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update demo

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This commit is contained in:
Justin Pinkney 2022-09-05 07:33:35 -04:00
parent 8c48f6005f
commit db17cd8f41
1 changed files with 37 additions and 6 deletions
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43
scripts/gradio_variations.py
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@ -74,6 +74,31 @@ def main(
return output_ims return output_ims
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.
"""
def run_demo( def run_demo(
device_idx=0, device_idx=0,
ckpt="models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt", ckpt="models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt",
@ -89,24 +114,30 @@ def run_demo(
gr.Slider(0, 25, value=3, step=1, label="cfg scale"), gr.Slider(0, 25, value=3, step=1, label="cfg scale"),
gr.Slider(1, 4, value=1, step=1, label="Number images"), gr.Slider(1, 4, value=1, step=1, label="Number images"),
gr.Checkbox(True, label="plms"), gr.Checkbox(True, label="plms"),
gr.Slider(5, 250, value=25, step=5, label="steps"), gr.Slider(5, 50, value=25, step=5, label="steps"),
] ]
output = gr.Gallery(label="Generated variations") output = gr.Gallery(label="Generated variations")
output.style(height="auto", grid=2) output.style(grid=2)
fn_with_model = partial(main, model, device) fn_with_model = partial(main, model, device)
fn_with_model.__name__ = "fn_with_model" fn_with_model.__name__ = "fn_with_model"
examples = [
["assets/im-examples/vermeer.jpg", 3, 1, True, 25],
["assets/im-examples/matisse.jpg", 3, 1, True, 25],
]
demo = gr.Interface( demo = gr.Interface(
fn=fn_with_model, fn=fn_with_model,
title="Stable Diffusion Image Variations", title="Stable Diffusion Image Variations",
description="Generate variations on an input image using a fine-tuned version of Stable Diffision", description=description,
article="TODO", article=article,
inputs=inputs, inputs=inputs,
outputs=output, outputs=output,
examples=examples,
allow_flagging="never",
) )
# demo.queue() demo.launch(enable_queue=True, share=True)
demo.launch(share=False, server_name="0.0.0.0")
if __name__ == "__main__": if __name__ == "__main__":
fire.Fire(run_demo) fire.Fire(run_demo)