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@@ -51,3 +51,12 @@ fp16onnxquantizedsd_in_ort/cyberrealistic/model.ort filter=lfs diff=lfs merge=lf
 fp16onnxquantizedsd_in_ort/cyberrealistic/model.with_runtime_opt.ort filter=lfs diff=lfs merge=lfs -text
 fp16onnxquantizedsd_textencoder_in_ort/cyberrealistic/model.ort filter=lfs diff=lfs merge=lfs -text
 fp16onnxquantizedsd_textencoder_in_ort/cyberrealistic/model.with_runtime_opt.ort filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/sd_env/bin/python filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/sd_env/bin/python3 filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/sd_env/bin/python3.10 filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/stable-diffusion-streamlit/doc/gif/use1.gif filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/stable-diffusion-streamlit/doc/gif/use2.gif filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/stable-diffusion-streamlit/doc/gif/use3.gif filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.onnx.data filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.ort filter=lfs diff=lfs merge=lfs -text
+patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.with_runtime_opt.ort filter=lfs diff=lfs merge=lfs -text

patchedstabledifftoonnx/LICENSE

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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
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+    (at your option) any later version.
+
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
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+
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+notice like this when it starts in an interactive mode:
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

patchedstabledifftoonnx/README.md

@@ -0,0 +1,293 @@
+# Stable Diffusion using ONNX, FP16 and DirectML
+
+This repository contains a conversion tool, some examples, and instructions on how to set up Stable Diffusion with ONNX models.
+This was mainly intended for use with AMD GPUs but should work just as well with other DirectML devices (e.g. Intel Arc).
+I'd be very interested to hear of any results with Intel Arc.  
+
+**MOST IMPORTANT RECENT UPDATES:**  
+**- ONNX Runtime 1.15 has been released! Updated model tuning code to better align with ORT.**  
+**- Realigned with latest version of diffusers, we were forced to switch to torch 2.1 nightly! (Install instructions updated accordingly)**  
+**- I have enabled GitHub discussions: If you have a generic question rather than an issue, start a discussion!**
+
+This focuses specifically on making it easy to get FP16 models. When using FP16, the VRAM footprint is significantly reduced and speed goes up.
+
+It's all fairly straightforward, but It helps to be comfortable with command line.
+
+You can use these instructions to convert models to FP16 and then use them in any tool that allows you to load ONNX models.
+We'll demonstrate this by downloading and setting up ONNXDiffusersUI specifically for use with our installation (no need to follow the ONNXDiffusersUI setup).
+
+## Set up
+
+First make sure you have Python 3.10 (or 3.11) installed. You can get it here: https://www.python.org/downloads/  
+**NOTE:** 3.10 is still the preferred version. Since the release of ONNX Runtime 1.15 all requirements now have proper Python 3.11 support but conversion is extremely slow on 3.11.
+
+If you don't have git, get it here: https://gitforwindows.org/
+
+Pick a directory that can contain your Stable Diffusion installation (make sure you've the diskspace to store the models).
+Open the commandline (Powershell or Command Prompt) and change into the directory you will use.
+
+Start by cloning this repository:
+```
+git clone https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16
+cd Stable-Diffusion-ONNX-FP16
+```
+
+Do the following:
+```
+pip install virtualenv
+python -m venv sd_env
+sd_env\scripts\activate
+python -m pip install --upgrade pip
+pip install torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu --pre
+pip install -r requirements.txt
+```
+
+Now first make sure you have an account on https://huggingface.co/  
+When you do make sure to create a token on https://huggingface.co/settings/tokens  
+And then on the commandline login using following command
+```
+huggingface-cli login
+```
+
+Now you're ready to download and convert models. Before we explain this, just a pointer on future use.  
+Whenever you want to make use of this post set up, open a command line, change into the directory and enable the environment.
+Say that you installed this on your D: drive in the root. You would open command line and then:
+```
+d:
+cd Stable-Diffusion-ONNX-FP16
+sd_env\scripts\activate
+```
+
+Remember this for whenver you want to use your installation. Let's now get to the fun part and convert some models:
+```
+mkdir model
+python conv_sd_to_onnx.py --model_path "stabilityai/stable-diffusion-2-1-base" --output_path "./model/sd2_1base-fp32" 
+python conv_sd_to_onnx.py --model_path "stabilityai/stable-diffusion-2-1-base" --output_path "./model/sd2_1base-fp16" --fp16
+```
+
+You now have 2 models. These are geared towards creating 512x512 images.
+
+Now we'll run our test script twice:
+```
+python test-txt2img.py --model "model\sd2_1base-fp32" --size 512 --seed 0
+python test-txt2img.py --model "model\sd2_1base-fp16" --size 512 --seed 0
+```
+
+You should now have 2 similar pictures. Note that there'll be differences between FP32 and FP16. But FP16 should not be specifically worse than FP32.
+The accuracy just shifts things a bit, but it may just as well shift them for the better.
+
+Next let's do 768x768. This requires your card to have enough VRAM but we'll make a VRAM friendly version too.
+Here we aren't bothering with FP32 because it just requires too much VRAM. 
+```
+python conv_sd_to_onnx.py --model_path "stabilityai/stable-diffusion-2-1" --output_path "./model/sd2_1-fp16" --fp16
+python test-txt2img.py --model "model\sd2_1-fp16" --size 768 --seed 0
+```
+
+You should now have a 768x768 picture.
+
+This will work fine on 12GB VRAM and above but 8GB may already be a stretch. The more VRAM friendly version is next.
+
+This method uses less VRAM and will be slightly slower when you're not VRAM limited. But, it'll allow you to use far larger resolutions than standard models.
+The output will be slightly different but should not be specifically worse. If you got the VRAM, see how well size 1024 works!
+
+```
+python conv_sd_to_onnx.py --model_path "stabilityai/stable-diffusion-2-1" --output_path "./model/sd2_1-fp16-autoslicing" --fp16 --attention-slicing auto
+python test-txt2img.py --model "model\sd2_1-fp16-autoslicing" --size 768 --seed 0
+```
+
+Now that we've got everything working and we can create pictures, let's get a GUI. We'll use ONNXDiffusersUI but make it so it doesn't break our workflow.  
+First we clone the repository:
+```
+git clone https://github.com/Amblyopius/OnnxDiffusersUI
+```
+Now we run the UI
+```
+python OnnxDiffusersUI\onnxUI.py
+```
+It'll take some time to load and then in your browser you can go to http://127.0.0.1:7860 (only accessible on the host you're running it).  
+If you're done you can go back to the CMD window and press Ctrl+C and it will quit.
+
+Note that it expects your models to be in the model directory (which is why we put them there in the instructions).  
+You can find your history and all the pictures you created in the directory called output.
+
+If you want to learn more about the UI be sure to visit https://github.com/azuritecoin/OnnxDiffusersUI  
+NOTE: We are using a fork as it simplifies keeping it aligned with our own updates
+
+## Advanced features
+### Support for ControlNet
+ControlNet was recently introduced. It allows conditional control on Text-to-Image Diffusion Models. If you want more in-depth information,
+get it here: https://github.com/lllyasviel/ControlNet
+
+As it has now been added to Diffusers I've added a fairly "elegant" ONNX implementation.
+
+The idea behind the implementation is:  
+- We use the same single tool to convert models  
+- We can load the Pipeline from disk by referencing a single model
+
+This has only 1 downside, it is not the most disk friendly solution as you'll get some duplication.
+We may eventually have to opt for a different disk layout for ONNX models.
+
+The current implementation consists of a simple demo. More to follow soon!
+
+First let's get ourselves a working model:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_ft_mse-autoslicing-cn_canny" --controlnet_path "lllyasviel/sd-controlnet-canny" --fp16 --attention-slicing auto --vae_path "stabilityai/sd-vae-ft-mse"
+```
+This model is an SD 1.5 model combined with Controlnet Canny. Now let's run the test script:
+```
+python test-controlnet-canny.py
+```
+Once the test is done you'll have an image called controlnet-canny-test.png.
+The new image is entirely different but the shape is very similar to the original input image.
+
+You can look at the test-controlnet-canny.py to see how it works.
+
+Next we'll use openpose. Note that the example is a demanding pose that you would ordinarily probably not go for.
+Without tweaking this also suffers a bit from bad hands/feet. For the sake of the test I decided to tolerate it.
+Let's make a ControlNet OpenPose model:
+```
+python conv_sd_to_onnx.py --model_path "Linaqruf/anything-v3.0" --output_path "./model/anyv3-fp16-autoslicing-cn_openpose" --controlnet_path "lllyasviel/sd-controlnet-openpose" --fp16 --attention-slicing auto
+```
+And now let's run the test:
+```
+python test-controlnet-openpose.py
+```
+This gives you controlnet-openpose-test.png
+
+As some may wonder where I got the openpose startpoint image from. I used https://zhuyu1997.github.io/open-pose-editor/  
+Create the pose.
+Press the button underneath height, then download the generated map on the left.
+You can further edit it locally to fit the canvas in the way you want it to.
+
+### Support for Instruct pix2pix
+Recently a special Stable Diffusion model was released, allowing you to have AI edit images based on instructions.
+Make sure you read the original documentation here: https://www.timothybrooks.com/instruct-pix2pix
+
+A pipeline was added to diffusers, but currently Huggingface does not add ONNX equivalents.
+In this repository I included the required ONNX pipeline and a basic UI (to simplify testing before it gets added to ONNXDiffusersUI)
+
+You can convert the model using this command (it'll fetch it from huggingface):
+```
+python conv_sd_to_onnx.py --model_path "timbrooks/instruct-pix2pix" --output_path "./model/ip2p-base-fp16-vae_ft_mse-autoslicing" --vae_path "stabilityai/sd-vae-ft-mse" --fp16 --attention-slicing auto
+```
+Once converted you can run the included UI like this:
+```
+python pix2pixUI.py
+```
+You'll need an image to start from (you can always create one with Stable Diffusion) and then you can test the pipeline.
+This first version is _very_ basic and you'll need to save the results (when you want them) using "save image as" in your browser.
+
+### Use alternative VAE
+Some models will suggest using an alternative VAE.
+It's possible to copy the model.onnx from an existing directory and put it in another one, but you may want to keep the conversion command line you use for reference.
+To simplify the task of using an alternative VAE you can now pass it as part of the conversion command.
+
+Say you want to have SD1.5 but with the updated MSE VAE that was released later and is the result of further training. You can do it like this:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_ft_mse" --vae_path "stabilityai/sd-vae-ft-mse" --fp16
+```
+
+You can also load a vae from a full model on huggingface. You add /vae to make that clear. Say you need the VAE from Anything v3.0:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_anythingv3" --vae_path "Linaqruf/anything-v3.0/vae" --fp16
+```
+
+Or if the model is on your local disk, you can just use the local directory. Say you have stable-diffusion 2.1 base on disk, you could it like this:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_2_1" --vae_path "stable-diffusion-2-1-base/vae" --fp16
+```
+
+### Clip Skip
+For some models people will suggest using "Clip Skip" for better results. As we can't arbitrarily change this with ONNX, we need to decide on it at model creation.  
+Therefore there's --clip-skip which you can set to 2, 3 or 4.  
+
+Example:
+```
+python conv_sd_to_onnx.py --model_path "Linaqruf/anything-v3.0" --output_path "./model/anythingv3_fp16_cs2" --fp16 --clip-skip 2
+```
+
+Clip Skip results in a change to the Text Encoder. 
+To stay compatible with other implementations we use the same numbering where 1 is the default behaviour and 2 skips 1 layer.
+This ensures that you see similar behaviour to other implementations when setting the same number for Clip Skip.
+
+### Reducing VRAM usage
+While FP16 already uses a lot less VRAM, you may still run into VRAM issues. The easiest solution is to load the Text Encoder on CPU rather than GPU. The Text Encoder is only used as part of prompt parsing and not during the iterations.
+You can expect some additional latency when the Text Encoder is on CPU, but this will be fairly minor as it is not compute intensive. You also gain more than that back during the iterations if you're near your VRAM limit.
+You'll bump into VRAM limits when it is limited (8GB or less), or you're trying to use a 768x768 model.
+
+In test-txt2img.py you can see how this works. You can pass --cpu-textenc and it will load the Text Encoder on CPU. This is how it's done:
+```
+            cputextenc=OnnxRuntimeModel.from_pretrained(args.model+"/text_encoder")
+            pipe = OnnxStableDiffusionPipeline.from_pretrained(args.model, provider="DmlExecutionProvider", text_encoder=cputextenc)
+```
+You can use this in your own code when needed. OnnxDiffusersUI supports --cpu-textenc too.
+
+In extreme circumstances you can also try to load VAE on CPU. This is likely to be only of use for cards that have limited VRAM. The need to load VAE on CPU can be identified when generation crashes after the steps.
+So if it goes through all the steps but then crashes when it needs to save the final image, VAE is your issue. If it crashes before steps is finished, changes to where VAE is loaded are unlikely to make much of a difference.  
+**You can pass --cpuvae to test-txt2img.py to load VAE on CPU (this will always also load CLIP on CPU).**  
+Note that having VAE loaded on CPU is CPU intensive (far more than CLIP is) and you'll see RAM use spike.
+
+### Conversion of .ckpt / .safetensors
+Did your model come as a single file ending in .safetensors or .ckpt? Don't worry, with the 0.12.0 release of diffusers I can now use diffusers to load these directly. I have updated (and renamed) the conversion tool and it 
+will convert directly from .ckpt to ONNX.
+
+This is probably the most requested feature as many of you have used https://www.civitai.com/ and have found the conversion process a bit cumbersome.
+
+To properly convert a file you do need a .yaml config file. Ideally this should be included but if not you're advised to try with the v1-inference.yaml included in this repository.  
+To convert a model you'd then do:
+```
+python conv_sd_to_onnx.py --model_path ".\downloaded.ckpt" --output_path "./model/downloaded-fp16" --ckpt-original-config-file downloaded.yaml --fp16
+```
+If it did not come with a .yaml config file, try with v1-inference.yaml.
+
+If you have a choice between .safetensors and .ckpt, go for .safetensors. In theory a .ckpt file can contain malicious code. I have not seen any reports of this happening but it's better to be safe than sorry.
+
+The conversion tool also has additional parameters you can set when converting from .ckpt/.safetensors. The best way to find all the parameters is by doing:
+```
+python conv_sd_to_onnx.py --help
+```
+You should generally not need these but some advanced users may want to have them just in case.
+
+## FAQ
+### Do the converted models work with other ONNX DirectML based implementations?
+While not tested extensively: yes they should! They are not full FP16, at the interface level they are the same as FP32.
+They are completely valid drop in replacements and transparently run in FP16 on ORT DirectML.
+This makes it possible to run both FP16 and FP32 models with the exact same code.
+
+### Can I convert non-official models?
+You should be able to convert any model. Most of the models can be found on https://huggingface.co/, but you may prefer using https://civitai.com/ instead.
+It's generally better to start from a model in diffusers form but if you only have the .ckpt/.safetensors file you now have instructions on how to convert these directly into ONNX.
+
+### Does this work for inpainting / img2img?
+Yes, it has been tested on the inpainting models and it works fine. Just like with txt2img, replacement is transparent as the interface is FP32.
+Additional example scripts may be added in the future to demonstrate inpainting in code. For now mainly useful for use with OnnxDiffusersUI
+
+### Why is Euler Ancestral not giving me the same image if I provide a seed?
+Due to how Euler Ancestral works, it adds noise as part of the scheduler that is apparently non-deterministic when interacting with ONNX diffusers pipeline.
+A clean ONNX implementation without diffusers, torch ... would likely be faster and bug free but it's a lot of work and it would not match SHARK.  
+Best advice is to live with it and to switch to SHARK as soon as your wished for feature is available there. For more on SHARK see the next answer.
+
+### This is still too slow / taxing on my VRAM
+Make sure to close as many applications as possible when running FP32 or 768x768 FP16 models.
+On my 6700XT I can do 768x768 at 1.2s/it but only if I close all applications.
+If I don't close enough applications, it very quickly goes beyond 2s/it.
+
+Also consider following https://github.com/nod-ai/SHARK which provides accelerated ML on AMD via MLIR/IREE.
+It (currently) lacks features and flexibility but it has a faster and more VRAM efficient Stable Diffusion implementation than we can currently get on ONNX.  
+The current motto also is "Things move fast" which means that in a single day you may get both new features and performance boosts. (On my 6700XT SHARK is close to being twice as fast as ONNX FP16!)  
+There's also an onnxdiffusers channel on the Discord where you can ask for help if you want to stick to ONNX for a bit longer. We'll convert you to a dedicated SHARK user there.
+
+If you are an advanced AMD user, switch to Linux+ROCm. It'll be faster and you can use any torch based solution directly.
+
+### Can you share any results?
+On my 6700XT I can get Stable Diffusion 2.1 768x768 down to 1.15s/it and 2.1 base 512x512 to 2.7it/s  
+Reported working for Vega56 and doing 512x512 at 1.75it/s  
+Reported working for RX 480 8GB and doing 512x512 at 1.75s/it  
+Reported working for 5600XT 6GB and doing 512x512 at 1.43s/it (about 4x times faster than using ONNX FP32)
+
+### All these model downloads seem to be eating my main drive's disk space?!
+This is an unfortunate side effect of where the huggingface library stores its cache by default.  
+On your main drive go to your users home directory (C:\users\...) and you'll find a .cache directory and in it a directory called huggingface.  
+Point an environment variable HF_HOME towards where you want to have it store things instead.  
+(You can probably move the existing directory to a different drive and point HF_HOME towards it but I have not tested this ...)  
+Once resolved you can remove the huggingface directory from .cache

