControlImages, autoAddBoardId } = useAppSelector(selector);
|
||||
const { pendingControlImages, autoAddBoardId, isConnected } =
|
||||
useAppSelector(selector);
|
||||
const activeTabName = useAppSelector(activeTabNameSelector);
|
||||
|
||||
const [isMouseOverImage, setIsMouseOverImage] = useState(false);
|
||||
|
||||
const { currentData: controlImage } = useGetImageDTOQuery(
|
||||
controlImageName ?? skipToken
|
||||
);
|
||||
const { currentData: controlImage, isError: isErrorControlImage } =
|
||||
useGetImageDTOQuery(controlImageName ?? skipToken);
|
||||
|
||||
const { currentData: processedControlImage } = useGetImageDTOQuery(
|
||||
processedControlImageName ?? skipToken
|
||||
);
|
||||
const {
|
||||
currentData: processedControlImage,
|
||||
isError: isErrorProcessedControlImage,
|
||||
} = useGetImageDTOQuery(processedControlImageName ?? skipToken);
|
||||
|
||||
const [changeIsIntermediate] = useChangeImageIsIntermediateMutation();
|
||||
const [addToBoard] = useAddImageToBoardMutation();
|
||||
@ -158,6 +161,17 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
|
||||
!pendingControlImages.includes(id) &&
|
||||
processorType !== 'none';
|
||||
|
||||
useEffect(() => {
|
||||
if (isConnected && (isErrorControlImage || isErrorProcessedControlImage)) {
|
||||
handleResetControlImage();
|
||||
}
|
||||
}, [
|
||||
handleResetControlImage,
|
||||
isConnected,
|
||||
isErrorControlImage,
|
||||
isErrorProcessedControlImage,
|
||||
]);
|
||||
|
||||
return (
|
||||
<Flex
|
||||
onMouseEnter={handleMouseEnter}
|
||||
|
||||
@ -73,7 +73,13 @@ const BoardContextMenu = ({
|
||||
addToast({
|
||||
title: t('gallery.preparingDownload'),
|
||||
status: 'success',
|
||||
...(response.response ? { description: response.response } : {}),
|
||||
...(response.response
|
||||
? {
|
||||
description: response.response,
|
||||
duration: null,
|
||||
isClosable: true,
|
||||
}
|
||||
: {}),
|
||||
})
|
||||
);
|
||||
} catch {
|
||||
|
||||
@ -59,7 +59,13 @@ const MultipleSelectionMenuItems = () => {
|
||||
addToast({
|
||||
title: t('gallery.preparingDownload'),
|
||||
status: 'success',
|
||||
...(response.response ? { description: response.response } : {}),
|
||||
...(response.response
|
||||
? {
|
||||
description: response.response,
|
||||
duration: null,
|
||||
isClosable: true,
|
||||
}
|
||||
: {}),
|
||||
})
|
||||
);
|
||||
} catch {
|
||||
|
||||
@ -234,14 +234,14 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
|
||||
icon={customStarUi ? customStarUi.off.icon : <MdStar />}
|
||||
onClickCapture={handleUnstarImage}
|
||||
>
|
||||
{customStarUi ? customStarUi.off.text : t('controlnet.unstarImage')}
|
||||
{customStarUi ? customStarUi.off.text : t('gallery.unstarImage')}
|
||||
</MenuItem>
|
||||
) : (
|
||||
<MenuItem
|
||||
icon={customStarUi ? customStarUi.on.icon : <MdStarBorder />}
|
||||
onClickCapture={handleStarImage}
|
||||
>
|
||||
{customStarUi ? customStarUi.on.text : `Star Image`}
|
||||
{customStarUi ? customStarUi.on.text : t('gallery.starImage')}
|
||||
</MenuItem>
|
||||
)}
|
||||
<MenuItem
|
||||
|
||||
@ -29,6 +29,7 @@ const ImageMetadataActions = (props: Props) => {
|
||||
recallNegativePrompt,
|
||||
recallSeed,
|
||||
recallCfgScale,
|
||||
recallCfgRescaleMultiplier,
|
||||
recallModel,
|
||||
recallScheduler,
|
||||
recallVaeModel,
|
||||
@ -85,6 +86,10 @@ const ImageMetadataActions = (props: Props) => {
|
||||
recallCfgScale(metadata?.cfg_scale);
|
||||
}, [metadata?.cfg_scale, recallCfgScale]);
|
||||
|
||||
const handleRecallCfgRescaleMultiplier = useCallback(() => {
|
||||
recallCfgRescaleMultiplier(metadata?.cfg_rescale_multiplier);
|
||||
}, [metadata?.cfg_rescale_multiplier, recallCfgRescaleMultiplier]);
|
||||
|
||||
const handleRecallStrength = useCallback(() => {
|
||||
recallStrength(metadata?.strength);
|
||||
}, [metadata?.strength, recallStrength]);
|
||||
@ -243,6 +248,14 @@ const ImageMetadataActions = (props: Props) => {
|
||||
onClick={handleRecallCfgScale}
|
||||
/>
|
||||
)}
|
||||
{metadata.cfg_rescale_multiplier !== undefined &&
|
||||
metadata.cfg_rescale_multiplier !== null && (
|
||||
<ImageMetadataItem
|
||||
label={t('metadata.cfgRescaleMultiplier')}
|
||||
value={metadata.cfg_rescale_multiplier}
|
||||
onClick={handleRecallCfgRescaleMultiplier}
|
||||
/>
|
||||
)}
|
||||
{metadata.strength && (
