finegrain_ai_image_enhancer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyObiPtRiqRI1JFN04wgNkZz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VimukthiRandika1997/Computer-Vision/blob/dev/finegrain_ai_image_enhancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "raks4DoH7GsD",
        "outputId": "b55efd8f-f61b-47f2-aeec-9ecab5bf12b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/finegrain-ai/refiners@a5d3c2971b84f6faa4762b1cf5a07f4f812bb1f5\n",
            "  Cloning https://github.com/finegrain-ai/refiners (to revision a5d3c2971b84f6faa4762b1cf5a07f4f812bb1f5) to /tmp/pip-req-build-bf03oeo3\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/finegrain-ai/refiners /tmp/pip-req-build-bf03oeo3\n",
            "  Running command git rev-parse -q --verify 'sha^a5d3c2971b84f6faa4762b1cf5a07f4f812bb1f5'\n",
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            "Building wheels for collected packages: refiners\n",
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            "  Created wheel for refiners: filename=refiners-0.4.0-py3-none-any.whl size=1537313 sha256=e401c6a31c3fae7c8230f7216d9de562908e3433ee91c1713a387b05dcf4613a\n",
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            "\u001b[?25hInstalling collected packages: pydub, websockets, tomlkit, semantic-version, ruff, python-multipart, orjson, h11, ffmpy, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio, gradio_imageslider\n",
            "Successfully installed aiofiles-23.2.1 fastapi-0.115.0 ffmpy-0.4.0 gradio-4.44.1 gradio-client-1.3.0 gradio_imageslider-0.0.20 h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 orjson-3.10.7 pydub-0.25.1 python-multipart-0.0.12 ruff-0.6.9 semantic-version-2.10.0 starlette-0.38.6 tomlkit-0.12.0 uvicorn-0.31.0 websockets-12.0\n",
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            "Downloading spaces-0.28.3-py3-none-any.whl (18 kB)\n",
            "Installing collected packages: spaces\n",
            "Successfully installed spaces-0.28.3\n",
            "/bin/bash: line 1: 2.0.0: No such file or directory\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/finegrain-ai/refiners@a5d3c2971b84f6faa4762b1cf5a07f4f812bb1f5\n",
        "!pip install gradio_imageslider==0.0.20\n",
        "!pip install spaces==0.28.3\n",
        "!pip install numpy<2.0.0\n",
        "!pip install pillow>=10.4.0\n",
        "!pip install pillow-heif>=0.18.0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 app.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYHHQzFg7tng",
        "outputId": "f7000bf5-4260-4eec-cf02-c94e3474d2e8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model.safetensors: 100% 1.72G/1.72G [00:59<00:00, 28.9MB/s]\n",
            "model.safetensors: 100% 246M/246M [00:02<00:00, 82.5MB/s]\n",
            "model.safetensors: 100% 167M/167M [00:01<00:00, 164MB/s]\n",
            "model.safetensors: 100% 723M/723M [00:09<00:00, 78.6MB/s]\n",
            "4x-UltraSharp.pth: 100% 67.0M/67.0M [00:00<00:00, 79.3MB/s]\n",
            "JuggernautNegative-neg.pt: 100% 13.3k/13.3k [00:00<00:00, 54.6MB/s]\n",
            "more_details.safetensors: 100% 9.55M/9.55M [00:00<00:00, 66.9MB/s]\n",
            "SDXLrender_v2.0.safetensors: 100% 181M/181M [00:00<00:00, 263MB/s]\n",
            "Will cache examples in '/content/gradio_cached_examples/22' directory at first use. \n",
            "\n",
            "\n",
            "Running on local URL:  http://127.0.0.1:7860\n",
            "Running on public URL: https://4950554fb9c46ee6e3.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",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/queueing.py\", line 536, in process_events\n",
            "    response = await route_utils.call_process_api(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/route_utils.py\", line 322, in call_process_api\n",