patchedstabledifftoonnx/README4GB.md

@@ -0,0 +1,136 @@
+# Stable Diffusion using ONNX, FP16 and DirectML
+
+This repository contains a conversion tool, some examples, and instructions on how to set up Stable Diffusion with ONNX models.
+
+**These instructions are specifically for people who have only 4GB VRAM**
+
+It's all fairly straightforward, but It helps to be comfortable with command line.
+
+We'll focus on making all of it work within limited VRAM. This will still include a UI.
+
+## Set up
+
+First make sure you have Python 3.10 or 3.11 installed. You can get it here: https://www.python.org/downloads/  
+
+If you don't have git, get it here: https://gitforwindows.org/
+
+Pick a directory that can contain your Stable Diffusion installation (make sure you've the diskspace to store the models).
+Open the commandline (Powershell or Command Prompt) and change into the directory you will use.
+
+Start by cloning this repository:
+```
+git clone https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16
+cd Stable-Diffusion-ONNX-FP16
+```
+
+Do the following:
+```
+pip install virtualenv
+python -m venv sd_env
+sd_env\scripts\activate
+python -m pip install --upgrade pip
+pip install torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu --pre
+pip install -r requirements.txt
+```
+
+Now first make sure you have an account on https://huggingface.co/  
+When you do make sure to create a token on https://huggingface.co/settings/tokens  
+And then on the commandline login using following command
+```
+huggingface-cli login
+```
+
+Now you're ready to download and convert models. Before we explain this, just a pointer on future use.  
+Whenever you want to make use of this post set up, open a command line, change into the directory and enable the environment.
+Say that you installed this on your D: drive in the root. You would open command line and then:
+```
+d:
+cd Stable-Diffusion-ONNX-FP16
+sd_env\scripts\activate
+```
+
+Remember this for whenver you want to use your installation. Let's now get to the fun part and convert a model. This will take some time!
+The extra time spend on creating the model is saved back by having it run fine on 4GB VRAM.
+```
+mkdir model
+python conv_sd_to_onnx.py --model_path "stabilityai/stable-diffusion-2-1-base" --output_path "./model/sd2_1base-fp16-maxslicing" --fp16 --attention-slicing max
+```
+
+That's your first model. Let's do a test:
+
+```
+python test-txt2img.py --model "model\sd2_1base-fp16-maxslicing" --size 512 --seed 0 --cpu-textenc --cpuvae
+```
+
+You should now have your first picture in the current directory.
+
+Now that we've got everything working and we can create pictures, let's get a GUI. We'll use ONNXDiffusersUI but make it so it doesn't break our workflow.  
+First we clone the repository:
+```
+git clone https://github.com/Amblyopius/OnnxDiffusersUI
+```
+Now we run the UI
+```
+python OnnxDiffusersUI\onnxUI.py --cpu-textenc --cpu-vaedec
+```
+It'll take some time to load and then in your browser you can go to http://127.0.0.1:7860 (only accessible on the host you're running it).  
+If you're done you can go back to the CMD window and press Ctrl+C and it will quit.
+
+Note that it expects your models to be in the model directory (which is why we put them there in the instructions).  
+You can find your history and all the pictures you created in the directory called output.
+
+If you want to learn more about the UI be sure to visit https://github.com/azuritecoin/OnnxDiffusersUI
+
+## Advanced features
+### Use alternative VAE
+Some models will suggest using an alternative VAE.
+It's possible to copy the model.onnx from an existing directory and put it in another one, but you may want to keep the conversion command line you use for reference.
+To simplify the task of using an alternative VAE you can now pass it as part of the conversion command.
+
+Say you want to have SD1.5 but with the updated MSE VAE that was released later and is the result of further training. You can do it like this:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_ft_mse" --vae_path "stabilityai/sd-vae-ft-mse" --fp16 --attention-slicing max
+```
+
+You can also load a vae from a full model on huggingface. You add /vae to make that clear. Say you need the VAE from Anything v3.0:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_anythingv3" --vae_path "Linaqruf/anything-v3.0/vae" --fp16 --attention-slicing max
+```
+
+Or if the model is on your local disk, you can just use the local directory. Say you have stable-diffusion 2.1 base on disk, you could it like this:
+```
+python conv_sd_to_onnx.py --model_path "runwayml/stable-diffusion-v1-5" --output_path "./model/sd1_5-fp16-vae_2_1" --vae_path "stable-diffusion-2-1-base/vae" --fp16 --attention-slicing max
+```
+
+### Clip Skip
+For some models people will suggest using "Clip Skip" for better results. As we can't arbitrarily change this with ONNX we need to decide on it at model creation.  
+Therefore there's --clip-skip which you can set to 2, 3 or 4.  
+
+Example:
+```
+python conv_sd_to_onnx.py --model_path "Linaqruf/anything-v3.0" --output_path "./model/anythingv3_fp16_cs2" --fp16 --clip-skip 2
+```
+
+Clip Skip results in a change to the Text Encoder. To stay compatible with other implementations we use the same numbering where 1 is the default behaviour and 2 skips 1 layer.
+This ensures that you see similar behaviour to other implementations when setting the same number for Clip Skip.
+
+### Conversion of .ckpt / .safetensors
+Did your model come as a single file ending in .safetensors or .ckpt? Don't worry, with the 0.12.0 release of diffusers I can now use diffusers to load these directly. I have updated (and renamed) the conversion tool and it 
+will convert directly from .ckpt to ONNX.
+
+This is probably the most requested feature as many of you have used https://www.civitai.com/ and have found the conversion process a bit cumbersome.
+
+To properly convert a file you do need a .yaml config file. Ideally this should be included but if not you're advised to try with the v1-inference.yaml included in this repository.  
+To convert a model you'd then do:
+```
+python conv_sd_to_onnx.py --model_path ".\downloaded.ckpt" --output_path "./model/downloaded-fp16" --ckpt-original-config-file downloaded.yaml --fp16
+```
+If it did not come with a .yaml config file, try with v1-inference.yaml.
+
+If you have a choice between .safetensors and .ckpt, go for .safetensors. In theory a .ckpt file can contain malicious code. I have not seen any reports of this happening but it's better to be safe than sorry.
+
+The conversion tool also has additional parameters you can set when converting from .ckpt/.safetensors. The best way to find all the parameters is by doing:
+```
+python conv_sd_to_onnx.py --help
+```
+You should generally not need these but some advanced users may want to have them just in case.

