Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from torchvision import transforms
+
+from diffusers import StableDiffusionImageVariationPipeline
+
+def main(
+    input_im,
+    scale=3.0,
+    n_samples=4,
+    steps=25,
+    seed=0,
+    ):
+    generator = torch.Generator(device=device).manual_seed(int(seed))
+
+    tform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize(
+        (224, 224),
+        interpolation=transforms.InterpolationMode.BICUBIC,
+        antialias=False,
+        ),
+        transforms.Normalize(
+          [0.48145466, 0.4578275, 0.40821073],
+          [0.26862954, 0.26130258, 0.27577711]),
+    ])
+    inp = tform(input_im).to(device)
+        
+    images_list = pipe(
+        inp.tile(n_samples, 1, 1, 1),
+        guidance_scale=scale,
+        num_inference_steps=steps,
+        generator=generator,
+        )
+
+    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 = \
+"""
+__Now using Image Variations v2!__
+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 8xA100 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud).
+More details are on the [model card](https://huggingface.co/lambdalabs/sd-image-variations-diffusers).
+"""
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionImageVariationPipeline.from_pretrained(
+    "lambdalabs/sd-image-variations-diffusers",
+    )
+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.Number(0, label="Seed", precision=0)
+]
+output = gr.Gallery(label="Generated variations")
+output.style(grid=2)
+
+
+
+demo = gr.Interface(
+    fn=main,
+    title="Stable Diffusion Image Variations",
+    description=description,
+    article=article,
+    inputs=inputs,
+    outputs=output
+    )
+demo.launch()