Delete gradio.ipynb

gradio.ipynb

@@ -1,317 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2024-06-28 00:45:26,702] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "import sys\n",
-    "import os \n",
-    "import tqdm\n",
-    "sys.path.append(os.path.abspath(os.path.join(\"\", \"..\")))\n",
-    "import torch\n",
-    "import gc\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "from PIL import Image\n",
-    "from utils import load_models, save_model_w2w, save_model_for_diffusers\n",
-    "from sampling import sample_weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "global device\n",
-    "global generator \n",
-    "global unet\n",
-    "global vae \n",
-    "global text_encoder\n",
-    "global tokenizer\n",
-    "global noise_scheduler\n",
-    "device = \"cuda:0\"\n",
-    "generator = torch.Generator(device=device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean = torch.load(\"files/mean.pt\").bfloat16().to(device)\n",
-    "std = torch.load(\"files/std.pt\").bfloat16().to(device)\n",
-    "v = torch.load(\"files/V.pt\").bfloat16().to(device)\n",
-    "proj = torch.load(\"files/proj_1000pc.pt\").bfloat16().to(device)\n",
-    "df = torch.load(\"files/identity_df.pt\")\n",
-    "weight_dimensions = torch.load(\"files/weight_dimensions.pt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.79it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "unet, vae, text_encoder, tokenizer, noise_scheduler = load_models(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "global network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sample_model():\n",
-    "    global unet\n",
-    "    del unet\n",
-    "    global network\n",
-    "    unet, _, _, _, _ = load_models(device)\n",
-    "    network = sample_weights(unet, proj, mean, std, v[:, :1000], device, factor = 1.00)\n",
-    " \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@torch.no_grad()\n",
-    "def inference( prompt, negative_prompt, guidance_scale, ddim_steps, seed):\n",
-    "    global device\n",
-    "    global generator \n",
-    "    global unet\n",
-    "    global vae \n",
-    "    global text_encoder\n",
-    "    global tokenizer\n",
-    "    global noise_scheduler\n",
-    "    generator = generator.manual_seed(seed)\n",
-    "    latents = torch.randn(\n",
-    "        (1, unet.in_channels, 512 // 8, 512 // 8),\n",
-    "        generator = generator,\n",
-    "        device = device\n",
-    "    ).bfloat16()\n",
-    "   \n",
-    "\n",
-    "    text_input = tokenizer(prompt, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "    text_embeddings = text_encoder(text_input.input_ids.to(device))[0]\n",
-    "\n",
-    "    max_length = text_input.input_ids.shape[-1]\n",
-    "    uncond_input = tokenizer(\n",
-    "                            [negative_prompt], padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n",
-    "                        )\n",
-    "    uncond_embeddings = text_encoder(uncond_input.input_ids.to(device))[0]\n",
-    "    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n",
-    "    noise_scheduler.set_timesteps(ddim_steps) \n",
-    "    latents = latents * noise_scheduler.init_noise_sigma\n",
-    "    \n",
-    "    for i,t in enumerate(tqdm.tqdm(noise_scheduler.timesteps)):\n",
-    "        latent_model_input = torch.cat([latents] * 2)\n",
-    "        latent_model_input = noise_scheduler.scale_model_input(latent_model_input, timestep=t)\n",
-    "        with network:\n",
-    "            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings, timestep_cond= None).sample\n",
-    "        #guidance\n",
-    "        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
-    "        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n",
-    "        latents = noise_scheduler.step(noise_pred, t, latents).prev_sample\n",
-    "    \n",
-    "    latents = 1 / 0.18215 * latents\n",
-    "    image = vae.decode(latents).sample\n",
-    "    image = (image / 2 + 0.5).clamp(0, 1)\n",
-    "    image = image.detach().cpu().float().permute(0, 2, 3, 1).numpy()[0]\n",
-    "\n",
-    "    image = Image.fromarray((image * 255).round().astype(\"uint8\"))\n",
-    "\n",
-    "    return [image] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7860\n",
-      "Running on public URL: https://bc89b27b9704787832.gradio.live\n",
-      "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"https://bc89b27b9704787832.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00,  8.95it/s]\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/gradio/routes.py\", line 437, in run_predict\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/gradio/blocks.py\", line 1352, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/gradio/blocks.py\", line 1077, in call_function\n",
-      "    prediction = await anyio.to_thread.run_sync(\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
-      "    return await get_async_backend().run_sync_in_worker_thread(\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/anyio/_backends/_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/anyio/_backends/_asyncio.py\", line 851, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"/home/amil/anaconda3/envs/dblora2/lib/python3.8/site-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n",
-      "    return func(*args, **kwargs)\n",
-      "  File \"/tmp/ipykernel_2844069/1186401021.py\", line 12, in inference\n",
-      "    (1, unet.in_channels, 512 // 8, 512 // 8),\n",
-      "NameError: name 'unet' is not defined\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "css = ''\n",
-    "with gr.Blocks(css=css) as demo:\n",
-    "    gr.Markdown(\"# <em>weights2weights</em> Demo\")\n",
-    "    gr.Markdown(\"Demo for the [h94/IP-Adapter-FaceID model](https://huggingface.co/h94/IP-Adapter-FaceID) - Generate AI images with your own face - Non-commercial license\")\n",
-    "    with gr.Row():\n",
-    "        with gr.Column():\n",
-    "            files = gr.Files(\n",
-    "                        label=\"Upload a photo of your face to invert, or sample a new model\",\n",
-    "                        file_types=[\"image\"]\n",
-    "                    )\n",
-    "            uploaded_files = gr.Gallery(label=\"Your images\", visible=False, columns=5, rows=1, height=125)\n",
-    "\n",
-    "            sample = gr.Button(\"Sample New Model\")\n",
-    "\n",
-    "            with gr.Column(visible=False) as clear_button:\n",
-    "                remove_and_reupload = gr.ClearButton(value=\"Remove and upload new ones\", components=files, size=\"sm\")\n",
-    "            prompt = gr.Textbox(label=\"Prompt\",\n",
-    "                       info=\"Make sure to include 'sks person'\" ,\n",
-    "                       placeholder=\"sks person\", \n",
-    "                       value=\"sks person\")\n",
-    "            negative_prompt = gr.Textbox(label=\"Negative Prompt\", placeholder=\"low quality, blurry, unfinished, cartoon\", value=\"low quality, blurry, unfinished, cartoon\")\n",
-    "            seed = gr.Number(value=5, precision=0, label=\"Seed\", interactive=True)\n",
-    "            cfg = gr.Slider(label=\"CFG\", value=3.0, step=0.1, minimum=0, maximum=10, interactive=True)\n",
-    "            steps = gr.Slider(label=\"Inference Steps\", precision=0, value=50, step=1, minimum=0, maximum=100, interactive=True)\n",
-    "\n",
-    "\n",
-    "            submit = gr.Button(\"Submit\")\n",
-    "\n",
-    "        with gr.Column():\n",
-    "            gallery = gr.Gallery(label=\"Generated Images\")\n",
-    "\n",
-    "        sample.click(fn=sample_model)\n",
-    "        \n",
-    "        submit.click(fn=inference,\n",
-    "                    inputs=[prompt, negative_prompt, cfg, steps, seed],\n",
-    "                    outputs=gallery)\n",
-    "            \n",
-    "\n",
-    "\n",
-    "\n",
-    "            \n",
-    "            \n",
-    "demo.launch(share=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "dblora2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}