patchedstabledifftoonnx/conv_sd_to_onnx.py

@@ -0,0 +1,658 @@
+
+# Copyright 2022 Dirk Moerenhout. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify it under the terms
+# of the GNU General Public License as published by the Free Software Foundation,
+# either version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
+# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with this program. If not,
+# see <https://www.gnu.org/licenses/>.
+#
+# *****
+# NOTE this was originally derived from:
+# https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
+#
+# Original file released under Apache License, Version 2.0
+# *****
+#
+# Version history
+# v1.2 First fully working version converting unet to fp16
+# v2.0 Refactored + enabled conversion to fp16 for Text Encoder
+# v2.1 Support for safetensors
+# v2.2 Reduce visible warnings
+# v3.0 You can now provide an alternative VAE
+# v3.1 Align with diffusers 0.12.0
+# v4.0 Support ckpt conversion (--> renamed to conv_sd_to_onnx.py)
+# v5.0 Use ONNX Runtime Transformers for model optimisation
+# v6.0 Support ControlNet
+# v6.1 Support for diffusers 0.15.0
+
+import warnings
+import argparse
+import os
+import shutil
+from pathlib import Path
+import json
+import tempfile
+from typing import Union, Optional, Tuple
+
+import torch
+from torch.onnx import export
+import safetensors
+
+import onnx
+from onnxruntime.transformers.float16 import convert_float_to_float16
+from diffusers.models import AutoencoderKL
+from diffusers import (
+    OnnxRuntimeModel,
+    OnnxStableDiffusionPipeline,
+    StableDiffusionPipeline,
+    ControlNetModel,
+    UNet2DConditionModel
+)
+from diffusers.models.attention_processor import AttnProcessor
+from diffusers.models.unet_2d_condition import UNet2DConditionOutput
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
+
+# To improve future development and testing, warnings should be limited to what is somewhat useful
+# Truncation warnings are expected as part of FP16 conversion and should not be shown
+warnings.filterwarnings('ignore','.*will be truncated.*')
+# We are ignoring prim::Constant type related warnings
+warnings.filterwarnings('ignore','.*The shape inference of prim::Constant type is missing.*')
+
+# ONNX Runtime Transformers offers ONNX model optimisation
+# It does not directly support DirectML but we can use a custom class
+# Based on onnx_model_unet.py in ONNX Runtime Transformers
+from onnx import ModelProto
+from onnxruntime.transformers.onnx_model_unet import UnetOnnxModel
+
+class UnetOnnxModelDML(UnetOnnxModel):
+    def __init__(self, model: ModelProto, num_heads: int = 0, hidden_size: int = 0):
+        """Initialize UNet ONNX Model.
+
+        Args:
+            model (ModelProto): the ONNX model
+            num_heads (int, optional): number of attention heads. Defaults to 0 (detect the parameter automatically).
+            hidden_size (int, optional): hidden dimension. Defaults to 0 (detect the parameter automatically).
+        """
+        assert (num_heads == 0 and hidden_size == 0) or (num_heads > 0 and hidden_size % num_heads == 0)
+
+        super().__init__(model, num_heads=num_heads, hidden_size=hidden_size)
+
+    def optimize(self, enable_shape_inference=False):
+        if not enable_shape_inference:
+            self.disable_shape_inference()
+        self.fuse_layer_norm()
+        self.preprocess()
+        self.postprocess()
+
+# We need a wrapper for UNet2DConditionModel as we need to pass tuples
+# We can't properly export tuples of Tensors with ONNX
+
+class UNet2DConditionModel_Cnet(UNet2DConditionModel):
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        down_block_add_res00: Optional[torch.Tensor] = None,
+        down_block_add_res01: Optional[torch.Tensor] = None,
+        down_block_add_res02: Optional[torch.Tensor] = None,
+        down_block_add_res03: Optional[torch.Tensor] = None,
+        down_block_add_res04: Optional[torch.Tensor] = None,
+        down_block_add_res05: Optional[torch.Tensor] = None,
+        down_block_add_res06: Optional[torch.Tensor] = None,
+        down_block_add_res07: Optional[torch.Tensor] = None,
+        down_block_add_res08: Optional[torch.Tensor] = None,
+        down_block_add_res09: Optional[torch.Tensor] = None,
+        down_block_add_res10: Optional[torch.Tensor] = None,
+        down_block_add_res11: Optional[torch.Tensor] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        return_dict: bool = False,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        down_block_add_res = (
+            down_block_add_res00, down_block_add_res01, down_block_add_res02,
+            down_block_add_res03, down_block_add_res04, down_block_add_res05,
+            down_block_add_res06, down_block_add_res07, down_block_add_res08,
+            down_block_add_res09, down_block_add_res10, down_block_add_res11)
+        return super().forward(
+            sample = sample,
+            timestep = timestep,
+            encoder_hidden_states = encoder_hidden_states,
+            down_block_additional_residuals = down_block_add_res,
+            mid_block_additional_residual = mid_block_additional_residual,
+            return_dict = return_dict
+        )
+
+def onnx_export(
+    model,
+    model_args: tuple,
+    output_path: Path,
+    ordered_input_names,
+    output_names,
+    dynamic_axes,
+    opset,
+):
+    '''export a PyTorch model as an ONNX model'''
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    export(
+        model,
+        model_args,
+        f=output_path.as_posix(),
+        input_names=ordered_input_names,
+        output_names=output_names,
+        dynamic_axes=dynamic_axes,
+        do_constant_folding=True,
+        opset_version=opset,
+    )
+
+@torch.no_grad()
+def convert_to_fp16(
+    model_path
+):
+    '''Converts an ONNX model on disk to FP16'''
+    model_dir=os.path.dirname(model_path)
+    # Breaking down in steps due to Windows bug in convert_float_to_float16_model_path
+    onnx.shape_inference.infer_shapes_path(model_path)
+    fp16_model = onnx.load(model_path)
+    fp16_model = convert_float_to_float16(
+        fp16_model, keep_io_types=True, disable_shape_infer=True
+    )
+    # clean up existing tensor files
+    shutil.rmtree(model_dir)
+    os.mkdir(model_dir)
+    # save FP16 model
+    onnx.save(fp16_model, model_path)
+
+@torch.no_grad()
+def convert_models(pipeline: StableDiffusionPipeline,
+                                        output_path: str,
+                                        opset: int,
+                                        fp16: bool,
+                                        notune: bool,
+                                        controlnet_path: str,
+                                        attention_slicing: str):
+    '''Converts the individual models in a path (UNET, VAE ...) to ONNX'''
+
+    output_path = Path(output_path)
+
+    # TEXT ENCODER
+    num_tokens = pipeline.text_encoder.config.max_position_embeddings
+    text_hidden_size = pipeline.text_encoder.config.hidden_size
+    text_input = pipeline.tokenizer(
+        "A sample prompt",
+        padding="max_length",
+        max_length=pipeline.tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+    textenc_path=output_path / "text_encoder" / "model.onnx"
+    onnx_export(
+        pipeline.text_encoder,
+        # casting to torch.int32 https://github.com/huggingface/transformers/pull/18515/files
+        model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)),
+        output_path=textenc_path,
+        ordered_input_names=["input_ids"],
+        output_names=["last_hidden_state", "pooler_output"],
+        dynamic_axes={
+            "input_ids": {0: "batch", 1: "sequence"},
+        },
+        opset=opset,
+    )
+    if fp16:
+        textenc_model_path = str(textenc_path.absolute().as_posix())
+        convert_to_fp16(textenc_model_path)
+
+    # UNET
+    unet_in_channels = pipeline.unet.config.in_channels
+    unet_sample_size = pipeline.unet.config.sample_size
+    unet_path = output_path / "unet" / "model.onnx"
+    if controlnet_path:
+        # reload UNET to get an ONNX exportable version with ControlNet support
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pl.unet.save_pretrained(tmpdirname)
+            controlnet_unet=UNet2DConditionModel_Cnet.from_pretrained(tmpdirname,
+                low_cpu_mem_usage=False)
+
+        controlnet_unet.set_attn_processor(AttnProcessor())
+
+        if attention_slicing:
+            pl.enable_attention_slicing(attention_slicing)
+            controlnet_unet.set_attention_slice(attention_slicing)
+
+        onnx_export(
+            controlnet_unet,
+            model_args=(
+                torch.randn(2, unet_in_channels, unet_sample_size,
+                    unet_sample_size).to(device=device, dtype=dtype),
+                torch.randn(2).to(device=device, dtype=dtype),
+                torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
+                torch.randn(2, 320, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
+                torch.randn(2, 320, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
+                torch.randn(2, 320, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
+                torch.randn(2, 320, unet_sample_size//2,unet_sample_size//2).to(device=device, dtype=dtype),
+                torch.randn(2, 640, unet_sample_size//2,unet_sample_size//2).to(device=device, dtype=dtype),
+                torch.randn(2, 640, unet_sample_size//2,unet_sample_size//2).to(device=device, dtype=dtype),
+                torch.randn(2, 640, unet_sample_size//4,unet_sample_size//4).to(device=device, dtype=dtype),
+                torch.randn(2, 1280, unet_sample_size//4,unet_sample_size//4).to(device=device, dtype=dtype),
+                torch.randn(2, 1280, unet_sample_size//4,unet_sample_size//4).to(device=device, dtype=dtype),
+                torch.randn(2, 1280, unet_sample_size//8,unet_sample_size//8).to(device=device, dtype=dtype),
+                torch.randn(2, 1280, unet_sample_size//8,unet_sample_size//8).to(device=device, dtype=dtype),
+                torch.randn(2, 1280, unet_sample_size//8,unet_sample_size//8).to(device=device, dtype=dtype),
+                torch.randn(2, 1280, unet_sample_size//8,unet_sample_size//8).to(device=device, dtype=dtype),
+                False,
+            ),
+            output_path=unet_path,
+            ordered_input_names=[
+                "sample",
+                "timestep",
+                "encoder_hidden_states",
+                "down_block_0",
+                "down_block_1",
+                "down_block_2",
+                "down_block_3",
+                "down_block_4",
+                "down_block_5",
+                "down_block_6",
+                "down_block_7",
+                "down_block_8",
+                "down_block_9",
+                "down_block_10",
+                "down_block_11",
+                "mid_block_additional_residual",
+                "return_dict"
+            ],
+            output_names=["out_sample"],  # has to be different from "sample" for correct tracing
+            dynamic_axes={
+                "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
+                "timestep": {0: "batch"},
+                "encoder_hidden_states": {0: "batch", 1: "sequence"},
+                "down_block_0": {0: "batch", 2: "height", 3: "width"},
+                "down_block_1": {0: "batch", 2: "height", 3: "width"},
+                "down_block_2": {0: "batch", 2: "height", 3: "width"},
+                "down_block_3": {0: "batch", 2: "height2", 3: "width2"},
+                "down_block_4": {0: "batch", 2: "height2", 3: "width2"},
+                "down_block_5": {0: "batch", 2: "height2", 3: "width2"},
+                "down_block_6": {0: "batch", 2: "height4", 3: "width4"},
+                "down_block_7": {0: "batch", 2: "height4", 3: "width4"},
+                "down_block_8": {0: "batch", 2: "height4", 3: "width4"},
+                "down_block_9": {0: "batch", 2: "height8", 3: "width8"},
+                "down_block_10": {0: "batch", 2: "height8", 3: "width8"},
+                "down_block_11": {0: "batch", 2: "height8", 3: "width8"},
+                "mid_block_additional_residual": {0: "batch", 2: "height8", 3: "width8"},
+            },
+            opset=opset,
+        )
+
+        controlnet = ControlNetModel.from_pretrained(args.controlnet_path, low_cpu_mem_usage=False)
+        if attention_slicing:
+            controlnet.set_attention_slice(attention_slicing)
+        cnet_path = output_path / "controlnet" / "model.onnx"
+        onnx_export(
+            controlnet,
+            model_args=(
+                torch.randn(2, 4, 64, 64).to(device=device, dtype=dtype),
+                torch.randn(2).to(device=device, dtype=torch.int32),
+                torch.randn(2, 77, 768).to(device=device, dtype=dtype),
+                torch.randn(2, 3, 512,512).to(device=device, dtype=dtype),
+                False,
+            ),
+            output_path=cnet_path,
+            ordered_input_names=["sample", "timestep", "encoder_hidden_states", "controlnet_cond","return_dict"],
+            output_names=["down_block_res_samples", "mid_block_res_sample"],
+            dynamic_axes={
+                "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
+                "timestep": {0: "batch"},
+                "encoder_hidden_states": {0: "batch", 1: "sequence"},
+                "controlnet_cond": {0: "batch", 2: "height", 3: "width"}
+            },
+            opset=opset,
+        )
+
+        if fp16:
+            cnet_path_model_path = str(cnet_path.absolute().as_posix())
+            convert_to_fp16(cnet_path_model_path)
+
+    else:
+        onnx_export(
+            pipeline.unet,
+            model_args=(
+                torch.randn(2, unet_in_channels, unet_sample_size,
+                    unet_sample_size).to(device=device, dtype=dtype),
+                torch.randn(2).to(device=device, dtype=torch.int32),
+                torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
+                False,
+            ),
+            output_path=unet_path,
+            ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"],
+            output_names=["out_sample"],  # has to be different from "sample" for correct tracing
+            dynamic_axes={
+                "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
+                "timestep": {0: "batch"},
+                "encoder_hidden_states": {0: "batch", 1: "sequence"},
+            },
+            opset=opset,
+        )
+    del pipeline.unet
+
+    unet_model_path = str(unet_path.absolute().as_posix())
+    unet_dir = os.path.dirname(unet_model_path)
+    unet = onnx.load(unet_model_path)
+    # clean up existing tensor files
+    shutil.rmtree(unet_dir)
+    os.mkdir(unet_dir)
+
+    optimizer = UnetOnnxModelDML(unet, 0, 0)
+    if not notune:
+        optimizer.optimize()
+        optimizer.topological_sort()
+
+    # collate external tensor files into one
+    onnx.save_model(
+        optimizer.model,
+        unet_model_path,
+        save_as_external_data=True,
+        all_tensors_to_one_file=True,
+        location="weights.pb",
+        convert_attribute=False,
+    )
+    if fp16:
+        convert_to_fp16(unet_model_path)
+    del unet, optimizer
+
+    # VAE ENCODER
+    vae_encoder = pipeline.vae
+    vae_in_channels = vae_encoder.config.in_channels
+    vae_sample_size = vae_encoder.config.sample_size
+    # need to get the raw tensor output (sample) from the encoder
+    vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample,
+        return_dict)[0].sample()
+    onnx_export(
+        vae_encoder,
+        model_args=(
+            torch.randn(1, vae_in_channels, vae_sample_size,
+                vae_sample_size).to(device=device, dtype=dtype),
+            False,
+        ),
+        output_path=output_path / "vae_encoder" / "model.onnx",
+        ordered_input_names=["sample", "return_dict"],
+        output_names=["latent_sample"],
+        dynamic_axes={
+            "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
+        },
+        opset=opset,
+    )
+
+    # VAE DECODER
+    vae_decoder = pipeline.vae
+    vae_latent_channels = vae_decoder.config.latent_channels
+    vae_out_channels = vae_decoder.config.out_channels
+    # forward only through the decoder part
+    vae_decoder.forward = vae_encoder.decode
+    onnx_export(
+        vae_decoder,
+        model_args=(
+            torch.randn(1, vae_latent_channels, unet_sample_size,
+                unet_sample_size).to(device=device, dtype=dtype),
+            False,
+        ),
+        output_path=output_path / "vae_decoder" / "model.onnx",
+        ordered_input_names=["latent_sample", "return_dict"],
+        output_names=["sample"],
+        dynamic_axes={
+            "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
+        },
+        opset=opset,
+    )
+    del pipeline.vae
+
+    # SAFETY CHECKER
+    # NOTE:
+    # Safety checker is excluded because it is a resource hog and you'd be turning it off anyway
+    # I'm not a legal expert but IMHO you are still bound by the model's license after conversion
+    # Check the license of the model you are converting and abide by it
+
+    safety_checker = None
+    feature_extractor = None
+
+    onnx_pipeline = OnnxStableDiffusionPipeline(
+        vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder",
+            low_cpu_mem_usage=False),
+        vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder",
+            low_cpu_mem_usage=False),
+        text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder",
+            low_cpu_mem_usage=False),
+        tokenizer=pipeline.tokenizer,
+        unet=OnnxRuntimeModel.from_pretrained(output_path / "unet",low_cpu_mem_usage=False),
+        scheduler=pipeline.scheduler,
+        safety_checker=safety_checker,
+        feature_extractor=feature_extractor,
+        requires_safety_checker=safety_checker is not None,
+    )
+
+    onnx_pipeline.save_pretrained(output_path)
+
+    if controlnet_path:
+        confname=f"{output_path}/model_index.json"
+        with open(confname, 'r', encoding="utf-8") as f:
+            modelconf = json.load(f)
+            modelconf['controlnet'] = ("diffusers","OnnxRuntimeModel")
+        with open(confname, 'w', encoding="utf-8") as f:
+            json.dump(modelconf, f, indent=1)
+
+    print("ONNX pipeline saved to", output_path)
+
+    del pipeline
+    del onnx_pipeline
+    _ = OnnxStableDiffusionPipeline.from_pretrained(output_path,
+        provider="DmlExecutionProvider",
+        low_cpu_mem_usage=False)
+    print("ONNX pipeline is loadable")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--model_path",
+        default="D:/cache/chilloutmix_NiPrunedFp32Fix.safetensors",
+        type=str,
+        required=False,
+        help=(
+            "Path to the `diffusers` checkpoint to convert (either local directory or on the Hub). "
+            "Or the path to a local checkpoint saved in .ckpt or .safetensors."
+        )
+    )
+
+    parser.add_argument(
+        "--output_path",
+        default="D:/source-fp32",
+        type=str,
+        required=False,
+        help="Path to the output model."
+    )
+
+    parser.add_argument(
+        "--vae_path",
+        default=None,
+        type=str,
+        help=(
+            "Path to alternate VAE `diffusers` checkpoint (either local or on the Hub). "
+        )
+    )
+
+    parser.add_argument(
+        "--controlnet_path",
+        default=None,
+        type=str,
+        help=(
+            "Path to controlnet model to import and convert (either local or on the Hub). "
+            "Setting this results in an SD model intended to be used with a specific ControlNet"
+        )
+    )
+
+    parser.add_argument(
+        "--opset",
+        default=15,
+        type=int,
+        help="The version of the ONNX operator set to use.",
+    )
+
+    parser.add_argument(
+        "--fp16",
+        action="store_true",
+        help="Export Text Encoder and UNET in mixed `float16` mode"
+    )
+
+    parser.add_argument(
+        "--notune",
+        action="store_true",
+        help="Turn off tuning UNET with ONNX Runtime Transformers"
+    )
+
+    parser.add_argument(
+        "--attention-slicing",
+        choices={"auto","max"},
+        type=str,
+        help=(
+            "Attention slicing reduces VRAM needed, off by default. Set to auto or max. "
+            "WARNING: max implies --notune"
+        )
+    )
+
+    parser.add_argument(
+        "--clip-skip",
+        choices={2,3,4},
+        type=int,
+        help="Add permanent clip skip to ONNX model."
+    )
+
+    parser.add_argument(
+        "--diffusers-output",
+        type=str,
+        help="Directory to dump a pre-conversion copy in diffusers format in."
+    )
+
+    parser.add_argument(
+        "--ckpt-original-config-file",
+        default="D:/python/diffusion-convert-FP/v1-inference.yaml",
+        type=str,
+        help="The YAML config file corresponding to the original architecture."
+    )
+
+    parser.add_argument(
+        "--ckpt-image-size",
+        default=None,
+        type=int,
+        help="The image size that the model was trained on. Typically 512 or 768"
+    )
+
+    parser.add_argument(
+        "--ckpt-prediction_type",
+        default=None,
+        type=str,
+        help=(
+            "Prediction type the model was trained on. "
+           "'epsilon' for SD v1.X and SD v2 Base, 'v-prediction' for SD v2"
+       )
+    )
+
+    parser.add_argument(
+        "--ckpt-pipeline_type",
+        default=None,
+        type=str,
+        help="The pipeline type. If `None` pipeline will be automatically inferred."
+    )
+
+    parser.add_argument(
+        "--ckpt-extract-ema",
+        action="store_true",
+        help=(
+            "Only relevant for checkpoints that have both EMA and non-EMA weights. "
+            "If set enables extraction of EMA weights (Default is non-EMA). "
+            "EMA weights usually yield higher quality images for inference. "
+            "Non-EMA weights are usually better to continue fine-tuning."
+        )
+    )
+
+    parser.add_argument(
+        "--ckpt-num-in-channels",
+        default=None,
+        type=int,
+        help=(
+            "The number of input channels. "
+            "If `None` number of input channels will be automatically inferred."
+        )
+    )
+
+    parser.add_argument(
+        "--ckpt-upcast-attention",
+        action="store_true",
+        help=(
+            "Whether the attention computation should always be upcasted. "
+            "Necessary when running SD 2.1"
+        )
+    )
+
+    args = parser.parse_args()
+
+    dtype=torch.float32
+    device = "cpu"
+    if args.model_path.endswith(".ckpt") or args.model_path.endswith(".safetensors"):
+        pl = download_from_original_stable_diffusion_ckpt(
+            checkpoint_path=args.model_path,
+            original_config_file=args.ckpt_original_config_file,
+            image_size=args.ckpt_image_size,
+            prediction_type=args.ckpt_prediction_type,
+            model_type=args.ckpt_pipeline_type,
+            extract_ema=args.ckpt_extract_ema,
+            scheduler_type="pndm",
+            num_in_channels=args.ckpt_num_in_channels,
+            upcast_attention=args.ckpt_upcast_attention,
+            from_safetensors=args.model_path.endswith(".safetensors")
+        )
+    else:
+        pl = StableDiffusionPipeline.from_pretrained(args.model_path,
+            torch_dtype=dtype,low_cpu_mem_usage=False).to(device)
+
+    if args.vae_path:
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pl.save_pretrained(tmpdirname)
+            if args.vae_path.endswith('/vae'):
+                vae = AutoencoderKL.from_pretrained(args.vae_path[:-4],subfolder='vae',
+                    low_cpu_mem_usage=False)
+            else:
+                vae = AutoencoderKL.from_pretrained(args.vae_path,low_cpu_mem_usage=False)
+            pl = StableDiffusionPipeline.from_pretrained(tmpdirname,
+                torch_dtype=dtype, vae=vae,low_cpu_mem_usage=False).to(device)
+
+    if args.clip_skip:
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pl.save_pretrained(tmpdirname)
+            confname=f"{tmpdirname}/text_encoder/config.json"
+            with open(confname, 'r', encoding="utf-8") as f:
+                clipconf = json.load(f)
+                clipconf['num_hidden_layers'] = clipconf['num_hidden_layers']-args.clip_skip+1
+            with open(confname, 'w', encoding="utf-8") as f:
+                json.dump(clipconf, f, indent=1)
+            pl = StableDiffusionPipeline.from_pretrained(tmpdirname,
+                torch_dtype=dtype,low_cpu_mem_usage=False).to(device)
+
+    pl.unet.set_attn_processor(AttnProcessor())
+
+    blocktune=False
+    if args.attention_slicing:
+        if args.attention_slicing == "max":
+            blocktune=True
+            print ("WARNING: attention_slicing max implies --notune")
+        pl.enable_attention_slicing(args.attention_slicing)
+
+    if args.diffusers_output:
+        pl.save_pretrained(args.diffusers_output)
+
+    #convert_models(pl, args.output_path,args.opset,args.fp16,args.notune or blocktune,args.controlnet_path,args.attention_slicing)
+    convert_models(pl, args.output_path, args.opset, False, args.notune or blocktune, args.controlnet_path,args.attention_slicing)

