initial test

app.py

@@ -1,7 +1,94 @@
 import gradio as gr
+import torch
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+from lambda_diffusers import StableDiffusionImageEmbedPipeline
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def main(
+    input_im,
+    scale=3.0,
+    n_samples=4,
+    seed=0,
+    steps=25,
+    ):
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    images_list = pipe(
+        n_samples*[input_im],
+        guidance_scale=scale,
+        num_inference_steps=steps,
+        generator=generator,
+        )
+
+    images = []
+    safe_image = Image.open(r"unsafe.png")
+    for i, image in enumerate(images_list["sample"]):
+        if(images_list["nsfw_content_detected"][i]):
+            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/)
+
+__Get the [code](https://github.com/justinpinkney/stable-diffusion) and [model](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned).__
+
+![](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.
+"""
+
+device = "cpu"
+pipe = StableDiffusionImageEmbedPipeline.from_pretrained(
+    "lambdalabs/sd-image-variations-diffusers",
+    revision="273115e88df42350019ef4d628265b8c29ef4af5",
+    )
+pipe = pipe.to(device)
+
+inputs = [
+    gr.Image(),
+    gr.Slider(0, 25, value=3, step=1, label="Guidance scale"),
+    gr.Slider(1, 4, value=1, step=1, label="Number images"),
+    gr.Slider(5, 50, value=25, step=5, label="Steps"),
+    gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=2147483647,
+            step=1,
+            randomize=True,
+        )
+]
+output = gr.Gallery(label="Generated variations")
+output.style(grid=2)
+
+examples = [
+    ["assets/im-examples/vermeer.jpg", 3, 1, True, 25],
+    ["assets/im-examples/matisse.jpg", 3, 1, True, 25],
+]
+
+demo = gr.Interface(
+    fn=main,
+    title="Stable Diffusion Image Variations",
+    description=description,
+    article=article,
+    inputs=inputs,
+    outputs=output,
+    examples=examples,
+    )
+demo.launch()

requirements.txt

@@ -0,0 +1 @@
+git+git://github.com/LambdaLabsML/lambda-diffusers.git#egg=lambda-diffusers