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base repository: apple/ml-stable-diffusion
base: 0.1.0
head repository: apple/ml-stable-diffusion
compare: main
Commits on Dec 14, 2022
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README improvements and reduceMemory option in Swift
Co-authored-by: msiracusa <msiracusa+github@gmail.com>
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Commits on Dec 15, 2022
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Add Filename Character Limit (#19)
Co-authored-by: Stefan-Hintz <github@corbusierhaus.de>
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Commits on Dec 16, 2022
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Commits on Dec 20, 2022
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Commits on Dec 21, 2022
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* Synced to main branch and minimizes line changes * Adds negative prompt argument to CLI Co-authored-by: Wanaldino Antimonio <carlosmm31093@gmail.com>
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Commits on Dec 22, 2022
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Add brief instructions to download weights from the Hub (#10)
* Add brief instructions to download weights from the Hub. This should be convenient and time-saving for many users. * Capitalize ORIGINAL. * Expand git lfs details. * Make download code robust to symlinks. coremltools seems to not like symlinks. * Add mention to demo app. * Add steps to git lfs process. * Minor text change. * Apply suggestions from review.
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Commits on Dec 24, 2022
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Move guidanceScale as generation parameter (#46)
* Move guidanceScale as generation parameter * Added guidanceScale in CLI * Reverted identation change
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Commits on Dec 28, 2022
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Fix timesteps of DPMSolverMultistepScheduler. (#88)
There were minor differences in the timesteps because the linspace was computed slightly differently. This PR makes the Swift implementation identical to the current Python implementation in diffusers, which was originally contributed by the DPM-Solver++ author. See https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L199-L204 for reference.
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Commits on Jan 3, 2023
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Commits on Jan 26, 2023
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Fix downsample channels. (#110)
Reference: https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py#L749-L756 This is usually not a problem for Stable Diffusion models. However, some architectures such as https://huggingface.co/OFA-Sys/small-stable-diffusion-v0 wouldn't convert.
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Update import to nested path (#109)
* Update import to nested path * Update python_coreml_stable_diffusion/unet.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Atila Orhon <64497909+atiorh@users.noreply.github.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
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Commits on Jan 31, 2023
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* Add Encoder model to torch2coreml for image2image and later for in-paining * diagonal test with randn * Revert "diagonal test with randn" This reverts commit 270afe1. * readme updates for encoder * pr comments
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Commits on Feb 3, 2023
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Commits on Feb 8, 2023
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Commits on Feb 9, 2023
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* Image2Image Encoder Encoder * Scheduler and pipeline * fix scheduler * cli * remove CLI comment * disable dpm multistep solver with image2image * clamp initial timestamp * Store timesteps in reverse order for consistency. * Report actual number of steps. * uint32 * PRComments * remove old initializer * pr comments * change name and add error handling also fix 512 hard coded * Add fix for Jpegs --------- Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
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Commits on Feb 10, 2023
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Commits on Feb 15, 2023
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Add random source that matches PyTorch (#124)
* Add random source that matches PyTorch This added random source that matches PyTorch on CPU. In particular, it matches: `torch.randn([], dtype=torch.float)` result. PyTorch's RNG is a bit convoluted and not claimed to be version-stable (will open a separate issue in PyTorch repo on this). However, the current implementation on CPU is fairly straightforward^*. 1. If it is less than 16 elements, it uses Gaussian distribution sampled from MT19937 for double + Box-Muller transformation. 2. If it is more than 16 (16 included), it first do uniform sampling with whatever the resulting data type would be (in this case, torch.float), and then apply Box-Muller transformation over 16-element segment at a type, treating the first floating-point and the 8th as a pair, so on so forth. 3. If it is not a multiple of 16, trace back from the end for 16 elements and redo step 2. * Update with configuration available in SwiftDiffusionCLI * Fix the RNG is not passed into pipelineConfig.
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Commits on Mar 4, 2023
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Commits on Mar 27, 2023
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Commits on Apr 3, 2023
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Simplify Encoder module (#145)
* Simplify encoder conversion * Add nextNormal to RandomSource * Add addNoise method to Scheduler * Update Encoder implementation * Clean up Pipeline * Remove AlphasCumprodCalculation * Revert Encoder’s input shape size * Make Encoder/Decoder's scale factor configurable
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Commits on Apr 13, 2023
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Commits on Apr 18, 2023
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* add controlnet tentatively * add controlnet in python code * implement swift part * support 8-bit quantization * add controlnet unload when reduce memory * remove irrelevant changes * add more description about controlnet option in swift * fix some for pr and update README * pre-allocate zero shapedArray + make multi-controlnet faster
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Commits on Apr 24, 2023
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Commits on Apr 26, 2023
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Commits on Apr 27, 2023
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Improve BPETokenizer.readMerges performance (#169)
From 240ms to 19ms on M1 MacBook Pro Co-authored-by: Alejandro Isaza <aisaza@apple.com>
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Verify ControlNet base model. (#172)
We currently display a warning if the ControlNet repo does not contain a `base_model` property and the `model-version` is not SD 1.5. If the ControlNet repo does indicate a `base_model` property, then we raise a hard error if the versions don't match.
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Commits on Apr 29, 2023
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Disable SDPA (PyTorch 2.0) in the VAE (#174)
* Disable SDPA (PyTorch 2.0) in the VAE Diffusers 0.16.0 made sdpa enabled by default in the vae. It had previously been enabled for the UNet, but it didn't affect this repo because it uses its own copy of the UNet. This fix will not work for diffusers 0.16.0, but it will for main or 0.16.1 (to be released today). * Link to coremltools issue.
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Commits on Apr 30, 2023
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Commits on May 1, 2023
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Commits on May 7, 2023
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Image encoder resources management fix (#182)
* Unload image encoder for image2image in reduceMemory mode * Include image encoder to resource management functions
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Commits on May 16, 2023
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Commits on Jun 14, 2023
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Co-authored-by: davidfindlay1 <david.arthur.findlay@icloud.com>
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Commits on Jul 10, 2023
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Extend ControlNet image conversion with hole punching for InPaint (#205)
* Extend ControlNet image conversion with hole punching for InPaint The inpainting ControlNet model expects the masked areas to be filled with -1. This never occured with the original conversion, as the pixel data was converted to values between [0;1], thus the inpainting CN model never had any effect (apart from maybe generating artifacts). This commit uses the _previously discarded_ alpha channel of the source image to mark masked pixels: any non-opaque pixel is cleared out to -1. For an end-user, this allows creating masks simply by erasing areas with Preview to transparent, saving the result as PNG, and feeding it to the ControlNet. * Mask only fully transparent pixels
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Commits on Jul 12, 2023
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