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fixing formatting in readme
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README.md
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README.md
@ -29,28 +29,33 @@ fast.
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Note that this has only been tested in the Linux environment!
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Note that this has only been tested in the Linux environment!
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(ldm) ~/stable-diffusion$ ./scripts/dream.py
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~~~~
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* Initializing, be patient...
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(ldm) ~/stable-diffusion$ ./scripts/dream.py
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* Initializing, be patient...
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Loading model from models/ldm/text2img-large/model.ckpt
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LatentDiffusion: Running in eps-prediction mode
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DiffusionWrapper has 872.30 M params.
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making attention of type 'vanilla' with 512 in_channels
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Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
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making attention of type 'vanilla' with 512 in_channels
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Loading Bert tokenizer from "models/bert"
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setting sampler to plms
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Loading model from models/ldm/text2img-large/model.ckpt
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* Initialization done! Awaiting your command...
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LatentDiffusion: Running in eps-prediction mode
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dream> ashley judd riding a camel -n2
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DiffusionWrapper has 872.30 M params.
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Outputs:
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making attention of type 'vanilla' with 512 in_channels
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outputs/txt2img-samples/00009.png: "ashley judd riding a camel" -n2 -S 416354203
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Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
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outputs/txt2img-samples/00010.png: "ashley judd riding a camel" -n2 -S 1362479620
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making attention of type 'vanilla' with 512 in_channels
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Loading Bert tokenizer from "models/bert"
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setting sampler to plms
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* Initialization done! Awaiting your command...
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dream> "your prompt here" -n6 -g
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dream> ashley judd riding a camel -n2
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...
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Outputs:
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~~~~
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outputs/txt2img-samples/00009.png: "ashley judd riding a camel" -n2 -S 416354203
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outputs/txt2img-samples/00010.png: "ashley judd riding a camel" -n2 -S 1362479620
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Command-line arguments ("./scripts/dream.py -h") allow you to change
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Command-line arguments (`./scripts/dream.py -h`) allow you to change
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various defaults, and select between the mature stable-diffusion
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various defaults, and select between the mature stable-diffusion
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weights (512x512) and the older (256x256) latent diffusion weights
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weights (512x512) and the older (256x256) latent diffusion weights
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(laion400m).
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(laion400m). Within the script, the switches are (mostly) identical to
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those used in the Discord bot, except you don't need to type "!dream".
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## No need for internet connectivity when loading the model
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## No need for internet connectivity when loading the model
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@ -64,11 +69,13 @@ expedient thing to do was to download the Bert tokenizer in advance,
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and patch stable-diffusion to read it from the local disk. The steps
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and patch stable-diffusion to read it from the local disk. The steps
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to do this are:
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to do this are:
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(ldm) ~/stable-diffusion$ mkdir ./models/bert
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~~~~
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> python3
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(ldm) ~/stable-diffusion$ mkdir ./models/bert
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>>> from transformers import BertTokenizerFast
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> python3
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>>> model = BertTokenizerFast.from_pretrained("bert-base-uncased")
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>>> from transformers import BertTokenizerFast
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>>> model.save_pretrained("./models/bert")
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>>> model = BertTokenizerFast.from_pretrained("bert-base-uncased")
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>>> model.save_pretrained("./models/bert")
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~~~~
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(Make sure you are in the stable-diffusion directory when you do
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(Make sure you are in the stable-diffusion directory when you do
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this!)
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this!)
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@ -85,9 +92,10 @@ I added the requirement for torchmetrics to environment.yaml.
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Follow the directions from the original README, which starts below, to
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Follow the directions from the original README, which starts below, to
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configure the environment and install requirements. For support,
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configure the environment and install requirements. For support,
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please use this repository's GitHub Issues tracking service.
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please use this repository's GitHub Issues tracking service. Feel free
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to send me an email if you use and like the script.
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Author: Lincoln D. Stein <lincoln.stein@gmail.com>
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*Author:* Lincoln D. Stein <lincoln.stein@gmail.com>
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# Original README from CompViz/stable-diffusion
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# Original README from CompViz/stable-diffusion
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*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
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*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
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