|
||||
<ImageMetadataItem
|
||||
label={t('metadata.strength')}
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import { Flex, Text } from '@chakra-ui/react';
|
||||
import { skipToken } from '@reduxjs/toolkit/dist/query';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import IAIDndImage from 'common/components/IAIDndImage';
|
||||
import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
|
||||
import {
|
||||
@ -13,7 +13,7 @@ import {
|
||||
ImageFieldInputTemplate,
|
||||
} from 'features/nodes/types/field';
|
||||
import { FieldComponentProps } from './types';
|
||||
import { memo, useCallback, useMemo } from 'react';
|
||||
import { memo, useCallback, useEffect, useMemo } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { FaUndo } from 'react-icons/fa';
|
||||
import { useGetImageDTOQuery } from 'services/api/endpoints/images';
|
||||
@ -24,8 +24,8 @@ const ImageFieldInputComponent = (
|
||||
) => {
|
||||
const { nodeId, field } = props;
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
const { currentData: imageDTO } = useGetImageDTOQuery(
|
||||
const isConnected = useAppSelector((state) => state.system.isConnected);
|
||||
const { currentData: imageDTO, isError } = useGetImageDTOQuery(
|
||||
field.value?.image_name ?? skipToken
|
||||
);
|
||||
|
||||
@ -67,6 +67,12 @@ const ImageFieldInputComponent = (
|
||||
[nodeId, field.name]
|
||||
);
|
||||
|
||||
useEffect(() => {
|
||||
if (isConnected && isError) {
|
||||
handleReset();
|
||||
}
|
||||
}, [handleReset, isConnected, isError]);
|
||||
|
||||
return (
|
||||
<Flex
|
||||
className="nodrag"
|
||||
|
||||
@ -43,10 +43,10 @@ export class NodeUpdateError extends Error {
|
||||
}
|
||||
|
||||
/**
|
||||
* FieldTypeParseError
|
||||
* FieldParseError
|
||||
* Raised when a field cannot be parsed from a field schema.
|
||||
*/
|
||||
export class FieldTypeParseError extends Error {
|
||||
export class FieldParseError extends Error {
|
||||
/**
|
||||
* Create FieldTypeParseError
|
||||
* @param {String} message
|
||||
@ -56,18 +56,3 @@ export class FieldTypeParseError extends Error {
|
||||
this.name = this.constructor.name;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* UnsupportedFieldTypeError
|
||||
* Raised when an unsupported field type is parsed.
|
||||
*/
|
||||
export class UnsupportedFieldTypeError extends Error {
|
||||
/**
|
||||
* Create UnsupportedFieldTypeError
|
||||
* @param {String} message
|
||||
*/
|
||||
constructor(message: string) {
|
||||
super(message);
|
||||
this.name = this.constructor.name;
|
||||
}
|
||||
}
|
||||
|
||||
@ -51,6 +51,7 @@ export const zCoreMetadata = z
|
||||
seed: z.number().int().nullish().catch(null),
|
||||
rand_device: z.string().nullish().catch(null),
|
||||
cfg_scale: z.number().nullish().catch(null),
|
||||
cfg_rescale_multiplier: z.number().nullish().catch(null),
|
||||
steps: z.number().int().nullish().catch(null),
|
||||
scheduler: z.string().nullish().catch(null),
|
||||
clip_skip: z.number().int().nullish().catch(null),
|
||||
|
||||
@ -43,6 +43,7 @@ export const buildCanvasImageToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -316,6 +317,7 @@ export const buildCanvasImageToImageGraph = (
|
||||
{
|
||||
generation_mode: 'img2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
width: !isUsingScaledDimensions
|
||||
? width
|
||||
: scaledBoundingBoxDimensions.width,
|
||||
|
||||
@ -45,6 +45,7 @@ export const buildCanvasSDXLImageToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -327,6 +328,7 @@ export const buildCanvasSDXLImageToImageGraph = (
|
||||
{
|
||||
generation_mode: 'img2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
width: !isUsingScaledDimensions
|
||||
? width
|
||||
: scaledBoundingBoxDimensions.width,
|
||||
|
||||
@ -43,6 +43,7 @@ export const buildCanvasSDXLTextToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -306,6 +307,7 @@ export const buildCanvasSDXLTextToImageGraph = (
|
||||
{
|
||||
generation_mode: 'txt2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
width: !isUsingScaledDimensions
|
||||
? width
|
||||
: scaledBoundingBoxDimensions.width,
|
||||
|
||||
@ -41,6 +41,7 @@ export const buildCanvasTextToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -294,6 +295,7 @@ export const buildCanvasTextToImageGraph = (
|
||||
{
|
||||
