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| import gradio as gr | |
| import torch | |
| from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline | |
| MODEL_ID = "verkaDerkaDerk/tiki-based-128" | |
| MODEL_ID = "verkaDerkaDerk/tiki-64" | |
| PIPELINE = DDPMPipeline.from_pretrained(MODEL_ID) | |
| ############################################################################# | |
| def tiki(batch_size=1,seed=0): | |
| generator = torch.manual_seed(seed) | |
| return PIPELINE(generator=generator, batch_size=batch_size)["sample"] | |
| def imagine4(): | |
| return tiki(batch_size=4) | |
| def imagine(): | |
| return tiki()[0] | |
| ############################################################################# | |
| def fancy(): | |
| # try some https://huggingface.co/spaces/stabilityai/stable-diffusion/blob/main/app.py trix | |
| css = ''' | |
| .output_image {height: 40rem !important; width: 100% !important; } | |
| .object-contain {height: 256px !important; width: 256px !important; } | |
| #gallery img { height: 256px !important; width: 256px !important; } | |
| #.center { text-align: center; } | |
| ''' | |
| block = gr.Blocks(css=css) | |
| with block: | |
| gr.HTML(''' | |
| <pre> | |
| This is an unconditioned diffusion model trained on around 500 | |
| miscellaneous tiki images from around the web. | |
| It was trained for around 4k epochs with a loss around 1.5%. | |
| The 64x64 version (used here) took around 12s/epoch. | |
| Despite the loss staying about the same, the visual quality | |
| continue[sd] to improve. Occasionally, the tiki generated | |
| require some suspension of disbelief. | |
| More training in the near future, but the current model is | |
| good enough to provide some limited play value. | |
| Image generation is slow, from 80s - 120s per image. | |
| When running 4 images concurrently it takes 4-5 minutes total. | |
| Despite the long wait time, it's more fun to generate 4 at a time. | |
| Different "tiki" values will give different tiki images if you can | |
| imagine. | |
| </pre> | |
| <p class="center"> | |
| <center whatever="i know, i know..."> | |
| <img src="https://freeimghost.net/images/2022/08/23/tiki-600e.md.png" height="256"> | |
| </center> | |
| </p> | |
| ''') | |
| with gr.Group(): | |
| with gr.Box(): | |
| with gr.Row(): | |
| btn = gr.Button("Generate image") | |
| gallery = gr.Gallery( | |
| label="Generated images", show_label=False, elem_id="gallery" | |
| ).style(grid=[4], height="256") | |
| #btn.click(imagine4, inputs=None, outputs=gallery) | |
| maximum = 4294967296 | |
| seed = torch.randint(maximum,[1])[0].item() | |
| seed = 2607725669 # lulz | |
| seed = 917832826 | |
| with gr.Row(elem_id="tiki-options"): | |
| batch_size = gr.Slider(label="image count", minimum=1, maximum=4, value=1, step=1) | |
| seed = gr.Slider(label="tiki", minimum=0, maximum=maximum, value=seed, step=1) | |
| btn.click(tiki, inputs=[batch_size,seed], outputs=gallery) | |
| gr.HTML(''' | |
| <p>Trained with <a href="https://github.com/huggingface/diffusers">huggingface/diffusers</a>.</p> | |
| ''') | |
| #block.queue(max_size=40).launch() | |
| block.queue().launch() | |
| ############################################################################# | |
| def plain(): | |
| # trix from https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial | |
| title = "Tiki Diffusion Model" | |
| description = ''' | |
| Diffusion model trained on random tiki images. | |
| FIXME: | |
| - runs very slow 120s - 240s | |
| - image is weirdly stretched | |
| ''' | |
| article = gr.HTML(''' | |
| <p>Trained with <a href="https://github.com/huggingface/diffusers">diffusers</a>.</p> | |
| ''') | |
| # ValueError: The parameter `examples` must either be a string directory or a list(if there is only 1 input component) or (more generally), a nested list, where each sublist represents a set of inputs. | |
| examples = ['tiki-600e.png'] | |
| interpretation = 'default' # no idea... | |
| enable_queue = True | |
| # https://github.com/gradio-app/gradio/issues/287 | |
| css = ''' | |
| .output_image {height: 40rem !important; width: 100% !important; } | |
| .object-contain {height: 256px !important; width: 256px !important; } | |
| ''' | |
| # css = ".output-image, .input-image, .image-preview {height: 600px !important}" | |
| inputs = None | |
| outputs = "pil" | |
| gr.Interface( | |
| fn=imagine, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| description=description, | |
| article=article, | |
| css=css, | |
| #examples=examples, | |
| interpretation=interpretation, | |
| enable_queue=enable_queue | |
| ).launch() | |
| ############################################################################# | |
| def main(): | |
| if True: | |
| return fancy() | |
| plain() | |
| main() | |
| # EOF | |
| ############################################################################# |