-The `dream>` prompt's arguments are pretty much identical to those
-used in the Discord bot, except you don't need to type "!dream" (it
-doesn't hurt if you do). A significant change is that creation of
-individual images is now the default unless --grid (-g) is given. A
-full list is given in [List of prompt arguments]
-(#list-of-prompt-arguments).
+The `dream>` prompt's arguments are pretty much identical to those used in the Discord bot, except
+you don't need to type "!dream" (it doesn't hurt if you do). A significant change is that creation
+of individual images is now the default unless --grid (-g) is given. A full list is given in [List
+of prompt arguments] (#list-of-prompt-arguments).
-# Arguments
+## Arguments
-The script itself also recognizes a series of command-line switches
-that will change important global defaults, such as the directory for
-image outputs and the location of the model weight files.
+The script itself also recognizes a series of command-line switches that will change important
+global defaults, such as the directory for image outputs and the location of the model weight files.
## List of arguments recognized at the command line
-These command-line arguments can be passed to dream.py when you first
-run it from the Windows, Mac or Linux command line. Some set defaults
-that can be overridden on a per-prompt basis (see [List of prompt
-arguments] (#list-of-prompt-arguments). Others
+These command-line arguments can be passed to dream.py when you first run it from the Windows, Mac
+or Linux command line. Some set defaults that can be overridden on a per-prompt basis (see [List of
+prompt arguments] (#list-of-prompt-arguments). Others
-| Argument | Shortcut | Default | Description |
-|--------------------|------------|---------------------|--------------|
-| --help | -h | | Print a concise help message. |
-| --outdir /GFPGANv1.3.pth | Path to GFPGAN model file, relative to --gfpgan_dir. | +| --device
Open and follow instructions to use an -isolated environment running Dream.
+alt="Open In Colab"/>
Open and follow instructions to use an isolated environment running +Dream.
-Output Example: - +Output Example:  --- ## **Seamless Tiling** -The seamless tiling mode causes generated images to seamlessly tile -with itself. To use it, add the `--seamless` option when starting the -script which will result in all generated images to tile, or for each -`dream>` prompt as shown here: +The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the +`--seamless` option when starting the script which will result in all generated images to tile, or +for each `dream>` prompt as shown here: ```python dream> "pond garden with lotus by claude monet" --seamless -s100 -n4 @@ -31,12 +30,11 @@ dream> "pond garden with lotus by claude monet" --seamless -s100 -n4 ## **Reading Prompts from a File** -You can automate `dream.py` by providing a text file with the prompts -you want to run, one line per prompt. The text file must be composed -with a text editor (e.g. Notepad) and not a word processor. Each line -should look like what you would type at the dream> prompt: +You can automate `dream.py` by providing a text file with the prompts you want to run, one line per +prompt. The text file must be composed with a text editor (e.g. Notepad) and not a word processor. +Each line should look like what you would type at the dream> prompt: -``` +```bash a beautiful sunny day in the park, children playing -n4 -C10 stormy weather on a mountain top, goats grazing -s100 innovative packaging for a squid's dinner -S137038382 @@ -58,12 +56,16 @@ You may read a series of prompts from standard input by providing a filename of ## **Shortcuts: Reusing Seeds** -Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version 1.11. Provide a `**-S**` (or `**--seed**`) -switch of `-1` to use the seed of the most recent image generated. If you produced multiple images with the `**-n**` switch, then you can go back further using -2, -3, etc. up to the first image generated by the previous command. Sorry, but you can't go back further than one command. +Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version +1.11. Provide a `**-S**` (or `**--seed**`) switch of `-1` to use the seed of the most recent image +generated. If you produced multiple images with the `**-n**` switch, then you can go back further +using -2, -3, etc. up to the first image generated by the previous command. Sorry, but you can't go +back further than one command. -Here's an example of using this to do a quick refinement. It also illustrates using the new `**-G**` switch