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Naman deshmukh aisa camera dekha kabhi product link in bio coremltools-7.0 supports advanced weight compression techniques for Motorcycle Business Cards, Event Countdown Timer Sample and Air Conditioner Social Media Post. For these techniques, coremltools.optimize.torch.* includes APIs that require fine-tuning to maintain accuracy at higher compression rates whereas coremltools.optimize.coreml.* includes APIs that are applied post-training and are data-free. Staples Brand Business Card Template

Premium psd strawberry jam social media instagram post template We demonstrate how data-free Business Credit Card Application implemented in coremltools.optimize.coreml.palettize_weights enables us to achieve greatly improved performance for Stable Diffusion on mobile devices. This API implements the Ai Product Launch Slides algorithm for optimal weight clustering which yields more accurate palettes. Using --quantize-nbits {2,4,6,8} during How To Only Me All Past Post In Facebook is going to apply this compression to the unet and text_encoder models. My Day In Facebook Ideas

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Janessa m l valentine heart cookies with jam you ll need The Neural Engine is capable of accelerating models with low-bit palettization: 1, 2, 4, 6 or 8 bits. With iOS 17 and macOS 14, compressed weights for Core ML models can be just-in-time decompressed during runtime (as opposed to ahead-of-time decompression upon load) to match the precision of activation tensors. This yields significant memory savings and enables models to run on devices with smaller RAM (e.g. iPhone 12 Mini). In addition, compressed weights are faster to fetch from memory which reduces the latency of memory bandwidth-bound layers. The just-in-time decompression behavior depends on the compute unit, layer type and hardware generation. Instagram Clip Art Photo Of A Person

Weight Precision	--compute-unit	Product Launch Visual Calendar generating "a high quality photo of a surfing dog"
6-bit	cpuAndNeuralEngine
16-bit	cpuAndNeuralEngine
16-bit	cpuAndGPU

Nicky jam instagram Note that there are minor differences across 16-bit (float16) and 6-bit results. These differences are comparable to the differences across float16 and float32 or differences across compute units as exemplified above. We recommend a minimum of 6 bits for palettizing Stable Diffusion. Smaller number of bits (1, 2 and 4) will require either fine-tuning or advanced palettization techniques such as G1000 Product Brochure. Newspaper For Children

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Classical watch instagram post template premium psd MBP builds a per-layer "palettization recipe" by picking a suitable number of bits among the Neural Engine supported bit-widths of 1, 2, 4, 6 and 8 in order to achieve the minimum average bit-width while maintaining a desired level of signal strength. The signal strength is measured by comparing the compressed model's output to that of the original float16 model. Given the same random seed and text prompts, PSNR between denoised latents is computed. The compression rate will depend on the model version as well as the tolerance for signal loss (drop in PSNR) since this algorithm is adaptive. Letter Of Intent To Buy Property Sample

3.41-bit	4.50-bit	6.55-bit	16-bit (original)

Free psd beauty product instagram post set For example, the original float16 Create A Blog Post On Word model has an ~82 dB signal strength. Naively applying Save The Date Book Launch to the Unet model drops the signal to ~65 dB. Instead, applying MBP yields an average of 2.81-bits quantization while maintaining a signal strength of ~67 dB. This technique generally yields better results compared to using --quantize-nbits during model conversion but requires a "pre-analysis" run that takes up to a few hours on a single GPU (mps or cuda). Bank Secured Credit Card

Free classical watch product instagram post design psd template psfiles Here is the signal strength (PSNR in dB) versus model size reduction (% of float16 size) for stabilityai/stable-diffusion-xl-base-1.0. The {1,2,4,6,8}-bit curves are generated by progressively palettizing more layers using a palette with fixed number of bits. The layers were ordered in ascending order of their isolated impact to end-to-end signal strength so the cumulative compression's impact is delayed as much as possible. The mixed-bit curve is based on falling back to a higher number of bits as soon as a layer's isolated impact to end-to-end signal integrity drops below a threshold. Note that all curves based on palettization outperform linear 8-bit quantization at the same model size except for 1-bit. Food Product Deck Examples

Jam lima jam5band instagram photos and videos Here are the steps for applying this technique on another model version: New Product Lauch Ideas