generation_mode: 'txt2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
width: !isUsingScaledDimensions
|
||||
? width
|
||||
: scaledBoundingBoxDimensions.width,
|
||||
|
||||
@ -41,6 +41,7 @@ export const buildLinearImageToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -316,6 +317,7 @@ export const buildLinearImageToImageGraph = (
|
||||
{
|
||||
generation_mode: 'img2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
height,
|
||||
width,
|
||||
positive_prompt: positivePrompt,
|
||||
|
||||
@ -43,6 +43,7 @@ export const buildLinearSDXLImageToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -336,6 +337,7 @@ export const buildLinearSDXLImageToImageGraph = (
|
||||
{
|
||||
generation_mode: 'sdxl_img2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
height,
|
||||
width,
|
||||
positive_prompt: positivePrompt,
|
||||
|
||||
@ -34,6 +34,7 @@ export const buildLinearSDXLTextToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
seed,
|
||||
steps,
|
||||
@ -230,6 +231,7 @@ export const buildLinearSDXLTextToImageGraph = (
|
||||
{
|
||||
generation_mode: 'sdxl_txt2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
height,
|
||||
width,
|
||||
positive_prompt: positivePrompt,
|
||||
|
||||
@ -38,6 +38,7 @@ export const buildLinearTextToImageGraph = (
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
cfgRescaleMultiplier: cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
steps,
|
||||
width,
|
||||
@ -84,6 +85,7 @@ export const buildLinearTextToImageGraph = (
|
||||
id: DENOISE_LATENTS,
|
||||
is_intermediate,
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
scheduler,
|
||||
steps,
|
||||
denoising_start: 0,
|
||||
@ -239,6 +241,7 @@ export const buildLinearTextToImageGraph = (
|
||||
{
|
||||
generation_mode: 'txt2img',
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
height,
|
||||
width,
|
||||
positive_prompt: positivePrompt,
|
||||
|
||||
@ -23,7 +23,12 @@ import {
|
||||
VAEModelFieldInputTemplate,
|
||||
isStatefulFieldType,
|
||||
} from 'features/nodes/types/field';
|
||||
import { InvocationFieldSchema } from 'features/nodes/types/openapi';
|
||||
import {
|
||||
InvocationFieldSchema,
|
||||
isSchemaObject,
|
||||
} from 'features/nodes/types/openapi';
|
||||
import { t } from 'i18next';
|
||||
import { FieldParseError } from 'features/nodes/types/error';
|
||||
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
type FieldInputTemplateBuilder<T extends FieldInputTemplate = any> = // valid `any`!
|
||||
@ -321,7 +326,28 @@ const buildImageFieldInputTemplate: FieldInputTemplateBuilder<
|
||||
const buildEnumFieldInputTemplate: FieldInputTemplateBuilder<
|
||||
EnumFieldInputTemplate
|
||||
> = ({ schemaObject, baseField, isCollection, isCollectionOrScalar }) => {
|
||||
const options = schemaObject.enum ?? [];
|
||||
let options: EnumFieldInputTemplate['options'] = [];
|
||||
if (schemaObject.anyOf) {
|
||||
const filteredAnyOf = schemaObject.anyOf.filter((i) => {
|
||||
if (isSchemaObject(i)) {
|
||||
if (i.type === 'null') {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
});
|
||||
const firstAnyOf = filteredAnyOf[0];
|
||||
if (filteredAnyOf.length !== 1 || !isSchemaObject(firstAnyOf)) {
|
||||
options = [];
|
||||
} else {
|
||||
options = firstAnyOf.enum ?? [];
|
||||
}
|
||||
} else {
|
||||
options = schemaObject.enum ?? [];
|
||||
}
|
||||
if (options.length === 0) {
|
||||
throw new FieldParseError(t('nodes.unableToExtractEnumOptions'));
|
||||
}
|
||||
const template: EnumFieldInputTemplate = {
|
||||
...baseField,
|
||||
type: {
|
||||
|
||||
@ -1,10 +1,4 @@
|
||||
import { t } from 'i18next';
|
||||
import { isArray } from 'lodash-es';
|
||||
import { OpenAPIV3_1 } from 'openapi-types';
|
||||
import {
|
||||
FieldTypeParseError,
|
||||
UnsupportedFieldTypeError,
|
||||
} from 'features/nodes/types/error';
|
||||
import { FieldParseError } from 'features/nodes/types/error';
|
||||
import { FieldType } from 'features/nodes/types/field';
|
||||
import {
|
||||
OpenAPIV3_1SchemaOrRef,
|
||||
@ -14,6 +8,9 @@ import {
|
||||
isRefObject,
|
||||
isSchemaObject,
|
||||
} from 'features/nodes/types/openapi';
|
||||
import { t } from 'i18next';
|
||||
import { isArray } from 'lodash-es';
|
||||
import { OpenAPIV3_1 } from 'openapi-types';
|
||||
|
||||
/**
|
||||
* Transforms an invocation output ref object to field type.