to turn on upscaling and face enhancement (see previous section): +Here's an example of using this to do a quick refinement. It also illustrates using the new `**-G**` +switch to turn on upscaling and face enhancement (see previous section): -``` +```bash dream> a cute child playing hopscotch -G0.5 [...] outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304 @@ -80,26 +82,26 @@ outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1. ## **Weighted Prompts** You may weight different sections of the prompt to tell the sampler to attach different levels of -priority to them, by adding `:(number)` to the end of the section you wish to up- or downweight. -For example consider this prompt: +priority to them, by adding `:(number)` to the end of the section you wish to up- or downweight. For +example consider this prompt: -``` - tabby cat:0.25 white duck:0.75 hybrid +```bash +tabby cat:0.25 white duck:0.75 hybrid ``` -This will tell the sampler to invest 25% of its effort on the tabby -cat aspect of the image and 75% on the white duck aspect -(surprisingly, this example actually works). The prompt weights can -use any combination of integers and floating point numbers, and they -do not need to add up to 1. +This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75% +on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any +combination of integers and floating point numbers, and they do not need to add up to 1. --- ## **Simplified API** -For programmers who wish to incorporate stable-diffusion into other products, this repository includes a simplified API for text to image generation, which lets you create images from a prompt in just three lines of code: +For programmers who wish to incorporate stable-diffusion into other products, this repository +includes a simplified API for text to image generation, which lets you create images from a prompt +in just three lines of code: -``` +```bash from ldm.generate import Generate g = Generate() outputs = g.txt2img("a unicorn in manhattan") @@ -113,16 +115,14 @@ Please see ldm/generate.py for more information. A set of example scripts is com ## **Preload Models** -In situations where you have limited internet connectivity or are -blocked behind a firewall, you can use the preload script to preload -the required files for Stable Diffusion to run. +In situations where you have limited internet connectivity or are blocked behind a firewall, you can +use the preload script to preload the required files for Stable Diffusion to run. -The preload script `scripts/preload_models.py` needs to be run once at -least while connected to the internet. In the following runs, it will -load up the cached versions of the required files from the `.cache` -directory of the system. +The preload script `scripts/preload_models.py` needs to be run once at least while connected to the +internet. In the following runs, it will load up the cached versions of the required files from the +`.cache` directory of the system. -``` +```bash (ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py preloading bert tokenizer... Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s] diff --git a/docs/features/TEXTUAL_INVERSION.md b/docs/features/TEXTUAL_INVERSION.md index 2bfb1e50bd..b8dbc21192 100644 --- a/docs/features/TEXTUAL_INVERSION.md +++ b/docs/features/TEXTUAL_INVERSION.md @@ -6,18 +6,19 @@ title: TEXTUAL_INVERSION You may personalize the generated images to provide your own styles or objects by training a new LDM checkpoint and introducing a new vocabulary to the fixed -model as a (.pt) embeddings file. Alternatively, you may use or train HuggingFace -Concepts embeddings files (.bin) from
+
---
## Step 2 - Generating Variations
-Let's try to generate some variations. Using the same seed, we pass the argument `-v0.1` (or --variant_amount), which generates a series of
-variations each differing by a variation amount of 0.2. This number ranges from `0` to `1.0`, with higher numbers being larger amounts of
-variation.
+Let's try to generate some variations. Using the same seed, we pass the argument
+`-v0.1` (or --variant_amount), which generates a series of variations each
+differing by a variation amount of 0.2. This number ranges from `0` to `1.0`,
+with higher numbers being larger amounts of variation.
-```
+```bash
dream> "prompt" -n6 -S3357757885 -v0.2
...
Outputs:
@@ -60,32 +68,41 @@ Outputs:
### **Variation Sub Seeding**
-Note that the output for each image has a `-V` option giving the "variant subseed" for that image, consisting of a seed followed by the
-variation amount used to generate it.