patchedstabledifftoonnx/pipeline_onnx_stable_diffusion_controlnet.py

@@ -0,0 +1,472 @@
+# Copyright 2023 The HuggingFace Team.
+# Converted for use with ONNX as part of https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16
+# Special thanks to https://github.com/uchuusen for the initial conversion effort
+
+import inspect
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+import PIL
+from transformers import CLIPFeatureExtractor, CLIPTokenizer
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging, PIL_INTERPOLATION
+from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+
+
+logger = logging.get_logger(__name__)
+
+
+class OnnxStableDiffusionControlNetPipeline(DiffusionPipeline):
+    vae_encoder: OnnxRuntimeModel
+    vae_decoder: OnnxRuntimeModel
+    text_encoder: OnnxRuntimeModel
+    tokenizer: CLIPTokenizer
+    unet: OnnxRuntimeModel
+    controlnet: OnnxRuntimeModel
+    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
+    safety_checker: OnnxRuntimeModel
+    feature_extractor: CLIPFeatureExtractor
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae_encoder: OnnxRuntimeModel,
+        vae_decoder: OnnxRuntimeModel,
+        text_encoder: OnnxRuntimeModel,
+        tokenizer: CLIPTokenizer,
+        unet: OnnxRuntimeModel,
+        controlnet: OnnxRuntimeModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: OnnxRuntimeModel,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.register_modules(
+            vae_encoder=vae_encoder,
+            vae_decoder=vae_decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+        
+        
+    def _default_height_width(self, height, width, image):
+        if isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, np.ndarray):
+                height = image.shape[3]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, np.ndarray):
+                width = image.shape[2]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+        
+    def prepare_image(self, image, width, height, batch_size, num_images_per_prompt, dtype):
+        if not isinstance(image, np.ndarray):
+            if isinstance(image, PIL.Image.Image):
+                image = [image]
+
+            if isinstance(image[0], PIL.Image.Image):
+                image = [
+                    np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image
+                ]
+                image = np.concatenate(image, axis=0)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = image.transpose(0, 3, 1, 2)
+                image = torch.from_numpy(image)
+            elif isinstance(image[0], np.ndarray):
+                image = np.concatenate(image, axis=0)
+                image = torch.from_numpy(image)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        return image
+        
+        
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // 8, width // 8)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = generator.randn(*shape).astype(dtype)
+
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta, torch_gen):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = torch_gen
+        return extra_step_kwargs
+
+    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                prompt to be encoded
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="np",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
+
+        if not np.array_equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
+        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt] * batch_size
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="np",
+            )
+            negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
+            negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[np.ndarray, PIL.Image.Image] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[np.random.RandomState] = None,
+        latents: Optional[np.ndarray] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+        controlnet_conditioning_scale: float = 1.0,
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+            
+            
+        if generator:
+            torch_seed = generator.randint(2147483647)
+            torch_gen = torch.Generator().manual_seed(torch_seed)
+        else:
+            generator = np.random
+            torch_gen = None
+            
+        height, width = self._default_height_width(height, width, image)
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        prompt_embeds = self._encode_prompt(
+            prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+        
+        # 4. Prepare image
+        image = self.prepare_image(
+            image,
+            width,
+            height,
+            batch_size * num_images_per_prompt,
+            num_images_per_prompt,
+            np.float32,
+        ).numpy()
+        
+        if do_classifier_free_guidance:
+            image = np.concatenate([image] * 2)
+
+        # get the initial random noise unless the user supplied it
+        latents_dtype = prompt_embeds.dtype
+        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        
+        num_channels_latents = 4
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            latents_dtype,
+            generator,
+            latents,
+        )
+        
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+        timesteps = self.scheduler.timesteps
+
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta, torch_gen)
+
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+        
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+                latent_model_input = latent_model_input.cpu().numpy()
+
+                timestep = np.array([t], dtype=timestep_dtype)
+
+                blocksamples = self.controlnet(
+                    sample=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_cond=image,
+                    conditioning_scale=1.0
+                )
+
+                mid_block_res_sample=blocksamples[12]
+                down_block_res_samples=blocksamples[0:12]
+
+                down_block_res_samples = [
+                    down_block_res_sample * controlnet_conditioning_scale
+                    for down_block_res_sample in down_block_res_samples
+                ]
+                mid_block_res_sample *= controlnet_conditioning_scale
+
+                # predict the noise residual
+                
+                noise_pred = self.unet(
+                    sample=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    down_block_0=down_block_res_samples[0],
+                    down_block_1=down_block_res_samples[1],
+                    down_block_2=down_block_res_samples[2],
+                    down_block_3=down_block_res_samples[3],
+                    down_block_4=down_block_res_samples[4],
+                    down_block_5=down_block_res_samples[5],
+                    down_block_6=down_block_res_samples[6],
+                    down_block_7=down_block_res_samples[7],
+                    down_block_8=down_block_res_samples[8],
+                    down_block_9=down_block_res_samples[9],
+                    down_block_10=down_block_res_samples[10],
+                    down_block_11=down_block_res_samples[11],
+                    mid_block_additional_residual=mid_block_res_sample
+                )
+                noise_pred = noise_pred[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                scheduler_output = self.scheduler.step(
+                    torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+                )
+                latents = scheduler_output.prev_sample.numpy()
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
+
+        image = np.clip(image / 2 + 0.5, 0, 1)
+        image = image.transpose((0, 2, 3, 1))
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(
+                self.numpy_to_pil(image), return_tensors="np"
+            ).pixel_values.astype(image.dtype)
+
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+

