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Merge branch 'comfyanonymous:master' into feature/blockweights
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@ -25,3 +25,7 @@ To update the ComfyUI code: update\update_comfyui.bat
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To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
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update\update_comfyui_and_python_dependencies.bat
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TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
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In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
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Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
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@ -45,6 +45,10 @@ There is a portable standalone build for Windows that should work for running on
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Just download, extract and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
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#### How do I share models between another UI and ComfyUI?
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See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
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## Colab Notebook
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To run it on colab or paperspace you can use my [Colab Notebook](notebooks/comfyui_colab.ipynb) here: [Link to open with google colab](https://colab.research.google.com/github/comfyanonymous/ComfyUI/blob/master/notebooks/comfyui_colab.ipynb)
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@ -102,7 +106,6 @@ With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"```
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And then you can use that terminal to run Comfyui without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI.
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# Running
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```python main.py```
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@ -221,7 +221,7 @@ class KSamplerX0Inpaint(torch.nn.Module):
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def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None):
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if denoise_mask is not None:
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latent_mask = 1. - denoise_mask
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x = x * denoise_mask + (self.latent_image + self.noise * sigma) * latent_mask
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x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
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out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat)
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if denoise_mask is not None:
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out *= denoise_mask
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