|
||||
@ -70,7 +67,7 @@ export const parseFieldType = (
|
||||
// This is a single ref type
|
||||
const name = refObjectToSchemaName(allOf[0]);
|
||||
if (!name) {
|
||||
throw new FieldTypeParseError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unableToExtractSchemaNameFromRef')
|
||||
);
|
||||
}
|
||||
@ -95,7 +92,7 @@ export const parseFieldType = (
|
||||
if (isRefObject(filteredAnyOf[0])) {
|
||||
const name = refObjectToSchemaName(filteredAnyOf[0]);
|
||||
if (!name) {
|
||||
throw new FieldTypeParseError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unableToExtractSchemaNameFromRef')
|
||||
);
|
||||
}
|
||||
@ -120,7 +117,7 @@ export const parseFieldType = (
|
||||
|
||||
if (filteredAnyOf.length !== 2) {
|
||||
// This is a union of more than 2 types, which we don't support
|
||||
throw new UnsupportedFieldTypeError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unsupportedAnyOfLength', {
|
||||
count: filteredAnyOf.length,
|
||||
})
|
||||
@ -167,7 +164,7 @@ export const parseFieldType = (
|
||||
};
|
||||
}
|
||||
|
||||
throw new UnsupportedFieldTypeError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unsupportedMismatchedUnion', {
|
||||
firstType,
|
||||
secondType,
|
||||
@ -186,7 +183,7 @@ export const parseFieldType = (
|
||||
if (isSchemaObject(schemaObject.items)) {
|
||||
const itemType = schemaObject.items.type;
|
||||
if (!itemType || isArray(itemType)) {
|
||||
throw new UnsupportedFieldTypeError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unsupportedArrayItemType', {
|
||||
type: itemType,
|
||||
})
|
||||
@ -196,7 +193,7 @@ export const parseFieldType = (
|
||||
const name = OPENAPI_TO_FIELD_TYPE_MAP[itemType];
|
||||
if (!name) {
|
||||
// it's 'null', 'object', or 'array' - skip
|
||||
throw new UnsupportedFieldTypeError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unsupportedArrayItemType', {
|
||||
type: itemType,
|
||||
})
|
||||
@ -212,7 +209,7 @@ export const parseFieldType = (
|
||||
// This is a ref object, extract the type name
|
||||
const name = refObjectToSchemaName(schemaObject.items);
|
||||
if (!name) {
|
||||
throw new FieldTypeParseError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unableToExtractSchemaNameFromRef')
|
||||
);
|
||||
}
|
||||
@ -226,7 +223,7 @@ export const parseFieldType = (
|
||||
const name = OPENAPI_TO_FIELD_TYPE_MAP[schemaObject.type];
|
||||
if (!name) {
|
||||
// it's 'null', 'object', or 'array' - skip
|
||||
throw new UnsupportedFieldTypeError(
|
||||
throw new FieldParseError(
|
||||
t('nodes.unsupportedArrayItemType', {
|
||||
type: schemaObject.type,
|
||||
})
|
||||
@ -242,9 +239,7 @@ export const parseFieldType = (
|
||||
} else if (isRefObject(schemaObject)) {
|
||||
const name = refObjectToSchemaName(schemaObject);
|
||||
if (!name) {
|
||||
throw new FieldTypeParseError(
|
||||
t('nodes.unableToExtractSchemaNameFromRef')
|
||||
);
|
||||
throw new FieldParseError(t('nodes.unableToExtractSchemaNameFromRef'));
|
||||
}
|
||||
return {
|
||||
name,
|
||||
@ -252,5 +247,5 @@ export const parseFieldType = (
|
||||
isCollectionOrScalar: false,
|
||||
};
|
||||
}
|
||||
throw new FieldTypeParseError(t('nodes.unableToParseFieldType'));
|
||||
throw new FieldParseError(t('nodes.unableToParseFieldType'));
|
||||
};
|
||||
|
||||
@ -1,12 +1,6 @@
|
||||
import { logger } from 'app/logging/logger';
|
||||
import { parseify } from 'common/util/serialize';
|
||||
import { t } from 'i18next';
|
||||
import { reduce } from 'lodash-es';
|
||||
import { OpenAPIV3_1 } from 'openapi-types';
|
||||
import {
|
||||
FieldTypeParseError,
|
||||
UnsupportedFieldTypeError,
|
||||
} from 'features/nodes/types/error';
|
||||
import { FieldParseError } from 'features/nodes/types/error';
|
||||
import {
|
||||
FieldInputTemplate,
|
||||
FieldOutputTemplate,
|
||||
@ -18,6 +12,9 @@ import {
|
||||
isInvocationOutputSchemaObject,
|
||||
isInvocationSchemaObject,
|
||||
} from 'features/nodes/types/openapi';
|
||||
import { t } from 'i18next';
|
||||
import { reduce } from 'lodash-es';
|
||||
import { OpenAPIV3_1 } from 'openapi-types';
|
||||
import { buildFieldInputTemplate } from './buildFieldInputTemplate';
|
||||
import { buildFieldOutputTemplate } from './buildFieldOutputTemplate';
|
||||
import { parseFieldType } from './parseFieldType';
|
||||