+Note that the output for each image has a `-V` option giving the "variant
+subseed" for that image, consisting of a seed followed by the variation amount
+used to generate it.
-This gives us a series of closely-related variations, including the two shown here.
+This gives us a series of closely-related variations, including the two shown
+here.
-
-
+
-I like the expression on Xena's face in the first one (subseed 3647897225), and the armor on her shoulder in the second one (subseed 1614299449). Can we combine them to get the best of both worlds?
+
-We combine the two variations using `-V` (--with_variations). Again, we must provide the seed for the originally-chosen image in order for
-this to work.
+I like the expression on Xena's face in the first one (subseed 3647897225), and
+the armor on her shoulder in the second one (subseed 1614299449). Can we combine
+them to get the best of both worlds?
-```
+We combine the two variations using `-V` (--with_variations). Again, we must
+provide the seed for the originally-chosen image in order for this to work.
+
+```bash
dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1
Outputs:
./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
```
-Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The resulting image is close, but not exactly what I wanted:
+Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The
+resulting image is close, but not exactly what I wanted:
-
+
-We could either try combining the images with different weights, or we can generate more variations around the almost-but-not-quite image. We do the latter, using both the `-V` (combining) and `-v` (variation strength) options. Note that we use `-n6` to generate 6 variations:
+We could either try combining the images with different weights, or we can
+generate more variations around the almost-but-not-quite image. We do the
+latter, using both the `-V` (combining) and `-v` (variation strength) options.
+Note that we use `-n6` to generate 6 variations:
-```
+```bash
dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1 -v0.05 -n6
Outputs:
./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
@@ -96,9 +113,11 @@ Outputs:
./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
```
-This produces six images, all slight variations on the combination of the chosen two images. Here's the one I like best:
+This produces six images, all slight variations on the combination of the chosen
+two images. Here's the one I like best:
-
+
-As you can see, this is a very powerful tool, which when combined with subprompt weighting, gives you great control over the content and
-quality of your generated images.
+As you can see, this is a very powerful tool, which when combined with subprompt
+weighting, gives you great control over the content and quality of your
+generated images.
diff --git a/docs/features/WEB.md b/docs/features/WEB.md
index 9eb6aa5bfc..1bbf5f3b55 100644
--- a/docs/features/WEB.md
+++ b/docs/features/WEB.md
@@ -1,13 +1,19 @@
-# Barebones Web Server
+---
+title: Barebones Web Server
+---
-As of version 1.10, this distribution comes with a bare bones web server (see screenshot). To use it, run the `dream.py` script by adding the `**--web**` option.
+As of version 1.10, this distribution comes with a bare bones web server (see
+screenshot). To use it, run the `dream.py` script by adding the `**--web**`
+option.
-```
+```bash
(ldm) ~/stable-diffusion$ python3 scripts/dream.py --web
```
-You can then connect to the server by pointing your web browser at http://localhost:9090, or to the network name or IP address of the server.
+You can then connect to the server by pointing your web browser at
+http://localhost:9090, or to the network name or IP address of the server.
-Kudos to [Tesseract Cat](https://github.com/TesseractCat) for contributing this code, and to [dagf2101](https://github.com/dagf2101) for refining it.
+Kudos to [Tesseract Cat](https://github.com/TesseractCat) for contributing this
+code, and to [dagf2101](https://github.com/dagf2101) for refining it.
diff --git a/docs/help/TROUBLESHOOT.md b/docs/help/TROUBLESHOOT.md
index b0d173a750..6458d8ceb9 100644
--- a/docs/help/TROUBLESHOOT.md
+++ b/docs/help/TROUBLESHOOT.md
@@ -1,72 +1,89 @@
---
-title: F.A.Q.
+title: F.A.Q.
---
-## **Frequently Asked Questions**
+## **Frequently-Asked-Questions**
-Here are a few common installation problems and their solutions. Often these are caused by incomplete installations or crashes during the
-install process.