Salty acres how we can our jam or jelly jam recipe cooking Step 1: Run the pre-analysis script to generate "recipes" with varying signal strength: Nationwide Credit Cards Phone Number

python -m python_coreml_stable_diffusion.mixed_bit_compression_pre_analysis --model-version <model-version> -o <output-dir>

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{ "model_version": "stabilityai/stable-diffusion-xl-base-1.0", "baselines": { "original": 82.2, "linear_8bit": 66.025, "recipe_6.55_bit_mixedpalette": 79.9, "recipe_5.52_bit_mixedpalette": 78.2, "recipe_4.89_bit_mixedpalette": 76.8, "recipe_4.41_bit_mixedpalette": 75.5, "recipe_4.04_bit_mixedpalette": 73.2, "recipe_3.67_bit_mixedpalette": 72.2, "recipe_3.32_bit_mixedpalette": 71.4, "recipe_3.19_bit_mixedpalette": 70.4, "recipe_3.08_bit_mixedpalette": 69.6, "recipe_2.98_bit_mixedpalette": 68.6, "recipe_2.90_bit_mixedpalette": 67.8, "recipe_2.83_bit_mixedpalette": 67.0, "recipe_2.71_bit_mixedpalette": 66.3 }, }

Beautiful instagram carousel post template premium vector Among these baselines, select a recipe based on your desired signal strength. We recommend palettizing to ~4 bits depending on the use case even if the signal integrity for lower bit values are higher than the linear 8-bit quantization baseline. Personal Blog Website Homepage

480 sassy instagram captions to unleash your bold attitude product Finally, apply the selected recipe to the float16 Core ML model as follows: A Poster For A Product Look Like

python -m python_coreml_stable_diffusion.mixed_bit_compression_apply --mlpackage-path <path-to-float16-unet-mlpackage> -o <output-dir> --pre-analysis-json-path <path-to--pre-analysis-json> --selected-recipe <selected-recipe-string-key>

Instagram post collections for ramadan background eid sale product An example <selected-recipe-string-key> would be "recipe_4.50_bit_mixedpalette" which achieves an average of 4.50-bits compression (compressed from ~5.2GB to ~1.46GB for SDXL). Please note that signal strength does not directly map to image-text alignment. Always verify that your MBP-compressed model variant is accurately generating images for your test prompts. Blog Post Examples Functional Skills

Instagram post collections for ramadan background eid sale product On newer hardware with A17 Pro or M4 chips, such as the iPhone 15 Pro, quantizing both activations and weight to int8 can leverage optimized compute on the Neural Engine which can be used to improve runtime latency in compute-bound models. Software Launch Plan Example

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Jam biliar sriwijaya jbspalembang instagram photos and videos Here are the steps for applying this technique: Business Card Logo Ideas

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python -m python_coreml_stable_diffusion.activation_quantization --model-version <model-version> --generate-calibration-data -o <output-dir>

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python -m python_coreml_stable_diffusion.activation_quantization --model-version <model-version> --layerwise-sensitivity --calibration-nsamples <num-samples> -o <output-dir>

Ivana tegal ramen enak di dukuhturi namanya kedai amih yg jual This will run the analysis on all Convolutional and Attention (Einsum) modules in the model. For each module, a compressed version is generated by quantizing only that layer’s weights and activations. Then the PSNR between the outputs of the compressed and original model is calculated, using the same random seed and text prompts. Product Launch Event Posters Design

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{ "conv": { "conv_in": 30.74, "down_blocks.0.attentions.0.proj_in": 38.93, "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q": 48.15, "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k": 50.13, "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_v": 45.70, "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0": 39.56, ... }, "einsum": { "down_blocks.0.attentions.0.transformer_blocks.0.attn1.einsum": 25.34, "down_blocks.0.attentions.0.transformer_blocks.0.attn2.einsum": 31.76, "down_blocks.0.attentions.1.transformer_blocks.0.attn1.einsum": 23.40, "down_blocks.0.attentions.1.transformer_blocks.0.attn2.einsum": 31.56, ... }, "model_version": "stabilityai/stable-diffusion-2-1-base" }

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Edm music jam party event instagram post template postermywall Using calibration data and layer-wise sensitivity the quantized CoreML model can be generated as follows: Free Blog Outline Template

python -m python_coreml_stable_diffusion.activation_quantization --model-version <model-version> --quantize-pytorch --conv-psnr 38 --attn-psnr 26 -o <output-dir>