@ -133,10 +130,7 @@ export const parseSchema = (
|
||||
|
||||
inputsAccumulator[propertyName] = fieldInputTemplate;
|
||||
} catch (e) {
|
||||
if (
|
||||
e instanceof FieldTypeParseError ||
|
||||
e instanceof UnsupportedFieldTypeError
|
||||
) {
|
||||
if (e instanceof FieldParseError) {
|
||||
logger('nodes').warn(
|
||||
{
|
||||
node: type,
|
||||
@ -225,10 +219,7 @@ export const parseSchema = (
|
||||
|
||||
outputsAccumulator[propertyName] = fieldOutputTemplate;
|
||||
} catch (e) {
|
||||
if (
|
||||
e instanceof FieldTypeParseError ||
|
||||
e instanceof UnsupportedFieldTypeError
|
||||
) {
|
||||
if (e instanceof FieldParseError) {
|
||||
logger('nodes').warn(
|
||||
{
|
||||
node: type,
|
||||
|
||||
@ -9,21 +9,41 @@ import { useTranslation } from 'react-i18next';
|
||||
import { ParamCpuNoiseToggle } from 'features/parameters/components/Parameters/Noise/ParamCpuNoise';
|
||||
import ParamSeamless from 'features/parameters/components/Parameters/Seamless/ParamSeamless';
|
||||
import ParamClipSkip from './ParamClipSkip';
|
||||
import ParamCFGRescaleMultiplier from './ParamCFGRescaleMultiplier';
|
||||
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
(state: RootState) => {
|
||||
const { clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise } =
|
||||
state.generation;
|
||||
const {
|
||||
clipSkip,
|
||||
model,
|
||||
seamlessXAxis,
|
||||
seamlessYAxis,
|
||||
shouldUseCpuNoise,
|
||||
cfgRescaleMultiplier,
|
||||
} = state.generation;
|
||||
|
||||
return { clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise };
|
||||
return {
|
||||
clipSkip,
|
||||
model,
|
||||
seamlessXAxis,
|
||||
seamlessYAxis,
|
||||
shouldUseCpuNoise,
|
||||
cfgRescaleMultiplier,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
export default function ParamAdvancedCollapse() {
|
||||
const { clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise } =
|
||||
useAppSelector(selector);
|
||||
const {
|
||||
clipSkip,
|
||||
model,
|
||||
seamlessXAxis,
|
||||
seamlessYAxis,
|
||||
shouldUseCpuNoise,
|
||||
cfgRescaleMultiplier,
|
||||
} = useAppSelector(selector);
|
||||
const { t } = useTranslation();
|
||||
const activeLabel = useMemo(() => {
|
||||
const activeLabel: string[] = [];
|
||||
@ -46,8 +66,20 @@ export default function ParamAdvancedCollapse() {
|
||||
activeLabel.push(t('parameters.seamlessY'));
|
||||
}
|
||||
|
||||
if (cfgRescaleMultiplier) {
|
||||
activeLabel.push(t('parameters.cfgRescale'));
|
||||
}
|
||||
|
||||
return activeLabel.join(', ');
|
||||
}, [clipSkip, model, seamlessXAxis, seamlessYAxis, shouldUseCpuNoise, t]);
|
||||
}, [
|
||||
cfgRescaleMultiplier,
|
||||
clipSkip,
|
||||
model,
|
||||
seamlessXAxis,
|
||||
seamlessYAxis,
|
||||
shouldUseCpuNoise,
|
||||
t,
|
||||
]);
|
||||
|
||||
return (
|
||||
<IAICollapse label={t('common.advanced')} activeLabel={activeLabel}>
|
||||
@ -61,6 +93,8 @@ export default function ParamAdvancedCollapse() {
|
||||
</>
|
||||
)}
|
||||
<ParamCpuNoiseToggle />
|
||||
<Divider />
|
||||
<ParamCFGRescaleMultiplier />
|
||||
</Flex>
|
||||
</IAICollapse>
|
||||
);
|
||||
|
||||
@ -0,0 +1,60 @@
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
|
||||
import IAISlider from 'common/components/IAISlider';
|
||||
import { setCfgRescaleMultiplier } from 'features/parameters/store/generationSlice';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
const selector = createSelector(
|
||||
[stateSelector],
|
||||
({ generation, hotkeys }) => {
|
||||
const { cfgRescaleMultiplier } = generation;
|
||||
const { shift } = hotkeys;
|
||||
|
||||
return {
|
||||
cfgRescaleMultiplier,
|
||||
shift,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const ParamCFGRescaleMultiplier = () => {
|
||||
const { cfgRescaleMultiplier, shift } = useAppSelector(selector);
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
|
||||
const handleChange = useCallback(
|
||||
(v: number) => dispatch(setCfgRescaleMultiplier(v)),
|
||||
[dispatch]
|
||||
);
|
||||
|
||||
const handleReset = useCallback(
|
||||
() => dispatch(setCfgRescaleMultiplier(0)),
|
||||
[dispatch]
|
||||
);
|
||||
|
||||
return (
|
||||
<IAIInformationalPopover feature="paramCFGRescaleMultiplier">
|
||||
<IAISlider
|
||||
label={t('parameters.cfgRescaleMultiplier')}
|
||||
step={shift ? 0.01 : 0.05}
|
||||
min={0}
|
||||
max={0.99}
|
