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"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
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"source": [
"# Run Dreambooth fine-tuned models for Stable Diffusion using d🧨ffusers \n",
"\n",
"This notebook allows you to run Stable Diffusion concepts trained via Dreambooth using 🤗 Hugging Face [🧨 Diffusers 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",
"\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"
],
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"source": [
"#@title Install and import requirements\n",
"!pip install -qqq diffusers==0.11.1 transformers gradio ftfy accelerate\n",
"\n",
"import diffusers\n",
"import gradio\n",
"from PIL import Image\n",
"def image_grid(imgs, rows, cols):\n",
" assert len(imgs) == rows*cols\n",
"\n",
" w, h = imgs[0].size\n",
" grid = Image.new('RGB', size=(cols*w, rows*h))\n",
" 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": [
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]
}
},
"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,
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]
}
},
"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",
" `;\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