patchedstabledifftoonnx/pipeline_onnx_stable_diffusion_instruct_pix2pix.py

@@ -0,0 +1,553 @@
+# Copyright 2023 The InstructPix2Pix Authors and The HuggingFace Team.
+# Converted for use with ONNX as part of https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16
+
+import inspect
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+try:
+    from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
+except ImportError:
+    ORT_TO_NP_TYPE = {
+        "tensor(bool)": np.bool_,
+        "tensor(int8)": np.int8,
+        "tensor(uint8)": np.uint8,
+        "tensor(int16)": np.int16,
+        "tensor(uint16)": np.uint16,
+        "tensor(int32)": np.int32,
+        "tensor(uint32)": np.uint32,
+        "tensor(int64)": np.int64,
+        "tensor(uint64)": np.uint64,
+        "tensor(float16)": np.float16,
+        "tensor(float)": np.float32,
+        "tensor(double)": np.float64,
+    }
+
+from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.schedulers import KarrasDiffusionSchedulers, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    deprecate,
+    logging,
+    randn_tensor,
+)
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Simplified and ONNX specific version (only allows 1 image, np over torch)
+def preprocess(image):
+    if isinstance(image, np.ndarray):
+        return image
+        
+    w, h = image.size
+    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+    image = np.array(image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image.transpose(0, 3, 1, 2)
+    image = 2.0 * image - 1.0
+    return image
+
+
+class OnnxStableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    vae_encoder: OnnxRuntimeModel
+    vae_decoder: OnnxRuntimeModel
+    text_encoder: OnnxRuntimeModel
+    tokenizer: CLIPTokenizer
+    unet: OnnxRuntimeModel
+    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
+    safety_checker: OnnxRuntimeModel
+    feature_extractor: CLIPFeatureExtractor
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae_encoder: OnnxRuntimeModel,
+        vae_decoder: OnnxRuntimeModel,
+        text_encoder: OnnxRuntimeModel,
+        tokenizer: CLIPTokenizer,
+        unet: OnnxRuntimeModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: OnnxRuntimeModel,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+        self.unet_in_channels = 8
+        self.vae_scale_factor = 8
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.register_modules(
+            vae_encoder=vae_encoder,
+            vae_decoder=vae_decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        #self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[np.ndarray, PIL.Image.Image] = None,
+        num_inference_steps: int = 100,
+        guidance_scale: float = 7.5,
+        image_guidance_scale: float = 1.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[np.random.RandomState] = None,
+        latents: Optional[np.ndarray] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be repainted according to `prompt`.
+            num_inference_steps (`int`, *optional*, defaults to 100):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                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. This pipeline requires a value of at least `1`.
+            image_guidance_scale (`float`, *optional*, defaults to 1.5):
+                Image guidance scale is to push the generated image towards the inital image `image`. Image guidance
+                scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to
+                generate images that are closely linked to the source image `image`, usually at the expense of lower
+                image quality. This pipeline requires a value of at least `1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
+                is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Examples:
+
+        ```py
+        >>> import PIL
+        >>> import requests
+        >>> import torch
+        >>> from io import BytesIO
+
+        >>> from diffusers import StableDiffusionInstructPix2PixPipeline
+
+
+        >>> def download_image(url):
+        ...     response = requests.get(url)
+        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+
+        >>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
+
+        >>> image = download_image(img_url).resize((512, 512))
+
+        >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+        ...     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
+        ... )
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "make the mountains snowy"
+        >>> image = pipe(prompt=prompt, image=image).images[0]
+        ```
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        
+        # We need a deterministic torch generator for schedulers if a (likely seeded) generator was provided
+        
+        if generator:
+            torch_seed = generator.randint(2147483647)
+            torch_gen = torch.Generator().manual_seed(torch_seed)
+        else:
+            generator = np.random
+            torch_gen = None
+        
+        # 0. Check inputs
+        self.check_inputs(prompt, callback_steps)
+
+        if image is None:
+            raise ValueError("`image` input cannot be undefined.")
+
+        # 1. Define call parameters
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0
+        # check if scheduler is in sigmas space
+        scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
+
+        # 2. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+        )
+
+        # 3. Preprocess image
+        image = preprocess(image)
+        height, width = image.shape[-2:]
+
+        # 4. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare Image latents
+        latents_dtype = prompt_embeds.dtype
+        image = image.astype(latents_dtype)
+        # encode the init image into latents and scale the latents
+        image_latents = self.vae_encoder(sample=image)[0]
+        if do_classifier_free_guidance:
+            uncond_image_latents = np.zeros_like(image_latents)
+            image_latents = np.concatenate((image_latents, image_latents, uncond_image_latents), axis=0)
+
+        # 6. Prepare latent variables
+        latents_dtype = prompt_embeds.dtype
+        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        if latents is None:
+            latents = generator.randn(*latents_shape).astype(latents_dtype)
+        elif latents.shape != latents_shape:
+            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+        latents = latents * self.scheduler.init_noise_sigma.numpy()
+
+        # 7. Check that shapes of latents and image match the UNet channels
+        num_channels_image = image_latents.shape[1]
+        if 4+ num_channels_image != self.unet_in_channels:
+            raise ValueError(
+                f"Incorrect configuration settings! The config of `pipeline.unet`: expects"
+                f" {self.unet_in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+                f" `num_channels_image`: {num_channels_image} "
+                f" = {num_channels_latents+num_channels_image}. Please verify the config of"
+                " `pipeline.unet` or your `image` input."
+            )
+            
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+
+        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta, torch_gen)
+
+        # 9. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Expand the latents if we are doing classifier free guidance.
+                # The latents are expanded 3 times because for pix2pix the guidance\
+                # is applied for both the text and the input image.
+                latent_model_input = np.concatenate([latents] * 3) if do_classifier_free_guidance else latents
+                
+                scaled_latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+                scaled_latent_model_input = scaled_latent_model_input.cpu().numpy()
+
+                scaled_latent_model_input = np.concatenate([scaled_latent_model_input, image_latents], axis=1)
+
+                # predict the noise residual
+                             
+                noise_pred = self.unet(
+                    sample=scaled_latent_model_input,
+                    timestep=np.array([t], dtype=timestep_dtype),
+                    encoder_hidden_states=prompt_embeds,
+                )[0]
+                
+                # Hack:
+                # For karras style schedulers the model does classifer free guidance using the
+                # predicted_original_sample instead of the noise_pred. So we need to compute the
+                # predicted_original_sample here if we are using a karras style scheduler.
+                if scheduler_is_in_sigma_space:
+                    step_index = (self.scheduler.timesteps == t).nonzero().item()
+                    sigma = self.scheduler.sigmas[step_index]
+                    noise_pred = latent_model_input - sigma.numpy() * noise_pred
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_text, noise_pred_image, noise_pred_uncond = np.split(noise_pred, 3)
+                    noise_pred = (
+                        noise_pred_uncond
+                        + guidance_scale * (noise_pred_text - noise_pred_image)
+                        + image_guidance_scale * (noise_pred_image - noise_pred_uncond)
+                    )
+
+                # Hack:
+                # For karras style schedulers the model does classifer free guidance using the
+                # predicted_original_sample instead of the noise_pred. But the scheduler.step function
+                # expects the noise_pred and computes the predicted_original_sample internally. So we
+                # need to overwrite the noise_pred here such that the value of the computed
+                # predicted_original_sample is correct.
+                if scheduler_is_in_sigma_space:
+                    noise_pred = (noise_pred - latents) / (-sigma)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                scheduler_output = self.scheduler.step(
+                    noise_pred, t, torch.from_numpy(latents), **extra_step_kwargs
+                )
+                latents = scheduler_output.prev_sample.numpy()
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents.numpy())
+
+        # 10. Post-processing
+        image = self.decode_latents(latents)
+
+        # 11. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image)
+
+        # 12. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                prompt to be encoded
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        negative_prompt_embeds = None
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="np",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
+
+        if not np.array_equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]  
+        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt] * batch_size
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="np",
+            )
+            negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
+            negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            # pix2pix has two  negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
+
+            prompt_embeds = np.concatenate((prompt_embeds, negative_prompt_embeds, negative_prompt_embeds))
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(
+                self.numpy_to_pil(image), return_tensors="np"
+            ).pixel_values.astype(image.dtype)
+            # safety_checker does not support batched inputs yet
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta, torch_gen):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = torch_gen
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
+        image = np.clip(image / 2 + 0.5, 0, 1)
+        image = image.transpose((0, 2, 3, 1))
+        return image
+
+    def check_inputs(self, prompt, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+

patchedstabledifftoonnx/pix2pixUI.py

@@ -0,0 +1,53 @@
+import numpy as np
+import gradio as gr
+
+from pipeline_onnx_stable_diffusion_instruct_pix2pix import OnnxStableDiffusionInstructPix2PixPipeline
+from diffusers import DDIMScheduler,  EulerAncestralDiscreteScheduler, LMSDiscreteScheduler
+
+def pix2pix(input_img, prompt, guide, iguide, steps, seed):
+    if seed == -1:
+        generator=None
+    else:
+        generator=np.random
+        generator.seed(seed)
+    img = pipe(
+        prompt=prompt,
+        image=input_img,
+        num_inference_steps=steps,
+        guidance_scale=guide,
+        image_guidance_scale=iguide,
+        generator=generator).images[0]
+    return img
+		
+if __name__ == "__main__":	
+    model="./model/ip2p-base-fp16-vae_ft_mse-autoslicing"
+    pipe = OnnxStableDiffusionInstructPix2PixPipeline.from_pretrained(model, provider="DmlExecutionProvider", safety_checker=None)
+    pipe.scheduler=EulerAncestralDiscreteScheduler.from_pretrained(model, subfolder="scheduler")
+
+    demo=gr.Interface(pix2pix, gr.Image(shape=(512,512)), "image")
+    title="ONNX Instruct Pix 2 Pix"
+    css = "#imgbox img {max-width: 100%  !important; }\n#imgbox div {height: auto;}"
+    with gr.Blocks(title=title, css=css) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                seed = gr.Number(value=-1, label="seed", precision=0)
+            with gr.Column(scale=14):
+                prompt = gr.Textbox(value="", lines=2, label="prompt")
+        with gr.Row():
+            with gr.Column(scale=1):
+                guide = gr.Slider(1.1, 10, value=3, step=0.1, label="Text guidance")
+            with gr.Column(scale=1):
+                iguide = gr.Slider(1, 10, value=1.1, step=0.1, label="Image guidance")
+            with gr.Column(scale=1):
+                steps = gr.Slider(10,100, value=30, step=1, label="Steps")
+        with gr.Row():
+            with gr.Column(scale=1):
+                input_img = gr.Image(label="Input Image", type="pil", elem_id="imgbox").style(width=600,height=600)
+            with gr.Column(scale=1):
+                image_out = gr.Image(value=None, label="Output Image", elem_id="imgbox").style(width=600,height=600)
+        gen_btn = gr.Button("Generate", variant="primary", elem_id="gen_button")        
+    
+        inputs=[input_img, prompt, guide, iguide, steps, seed]
+        gen_btn.click(fn=pix2pix, inputs=inputs, outputs=[image_out])
+    
+    demo.launch()

patchedstabledifftoonnx/quantization.py

@@ -0,0 +1 @@
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patchedstabledifftoonnx/requirements.txt

@@ -0,0 +1,14 @@
+protobuf < 4.0
+numpy
+transformers
+diffusers
+ftfy
+spacy
+scipy
+safetensors
+gradio
+omegaconf
+onnx
+onnxconverter-common
+onnxruntime-directml
+opencv-python

patchedstabledifftoonnx/run-batch.md

@@ -0,0 +1,50 @@
+# Running Stable Diffusion ONNX DirectML batches
+
+Ever feel like it's a struggle to compare schedulers, guidance scale ... while using a UI?  
+Not really interested in coding in Python to resolve it?
+
+Hopefully run-batch.py will make your life a bit easier.
+
+## Set up
+Drop run-batch.py where you've installed OnnxDiffusersUI (it'll use the same lwp_pipe.py).
+
+As parameter run-batch.py accepts 1 or more paths where it will then check for the existence of settings.json.
+It'll read settings.json, create a batch of images and save them in the directory it just got the settings from.
+
+In settings.json you can define the following things:
+- The model, set with the key 'model'. It will look for a directory with that name in the model subdirectory (just like OnnxDiffusersUI).
+- The scheduler, set with the key 'scheduler'. You can also set a list of schedulers to iterate over with the key 'schedulerlist'.
+It currently accepts following values: ddim, deis, dpms_ms, dpms_ss, euler_anc, euler, heun, kdpm2, lms, pndm,unipc.
+If the value is not recognised, it'll switch to pndm. Not all schedulers have been extensively tested and may behave unexpectendly.
+- Guidance scale, set with the key 'scale'. You can also set a list of guidance scales to iterate over with the key 'scalelist'
+- Iteration steps, set with the key 'steps'. You can also set a list of steps to iterate over with the key 'stepslist'
+- Width and height, with the keys 'width' and 'height'. You can also set a list of resolutions to iterate over with the key 'reslist' (e.g. 'reslist': ["512x512","512x768"])
+- The seed, set with the key 'seed'. If you want to iterate over seeds you can define the end with the key 'seedend'.
+Alternatively, you can provide a list of seeds using the key 'seedlist'.
+- The task to perform, set with the key 'task', default is 'txt2img'. Only txt2img has been tested enough, but it also supports img2img and controlnet (more on that below).
+- The prompt, set with the key 'prompt'. If you want to iterate over prompts, you can define them with the key 'promptlist'.
+- A negative prompt, set with the key 'negative_prompt'. Note that the same negative prompt will apply to all prompts you provided.
+- How to parse the prompt, set with the key 'textenc'. Supports 2 values, 'standard' and 'lwp'. Use 'lwp' when you want to use weights and long prompts.
+
+If you are doing img2img or controlnet there's more options:
+- Strength, set with the key 'strength'. Expected to be between 0 to 1. You can iterate over a list by setting 'strengthlist'.
+
+For img2img or controlnet the directory will also need to contain an image file called input.png. For img2img this acts as source image, for controlnet it is the input for the Controlnet.
+
+## Example
+
+Compare the results for a prompt with deis and euler at 20, 30 and 40 steps. Using SD 2.1.
+
+```
+{
+	"model": "sd2_1-fp16",
+	"seed": 0,
+	"seedend": 10,
+	"stepslist": [20,30,40],
+	"scale": 8.5,
+	"schedulerlist": ["deis","euler"],
+	"prompt": "(photo portrait) of a ((beautiful)) (woman) wearing (summer dress) in a park, eyes, detailed, high resolution, prime lens",
+	"negative_prompt": "bad quality, low resolution",
+	"textenc": "lwp"
+}
+```