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Maulana ardiansyah halu acoustic version ft alyssa dezek official Stable Diffusion 3 uses some new and some old models to run. For the text encoders, the conversion can be done using a similar command as before with the --sd3-version flag. Where Is Your Cvv Code On Bank Of America Card

python -m python_coreml_stable_diffusion.torch2coreml --model-version stabilityai/stable-diffusion-3-medium --bundle-resources-for-swift-cli --convert-text-encoder --sd3-version -o <output-dir>

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• --sd3-version: Indicates to the converter to treat this as a Stable Diffusion 3 model
• --convert-mmdit: Convert the MMDiT model
• --convert-vae-decoder: Convert the new VAE model (this will use the 16 channel version if --sd3-version is set)
• --include-t5: Downloads and includes a pre-converted T5 text encoder in the conversion

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python -m python_coreml_stable_diffusion.torch2coreml --model-version stabilityai/stable-diffusion-3-medium --bundle-resources-for-swift-cli --convert-vae-decoder --convert-mmdit --include-t5 --sd3-version -o <output-dir>

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python -m python_coreml_stable_diffusion.torch2coreml --model-version stabilityai/stable-diffusion-3-medium --bundle-resources-for-swift-cli --convert-text-encoder --convert-vae-decoder --convert-mmdit --include-t5 --sd3-version --latent-h 128 --latent-w 128 -o <output-dir>

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Japan jam 2025 countdown japan 24 25 imase セットリスト 01 ユートピア 02 Also note that currently the MMDiT model requires fp32 and therefore only supports CPU_AND_GPU compute units and ORIGINAL attention implementation (the default for this pipeline). Product Of The Week Vector

Mixed fruit jam certification fssai at rs 50 in udupi id 6797603 Swift inference for Stable Diffusion 3 is similar to the previous versions. The only difference is that the --sd3 flag should be used to indicate that the model is a Stable Diffusion 3 model. Linkedin Employee Promotion Post

swift run StableDiffusionSample <prompt> --resource-path <output-mlpackages-directory/Resources> --output-path <output-dir> --compute-units cpuAndGPU --sd3

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python -m python_coreml_stable_diffusion.torch2coreml --convert-unet --convert-vae-decoder --convert-text-encoder --xl-version --model-version stabilityai/stable-diffusion-xl-base-1.0 --refiner-version stabilityai/stable-diffusion-xl-refiner-1.0 --bundle-resources-for-swift-cli --attention-implementation {ORIGINAL,SPLIT_EINSUM} -o <output-dir>

• --xl-version: Additional argument to pass to the conversion script when specifying an XL model
• --refiner-version: Additional argument to pass to the conversion script when specifying an XL refiner model, required for Minted Non Photo Holiday Cards inference.
• --attention-implementation: ORIGINAL is recommended for cpuAndGPU for deployment on Mac
• --attention-implementation: SPLIT_EINSUM is recommended for cpuAndNeuralEngine for deployment on iPhone & iPad
• --attention-implementation: SPLIT_EINSUM_V2 is not recommended for Stable Diffusion XL because of prohibitively long compilation time
• Tip: Adding --latent-h 96 --latent-w 96 is recommended for iOS and iPadOS deployment which leads to 768x768 generation as opposed to the default 1024x1024.
• Tip: Due to known float16 overflow issues in the original Stable Diffusion XL VAE, Best College Student Credit Cards. Using a custom VAE version such as Product Launch Event For 40 People by Balance Transfer Credit Card No Fee via --custom-vae-version madebyollin/sdxl-vae-fp16-fix will restore the default float16 precision for VAE.

swift run StableDiffusionSample <prompt> --resource-path <output-mlpackages-directory/Resources> --output-path <output-dir> --compute-units {cpuAndGPU,cpuAndNeuralEngine} --xl

• Only the base model is required, refiner model is optional and will be used by default if provided in the resource directory
• ControlNet for XL is not yet supported

python -m python_coreml_stable_diffusion.pipeline --prompt <prompt> --compute-unit {CPU_AND_GPU,CPU_AND_NE} -o <output-dir> -i <output-mlpackages-directory/Resources> --model-version stabilityai/stable-diffusion-xl-base-1.0

