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library](https://github.com/huggingface/diffusers). \n", + "\n", + "Train your own using [here](#) and navigate the [public library concepts](#) to pick yours. You may also want to use the [Spaces](#) to browse the library\n", + "\n", + "\n", + "![Dreambooth Example](https://dreambooth.github.io/DreamBooth_files/teaser_static.jpg)\n", + "_By using just 3-5 images you can teach new concepts to Stable Diffusion and personalize the model on your own images_ \n", + "\n", + "Differently from Textual Inversion, this approach trains the whole model, which can yield better results to the cost of bigger models.\n", + "\n" + ], + "metadata": { + "id": "0tb1ywtV_EEO" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "id": "KFOytKQG-gGY", + "outputId": "4fcfac01-5914-4da2-cfdd-15a7efce2b2b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m524.9/524.9 KB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + 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grid_w, grid_h = grid.size\n", + " \n", + " for i, img in enumerate(imgs):\n", + " grid.paste(img, box=(i%cols*w, i//cols*h))\n", + " return grid" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Login to the Hugging Face Hub\n", + "#@markdown Optional step, do it if you want to run private concepts\n", + "from huggingface_hub import notebook_login\n", + "!git config --global credential.helper store\n", + "\n", + "notebook_login()" + ], + "metadata": { + "cellView": "form", + "id": "cG56KJga44EM", + "outputId": "57167967-e2e3-4a66-fb43-4e5650fe14fd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359, + "referenced_widgets": [ + "f99c8fd551364f9897cd0d0dd0c83a15", + "0b97363564504105aef130e312453fc7", + "a882a90459814a23bbb742891baf75e3", + "e2d25f306744497881e2edf482a1d38d", + "49ea3bc3b2c54dc280a85c38c0d98190", + "4a34db8873f14d88beb7563521865e40", + "ac3d60bdca214867b6505e5f22e55f77", + "41364862b2984e928a252257ca193ef2", + "5aa7c8f52b0c468cb0b77db6a38e5944", + "7d255e2766a54598a3ba91d2ed626a70", + "537e10605e164aa390f45d17bf391098", + "194463ebf71c47d9810a3aac12446f27", + "18e5bdadb79e45e38e92c0d4f0fd6e17", + "898d8c8d921d4366bc6970c1dd453b18", + "c49c7372e7564e90bd515746d8471fbf", + "ee368dece16e4a57a24cdc630735576c", + "7779ef31eb6e41f8aa5c498585a2c557" + ] + } + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
Stable Diffusion Dreambooth - Run Concept\")\n", + " with gr.Row():\n", + " with gr.Column():\n", + " prompt = gr.Textbox(label=\"prompt\")\n", + " samples = gr.Slider(label=\"Samples\",value=1)\n", + " run = gr.Button(value=\"Run\")\n", + " with gr.Column():\n", + " gallery = gr.Gallery(show_label=False)\n", + "\n", + " run.click(inference, inputs=[prompt,samples], outputs=gallery)\n", + " gr.Examples([[\"a photo of sks toy riding a bicycle\", 1,1]], [prompt,samples], gallery, inference, cache_examples=False)\n", + "\n", + "\n", + "demo.launch(debug=True)" + ], + "metadata": { + "cellView": "form", + "id": "sUE8A7znAlvM", + "outputId": "9697ea3a-3cb6-42e4-dfc9-0a5ab1d70d71", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "4d73f8f4ccc44e49816168ecce450fa9", + "77b646b9d6974468b54fd3add40c2fa8", + "d7aa25036968410c98e36c6b987e07d9", + "16ed2dbd6f7d48328fb31d6e06126bd8", + "acf3ed7564394a348140466f3eb8d305", + "11566248b50a4e3e8121e7637a54f8d5", + "3e5f15d5f74543388601c673ca5d6e58", + "4560df40cdc14b5c8cbca8fa6386aa48", + "aae508b64a61450487e035887729e9aa", + "c51e617a28244f5591003e8c8ec1e9c7", + "7d9a07c8c09243b68fccdf5be48744fc", + "cf93681237c94f98852b1d8e450abe63", + "dabe55e6f79b4cd6b273da1d4f61ebfb", + "11eb14cf557444dfbb89219427ccee75", + "503222c6f9934e41902586810ee67b45", + "12d1d4c9bb924042bb24884827cd1dcf", + "887eff67544b493cad7b778690569f32", + "1557a5bde8f04c99ad6b4d27bf1e77c0", + "df35bf5397204086806ac5064f7b2f28", + "a2a0823aadac419fbbd448006c912a45", + "6d6e4f4faff54a5482aeaefd985c0186", + "08a25a0472004388b3037ede8eca7795", + "26fc314f97204673bf9ce61d8ebfb06f", + "6514dce0fe734ed8bb0ad2bc30064ece", + "cc59a974305f45ea9f03051f014d969c", + "432ccca4cea84d858e1c59b7c98f0949", + "b4fa575482b04f0493bed2340ce23524", + "378ac0ef1640433da568349697356e63", + "4deddbd7cd9b4ac397c16f00c83798c7", + "247528410cb7466d8b8ff9d330c53073", + "8a85901a774d449db865bd2c4d6cd3aa", + "068863b48de041489b012409dca151ba", + "7613d2a4659b4dbf94c6857dd3e0d16a", + "f2834efa17d34d4f9025e2f89ba6a139", + "faad12d47ef647c9a1e614a9ad99327f", + "1619057d5d8a43d29d370566439bdee0", + "535527ae9bf54819aa4e73fc97750e7f", + "967ded24ca2345f290a60dfdb8678229", + "e8391e5e4b014f3c82fa1cf10e18c3aa", + "aa08c637629b45728918adb170587405", + "743018a809d74669bb7f7f84d13a28af", + "fd1ba1d18aac4dca8c0770b3689a7b2c", + "6a6f30620cc54b8797c1452021ae7d61", + "90d43de659c34c1fa98ce00778aca0c2", + "e8abda152e9a4381b4f4e04132e9cb92", + "b1db2c9c512a4498afb3d292b3f5a8ee", + "a333484ec4f8465387fd74060e8341d3", + "b41836c237a34ac09cb141f3a7772658", + "7b354b2f9b7b43d7b071af2aac9e315b", + "04afe58ec1cc412a9782a0c66ca01b9c", + "76abd49530a24a64ae1f832190c3430e", + "a5fb9b89956b42fe9a95016870a79bb0", + "cb32260ba19b4aa18d9c752d38fab12b", + "451c8e302cd04c86b6845a6df24698cb", + "7006ff52294f4dca9bdc8591578f1c89", + "0df07f06036947dc97f311728921c4e9", + "7045f7f1982240368257680cc723d736", + "41b9c3eecf7e4b21a86afc6107f7a898", + "013a776b588144818481ec07b14b7ec6", + "db2388459588452283e185b1f825d479", + "42d78114fdae47bb9985ce6ef9b84b4a", + "2aea87ab20f44811a7078c727287ef4f", + "2c1b776d14e34b8caf9500d534230c78", + "7443c5d8b80a40c5b6bea410d19e7bd5", + "35fb0371aad34e439c2b1ae7a40be260", + "69b614ee9e8149a09313655b40fcf6d6", + "83b5cac72fb24f04832a356035d27345", + "3a8cfb9a3c6544c384a390995c507e9b", + "e715d77117054aceb8353863434c85a1", + "c98fe035337d42918c622670e2e966cd", + "7ce80bbf92cd4343b87371ae57352964", + "e6fd6eeb82b048758008d1a4fae9a701", + "fddae4d308724d588e2ba3cbd959555e", + "d1f071d43e2c4a2eb39d535cef652306", + "3a851c61e5a343d4ba701fda7c0461e9", + "9e422932162c497a9417abbd0754d577", + "cf9544ac7e7b48cfbf6c3d13ca8b1bd5" + ] + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n", + "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "(async (port, path, width, height, cache, element) => {\n", + " if (!google.colab.kernel.accessAllowed && !cache) {\n", + " return;\n", + " }\n", + " element.appendChild(document.createTextNode(''));\n", + " const url = await google.colab.kernel.proxyPort(port, {cache});\n", + "\n", + " const external_link = document.createElement('div');\n", + " external_link.innerHTML = `\n", + "
\n", + " Running on \n", + " https://localhost:${port}${path}\n", + " \n", + "
\n", + " `;\n", + " element.appendChild(external_link);\n", + "\n", + " const iframe = document.createElement('iframe');\n", + " iframe.src = new URL(path, url).toString();\n", + " iframe.height = height;\n", + " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n", + " iframe.width = width;\n", + " iframe.style.border = 0;\n", + " element.appendChild(iframe);\n", + " })(7860, \"/\", \"100%\", 500, false, window.element)" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/50 [00:00\", line 9, in inference\n", + " images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images\n", + " File \"/usr/local/lib/python3.9/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n", + " return func(*args, **kwargs)\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py\", line 537, in __call__\n", + " latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_pndm.py\", line 221, in step\n", + " return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_pndm.py\", line 337, in step_plms\n", + " prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_pndm.py\", line 371, in _get_prev_sample\n", + " alpha_prod_t = self.alphas_cumprod[timestep]\n", + "IndexError: index 1001 is out of bounds for dimension 0 with size 1000\n", + "Traceback (most recent call last):\n", + " File \"/usr/local/lib/python3.9/dist-packages/gradio/routes.py\", line 393, in run_predict\n", + " output = await app.get_blocks().process_api(\n", + " File \"/usr/local/lib/python3.9/dist-packages/gradio/blocks.py\", line 1108, in process_api\n", + " result = await self.call_function(\n", + " File \"/usr/local/lib/python3.9/dist-packages/gradio/blocks.py\", line 915, in call_function\n", + " prediction = await anyio.to_thread.run_sync(\n", + " File \"/usr/local/lib/python3.9/dist-packages/anyio/to_thread.py\", line 31, in run_sync\n", + " return await get_asynclib().run_sync_in_worker_thread(\n", + " File \"/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n", + " return await future\n", + " File \"/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py\", line 867, in run\n", + " result = context.run(func, *args)\n", + " File \"\", line 9, in inference\n", + " images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images\n", + " File \"/usr/local/lib/python3.9/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n", + " return func(*args, **kwargs)\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py\", line 537, in __call__\n", + " latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_pndm.py\", line 221, in step\n", + " return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_pndm.py\", line 337, in step_plms\n", + " prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)\n", + " File \"/usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_pndm.py\", line 396, in _get_prev_sample\n", + " sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff\n", + "TypeError: unsupported operand type(s) for *: 'Tensor' and 'NoneType'\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/50 [00:00