patchedstabledifftoonnx/run-batch.py

@@ -0,0 +1,330 @@
+# Copyright 2022 Dirk Moerenhout. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify it under the terms
+# of the GNU General Public License as published by the Free Software Foundation,
+# either version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
+# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along with this program. If not,
+# see <https://www.gnu.org/licenses/>.
+
+# We need sys for argv
+import sys
+# We need os.path for isdir, isfile
+import os.path
+# Our settings are in json format
+import json
+# To be safe we force gc to lower RAM pressure
+import gc
+# We want to replace the text encoder in the pipeline
+import functools
+# We want to parse arguments
+import argparse
+# Numpy is used to provide a random generator
+import numpy
+# We need to load images for img2img
+# We want to save data to PNG
+from PIL import Image, PngImagePlugin
+
+# The pipelines
+from diffusers import OnnxStableDiffusionPipeline, OnnxStableDiffusionImg2ImgPipeline
+from pipeline_onnx_stable_diffusion_controlnet import OnnxStableDiffusionControlNetPipeline
+# Model needed to load Text Encoder on CPU
+from diffusers import OnnxRuntimeModel
+# The schedulers
+from diffusers import (
+    DDIMScheduler,
+    DEISMultistepScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    KDPM2DiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UniPCMultistepScheduler
+)
+
+# Support special text encoders
+import OnnxDiffusersUI.lpw_pipe
+
+# Default settings
+defSettings = {
+    "width": 512,
+    "height": 512,
+    "reslist": [],
+    "steps": 30,
+    "stepslist": [],
+    "scale": 7.5,
+    "scalelist":[],
+    "seed":0,
+    "seedend":0,
+    "seedlist":[],
+    "task": "txt2img",
+    "model":"sd2_1-fp16",
+    "prompt": "",
+    "promptlist":[],
+    "negative_prompt": "",
+    "textenc": "standard",
+    "scheduler": "pndm",
+    "schedulerlist": [],
+    "strength": 0.9,
+    "strengthlist": []
+}
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument(
+    "--cpu-textenc",
+    action="store_true",
+    help="Load Text Encoder on CPU to save VRAM"
+)
+
+parser.add_argument(
+    "--subdirs",
+    action="store_true",
+    help="Add subdirs with settings.json to projects to run"
+)
+
+parser.add_argument(
+    'project',
+    nargs='+',
+    type=str,
+    help="Provide projects as directories that contain settings.json"
+)
+
+args = parser.parse_args()
+
+projects=args.project
+if args.subdirs:
+    for proj in args.project:
+        obj = os.scandir(proj)
+        for entry in obj:
+            if entry.is_dir():
+                if os.path.isfile(f"{proj}/{entry.name}/settings.json"):
+                    projects.append(f"{proj}/{entry.name}")
+
+for proj in projects:
+    print("Running project "+proj)
+    # Check for directory
+    if os.path.isdir(proj):
+        if os.path.isfile(proj+"/settings.json"):
+            with open(proj+"/settings.json", encoding="utf-8") as confFile:
+                projSettings=json.load(confFile)
+            # Merge dictionaries with project settings taking precedence
+            runSettings = defSettings | projSettings
+            # We need prompts
+            prereqmet=len(runSettings['prompt'])>0 or len(runSettings['promptlist'])>0
+            # We need a model
+            model="model/"+runSettings['model']
+            prereqmet=prereqmet and os.path.isfile(model+"/unet/model.onnx")
+            # We need a start image to do img2img or controlnet
+            if runSettings['task']=="img2img" or runSettings['task']=="controlnet":
+                infile=proj+"/input.png"
+                prereqmet = prereqmet and os.path.isfile(infile)
+            if prereqmet:
+                sched = {
+                    "ddim": DDIMScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "deis": DEISMultistepScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "dpms_ms": DPMSolverMultistepScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "dpms_ss": DPMSolverSinglestepScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "euler_anc": EulerAncestralDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "euler": EulerDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "heun": HeunDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "kdpm2": KDPM2DiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "lms": LMSDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "pndm": PNDMScheduler.from_pretrained(model, subfolder="scheduler"),
+                    "unipc": UniPCMultistepScheduler.from_pretrained(model, subfolder="scheduler")
+                }
+                if runSettings['task']=="img2img":
+                    init_image = Image.open(infile).convert("RGB")
+                    if args.cpu_textenc:
+                        cputextenc=OnnxRuntimeModel.from_pretrained(model+"/text_encoder")
+                        pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
+                            model,
+                            provider="DmlExecutionProvider",
+                            revision="onnx",
+                            scheduler=sched['pndm'],
+                            text_encoder=cputextenc,
+                            safety_checker=None,
+                            feature_extractor=None
+                        )
+                    else:
+                        pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
+                            model,
+                            provider="DmlExecutionProvider",
+                            revision="onnx",
+                            scheduler=sched['pndm'],
+                            safety_checker=None,
+                            feature_extractor=None
+                        )
+                elif runSettings['task']=="controlnet": 
+                    init_image = Image.open(infile).convert("RGB")
+                    if args.cpu_textenc:
+                        cputextenc=OnnxRuntimeModel.from_pretrained(model+"/text_encoder")
+                        pipe = OnnxStableDiffusionControlNetPipeline.from_pretrained(
+                            model,
+                            provider="DmlExecutionProvider",
+                            revision="onnx",
+                            scheduler=sched['pndm'],
+                            text_encoder=cputextenc,
+                            safety_checker=None,
+                            feature_extractor=None
+                        )
+                    else:
+                        pipe = OnnxStableDiffusionControlNetPipeline.from_pretrained(
+                            model,
+                            provider="DmlExecutionProvider",
+                            revision="onnx",
+                            scheduler=sched['pndm'],
+                            safety_checker=None,
+                            feature_extractor=None
+                        )                 
+                else:
+                    if args.cpu_textenc:
+                        cputextenc=OnnxRuntimeModel.from_pretrained(model+"/text_encoder")
+                        pipe = OnnxStableDiffusionPipeline.from_pretrained(
+                            model,
+                            provider="DmlExecutionProvider",
+                            revision="onnx",
+                            scheduler=sched['pndm'],
+                            text_encoder=cputextenc,
+                            safety_checker=None,
+                            feature_extractor=None
+                        )
+                    else:
+                        pipe = OnnxStableDiffusionPipeline.from_pretrained(
+                            model,
+                            provider="DmlExecutionProvider",
+                            revision="onnx",
+                            scheduler=sched['pndm'],
+                            safety_checker=None,
+                            feature_extractor=None
+                        )                       
+                if runSettings['textenc'] == "lpw":
+                    pipe._encode_prompt = functools.partial(lpw_pipe._encode_prompt, pipe)
+                generator = numpy.random
+                # Set schedulers for projects
+                if len(runSettings['schedulerlist'])==0:
+                    schedulerlist=[runSettings['scheduler']]
+                else:
+                    schedulerlist=runSettings['schedulerlist']
+                # Set seeds for project
+                if len(runSettings['seedlist'])==0:
+                    if runSettings['seed']>runSettings['seedend']:
+                        runSettings['seedend']=runSettings['seed']
+                    seedlist=range(runSettings['seed'],runSettings['seedend']+1)
+                else:
+                    seedlist=runSettings['seedlist']
+                # Set resolustions for project
+                if len(runSettings['reslist'])==0:
+                    restuples=[(runSettings['width'],runSettings['height'])]
+                else:
+                    restuples=[]
+                    for resstr in runSettings['reslist']:
+                        restuples.append(tuple(map(int, resstr.split("x"))))
+                # Set steps for project
+                if len(runSettings['stepslist'])==0:
+                    stepslist=[runSettings['steps']]
+                else:
+                    stepslist=runSettings['stepslist']
+                # Set guidance scales for project
+                if len(runSettings['scalelist'])==0:
+                    scalelist=[runSettings['scale']]
+                else:
+                    scalelist=runSettings['scalelist']
+                # Set prompts for project
+                if len(runSettings['promptlist'])==0:
+                    promptlist=[runSettings['prompt']]
+                else:
+                    promptlist=runSettings['promptlist']
+                # Set strengths for project
+                if len(runSettings['strengthlist'])==0:
+                    strengthlist=[runSettings['strength']]
+                else:
+                    strengthlist=runSettings['strengthlist']
+                imgnr=len(schedulerlist)*len(promptlist)*len(seedlist)*len(restuples)*len(stepslist)*len(scalelist)*len(strengthlist)
+                imgdone=0
+                for scheduler in schedulerlist:
+                    if not sched[scheduler]:
+                        scheduler="pndm"
+                    pipe.scheduler=sched[scheduler]
+                    promptnum=0
+                    for prompt in promptlist:
+                        for seed in seedlist:
+                            for res in restuples:
+                                for steps in stepslist:
+                                    for scale in scalelist:
+                                        for strength in strengthlist:
+                                            if runSettings['task']=="img2img":
+                                                filename=(
+                                                    f"{proj}/result-p{promptnum}-seed{seed}-{res[0]}x{res[1]}-"+
+                                                    f"-steps-{steps}-{scheduler}-scale-"+str(scale).replace(".","_")+
+                                                    "-strength-"+str(strength).replace(".","_")+".png"
+                                                )
+                                            elif runSettings['task']=="controlnet":
+                                                filename=(
+                                                    f"{proj}/result-p{promptnum}-seed{seed}-{res[0]}x{res[1]}-"+
+                                                    f"-steps-{steps}-{scheduler}-scale-"+str(scale).replace(".","_")+
+                                                    "-strength-"+str(strength).replace(".","_")+".png"      
+                                                )
+                                            else:
+                                                filename=(
+                                                    f"{proj}/result-p{promptnum}-seed{seed}-{res[0]}x{res[1]}-"+
+                                                    f"-steps-{steps}-{scheduler}-scale-"+str(scale).replace(".","_")+".png"
+                                                )
+                                            if not os.path.isfile(filename):
+                                                generator.seed(seed)
+                                                if runSettings['task']=="img2img":
+                                                    image = pipe(
+                                                        image=init_image,
+                                                        strength=strength,
+                                                        prompt=prompt,
+                                                        negative_prompt=runSettings['negative_prompt'],
+                                                        num_inference_steps=steps,
+                                                        guidance_scale=scale,
+                                                        generator=generator).images[0]
+                                                elif runSettings['task']=="controlnet":
+                                                    image = pipe(
+                                                        image=init_image,
+                                                        controlnet_conditioning_scale=strength,
+                                                        prompt=prompt,
+                                                        negative_prompt=runSettings['negative_prompt'],
+                                                        num_inference_steps=steps,
+                                                        guidance_scale=scale,
+                                                        generator=generator).images[0]
+                                                else:
+                                                    image = pipe(
+                                                        prompt=prompt,
+                                                        negative_prompt=runSettings['negative_prompt'],
+                                                        width=res[0],
+                                                        height=res[1],
+                                                        num_inference_steps=steps,
+                                                        guidance_scale=scale,
+                                                        generator = generator).images[0]
+                                                metadata = PngImagePlugin.PngInfo()
+                                                metadata.add_text("Generator","Stable Diffusion ONNX https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16")
+                                                metadata.add_text("SD Model (local name)",model)
+                                                metadata.add_text("SD Prompt",prompt)
+                                                metadata.add_text("SD Negative Prompt",runSettings['negative_prompt'])
+                                                metadata.add_text("SD Scheduler",scheduler)
+                                                metadata.add_text("SD Steps",str(steps))
+                                                metadata.add_text("SD Guidance Scale",str(scale))
+                                                image.save(filename, pnginfo = metadata)
+                                            else:
+                                                print("Skipping existing image!")
+                                            imgdone+=1
+                                            print(f"Finished {imgdone}/{imgnr}")
+                        promptnum+=1
+                del pipe
+                gc.collect()
+            else:
+                print("Minimum requirements not met! Skipping")
+        else:
+            print("Settings not found! Skipping")
+    else:
+        print("Path not found! Skipping")

patchedstabledifftoonnx/sd_env/bin/python

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:682d5b916b076ae9e1f1399b89e7f4b521599cf04f669cbd65af0a317bc6033e
+size 5912968

patchedstabledifftoonnx/sd_env/bin/python3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:682d5b916b076ae9e1f1399b89e7f4b521599cf04f669cbd65af0a317bc6033e
+size 5912968

patchedstabledifftoonnx/sd_env/bin/python3.10

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:682d5b916b076ae9e1f1399b89e7f4b521599cf04f669cbd65af0a317bc6033e
+size 5912968

patchedstabledifftoonnx/sd_env/pyvenv.cfg

@@ -0,0 +1,3 @@
+home = /usr/bin
+include-system-site-packages = false
+version = 3.10.6

patchedstabledifftoonnx/stable-diffusion-streamlit/.dockerignore

@@ -0,0 +1,6 @@
+src/stable-diffusion-streamlit/pages/model/result
+src/stable-diffusion-streamlit/pages/model/onnx
+__pycache__
+docker/.env
+tag.sh
+docker/volume

patchedstabledifftoonnx/stable-diffusion-streamlit/.github/workflows/build-image.yml

@@ -0,0 +1,16 @@
+name: Build Image
+
+on: push
+
+jobs:
+  build: 
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v2
+      - name: Login to Registry
+        if: startsWith(github.ref, 'refs/tags')
+        run: docker login --username=${{ secrets.DOCKER_USERNAME }} --password ${{ secrets.DOCKER_PASSWORD }}
+      - name: Push Image
+        if: startsWith(github.ref, 'refs/tags')
+        run: |
+          cd docker && bash build.sh ${{ secrets.HUGGINGFACE_TOKEN }}