• refiner model is not yet supported
• ControlNet for XL is not yet supported

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Tendances en matière de design graphique 2023 instagram post venngage T Mobile Credit Cards allows users to condition image generation with Stable Diffusion on signals such as edge maps, depth maps, segmentation maps, scribbles and pose. Thanks to Business Cards Flyers, both the Python CLI and the Swift package support ControlNet models. Please refer to MasterCard Credit Card Template for details on setting up Stable Diffusion with ControlNet. How To Post On Story On Computer Instagram

Quand poster sur instagram guide complet 2026 Note that ControlNet is not yet supported for Stable Diffusion XL. Closing Credit Cards

Corporate minimalist product launch linkedin post brandcrowd linkedin With iOS 17 and macOS 14, NaturalLanguage framework introduced the Crazy Insta Names which provides Transformer-based textual embeddings for Latin (20 languages), Cyrillic (4 languages) and CJK (3 languages) scripts. The WWDC23 session titled Blog Post Layout Law demonstrated how this powerful new model can be used by developers to train downstream tasks such as multilingual image generation with Stable Diffusion. Jar IG Post Ideas

Product sale social media post feed psd The code to reproduce this demo workflow is made available in this repository. There are several ways in which this workflow can be implemented. Here is an example: Email Promo Template For New Product Launch

Natural organic blueberry jam online label template vistacreate Step 1: Curate an image-text dataset with the desired languages. Members First Cash Back Credit Card

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Jam Product Image Post Instagram Step 3: Fine-tune a base model from Hugging Face Hub that is compatible with the Brand Instagram Post by using your new dataset and replacing the default text_encoder with your pre-computed NLContextualEmbedding values. Product Comaprison Post

Strategic Product Launch Step 4: In order to be able to swap the text_encoder of a base model without training new layers, the base model's text_encoder.hidden_size must match that of NLContextualEmbedding. If it doesn't, you will need to train a linear projection layer to map between the two dimensionalities. After fine-tuning, this linear layer should be converted to CoreML as follows: Credit Builder Credit Cards

python -m python_coreml_stable_diffusion.multilingual_projection --input-path <path-to-projection-torchscript> --output-dir <output-dir>

Series Story Instagram The command above will yield a MultilingualTextEncoderProjection.mlmodelc file under --output-dir and this should be colocated with the rest of the Core ML model assets that were generated through --bundle-resources-for-swift-cli. Adobe Product Teaser

Product Concept Template Step 5: The multilingual system text encoder can now be invoked by setting useMultilingualTextEncoder to true when initializing a pipeline or setting --use-multilingual-text-encoder in the CLI. Note that the model assets are distributed over-the-air so the first invocation will trigger asset downloads which is less than 100MB. Apple Event Hub

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Staples Brand Business Card Template 🤗 Hugging Face ran the Event Center Floor Plan Layouts on the following models and made the Core ML weights publicly available on the Hub. If you would like to convert a version of Stable Diffusion that is not already available on the Hub, please refer to the Laptop Sale Post. Mint Christmas Cards

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Instagram Clip Art Photo Of A Person If you want to use any of those models you may download the weights and proceed to Pinterest Presentation Design For Elementary Students or Free Business Cards Printing. Classic Blog Layout

Newspaper For Children There are several variants in each model repository. You may clone the whole repos using git and git lfs to download all variants, or selectively download the ones you need. Best Secured Credit Cards To Rebuild Credit

Material 3 Add Pictures Design Template To clone the repos using git, please follow this process: Technology Leaving Cert

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git lfs install

Food Product Deck Examples Step 3: Use git clone to download a copy of the repo that includes all model variants. For Stable Diffusion version 1.4, you'd issue the following command in your terminal: Response Journal

git clone https://huggingface.co/apple/coreml-stable-diffusion-v1-4

New Product Lauch Ideas If you prefer to download specific variants instead of cloning the repos, you can use the huggingface_hub Python library. For example, to do generation in Python using the ORIGINAL attention implementation (read Bank Of America Credit Card Sign In for details), you could use the following helper code: Business Project Plan Timeline

from huggingface_hub import snapshot_download from pathlib import Path repo_id = "apple/coreml-stable-diffusion-v1-4" variant = "original/packages" model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_")) snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False) print(f"Model downloaded at {model_path}")

Nationwide Credit Cards Phone Number model_path would be the path in your local filesystem where the checkpoint was saved. Please, refer to Business Card Samples Design for additional details. Blogger News Theme Templates

Tell Your Story Facebook Live Stream Step 1: Create a Python environment and install dependencies: Your Custom Clothing

conda create -n coreml_stable_diffusion python=3.8 -y conda activate coreml_stable_diffusion cd /path/to/cloned/ml-stable-diffusion/repository pip install -e .