||||
onChange={handleChange}
|
||||
handleReset={handleReset}
|
||||
value={cfgRescaleMultiplier}
|
||||
sliderNumberInputProps={{ max: 0.99 }}
|
||||
withInput
|
||||
withReset
|
||||
withSliderMarks
|
||||
isInteger={false}
|
||||
/>
|
||||
</IAIInformationalPopover>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(ParamCFGRescaleMultiplier);
|
||||
@ -1,7 +1,7 @@
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { skipToken } from '@reduxjs/toolkit/dist/query';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
import IAIDndImage from 'common/components/IAIDndImage';
|
||||
import { IAINoContentFallback } from 'common/components/IAIImageFallback';
|
||||
@ -9,25 +9,30 @@ import {
|
||||
TypesafeDraggableData,
|
||||
TypesafeDroppableData,
|
||||
} from 'features/dnd/types';
|
||||
import { memo, useMemo } from 'react';
|
||||
import { clearInitialImage } from 'features/parameters/store/generationSlice';
|
||||
import { memo, useEffect, useMemo } from 'react';
|
||||
import { useGetImageDTOQuery } from 'services/api/endpoints/images';
|
||||
|
||||
const selector = createSelector(
|
||||
[stateSelector],
|
||||
(state) => {
|
||||
const { initialImage } = state.generation;
|
||||
const { isConnected } = state.system;
|
||||
|
||||
return {
|
||||
initialImage,
|
||||
isResetButtonDisabled: !initialImage,
|
||||
isConnected,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const InitialImage = () => {
|
||||
const { initialImage } = useAppSelector(selector);
|
||||
const dispatch = useAppDispatch();
|
||||
const { initialImage, isConnected } = useAppSelector(selector);
|
||||
|
||||
const { currentData: imageDTO } = useGetImageDTOQuery(
|
||||
const { currentData: imageDTO, isError } = useGetImageDTOQuery(
|
||||
initialImage?.imageName ?? skipToken
|
||||
);
|
||||
|
||||
@ -49,6 +54,13 @@ const InitialImage = () => {
|
||||
[]
|
||||
);
|
||||
|
||||
useEffect(() => {
|
||||
if (isError && isConnected) {
|
||||
// The image doesn't exist, reset init image
|
||||
dispatch(clearInitialImage());
|
||||
}
|
||||
}, [dispatch, isConnected, isError]);
|
||||
|
||||
return (
|
||||
<IAIDndImage
|
||||
imageDTO={imageDTO}
|
||||
|
||||
@ -57,6 +57,7 @@ import {
|
||||
modelSelected,
|
||||
} from 'features/parameters/store/actions';
|
||||
import {
|
||||
setCfgRescaleMultiplier,
|
||||
setCfgScale,
|
||||
setHeight,
|
||||
setHrfEnabled,
|
||||
@ -94,6 +95,7 @@ import {
|
||||
isParameterStrength,
|
||||
isParameterVAEModel,
|
||||
isParameterWidth,
|
||||
isParameterCFGRescaleMultiplier,
|
||||
} from 'features/parameters/types/parameterSchemas';
|
||||
|
||||
const selector = createSelector(
|
||||
@ -282,6 +284,21 @@ export const useRecallParameters = () => {
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall CFG rescale multiplier with toast
|
||||
*/
|
||||
const recallCfgRescaleMultiplier = useCallback(
|
||||
(cfgRescaleMultiplier: unknown) => {
|
||||
if (!isParameterCFGRescaleMultiplier(cfgRescaleMultiplier)) {
|
||||
parameterNotSetToast();
|
||||
return;
|
||||
}
|
||||
dispatch(setCfgRescaleMultiplier(cfgRescaleMultiplier));
|
||||
parameterSetToast();
|
||||
},
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall model with toast
|
||||
*/
|
||||
@ -799,6 +816,7 @@ export const useRecallParameters = () => {
|
||||
|
||||
const {
|
||||
cfg_scale,
|
||||
cfg_rescale_multiplier,
|
||||
height,
|
||||
model,
|
||||
positive_prompt,
|
||||
@ -831,6 +849,10 @@ export const useRecallParameters = () => {
|
||||
dispatch(setCfgScale(cfg_scale));
|
||||
}
|
||||
|
||||
if (isParameterCFGRescaleMultiplier(cfg_rescale_multiplier)) {
|
||||
dispatch(setCfgRescaleMultiplier(cfg_rescale_multiplier));
|
||||
}
|
||||
|
||||
if (isParameterModel(model)) {
|
||||
dispatch(modelSelected(model));
|
||||
}
|
||||
@ -985,6 +1007,7 @@ export const useRecallParameters = () => {
|
||||
recallSDXLNegativeStylePrompt,
|
||||
recallSeed,
|
||||
recallCfgScale,
|
||||
recallCfgRescaleMultiplier,
|
||||
recallModel,
|
||||
recallScheduler,
|
||||
recallVaeModel,
|
||||
|
||||
@ -24,6 +24,7 @@ import {
|
||||
ParameterVAEModel,
|
||||
ParameterWidth,
|
||||
zParameterModel,
|
||||
ParameterCFGRescaleMultiplier,
|
||||
} from 'features/parameters/types/parameterSchemas';
|
||||
|
||||
export interface GenerationState {
|
||||
