Spaces:
Running
Running
File size: 8,553 Bytes
be7fa12 084ed36 be7fa12 084ed36 be7fa12 084ed36 be7fa12 084ed36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1, shrink-to-fit=no, maximum-scale=1"
/>
<script>
window.__gradio_mode__ = "app";
window.gradio_config = {"version": "3.0.17", "mode": "blocks", "dev_mode": false, "components": [{"id": 1, "type": "column", "props": {"type": "column", "variant": "default", "visible": true, "style": {}}}, {"id": 2, "type": "markdown", "props": {"value": "<h1><center>DALL\u00b7E mini</center></h1>", "name": "markdown", "visible": true, "style": {}}}, {"id": 3, "type": "markdown", "props": {"value": "<center>AI model generating images from any prompt!</center>", "name": "markdown", "visible": true, "style": {}}}, {"id": 4, "type": "group", "props": {"type": "group", "visible": true, "style": {}}}, {"id": 5, "type": "box", "props": {"type": "box", "visible": true, "style": {}}}, {"id": 6, "type": "row", "props": {"type": "row", "visible": true, "style": {"equal_height": true, "mobile_collapse": false}}}, {"id": 7, "type": "textbox", "props": {"lines": 1, "max_lines": 1, "value": "", "label": "Enter your prompt", "show_label": false, "name": "textbox", "visible": true, "elem_id": "prompt", "style": {"container": false}}}, {"id": 8, "type": "button", "props": {"value": "Run", "variant": "primary", "name": "button", "visible": true, "style": {}}}, {"id": 9, "type": "gallery", "props": {"label": "Generated images", "show_label": false, "name": "gallery", "visible": true, "elem_id": "gallery", "style": {"grid": [3], "height": "auto"}}}, {"id": 10, "type": "column", "props": {"type": "column", "variant": "default", "visible": true, "style": {}}}, {"id": 11, "type": "button", "props": {"value": "Screenshot", "variant": "secondary", "name": "button", "visible": true, "elem_id": "screenshot", "style": {"full_width": true}}}, {"id": 12, "type": "markdown", "props": {"value": "<details>\n<summary>Bias and Limitations</summary>\n<p style='line-height: normal; font-size: small'>\nWhile the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. While the extent and nature of the biases of the DALL\u00b7E mini model have yet to be fully documented, given the fact that the model was trained on unfiltered data from the Internet, it may generate images that contain stereotypes against minority groups. Work to analyze the nature and extent of these limitations is ongoing, and will be documented in more detail in the <a href=\"https://huggingface.co/dalle-mini/dalle-mini\" target=\"_blank\">DALL\u00b7E mini model card</a>.\n</p>\n</details>", "name": "markdown", "visible": true, "style": {}}}, {"id": 13, "type": "markdown", "props": {"value": "<hr />\n<p style='text-align: center'>\nCreated by <a href=\"https://twitter.com/borisdayma\" target=\"_blank\">Boris Dayma</a> et al. 2021-2022\n<br/>\n<a href=\"https://github.com/borisdayma/dalle-mini\" target=\"_blank\">GitHub</a> | <a href=\"https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy\" target=\"_blank\">Project Report</a>\n<p style='text-align: center'>Powered by Google <a href=\"https://sites.research.google/trc/\" target=\"_blank\">TPU Research Cloud</a>\n</p>", "name": "markdown", "visible": true, "style": {}}}], "theme": "default", "css": ".container { max-width: 800px; margin: auto; }", "enable_queue": false, "layout": {"id": 0, "children": [{"id": 1, "children": [{"id": 2}, {"id": 3}, {"id": 4, "children": [{"id": 5, "children": [{"id": 6, "children": [{"id": 7}, {"id": 8}]}]}, {"id": 9}]}]}, {"id": 10, "children": [{"id": 11}, {"id": 12}, {"id": 13}]}]}, "dependencies": [{"targets": [8], "trigger": "click", "inputs": [7], "outputs": [9], "backend_fn": false, "js": "\n async (text) => {\n try {\n document.querySelector('#screenshot').style.display = 'none';\n response = await fetch('https://bf.dallemini.ai/generate', {\n method: 'POST',\n headers: {\n 'Accept': 'application/json',\n 'Content-Type': 'application/json'\n },\n body: JSON.stringify({\n prompt: text\n })\n });\n response = await response.json()\n let imgs = response.images.map(r => \"data:image/png;base64,\" + r)\n document.querySelector('#screenshot').style.display = 'block';\n return imgs\n } catch (e) {\n alert(\"Too much traffic, please try again.\")\n IMG = \"data:image/png;base64,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\"\n document.querySelector('#screenshot').style.display = 'block';\n return Array(9).fill(IMG)\n }\n }\n ", "status_tracker": null, "queue": null, "api_name": null, "scroll_to_output": false, "show_progress": true}, {"targets": [11], "trigger": "click", "inputs": [], "outputs": [], "backend_fn": false, "js": "\n () => {\n const captureElement = document.getElementById(1)\n let bg_color = getComputedStyle(document.querySelector(\"#root .container\"))[\"background-color\"]\n captureElement.style.backgroundColor = bg_color; \n html2canvas(captureElement)\n .then(canvas => {\n canvas.style.display = 'none'\n document.body.appendChild(canvas)\n return canvas\n })\n .then(canvas => {\n const image = canvas.toDataURL('image/png').replace('image/png', 'image/octet-stream')\n const a = document.createElement('a')\n const date = new Date()\n const filename = `dallemini_${date.getFullYear()}-${date.getMonth() + 1}-${date.getDate()}_${date.getHours()}-${date.getMinutes()}-${date.getSeconds()}.png`\n a.setAttribute('download', filename)\n a.setAttribute('href', image)\n a.click()\n canvas.remove()\n })\n }\n ", "status_tracker": null, "queue": null, "api_name": null, "scroll_to_output": false, "show_progress": true}]};
</script>
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link
rel="preconnect"
href="https://fonts.gstatic.com"
crossorigin="anonymous"
/>
<link
href="https://fonts.googleapis.com/css?family=Source Sans Pro"
rel="stylesheet"
/>
<link
href="https://fonts.googleapis.com/css?family=IBM Plex Mono"
rel="stylesheet"
/>
<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
<script type="module" crossorigin src="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.8eca4ae7.js"></script>
<link rel="stylesheet" href="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.cbea297d.css">
<style>
#screenshot {
display: none;
}
.container > div > div {
padding: 0.5rem;
}
footer a {
color: rgb(156 163 175) !important;
}
footer img {
display: none !important;
}
</style>
</head>
<body
style="
margin: 0;
padding: 0;
display: flex;
flex-direction: column;
flex-grow: 1;
"
>
<div
id="root"
style="display: flex; flex-direction: column; flex-grow: 1"
></div>
<script src="html2canvas.js"></script>
</body>
</html>
|