+Here are a few common installation problems and their solutions. Often these are caused by
+incomplete installations or crashes during the install process.
---
-**QUESTION**
+### **QUESTION**
During `conda env create -f environment.yaml`, conda hangs indefinitely.
-**SOLUTION**
+### **SOLUTION**
-Enter the stable-diffusion directory and completely remove the `src` directory and all its contents. The safest way to do this is to enter the stable-diffusion directory and give the command `git clean -f`. If this still doesn't fix the problem, try "conda clean -all" and then restart at the `conda env create` step.
+Enter the stable-diffusion directory and completely remove the `src` directory and all its contents.
+The safest way to do this is to enter the stable-diffusion directory and give the command
+`git clean -f`. If this still doesn't fix the problem, try "conda clean -all" and then restart at
+the `conda env create` step.
---
-**QUESTION**
+### **QUESTION**
-`dream.py` crashes with the complaint that it can't find `ldm.simplet2i.py`. Or it complains that function is being passed incorrect parameters.
+`dream.py` crashes with the complaint that it can't find `ldm.simplet2i.py`. Or it complains that
+function is being passed incorrect parameters.
-**SOLUTION**
+### **SOLUTION**
-Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory and give the command `pip install -e .`
+Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory and give the command
+`pip install -e .`
---
-**QUESTION**
+### **QUESTION**
`dream.py` dies, complaining of various missing modules, none of which starts with `ldm``.
-**SOLUTION**
+### **SOLUTION**
-From within the `stable-diffusion` directory, run `conda env update -f environment.yaml` This is also frequently the solution to
-complaints about an unknown function in a module.
+From within the `stable-diffusion` directory, run `conda env update -f environment.yaml` This is
+also frequently the solution to complaints about an unknown function in a module.
---
-**QUESTION**
+### **QUESTION**
There's a feature or bugfix in the Stable Diffusion GitHub that you want to try out.
-**SOLUTION**
+### **SOLUTION**
-**Main Branch**
+#### **Main Branch**
-If the fix/feature is on the `main` branch, enter the stable-diffusion directory and do a `git pull`.
+If the fix/feature is on the `main` branch, enter the stable-diffusion directory and do a
+`git pull`.
-Usually this will be sufficient, but if you start to see errors about missing or incorrect modules, use the command `pip install -e .` and/or `conda env update -f environment.yaml` (These commands won't break anything.)
+Usually this will be sufficient, but if you start to see errors about missing or incorrect modules,
+use the command
-**Sub Branch**
+`pip install -e .` and/or
-If the feature/fix is on a branch (e.g. "_foo-bugfix_"), the recipe is similar, but do a `git pull Stable Diffusion Dream Script
-{align='center'} +
+
+
[Robin Rombach](https://github.com/rromb)\*,
@@ -15,28 +17,36 @@ _Stable Diffusion was made possible thanks to a collaboration with [Stability AI
## **CVPR '22 Oral**
-which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).
+which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF
+at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our
+[Project page](https://ommer-lab.com/research/latent-diffusion-models/).
[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
-model.
-Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
-Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
-this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
-With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
-See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
+model. Thanks to a generous compute donation from
+[Stability AI](https://stability.ai/) and support from
+[LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on
+512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/)
+database. Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), this
+model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text
+prompts. With its 860M UNet and 123M text encoder, the model is relatively
+lightweight and runs on a GPU with at least 10GB VRAM. See
+[this section](#stable-diffusion-v1) below and the
+[model card](https://huggingface.co/CompVis/stable-diffusion).
## Requirements
-A suitable [conda](https://conda.io/) environment named `ldm` can be created
-and activated with:
+A suitable [conda](https://conda.io/) environment named `ldm` can be created and
+activated with:
```bash
conda env create -f environment.yaml
conda activate ldm
```
-You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
+You can also update an existing
+[latent diffusion](https://github.com/CompVis/latent-diffusion) environment by
+running
```bash
conda install pytorch torchvision -c pytorch
@@ -46,42 +56,57 @@ pip install -e .