@ -31,6 +32,7 @@ export interface GenerationState {
|
||||
hrfStrength: ParameterStrength;
|
||||
hrfMethod: ParameterHRFMethod;
|
||||
cfgScale: ParameterCFGScale;
|
||||
cfgRescaleMultiplier: ParameterCFGRescaleMultiplier;
|
||||
height: ParameterHeight;
|
||||
img2imgStrength: ParameterStrength;
|
||||
infillMethod: string;
|
||||
@ -76,6 +78,7 @@ export const initialGenerationState: GenerationState = {
|
||||
hrfEnabled: false,
|
||||
hrfMethod: 'ESRGAN',
|
||||
cfgScale: 7.5,
|
||||
cfgRescaleMultiplier: 0,
|
||||
height: 512,
|
||||
img2imgStrength: 0.75,
|
||||
infillMethod: 'patchmatch',
|
||||
@ -145,9 +148,15 @@ export const generationSlice = createSlice({
|
||||
state.steps
|
||||
);
|
||||
},
|
||||
setCfgScale: (state, action: PayloadAction<number>) => {
|
||||
setCfgScale: (state, action: PayloadAction<ParameterCFGScale>) => {
|
||||
state.cfgScale = action.payload;
|
||||
},
|
||||
setCfgRescaleMultiplier: (
|
||||
state,
|
||||
action: PayloadAction<ParameterCFGRescaleMultiplier>
|
||||
) => {
|
||||
state.cfgRescaleMultiplier = action.payload;
|
||||
},
|
||||
setThreshold: (state, action: PayloadAction<number>) => {
|
||||
state.threshold = action.payload;
|
||||
},
|
||||
@ -336,6 +345,7 @@ export const {
|
||||
resetParametersState,
|
||||
resetSeed,
|
||||
setCfgScale,
|
||||
setCfgRescaleMultiplier,
|
||||
setWidth,
|
||||
setHeight,
|
||||
toggleSize,
|
||||
|
||||
@ -77,6 +77,17 @@ export const isParameterCFGScale = (val: unknown): val is ParameterCFGScale =>
|
||||
zParameterCFGScale.safeParse(val).success;
|
||||
// #endregion
|
||||
|
||||
// #region CFG Rescale Multiplier
|
||||
export const zParameterCFGRescaleMultiplier = z.number().gte(0).lt(1);
|
||||
export type ParameterCFGRescaleMultiplier = z.infer<
|
||||
typeof zParameterCFGRescaleMultiplier
|
||||
>;
|
||||
export const isParameterCFGRescaleMultiplier = (
|
||||
val: unknown
|
||||
): val is ParameterCFGRescaleMultiplier =>
|
||||
zParameterCFGRescaleMultiplier.safeParse(val).success;
|
||||
// #endregion
|
||||
|
||||
// #region Scheduler
|
||||
export const zParameterScheduler = zSchedulerField;
|
||||
export type ParameterScheduler = z.infer<typeof zParameterScheduler>;
|
||||
|
||||
@ -14,11 +14,11 @@ import {
|
||||
} from '@chakra-ui/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { VALID_LOG_LEVELS } from 'app/logging/logger';
|
||||
import { LOCALSTORAGE_KEYS, LOCALSTORAGE_PREFIX } from 'app/store/constants';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import IAIButton from 'common/components/IAIButton';
|
||||
import IAIMantineSelect from 'common/components/IAIMantineSelect';
|
||||
import { useClearStorage } from 'common/hooks/useClearStorage';
|
||||
import {
|
||||
consoleLogLevelChanged,
|
||||
setEnableImageDebugging,
|
||||
@ -164,20 +164,14 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
|
||||
shouldEnableInformationalPopovers,
|
||||
} = useAppSelector(selector);
|
||||
|
||||
const clearStorage = useClearStorage();
|
||||
|
||||
const handleClickResetWebUI = useCallback(() => {
|
||||
// Only remove our keys
|
||||
Object.keys(window.localStorage).forEach((key) => {
|
||||
if (
|
||||
LOCALSTORAGE_KEYS.includes(key) ||
|
||||
key.startsWith(LOCALSTORAGE_PREFIX)
|
||||
) {
|
||||
localStorage.removeItem(key);
|
||||
}
|
||||
});
|
||||
clearStorage();
|
||||
onSettingsModalClose();
|
||||
onRefreshModalOpen();
|
||||
setInterval(() => setCountdown((prev) => prev - 1), 1000);
|
||||
}, [onSettingsModalClose, onRefreshModalOpen]);
|
||||
}, [clearStorage, onSettingsModalClose, onRefreshModalOpen]);
|
||||
|
||||
useEffect(() => {
|
||||
if (countdown <= 0) {
|
||||
|
||||
114
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
114
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
File diff suppressed because one or more lines are too long
@ -4158,6 +4158,11 @@ i18next@^23.6.0:
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.22.5"
|
||||
|
||||
idb-keyval@^6.2.1:
|
||||
version "6.2.1"
|
||||
resolved "https://registry.yarnpkg.com/idb-keyval/-/idb-keyval-6.2.1.tgz#94516d625346d16f56f3b33855da11bfded2db33"
|
||||
integrity sha512-8Sb3veuYCyrZL+VBt9LJfZjLUPWVvqn8tG28VqYNFCo43KHcKuq+b4EiXGeuaLAQWL2YmyDgMp2aSpH9JHsEQg==
|
||||
|
||||
ieee754@^1.1.13:
|
||||
version "1.2.1"
|
||||
resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352"