patchedstabledifftoonnx/stable-diffusion-streamlit/.gitignore

@@ -0,0 +1,6 @@
+result
+onnx
+__pycache__
+docker/.env
+tag.sh
+docker/volume

patchedstabledifftoonnx/stable-diffusion-streamlit/LICENSE

@@ -0,0 +1,254 @@
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors 
+ 
+CreativeML Open RAIL-M 
+dated August 22, 2022 
+ 
+Section I: PREAMBLE 
+ 
+Multimodal generative models are being widely adopted and used, and have 
+the potential to transform the way artists, among other individuals, 
+conceive and benefit from AI or ML technologies as a tool for content 
+creation. 
+ 
+Notwithstanding the current and potential benefits that these artifacts 
+can bring to society at large, there are also concerns about potential 
+misuses of them, either due to their technical limitations or ethical 
+considerations. 
+ 
+In short, this license strives for both the open and responsible 
+downstream use of the accompanying model. When it comes to the open 
+character, we took inspiration from open source permissive licenses 
+regarding the grant of IP rights. Referring to the downstream responsible 
+use, we added use-based restrictions not permitting the use of the Model 
+in very specific scenarios, in order for the licensor to be able to 
+enforce the license in case potential misuses of the Model may occur. At 
+the same time, we strive to promote open and responsible research on 
+generative models for art and content generation. 
+ 
+Even though downstream derivative versions of the model could be released 
+under different licensing terms, the latter will always have to include - 
+at minimum - the same use-based restrictions as the ones in the original 
+license (this license). We believe in the intersection between open and 
+responsible AI development; thus, this License aims to strike a balance 
+between both in order to enable responsible open-science in the field of 
+AI. 
+ 
+This License governs the use of the model (and its derivatives) and is 
+informed by the model card associated with the model. 
+ 
+NOW THEREFORE, You and Licensor agree as follows: 
+ 
+1. Definitions 
+ 
+- "License" means the terms and conditions for use, reproduction, and 
+Distribution as defined in this document. 
+- "Data" means a collection of information and/or content extracted from 
+the dataset used with the Model, including to train, pretrain, or 
+otherwise evaluate the Model. The Data is not licensed under this 
+License. 
+- "Output" means the results of operating a Model as embodied in 
+informational content resulting therefrom. 
+- "Model" means any accompanying machine-learning based assemblies 
+(including checkpoints), consisting of learnt weights, parameters 
+(including optimizer states), corresponding to the model architecture as 
+embodied in the Complementary Material, that have been trained or tuned, 
+in whole or in part on the Data, using the Complementary Material. 
+- "Derivatives of the Model" means all modifications to the Model, works 
+based on the Model, or any other model which is created or initialized by 
+transfer of patterns of the weights, parameters, activations or output of 
+the Model, to the other model, in order to cause the other model to 
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+representations or methods based on the generation of synthetic data by 
+the Model for training the other model. 
+- "Complementary Material" means the accompanying source code and scripts 
+used to define, run, load, benchmark or evaluate the Model, and used to 
+prepare data for training or evaluation, if any. This includes any 
+accompanying documentation, tutorials, examples, etc, if any. 
+- "Distribution" means any transmission, reproduction, publication or 
+other sharing of the Model or Derivatives of the Model to a third party, 
+including providing the Model as a hosted service made available by 
+electronic or other remote means - e.g. API-based or web access. 
+- "Licensor" means the copyright owner or entity authorized by the 
+copyright owner that is granting the License, including the persons or 
+entities that may have rights in the Model and/or distributing the Model. 
+- "You" (or "Your") means an individual or Legal Entity exercising 
+permissions granted by this License and/or making use of the Model for 
+whichever purpose and in any field of use, including usage of the Model 
+in an end-use application - e.g. chatbot, translator, image generator. 
+- "Third Parties" means individuals or legal entities that are not under 
+common control with Licensor or You. 
+- "Contribution" means any work of authorship, including the original 
+version of the Model and any modifications or additions to that Model or 
+Derivatives of the Model thereof, that is intentionally submitted to 
+Licensor for inclusion in the Model by the copyright owner or by an 
+individual or Legal Entity authorized to submit on behalf of the 
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+Licensor or its representatives, including but not limited to 
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+Licensor for the purpose of discussing and improving the Model, but 
+excluding communication that is conspicuously marked or otherwise 
+designated in writing by the copyright owner as "Not a Contribution." 
+- "Contributor" means Licensor and any individual or Legal Entity on 
+behalf of whom a Contribution has been received by Licensor and 
+subsequently incorporated within the Model. 
+ 
+Section II: INTELLECTUAL PROPERTY RIGHTS 
+ 
+Both copyright and patent grants apply to the Model, Derivatives of the 
+Model and Complementary Material. The Model and Derivatives of the Model 
+are subject to additional terms as described in Section III. 
+ 
+2. Grant of Copyright License. Subject to the terms and conditions of 
+this License, each Contributor hereby grants to You a perpetual, 
+worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright 
+license to reproduce, prepare, publicly display, publicly perform, 
+sublicense, and distribute the Complementary Material, the Model, and 
+Derivatives of the Model. 
+3. Grant of Patent License. Subject to the terms and conditions of this 
+License and where and as applicable, each Contributor hereby grants to 
+You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, 
+irrevocable (except as stated in this paragraph) patent license to make, 
+have made, use, offer to sell, sell, import, and otherwise transfer the 
+Model and the Complementary Material, where such license applies only to 
+those patent claims licensable by such Contributor that are necessarily 
+infringed by their Contribution(s) alone or by combination of their 
+Contribution(s) with the Model to which such Contribution(s) was 
+submitted. If You institute patent litigation against any entity 
+(including a cross-claim or counterclaim in a lawsuit) alleging that the 
+Model and/or Complementary Material or a Contribution incorporated within 
+the Model and/or Complementary Material constitutes direct or 
+contributory patent infringement, then any patent licenses granted to You 
+under this License for the Model and/or Work shall terminate as of the 
+date such litigation is asserted or filed. 
+ 
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION 
+ 
+4. Distribution and Redistribution. You may host for Third Party remote 
+access purposes (e.g. software-as-a-service), reproduce and distribute 
+copies of the Model or Derivatives of the Model thereof in any medium, 
+with or without modifications, provided that You meet the following 
+conditions: 
+Use-based restrictions as referenced in paragraph 5 MUST be included as 
+an enforceable provision by You in any type of legal agreement (e.g. a 
+license) governing the use and/or distribution of the Model or 
+Derivatives of the Model, and You shall give notice to subsequent users 
+You Distribute to, that the Model or Derivatives of the Model are subject 
+to paragraph 5. This provision does not apply to the use of Complementary 
+Material. 
+You must give any Third Party recipients of the Model or Derivatives of 
+the Model a copy of this License; 
+You must cause any modified files to carry prominent notices stating that 
+You changed the files; 
+You must retain all copyright, patent, trademark, and attribution notices 
+excluding those notices that do not pertain to any part of the Model, 
+Derivatives of the Model. 
+You may add Your own copyright statement to Your modifications and may 
+provide additional or different license terms and conditions - respecting 
+paragraph 4.a. - for use, reproduction, or Distribution of Your 
+modifications, or for any such Derivatives of the Model as a whole, 
+provided Your use, reproduction, and Distribution of the Model otherwise 
+complies with the conditions stated in this License. 
+5. Use-based restrictions. The restrictions set forth in Attachment A are 
+considered Use-based restrictions. Therefore You cannot use the Model and 
+the Derivatives of the Model for the specified restricted uses. You may 
+use the Model subject to this License, including only for lawful purposes 
+and in accordance with the License. Use may include creating any content 
+with, finetuning, updating, running, training, evaluating and/or 
+reparametrizing the Model. You shall require all of Your users who use 
+the Model or a Derivative of the Model to comply with the terms of this 
+paragraph (paragraph 5). 
+6. The Output You Generate. Except as set forth herein, Licensor claims 
+no rights in the Output You generate using the Model. You are accountable 
+for the Output you generate and its subsequent uses. No use of the output 
+can contravene any provision as stated in the License. 
+ 
+Section IV: OTHER PROVISIONS 
+ 
+7. Updates and Runtime Restrictions. To the maximum extent permitted by 
+law, Licensor reserves the right to restrict (remotely or otherwise) 
+usage of the Model in violation of this License, update the Model through 
+electronic means, or modify the Output of the Model based on updates. You 
+shall undertake reasonable efforts to use the latest version of the 
+Model. 
+8. Trademarks and related. Nothing in this License permits You to make 
+use of Licensors’ trademarks, trade names, logos or to otherwise suggest 
+endorsement or misrepresent the relationship between the parties; and any 
+rights not expressly granted herein are reserved by the Licensors. 
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to 
+in writing, Licensor provides the Model and the Complementary Material 
+(and each Contributor provides its Contributions) on an "AS IS" BASIS, 
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, 
+including, without limitation, any warranties or conditions of TITLE, 
+NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. 
+You are solely responsible for determining the appropriateness of using 
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+Complementary Material and assume any risks associated with Your exercise 
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+10. Limitation of Liability. In no event and under no legal theory, 
+whether in tort (including negligence), contract, or otherwise, unless 
+required by applicable law (such as deliberate and grossly negligent 
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+11. Accepting Warranty or Additional Liability. While redistributing the 
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+12. If any provision of this License is held to be invalid, illegal or 
+unenforceable, the remaining provisions shall be unaffected thereby and 
+remain valid as if such provision had not been set forth herein. 
+END OF TERMS AND CONDITIONS 
+ 
+ 
+ 
+ 
+Attachment A 
+ 
+Use Restrictions 
+ 
+You agree not to use the Model or Derivatives of the Model: 
+- In any way that violates any applicable national, federal, state, local 
+or international law or regulation; 
+- For the purpose of exploiting, harming or attempting to exploit or harm 
+minors in any way; 
+- To generate or disseminate verifiably false information and/or content 
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+be used to harm an individual; 
+- To defame, disparage or otherwise harass others; 
+- For fully automated decision making that adversely impacts an 
+individual’s legal rights or otherwise creates or modifies a binding, 
+enforceable obligation; 
+- For any use intended to or which has the effect of discriminating 
+against or harming individuals or groups based on online or offline 
+social behavior or known or predicted personal or personality 
+characteristics; 
+- To exploit any of the vulnerabilities of a specific group of persons 
+based on their age, social, physical or mental characteristics, in order 
+to materially distort the behavior of a person pertaining to that group 
+in a manner that causes or is likely to cause that person or another 
+person physical or psychological harm; 
+- For any use intended to or which has the effect of discriminating 
+against individuals or groups based on legally protected characteristics 
+or categories; 
+- To provide medical advice and medical results interpretation; 
+- To generate or disseminate information for the purpose to be used for 
+administration of justice, law enforcement, immigration or asylum 
+processes, such as predicting an individual will commit fraud/crime 
+commitment (e.g. by text profiling, drawing causal relationships between 
+assertions made in documents, indiscriminate and arbitrarily-targeted 
+use). 

patchedstabledifftoonnx/stable-diffusion-streamlit/README.md

@@ -0,0 +1,114 @@
+[**中文说明**](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/README_CN.md) | [**English**](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/README.md)
+
+# stable-diffusion-streamlit
+
+- [1.Introduction](#1-introduction)
+- [2.Getting Started](#2-getting-started)
+- [3.Quantization Performance](#3-quantization-performance)
+- [4.Streamlit Progress Bar](#4-streamlit-progress-bar)
+- [5.To Do](#5-to-do)
+- [6.Get Help](#6-get-help)
+- [7.Acknowledgements](#7-acknowledgements)
+
+## 1. Introduction
+
++ Create beautiful apps using [Streamlit](https://github.com/streamlit/streamlit) to test [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) model quantized by [OnnxRuntime](https://github.com/microsoft/onnxruntime) **cutting down memory 75%**.
+    + **Streamlit**:
+      + an open-source app framework for Machine Learning and Data Science teams. Create beautiful web apps in minutes.
+    + **CompVis/stable-diffusion-v1-4**:
+        + a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
+    + **OnnxRuntime**:
+        + a cross-platform, faster inference and lower costs accelerator for machine learning models.
+
+    
+## 2. Getting Started
+
+### 2.1. Deployment
++ docker-compose up -d
+```yaml
+version: "2.3"
+services:
+  stable-diffusion-streamlit-onnxquantized:
+    container_name: stable-diffusion-streamlit-onnxquantized
+    image: lowinli98/stable-diffusion-streamlit-onnxquantized:v0.2
+    expose:
+      - 8501
+    ports:
+      - "8501:8501"
+    environment:
+      - APP_TITLE=Stable Diffusion Streamlit
+    restart: always
+    volumes:
+      - /etc/localtime:/etc/localtime
+      - ./volume:/app/pages/model/result
+```
+
+### 2.2. Usage
++ 2.2.1. Copy an awesome prompt from Blogs like [best-100-stable-diffusion-prompts](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts/) or [50-text-to-image-prompts-for-ai](https://decentralizedcreator.com/50-text-to-image-prompts-for-ai-art-generator-stable-diffusion-a-visual-treat-inside/)
++ 2.2.2. Open http://localhost:8501 and click "文本转图片" on the left sidebar.
++ 2.2.3. Fix the runtime parameters, paste your prompt into the text area and click the "开始生成" button.
+
+    ![](./doc/gif/use1.gif)
+
++ 2.2.4. Wait for a while until the progress bar goes to the end, then you will get a generated image.
+
+    ![](./doc/gif/use2.gif)
+
++ 2.2.5. Click "画廊" on the left sidebar to see all the images you had generated.
+
+    ![](./doc/gif/use3.gif)
+
+
+## 3. Quantization Performance
++ The model in the docker container has been quantized by OnnxRuntime in the building of the docker image.
+
+  + [dockerfile](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/docker/dockerfile)
+  + [building progress in Github Action](https://github.com/LowinLi/stable-diffusion-streamlit/actions/runs/3202674839/jobs/5231895605)
+
++ The quantized model will be smaller and cut down the inference time a little(UINT8), while the performance of the image generated is almost the same as the original model.
++ This is an amazing feature because [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) can be deployed on most home computers. The following table shows the comparison of the quantized model and the original model.
+
+---
+| model | memory used | inference 49 steps waste time |
+| --- | --- | --- |
+| pytorch | 5.2GB | 6m56s |
+| onnx | 5.0GB | 4m34s |
+| onnx-quantized(UINT8) | 1.3GB | 4m29s |
+
++ CPU: 
+  + Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz 
+  + 10 core
+
+
++ image generated by PyTorch model
+
+    ![](./doc/pic/torch.png)
++ image generated by Onnx model
+
+    ![](./doc/pic/onnx.png)
++ image generated by Onnx-Quantized(UINT8) model
+
+    ![](./doc/pic/onnxquantized.png)
+
+## 4. Streamlit Progress Bar
+To generate an awesome image, the model needs to be inferences with many steps. So it will take a long time to finish the whole pipeline. To make the user experience better, a progress bar is added to show the pipeline progress. 
+With another thread in Python, the progress bar can be updated by the pipeline scheduler counter.
+
+
+
+## 5. To Do
+
+- [ ] Add the Text-Guided Image-to-Image Pipeline in [Huggingface/Diffusers](https://huggingface.co/docs/diffusers/using-diffusers/img2img)
+- [ ] Add the Text-Guided Image-Inpainting Pipeline in [Huggingface/Diffusers](https://huggingface.co/docs/diffusers/using-diffusers/inpaint)
+
+## 6. Get Help
+
++ Contact me at [email protected]
++ If appropriate, open an issue on GitHub
+
+## 7. Acknowledgements
+
++ [Huggingface/Diffusers](https://github.com/huggingface/diffusers)
++ [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
++ [Streamlit](https://github.com/streamlit/streamlit)
++ [OnnxRuntime](https://github.com/microsoft/onnxruntime)

