fixed a typo that introduced a crash

README.md

@@ -3,21 +3,7 @@
 This is a fork of CompVis/stable-diffusion, the wonderful open source
 text-to-image generator.
 
-The original has been modified in several minor ways:
-
-## Simplified API for text to image generation
-
-There is now a simplified API for text to image generation, which
-lets you create images from a prompt in just three lines of code:
-
-~~~~
-from ldm.simplet2i import T2I
-model   = T2I()
-outputs = model.text2image("a unicorn in manhattan")
-~~~~
-
-Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
-Please see ldm/simplet2i.py for more information.
+The original has been modified in several ways:
 
 ## Interactive command-line interface similar to the Discord bot
 
@@ -63,8 +49,25 @@ weights (512x512) and the older (256x256) latent diffusion weights
 identical to those used in the Discord bot, except you don't need to
 type "!dream". Pass "-h" (or "--help") to list the arguments.
 
+This script also provides an img2img feature that lets you seed your
+creations with a drawing or photo.
+
 For command-line help, type -h (or --help) at the dream> prompt.
 
+## Simplified API for text to image generation
+
+There is now a simplified API for text to image generation, which
+lets you create images from a prompt in just three lines of code:
+
+~~~~
+from ldm.simplet2i import T2I
+model   = T2I()
+outputs = model.text2image("a unicorn in manhattan")
+~~~~
+
+Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
+Please see ldm/simplet2i.py for more information.
+
 ## Workaround for machines with limited internet connectivity
 
 My development machine is a GPU node in a high-performance compute
@@ -116,14 +119,16 @@ to send me an email if you use and like the script.
 # Original README from CompViz/stable-diffusion
 *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:*
 
-[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)<br/>
+[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
 [Robin Rombach](https://github.com/rromb)\*,
 [Andreas Blattmann](https://github.com/ablattmann)\*,
 [Dominik Lorenz](https://github.com/qp-qp)\,
 [Patrick Esser](https://github.com/pesser),
 [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
 
-which is available on [GitHub](https://github.com/CompVis/latent-diffusion).
+**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/).
 
 ![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)
 [Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion

ldm/simplet2i.py

@@ -197,7 +197,7 @@ class T2I:
                             shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
                             samples_ddim, _ = sampler.sample(S=steps,
                                                              conditioning=c,
-                                                             batch_size_size=batch_size,
+                                                             batch_size=batch_size,
                                                              shape=shape,
                                                              verbose=False,
                                                              unconditional_guidance_scale=cfg_scale,