            "    output = await app.get_blocks().process_api(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1935, in process_api\n",
            "    result = await self.call_function(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1520, in call_function\n",
            "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py\", line 33, in run_sync\n",
            "    return await get_asynclib().run_sync_in_worker_thread(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
            "    return await future\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 807, in run\n",
            "    result = context.run(func, *args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/utils.py\", line 826, in wrapper\n",
            "    response = f(*args, **kwargs)\n",
            "  File \"/content/app.py\", line 143, in process\n",
            "    enhanced_image = enhancer.upscale(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_upscaler.py\", line 343, in upscale\n",
            "    return self.diffuse_targets(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_upscaler.py\", line 245, in diffuse_targets\n",
            "    x = self(x, noise=noise, step=step, targets=targets)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/multi_diffusion.py\", line 112, in __call__\n",
            "    view = self.diffuse_target(x=view, step=step, target=target)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_upscaler.py\", line 354, in diffuse_target\n",
            "    return self.diffuse_upscaler_target(x=x, step=step, target=target)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_upscaler.py\", line 167, in diffuse_upscaler_target\n",
            "    return self.sd(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py\", line 72, in __call__\n",
            "    return super().__call__(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/foundationals/latent_diffusion/model.py\", line 142, in forward\n",
            "    unconditional_prediction, conditional_prediction = self.unet(latents).chunk(2)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/fluxion/layers/chain.py\", line 249, in forward\n",
            "    result = self._call_layer(layer, name, *intermediate_args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/refiners/fluxion/layers/chain.py\", line 243, in _call_layer\n",
            "    raise ChainError(message) from None\n",
            "refiners.fluxion.layers.chain.ChainError: \n",
            "\n",
            "OutOfMemoryError:\n",
            "   File \"/usr/local/lib/python3.10/dist-packages/refiners/fluxion/layers/attentions.py\", line 29, in scaled_dot_product_attention\n",
            "    return _scaled_dot_product_attention(\n",
            "CUDA out of memory. Tried to allocate 7.75 GiB. GPU 0 has a total capacity of 14.75 GiB of which 3.99 GiB is free. Process 28291 has 10.76 GiB memory in use. Of the allocated memory 10.58 GiB is allocated by PyTorch, and 54.67 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n",
            "---------------\n",
            "(CHAIN) SelfAttention(embedding_dim=320, num_heads=8, inner_dim=320, use_bias=False)\n",
            "    ├── (PAR)\n",
            "    │   └── Identity() (x3)\n",
            "    ├── (DISTR)\n",
            "    │   └── Linear(in_features=320, out_features=320, device=cuda:0, dtype=bfloat16) (x3)\n",
            "    ├── >>> ScaledDotProductAttention(num_heads=8) | SD1UNet.Controlnet.DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.ScaledDotProductAttention\n",
            "    └── Linear(in_features=320, out_features=320, device=cuda:0, dtype=bfloat16)\n",
            "0: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-7.75, max=7.75, mean=0.03, std=1.34, norm=4302.18, grad=False)\n",
            "1: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-6.84, max=7.31, mean=0.06, std=1.35, norm=4328.33, grad=False)\n",
            "2: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-3.45, max=3.47, mean=0.00, std=0.55, norm=1756.26, grad=False)\n",
