Update app.py

app.py

@@ -4,12 +4,12 @@ from PIL import Image
 
 from lambda_diffusers import StableDiffusionImageEmbedPipeline
 
-def ask(input_im, scale, n_samples, steps, seed, images):
+def ask(input_im, scale, steps, seed, images):
     images = images
     generator = torch.Generator(device=device).manual_seed(int(seed))
     
     images_list = pipe(
-        n_samples*[input_im],
+        2*[input_im],
         guidance_scale=scale,
         num_inference_steps=steps,
         generator=generator,
@@ -23,7 +23,7 @@ def ask(input_im, scale, n_samples, steps, seed, images):
             images.append(image)
     return images
 
-def main(input_im, scale, n_samples, steps, seed):
+def main(input_im, scale, steps, seed):
     
     images = []
     images = ask(input_im, scale, n_samples, steps, seed, images)
@@ -41,12 +41,33 @@ pipe = pipe.to(device)
 inputs = [
     gr.Image(),
     gr.Slider(0, 25, value=3, step=1, label="Guidance scale"),
-    gr.Slider(1, 2, value=2, step=1, label="Number images"),
     gr.Slider(5, 50, value=25, step=5, label="Steps"),
-    gr.Number(0, labal="Seed", precision=0)
+    gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True)
 ]
 output = gr.Gallery(label="Generated variations")
-output.style(grid=2)
+output.style(grid=2, height="")
+
+description = \
+"""
+
+Generate variations on an input image using a fine-tuned version of Stable Diffision.
+Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/)
+This version has been ported to 🤗 Diffusers library, see more details on how to use this version in the [Lambda Diffusers repo](https://github.com/LambdaLabsML/lambda-diffusers).
+__For the original training code see [this repo](https://github.com/justinpinkney/stable-diffusion).
+![](https://raw.githubusercontent.com/justinpinkney/stable-diffusion/main/assets/im-vars-thin.jpg)
+"""
+
+article = \
+"""
+## How does this work?
+The normal Stable Diffusion model is trained to be conditioned on text input. This version has had the original text encoder (from CLIP) removed, and replaced with
+the CLIP _image_ encoder instead. So instead of generating images based a text input, images are generated to match CLIP's embedding of the image.
+This creates images which have the same rough style and content, but different details, in particular the composition is generally quite different.
+This is a totally different approach to the img2img script of the original Stable Diffusion and gives very different results.
+The model was fine tuned on the [LAION aethetics v2 6+ dataset](https://laion.ai/blog/laion-aesthetics/) to accept the new conditioning.
+Training was done on 4xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud).
+More details on the method and training will come in a future blog post.
+"""
 
 demo = gr.Interface(
     fn=main,