Updating app.py to support generating multiple variations and updated text

app.py

@@ -10,13 +10,18 @@ def main(
     edit_prompt=None,
     edit_prompt_weight=1.0,
     scale=3.0,
+    n_samples=4,
     steps=25,
     seed=0,
     ):
 
     generator = torch.Generator(device=device).manual_seed(int(seed))
 
-    n_samples = 1
+    if len(base_prompt) == 0:
+        base_prompt = None
+    if len(edit_prompt) == 0:
+        edit_prompt = None
+
     images_list = pipe(
         n_samples*[input_im],
         base_prompt=base_prompt,
@@ -29,26 +34,20 @@ def main(
 
     return images_list.images
 
-    images = []
-    for i, image in enumerate(images_list.images):
-        if(images_list["nsfw_content_detected"][i]):
-            safe_image = Image.open(r"unsafe.png")
-            images.append(safe_image)
-        else:
-            images.append(image)
-    return images
+    # images = []
+    # for i, image in enumerate(images_list.images):
+    #     if(images_list["nsfw_content_detected"][i]):
+    #         safe_image = Image.open(r"unsafe.png")
+    #         images.append(safe_image)
+    #     else:
+    #         images.append(image)
+    # return images
 
 
 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)
-
+Generate variations on an input image using a fine-tuned version of Stable Diffision.  Edit images by applying an "edit" vector to the image embedding,
+created by taking the difference between a base prompt describing an attribute of the image and an edit prompt describing the desired attribute of the edit.
 """
 
 article = \
@@ -60,9 +59,9 @@ the CLIP _image_ encoder instead. So instead of generating images based a text i
 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.
 
+Original model trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)).
 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.
 """
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -80,18 +79,19 @@ inputs = [
     gr.Image(),
     gr.Textbox(label="Base prompt"),
     gr.Textbox(label="Edit prompt"),
-    gr.Slider(0.0, 2.0, value=1.0, step=0.1, label="Edit prompt weight"),
+    gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="Edit prompt weight"),
     gr.Slider(0, 25, value=3, step=1, label="Guidance scale"),
-    gr.Slider(5, 50, value=25, step=5, label="Steps"),
-    gr.Number(0, labal="Seed", precision=0)
+    gr.Slider(1, 4, value=1, step=1, label="Number images"),
+    gr.Slider(5, 100, value=25, step=5, label="Steps"),
+    gr.Number(0, label="Seed", precision=0)
 ]
 output = gr.Gallery(label="Generated variations")
 output.style(grid=2)
 
 examples = [
-    ["examples/painted ladies.png", None, None, 1.0, 3, 25, 0],
-    ["examples/painted ladies.png", "a color photograph", "a black and white photograph", 1.0, 3, 25, 0],
-    ["examples/painted ladies.png", "a color photograph", "a brightly colored oil painting", 1.0, 3, 25, 0],
+    ["examples/painted ladies.png", "", "", 1.0, 3, 4, 25, 0],
+    ["examples/painted ladies.png", "a color photograph", "a black and white photograph", 1.0, 3, 1, 25, 0],
+    ["examples/painted ladies.png", "a color photograph", "a brightly colored oil painting", 1.0, 3, 1, 25, 0],
 ]
 
 demo = gr.Interface(