## Stable Diffusion v1
-Stable Diffusion v1 refers to a specific configuration of the model
-architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
-and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
-then finetuned on 512x512 images.
+Stable Diffusion v1 refers to a specific configuration of the model architecture
+that uses a downsampling-factor 8 autoencoder with an 860M UNet and CLIP
+ViT-L/14 text encoder for the diffusion model. The model was pretrained on
+256x256 images and then finetuned on 512x512 images.
-\*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
-in its training data.
-Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).
-Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](https://stability.ai/academia-access-form).
-**This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.\***
+\*Note: Stable Diffusion v1 is a general text-to-image diffusion model and
+therefore mirrors biases and (mis-)conceptions that are present in its training
+data. Details on the training procedure and data, as well as the intended use of
+the model can be found in the corresponding
+[model card](https://huggingface.co/CompVis/stable-diffusion). Research into the
+safe deployment of general text-to-image models is an ongoing effort. To prevent
+misuse and harm, we currently provide access to the checkpoints only for
+[academic research purposes upon request](https://stability.ai/academia-access-form).
+**This is an experiment in safe and community-driven publication of a capable
+and general text-to-image model. We are working on a public release with a more
+permissive license that also incorporates ethical considerations.\***
[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form)
### Weights
-We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
-which were trained as follows,
+We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and
+`sd-v1-3.ckpt`, which were trained as follows,
-- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
- 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
-- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
- 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
- filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
-- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on
+ [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at
+ resolution `512x512` on
+ [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution)
+ (170M examples from LAION-5B with resolution `>= 1024x1024`).
+- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution
+ `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to
+ images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`,
+ and an estimated watermark probability `< 0.5`. The watermark estimate is from
+ the LAION-5B metadata, the aesthetics score is estimated using an
+ [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
+- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution
+ `512x512` on "laion-improved-aesthetics" and 10\% dropping of the
+ text-conditioning to improve
+ [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
-steps show the relative improvements of the checkpoints:
-
+5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of
+the checkpoints: 
### Text-to-Image with Stable Diffusion
-Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
+Stable Diffusion is a latent diffusion model conditioned on the (non-pooled)
+text embeddings of a CLIP ViT-L/14 text encoder.
#### Sampling Script
@@ -98,8 +123,11 @@ and sample with
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
```
-By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler,
-and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
+By default, this uses a guidance scale of `--scale 7.5`,
+[Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51)
+of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, and renders images of
+size 512x512 (which it was trained on) in 50 steps. All supported arguments are
+listed below (type `python scripts/txt2img.py --help`).
```commandline
usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS]
@@ -137,14 +165,17 @@ optional arguments:
```
-Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints.
-For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
-non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
-which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
+Note: The inference config for all v1 versions is designed to be used with
+EMA-only checkpoints. For this reason `use_ema=False` is set in the
+configuration, otherwise the code will try to switch from non-EMA to EMA
+weights. If you want to examine the effect of EMA vs no EMA, we provide "full"
+checkpoints which contain both types of weights. For these, `use_ema=False` will
+load and use the non-EMA weights.
#### Diffusers Integration
-Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
+Another way to download and sample Stable Diffusion is by using the
+[diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
```py
# make sure you're logged in with `huggingface-cli login`
@@ -165,18 +196,23 @@ image.save("astronaut_rides_horse.png")
### Image Modification with Stable Diffusion
-By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
-tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
-we provide a script to perform image modification with Stable Diffusion.
+By using a diffusion-denoising mechanism as first proposed by
+[SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
+tasks such as text-guided image-to-image translation and upscaling. Similar to
+the txt2img sampling script, we provide a script to perform image modification
+with Stable Diffusion.
-The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
+The following describes an example where a rough sketch made in
+[Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
```
python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img