|
||||
|
||||
@ -54,7 +54,8 @@ dependencies = [
|
||||
"invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
|
||||
"matplotlib", # needed for plotting of Penner easing functions
|
||||
"mediapipe", # needed for "mediapipeface" controlnet model
|
||||
"numpy",
|
||||
# Minimum numpy version of 1.24.0 is needed to use the 'strict' argument to np.testing.assert_array_equal().
|
||||
"numpy>=1.24.0",
|
||||
"npyscreen",
|
||||
"omegaconf",
|
||||
"onnx",
|
||||
|
||||
@ -37,6 +37,14 @@ def build_dummy_sd15_unet_input(torch_device):
|
||||
"unet_model_id": "runwayml/stable-diffusion-v1-5",
|
||||
"unet_model_name": "stable-diffusion-v1-5",
|
||||
},
|
||||
# SD1.5, IPAdapterFull
|
||||
{
|
||||
"ip_adapter_model_id": "InvokeAI/ip-adapter-full-face_sd15",
|
||||
"ip_adapter_model_name": "ip-adapter-full-face_sd15",
|
||||
"base_model": BaseModelType.StableDiffusion1,
|
||||
"unet_model_id": "runwayml/stable-diffusion-v1-5",
|
||||
"unet_model_name": "stable-diffusion-v1-5",
|
||||
},
|
||||
],
|
||||
)
|
||||
@pytest.mark.slow
|
||||
|
||||
57
tests/backend/util/test_logging.py
Normal file
57
tests/backend/util/test_logging.py
Normal file
@ -0,0 +1,57 @@
|
||||
"""
|
||||
Test interaction of logging with configuration system.
|
||||
"""
|
||||
import io
|
||||
import logging
|
||||
import re
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.backend.util.logging import LOG_FORMATTERS, InvokeAILogger
|
||||
|
||||
|
||||
# test formatting
|
||||
# Would prefer to use the capfd/capsys fixture here, but it is broken
|
||||
# when used with the logging module: https://github.com/pytest-dev/pytest/issue
|
||||
def test_formatting():
|
||||
logger = InvokeAILogger.get_logger()
|
||||
stream = io.StringIO()
|
||||
handler = logging.StreamHandler(stream)
|
||||
handler.setFormatter(LOG_FORMATTERS["plain"]())
|
||||
logger.addHandler(handler)
|
||||
logger.info("test1")
|
||||
output = stream.getvalue()
|
||||
assert re.search(r"\[InvokeAI\]::INFO --> test1$", output)
|
||||
|
||||
handler.setFormatter(LOG_FORMATTERS["legacy"]())
|
||||
logger.info("test2")
|
||||
output = stream.getvalue()
|
||||
assert re.search(r">> test2$", output)
|
||||
|
||||
|
||||
# test independence of two loggers with different names
|
||||
def test_independence():
|
||||
logger1 = InvokeAILogger.get_logger()
|
||||
logger2 = InvokeAILogger.get_logger("Test")
|
||||
assert logger1.name == "InvokeAI"
|
||||
assert logger2.name == "Test"
|
||||
assert logger1.level == logging.INFO
|
||||
assert logger2.level == logging.INFO
|
||||
logger2.setLevel(logging.DEBUG)
|
||||
assert logger1.level == logging.INFO
|
||||
assert logger2.level == logging.DEBUG
|
||||
|
||||
|
||||
# test that the logger is returned from two similar get_logger() calls
|
||||
def test_retrieval():
|
||||
logger1 = InvokeAILogger.get_logger()
|
||||
logger2 = InvokeAILogger.get_logger()
|
||||
logger3 = InvokeAILogger.get_logger("Test")
|
||||
assert logger1 == logger2
|
||||
assert logger1 != logger3
|
||||
|
||||
|
||||
# test that the configuration is used to set the initial logging level
|
||||
def test_config():
|
||||
config = InvokeAIAppConfig(log_level="debug")
|
||||
logger1 = InvokeAILogger.get_logger("DebugTest", config=config)
|
||||
assert logger1.level == logging.DEBUG
|
||||
Loading…
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Reference in New Issue
Block a user