            "---------------\n",
            "(RES) Residual()\n",
            "    ├── LayerNorm(normalized_shape=(320,), device=cuda:0, dtype=bfloat16)\n",
            "    └── >>> (CHAIN) SelfAttention(embedding_dim=320, num_heads=8, inner_dim=320, use_bias=False) | SD1UNet.Controlnet.DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention\n",
            "        ├── (PAR)\n",
            "        │   └── Identity() (x3)\n",
            "        ├── (DISTR)\n",
            "        │   └── Linear(in_features=320, out_features=320, device=cuda:0, dtype=bfloat16) (x3)\n",
            "        ├── ScaledDotProductAttention(num_heads=8)\n",
            "        └── Linear(in_features=320, out_features=320, device=cuda:0, dtype=bfloat16)\n",
            "0: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-2.67, max=3.08, mean=0.00, std=0.51, norm=1643.53, grad=False)\n",
            "---------------\n",
            "(CHAIN) CrossAttentionBlock(embedding_dim=320, context_embedding_dim=768, context_key=clip_text_embedding, num_heads=8, use_bias=False)\n",
            "    ├── >>> (RES) Residual() | SD1UNet.Controlnet.DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1 #1\n",
            "    │   ├── LayerNorm(normalized_shape=(320,), device=cuda:0, dtype=bfloat16)\n",
            "    │   └── (CHAIN) SelfAttention(embedding_dim=320, num_heads=8, inner_dim=320, use_bias=False)\n",
            "    │       ├── (PAR) ...\n",
            "    │       ├── (DISTR) ...\n",
            "    │       ├── ScaledDotProductAttention(num_heads=8)\n",
            "    │       └── Linear(in_features=320, out_features=320, device=cuda:0, dtype=bfloat16)\n",
            "    ├── (RES) Residual() #2\n",
            "    │   ├── LayerNorm(normalized_shape=(320,), device=cuda:0, dtype=bfloat16)\n",
            "    │   ├── (PAR)\n",
            "0: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-2.06, max=2.38, mean=-0.00, std=0.21, norm=688.11, grad=False)\n",
            "---------------\n",
            "(CHAIN)\n",
            "    └── >>> (CHAIN) CrossAttentionBlock(embedding_dim=320, context_embedding_dim=768, context_key=clip_text_embedding, num_heads=8, use_bias=False) | SD1UNet.Controlnet.DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock\n",
            "        ├── (RES) Residual() #1\n",
            "        │   ├── LayerNorm(normalized_shape=(320,), device=cuda:0, dtype=bfloat16)\n",
            "        │   └── (CHAIN) SelfAttention(embedding_dim=320, num_heads=8, inner_dim=320, use_bias=False) ...\n",
            "        ├── (RES) Residual() #2\n",
            "        │   ├── LayerNorm(normalized_shape=(320,), device=cuda:0, dtype=bfloat16)\n",
            "        │   ├── (PAR) ...\n",
            "        │   └── (CHAIN) Attention(embedding_dim=320, num_heads=8, key_embedding_dim=768, value_embedding_dim=768, inner_dim=320, use_bias=False) ...\n",
            "        └── (RES) Residual() #3\n",
            "            ├── LayerNorm(normalized_shape=(320,), device=cuda:0, dtype=bfloat16)\n",
            "0: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-2.06, max=2.38, mean=-0.00, std=0.21, norm=688.11, grad=False)\n",
            "---------------\n",
            "(RES) CLIPLCrossAttention(channels=320)\n",
            "    ├── (CHAIN) #1\n",
            "    │   ├── GroupNorm(num_groups=32, eps=1e-06, channels=320, device=cuda:0, dtype=bfloat16)\n",
            "    │   ├── Conv2d(in_channels=320, out_channels=320, kernel_size=(1, 1), device=cuda:0, dtype=bfloat16)\n",
            "    │   ├── (CHAIN) StatefulFlatten(start_dim=2)\n",
            "    │   │   ├── SetContext(context=flatten, key=sizes)\n",
            "    │   │   └── Flatten(start_dim=2)\n",
            "    │   └── Transpose(dim0=1, dim1=2)\n",
            "    ├── >>> (CHAIN) | SD1UNet.Controlnet.DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2 #2\n",