patchedstabledifftoonnx/stable-diffusion-streamlit/README_CN.md

@@ -0,0 +1,111 @@
+[**中文说明**](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/README_CN.md) | [**English**](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/README.md)
+
+# stable-diffusion-streamlit
+
+- [1.简介](#1-简介)
+- [2.快速开始](#2-快速开始)
+- [3.模型量化提速表现](#3-模型量化提速表现)
+- [4.Streamlit进度条](#4-Streamlit进度条)
+- [5.下一步](#5-下一步)
+- [6.帮助](#6-帮助)
+- [7.致谢](#7-致谢)
+
+## 1. 简介
+
++ **使用[Streamlit](https://github.com/streamlit/streamlit)构建一个Web服务，测试使用[OnnxRuntime](https://github.com/microsoft/onnxruntime)量化压缩后的[CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)模型做图片生成**.
+    + **Streamlit**:
+      + 一个流行的开源框架，可以快速搭建机器学习和数据科学团队的Web应用。
+    + **CompVis/stable-diffusion-v1-4**:
+        + 一个流行的扩散模型，可以通过文字提示生成栩栩如生的高质量图片。
+    + **OnnxRuntime**:
+        + 微软推出的一款推理框架，用户可以非常便利的量化压缩模型。
+
+    
+## 2. 快速开始
+
+### 2.1. 部署
++ docker-compose up -d
+```yaml
+version: "2.3"
+services:
+  stable-diffusion-streamlit-onnxquantized:
+    container_name: stable-diffusion-streamlit-onnxquantized
+    image: lowinli98/stable-diffusion-streamlit-onnxquantized:v0.2
+    expose:
+      - 8501
+    ports:
+      - "8501:8501"
+    environment:
+      - APP_TITLE=Stable Diffusion Streamlit
+    restart: always
+    volumes:
+      - /etc/localtime:/etc/localtime
+      - ./volume:/app/pages/model/result
+```
+
+### 2.2. 使用
++ 2.2.1. 从博客[best-100-stable-diffusion-prompts](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts/)或[50-text-to-image-prompts-for-ai](https://decentralizedcreator.com/50-text-to-image-prompts-for-ai-art-generator-stable-diffusion-a-visual-treat-inside/)复制一个文本提示。
++ 2.2.2. 打开http://localhost:8501，在侧边栏点击"文本转图片"。
++ 2.2.3. 修改运行参数, 粘贴提示，点击"开始生成"。
+
+    ![](./doc/gif/use1.gif)
+
++ 2.2.4. 待进度条走完后，页面直接展示生成的图片
+
+    ![](./doc/gif/use2.gif)
+
++ 2.2.5. 点击侧边栏的"画廊"查看所有历史生成的图片
+
+    ![](./doc/gif/use3.gif)
+
+
+## 3. 模型量化提速表现
++ 服务中使用的模型已经在打镜像阶段就做了OnnxRuntime量化压缩处理，详见：
+
+  + [dockerfile](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/docker/dockerfile)
+  + [Github Action的镜像构建日志](https://github.com/LowinLi/stable-diffusion-streamlit/actions/runs/3202674839/jobs/5231895605)
+
++ 量化后的模型尺寸降低很多、推理速度提高一点点(UINT8), 同时保持和原模型几乎一样的生成图片质量.
++ 这一点意味着，[CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)模型可以被部署在大多数家用电脑上，并进行调试。以下是几种模型的比较：
+
+---
+| 模型 | 内存 | 49步推断用时 |
+| --- | --- | --- |
+| pytorch | 5.2GB | 6m56s |
+| onnx | 5.0GB | 4m34s |
+| onnx-quantized(UINT8) | 1.3GB | 4m29s |
+
++ CPU: 
+  + Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz 
+  + 10 core
+
+
++ PyTorch模型生成的图片
+
+    ![](./doc/pic/torch.png)
++ Onnx模型生成的图片
+
+    ![](./doc/pic/onnx.png)
++ Onnx-quantized(UINT8)模型生成的图片
+
+    ![](./doc/pic/onnxquantized.png)
+
+## 4. Streamlit进度条
+为了生成高质量图片，扩散模型一般要推断很多步，这会比较耗时。为了提升用户体验，在Streamlit页面做了一个进度条，通过另一个线程，监控推断步数，并更新到进度条中。
+
+## 5. 下一步
+
+- [ ] 增加[Huggingface/Diffusers](https://huggingface.co/docs/diffusers/using-diffusers/img2img)中的图像生成图像流程
+- [ ] 增加[Huggingface/Diffusers](https://huggingface.co/docs/diffusers/using-diffusers/inpaint)中的抠图生成图像流程
+
+## 6. 帮助
+
++ 联系我的邮箱[email protected]
++ 有任何问题，欢迎在Github上提Issue
+
+## 7. 致谢
+
++ [Huggingface/Diffusers](https://github.com/huggingface/diffusers)
++ [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
++ [Streamlit](https://github.com/streamlit/streamlit)
++ [OnnxRuntime](https://github.com/microsoft/onnxruntime)

patchedstabledifftoonnx/stable-diffusion-streamlit/docker/build.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+BUILDROOT=$DIR/..
+ 
+cd $BUILDROOT
+ 
+CONTAINER="lowinli98/stable-diffusion-streamlit-onnxquantized"   #替换成你的容器名称
+VERSION=`git describe --abbrev=0 --tags`
+ 
+IMAGE_NAME="${CONTAINER}:${VERSION}"
+cmd="docker build -t $IMAGE_NAME -f $DIR/dockerfile $BUILDROOT --build-arg HUGGINGFACE_TOKEN=$1"
+echo $cmd
+eval $cmd
+
+docker push $IMAGE_NAME

patchedstabledifftoonnx/stable-diffusion-streamlit/docker/docker-compose.yaml

@@ -0,0 +1,15 @@
+version: "2.3"
+services:
+  stable-diffusion-streamlit-onnxquantized:
+    container_name: stable-diffusion-streamlit-onnxquantized
+    image: lowinli98/stable-diffusion-streamlit-onnxquantized:v0.2
+    expose:
+      - 8501
+    ports:
+      - "8501:8501"
+    environment:
+      - APP_TITLE=Stable Diffusion Streamlit
+    restart: always
+    volumes:
+      - /etc/localtime:/etc/localtime
+      - ./volume:/app/pages/model/result

patchedstabledifftoonnx/stable-diffusion-streamlit/docker/dockerfile

@@ -0,0 +1,21 @@
+FROM python:3.8.7-slim as builder
+ARG HUGGINGFACE_TOKEN
+ENV HUGGINGFACE_TOKEN=${HUGGINGFACE_TOKEN}
+COPY docker/requirements.txt /app/
+
+# install
+RUN echo "==> Installing ..."  && \
+    pip3 install --no-cache-dir --upgrade pip && \
+    pip3 install virtualenv && \
+    virtualenv -p /usr/local/bin/python /app/env && \
+    /app/env/bin/pip install --no-cache-dir --upgrade pip && \
+    /app/env/bin/pip install --no-cache-dir -r /app/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+
+COPY src/stable-diffusion-streamlit /app/
+RUN cd /app/pages/model/ && \
+    /app/env/bin/python prepare.py
+
+FROM python:3.8.7-slim
+COPY --from=builder /app /app
+WORKDIR /app
+CMD ["/app/env/bin/streamlit", "run", "主页.py"]

patchedstabledifftoonnx/stable-diffusion-streamlit/docker/entrypoint.sh

@@ -0,0 +1 @@
+streamlit run 主页.py

patchedstabledifftoonnx/stable-diffusion-streamlit/docker/requirements.txt

@@ -0,0 +1,7 @@
+ftfy==6.1.1
+onnx==1.12.0
+onnxruntime==1.12.1
+streamlit==1.13.0
+transformers==4.22.2
+diffusers==0.4.0
+torch==1.10.0+cpu

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/entrypoint.sh

@@ -0,0 +1 @@
+streamlit run 主页.py

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/copy_pb.py

@@ -0,0 +1,17 @@
+import os
+import shutil
+
+
+def copy_weights():
+    for (root, _, file_list) in os.walk(
+        os.path.join(
+            os.environ["HOME"],
+            ".cache/huggingface/diffusers/models--CompVis--stable-diffusion-v1-4",
+        )
+    ):
+        if "weights.pb" in file_list:
+            shutil.copyfile(os.path.join(root, "weights.pb"), "./onnx/unet/weights.pb")
+
+
+if __name__ == "__main__":
+    copy_weights()

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/download_onnx.py

@@ -0,0 +1,22 @@
+import os
+from diffusers import StableDiffusionOnnxPipeline
+
+
+def download_save():
+    token = os.environ.get("HUGGINGFACE_TOKEN")
+
+    pipe = StableDiffusionOnnxPipeline.from_pretrained(
+        "CompVis/stable-diffusion-v1-4",
+        revision="onnx",
+        provider="CPUExecutionProvider",
+        use_auth_token=token,
+    )
+    for tmp_dir in ["safety_checker", "text_encoder", "unet", "vae_decoder"]:
+        os.makedirs(os.path.join("./onnx", tmp_dir), exist_ok=True)
+        with open(os.path.join("./onnx", tmp_dir, "model.onnx"), "wb") as f:
+            f.write(b"")
+    pipe.save_pretrained("./onnx")
+
+
+if __name__ == "__main__":
+    download_save()

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/inference.py

@@ -0,0 +1,40 @@
+from diffusers import StableDiffusionOnnxPipeline, StableDiffusionPipeline
+import os
+import json
+
+root = os.getcwd()
+last_dir = os.path.split(root)[-1]
+if last_dir == "stable-diffusion-streamlit":
+    model_dir = os.path.join(root, "pages/model/onnx")
+    result_dir = os.path.join(root, "pages/model/result")
+elif last_dir == "pages":
+    model_dir = os.path.join(root, "model/onnx")
+    result_dir = os.path.join(root, "model/result")
+elif last_dir == "app":
+    model_dir = os.path.join(root, "pages/model/onnx")
+    result_dir = os.path.join(root, "pages/model/result")
+else:
+    model_dir = os.path.join(root, "onnx")
+    result_dir = os.path.join(root, "result")
+
+global quant_pipe
+
+quant_pipe = StableDiffusionOnnxPipeline.from_pretrained(
+    model_dir, provider="CPUExecutionProvider", local_files_only=True
+)
+# quant_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", local_files_only=True)
+
+
+def inference(uid, **args):
+    image = quant_pipe(**args)["sample"][0]
+    target = os.path.join(result_dir, uid)
+    os.makedirs(target, exist_ok=True)
+    image.save(os.path.join(target, "image.png"))
+    with open(os.path.join(target, "config.json"), "w") as f:
+        json.dump(args, f, indent=4, ensure_ascii=False)
+
+
+if __name__ == "__main__":
+    from pympler.asizeof import asizeof
+
+    print(asizeof(quant_pipe))

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8613d46ab19c61e528f7f9bb37932d55ca6d73f59ff9349e5aaa3e97fc5e0700
+size 17745692

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.onnx.data

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d12bc8352e8bb54e7a3225883ca80e3aac9201ad21fe2dcaa142c6337d35269e
+size 860852480

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.ort

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d9c740b06118205354d251d123597c59c2550bd1870197bd7c393c4aa905505
+size 874459960

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.with_runtime_opt.ort

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7762dc164ef1b7d50a51b4efff243c650e2a87cfc8fddb00ebc6be1c2056bbe0
+size 874913120

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/required_operators.config

@@ -0,0 +1,12 @@
+# Generated from model/s:
+# - /content/Stable-Diffusion-ONNX-FP16/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.ort
+ai.onnx;1;LayerNormalization
+ai.onnx;6;InstanceNormalization
+ai.onnx;7;Cos,Sin
+ai.onnx;9;ConstantOfShape,Where
+ai.onnx;10;ConvInteger
+ai.onnx;11;DynamicQuantizeLinear,Range
+ai.onnx;13;Cast,Concat,Equal,Expand,Gather,MatMul,Resize,ScatterND,Slice,Softmax,Transpose,Unsqueeze
+ai.onnx;14;Add,Div,Mul,Reshape
+ai.onnx;15;Shape
+com.microsoft;1;DynamicQuantizeMatMul,FusedMatMul,Gelu,MatMulIntegerToFloat,QuickGelu

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/required_operators.with_runtime_opt.config

@@ -0,0 +1,11 @@
+# Generated from model/s:
+# - /content/Stable-Diffusion-ONNX-FP16/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/model.with_runtime_opt.ort
+# - /content/Stable-Diffusion-ONNX-FP16/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/onnx/tmpmaqb65r3.without_runtime_opt/model.ort
+ai.onnx;6;InstanceNormalization
+ai.onnx;7;Cos,Sin
+ai.onnx;9;ConstantOfShape,Where
+ai.onnx;10;ConvInteger,MatMulInteger
+ai.onnx;11;DynamicQuantizeLinear,Range
+ai.onnx;13;Cast,Concat,Equal,Erf,Expand,Gather,MatMul,ReduceMean,Resize,ScatterND,Sigmoid,Slice,Softmax,Sqrt,Transpose,Unsqueeze
+ai.onnx;14;Add,Div,Mul,Reshape,Sub
+ai.onnx;15;Pow,Shape

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/prepare.py

@@ -0,0 +1,10 @@
+from copy_pb import copy_weights
+from download_onnx import download_save
+from quantization import quant
+import shutil
+
+if __name__ == "__main__":
+    shutil.rmtree("./onnx", ignore_errors=True)
+    download_save()
+    copy_weights()
+    quant()

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/quantization.py

@@ -0,0 +1,28 @@
+import os
+from onnxruntime.quantization import quantize_dynamic, QuantType
+
+
+def quant():
+    for root, dirs, filenames in os.walk("./onnx"):
+        if "model.onnx" in filenames:
+            if "weights.pb" in filenames:
+                external_data = True
+            else:
+                external_data = False
+            quantize_dynamic(
+                model_input=os.path.join(root, "model.onnx"),
+                model_output=os.path.join(root, "model.onnx"),  # 量化后直接覆盖原onnx文件
+                per_channel=True,
+                reduce_range=True,
+                weight_type=QuantType.QUInt8,
+                optimize_model=True,
+                use_external_data_format=external_data,
+            )
+            print("Quantized model saved at: ", os.path.join(root, "model.onnx"))
+            if "weights.pb" in filenames:
+                os.remove(os.path.join(root, "weights.pb"))
+                print("Removed weights.pb")
+
+
+if __name__ == "__main__":
+    quant()

patchedstabledifftoonnx/stable-diffusion-streamlit/src/stable-diffusion-streamlit/pages/model/thread.py

@@ -0,0 +1,27 @@
+import threading
+import copy
+from diffusers import StableDiffusionOnnxPipeline, StableDiffusionPipeline
+
+
+class PipelineThread(threading.Thread):
+    def __init__(self, func, args=()):
+        super(PipelineThread, self).__init__()
+        self.func = StableDiffusionOnnxPipeline(
+            func.vae_decoder,
+            func.text_encoder,
+            func.tokenizer,
+            func.unet,
+            copy.deepcopy(func.scheduler),
+            func.safety_checker,
+            func.feature_extractor,
+        )
+        self.args = args
+
+    def run(self):
+        self.result = self.func(*self.args)
+
+    def get_result(self):
+        try:
+            return self.result
+        except Exception:
+            return None