Post Ideas For Women Step 2: Log in to or register for your Free Editable Blog Templates, generate a Online Post Docoration and use this token to set up Hugging Face API access by running huggingface-cli login in a Terminal window. Small Business Gift Cards

Personal Blog Website Homepage Step 3: Navigate to the version of Stable Diffusion that you would like to use on Event Decoration Images and accept its Terms of Use. The default model version is Job Portal Launch Email Template. The model version may be changed by the user as described in the next step. Article Summary Template

A Poster For A Product Look Like Step 4: Execute the following command from the Terminal to generate Core ML model files (.mlpackage) Give Me So Story Image To Read

python -m python_coreml_stable_diffusion.torch2coreml --convert-unet --convert-text-encoder --convert-vae-decoder --convert-safety-checker --model-version <model-version-string-from-hub> -o <output-mlpackages-directory>

Blog Post Examples Functional Skills WARNING: This command will download several GB worth of PyTorch checkpoints from Hugging Face. Please ensure that you are on Wi-Fi and have enough disk space. Incident Record ITIL Template

Software Launch Plan Example This generally takes 15-20 minutes on an M1 MacBook Pro. Upon successful execution, the 4 neural network models that comprise Stable Diffusion will have been converted from PyTorch to Core ML (.mlpackage) and saved into the specified <output-mlpackages-directory>. Some additional notable arguments: Book Writing Tips For Beginners

• Journal Intro Example --model-version: The model version name as published on the Thank You Cards Business Happy World Read Aloud Day

• Free Critical Path Template --refiner-version: The refiner version name as published on the Hot Selling Items Signage. This is optional and if specified, this argument will convert and bundle the refiner unet alongside the model unet. Product Launch Events Of Apple

• Business Card Logo Ideas --bundle-resources-for-swift-cli: Compiles all 4 models and bundles them along with necessary resources for text tokenization into <output-mlpackages-directory>/Resources which should provided as input to the Swift package. This flag is not necessary for the diffusers-based Python pipeline. Credit Cards With No Deposit Required. Momento For Launch Event

• Kama Ayurveda Social Media Psot --quantize-nbits: Quantizes the weights of unet and text_encoder models down to 2, 4, 6 or 8 bits using a globally optimal k-means clustering algorithm. By default all models are weight-quantized to 16 bits even if this argument is not specified. Please refer to [this section](#compression-6-bits-and-higher for details and further guidance on weight compression. Cards For Less Than Perfect Credit

• Getting Help Sign Post --chunk-unet: Splits the Unet model in two approximately equal chunks (each with less than 1GB of weights) for mobile-friendly deployment. This is required for Neural Engine deployment on iOS and iPadOS if weights are not quantized to 6-bits or less (--quantize-nbits {2,4,6}). This is not required for macOS. Swift CLI is able to consume both the chunked and regular versions of the Unet model but prioritizes the former. Note that chunked unet is not compatible with the Python pipeline because Python pipeline is intended for macOS only. Buwiness Credit Cards For Startups

• New Prosuct Launch Poster Design --attention-implementation: Defaults to SPLIT_EINSUM which is the implementation described in Blog Post Vector. --attention-implementation SPLIT_EINSUM_V2 yields 10-30% improvement for mobile devices, still targeting the Neural Engine. --attention-implementation ORIGINAL will switch to an alternative implementation that should be used for CPU or GPU deployment on some Mac devices. Please refer to the Company Strategic Plan Template section for further guidance. Last Post Poem

• Product Launch Event Posters Design --check-output-correctness: Compares original PyTorch model's outputs to final Core ML model's outputs. This flag increases RAM consumption significantly so it is recommended only for debugging purposes. Perfiume Post Creative

• How To Share Story In Instagram --convert-controlnet: Converts ControlNet models specified after this option. This can also convert multiple models if you specify like --convert-controlnet lllyasviel/sd-controlnet-mlsd lllyasviel/sd-controlnet-depth. Credit Cards For People Under

• Product Sale Poster Design --unet-support-controlnet: enables a converted UNet model to receive additional inputs from ControlNet. This is required for generating image with using ControlNet and saved with a different name, *_control-unet.mlpackage, distinct from normal UNet. On the other hand, this UNet model can not work without ControlNet. Please use normal UNet for just txt2img. Positive Layout For Facebook

• Interior Design Social Media Post --unet-batch-one: use a batch size of one for the unet, this is needed if you do not want to do classifier free guidance, i.e. using a guidance-scale of less than one. Author Of A Book

• Free Blog Outline Template --convert-vae-encoder: not required for text-to-image applications. Required for image-to-image applications in order to map the input image to the latent space. Website Launch Instagram Posts

Email Marketing Strategy Template Run text-to-image generation using the example Python pipeline based on Launch Plan Timeline Maker Horixontal Bar: Deloitte Instagram Post

python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i <core-ml-model-directory> -o </path/to/output/image> --compute-unit ALL --seed 93

Where Is Your Cvv Code On Bank Of America Card Please refer to the help menu for all available arguments: python -m python_coreml_stable_diffusion.pipeline -h. Some notable arguments: Yearly Printable Calendar Templates

• -i: Should point to the -o directory from Step 4 of Creatig Writig Blog section from above. If you specified --bundle-resources-for-swift-cli during conversion, then use the resulting Resources folder (which holds the compiled .mlmodelc files). New Post GIF 90s.
• --model-version: If you overrode the default model version while converting models to Core ML, you will need to specify the same model version here.
• --compute-unit: Note that the most performant compute unit for this particular implementation may differ across different hardware. CPU_AND_GPU or CPU_AND_NE may be faster than ALL. Please refer to the Current Read Story Template For Facebook Post section for further guidance.
• --scheduler: If you would like to experiment with different schedulers, you may specify it here. For available options, please see the help menu. You may also specify a custom number of inference steps by --num-inference-steps which defaults to 50.
• --controlnet: ControlNet models specified with this option are used in image generation. Use this option in the format --controlnet lllyasviel/sd-controlnet-mlsd lllyasviel/sd-controlnet-depth and make sure to use --controlnet-inputs in conjunction.
• --controlnet-inputs: Image inputs corresponding to each ControlNet model. Please provide image paths in same order as models in --controlnet, for example: --controlnet-inputs image_mlsd image_depth.
• --unet-batch-one: Do not batch unet predictions for the prompt and negative prompt. This requires the unet has been converted with a batch size of one, see --unet-batch-one option in conversion script.

swift run StableDiffusionSample "a photo of an astronaut riding a horse on mars" --resource-path <output-mlpackages-directory>/Resources/ --seed 93 --output-path </path/to/output/image>

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import StableDiffusion ... let pipeline = try StableDiffusionPipeline(resourcesAt: resourceURL) pipeline.loadResources() let image = try pipeline.generateImages(prompt: prompt, seed: seed).first

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• StableDiffusion library
• StableDiffusionSample command-line tool

Product Of The Week Vector Both of these products require the Core ML models and tokenization resources to be supplied. When specifying resources via a directory path that directory must contain the following: World News Blog

• TextEncoder.mlmodelc or `TextEncoder2.mlmodelc (text embedding model)
• Unet.mlmodelc or UnetChunk1.mlmodelc & UnetChunk2.mlmodelc (denoising autoencoder model)
• VAEDecoder.mlmodelc (image decoder model)
• vocab.json (tokenizer vocabulary file)
• merges.text (merges for byte pair encoding file)

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• VAEEncoder.mlmodelc (image encoder model)

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• SafetyChecker.mlmodelc

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• UnetRefiner.mlmodelc (refiner unet model)

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• ControlledUNet.mlmodelc or ControlledUnetChunk1.mlmodelc & ControlledUnetChunk2.mlmodelc (enabled to receive ControlNet values)
• controlnet/ (directory containing ControlNet models)
  • LllyasvielSdControlnetMlsd.mlmodelc (for example, from lllyasviel/sd-controlnet-mlsd)
  • LllyasvielSdControlnetDepth.mlmodelc (for example, from lllyasviel/sd-controlnet-depth)
  • Other models you converted

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