            "    │   └── (CHAIN) CrossAttentionBlock(embedding_dim=320, context_embedding_dim=768, context_key=clip_text_embedding, num_heads=8, use_bias=False)\n",
            "    │       ├── (RES) Residual() #1 ...\n",
            "0: Tensor(shape=(2, 16128, 320), dtype=bfloat16, device=cuda:0, min=-2.06, max=2.38, mean=-0.00, std=0.21, norm=688.11, grad=False)\n",
            "---------------\n",
            "(CHAIN)\n",
            "    ├── (SUM) ResidualBlock(in_channels=320, out_channels=320)\n",
            "    │   ├── (CHAIN)\n",
            "    │   │   ├── GroupNorm(num_groups=32, channels=320, device=cuda:0, dtype=bfloat16) #1\n",
            "    │   │   ├── SiLU() #1\n",
            "    │   │   ├── (SUM) RangeAdapter2d(channels=320, embedding_dim=1280) ...\n",
            "    │   │   ├── GroupNorm(num_groups=32, channels=320, device=cuda:0, dtype=bfloat16) #2\n",
            "    │   │   ├── SiLU() #2\n",
            "    │   │   └── Conv2d(in_channels=320, out_channels=320, kernel_size=(3, 3), padding=(1, 1), device=cuda:0, dtype=bfloat16)\n",
            "    │   └── Identity()\n",
            "    ├── >>> (RES) CLIPLCrossAttention(channels=320) | SD1UNet.Controlnet.DownBlocks.Chain_2.CLIPLCrossAttention\n",
            "0: Tensor(shape=(2, 320, 144, 112), dtype=bfloat16, device=cuda:0, min=-8.38, max=6.22, mean=-0.09, std=0.61, norm=1995.25, grad=False)\n",
            "---------------\n",
            "(CHAIN) DownBlocks(in_channels=4)\n",
            "    ├── (CHAIN) #1\n",
            "    │   ├── Conv2d(in_channels=4, out_channels=320, kernel_size=(3, 3), padding=(1, 1), device=cuda:0, dtype=bfloat16)\n",
            "    │   ├── (RES) Residual()\n",
            "    │   │   ├── UseContext(context=controlnet, key=condition_tile)\n",
            "    │   │   └── (CHAIN) ConditionEncoder() ...\n",
            "    │   └── (PASS)\n",
            "    │       ├── Conv2d(in_channels=320, out_channels=320, kernel_size=(1, 1), device=cuda:0, dtype=bfloat16)\n",
            "    │       └── Lambda(_store_residual(x: torch.Tensor))\n",
            "    ├── >>> (CHAIN) (x2) | SD1UNet.Controlnet.DownBlocks.Chain_2 #2\n",
            "    │   ├── (SUM) ResidualBlock(in_channels=320, out_channels=320)\n",
            "0: Tensor(shape=(2, 320, 144, 112), dtype=bfloat16, device=cuda:0, min=-5.44, max=5.25, mean=-0.00, std=0.39, norm=1264.76, grad=False)\n",
            "---------------\n",
            "(PASS) Controlnet(name=tile, scale=0.6)\n",
            "    ├── (PASS) TimestepEncoder()\n",
            "    │   ├── UseContext(context=diffusion, key=timestep)\n",
            "    │   ├── (CHAIN) RangeEncoder(sinusoidal_embedding_dim=320, embedding_dim=1280)\n",
            "    │   │   ├── Lambda(compute_sinusoidal_embedding(x: jaxtyping.Int[Tensor, '*batch 1']) -> jaxtyping.Float[Tensor, '*batch 1 embedding_dim'])\n",
            "    │   │   ├── Converter(set_device=False)\n",
            "    │   │   ├── Linear(in_features=320, out_features=1280, device=cuda:0, dtype=bfloat16) #1\n",
            "    │   │   ├── SiLU()\n",
            "    │   │   └── Linear(in_features=1280, out_features=1280, device=cuda:0, dtype=bfloat16) #2\n",
            "    │   └── SetContext(context=range_adapter, key=timestep_embedding_tile)\n",
            "    ├── Slicing(dim=1, end=4)\n",
            "0: Tensor(shape=(2, 4, 144, 112), dtype=bfloat16, device=cuda:0, min=-3.75, max=4.19, mean=0.03, std=0.87, norm=311.07, grad=False)\n",
            "---------------\n",
            "(CHAIN) SD1UNet(in_channels=4)\n",
            "    ├── >>> (PASS) Controlnet(name=tile, scale=0.6) | SD1UNet.Controlnet\n",
            "    │   ├── (PASS) TimestepEncoder()\n",
            "    │   │   ├── UseContext(context=diffusion, key=timestep)\n",
            "    │   │   ├── (CHAIN) RangeEncoder(sinusoidal_embedding_dim=320, embedding_dim=1280) ...\n",
            "    │   │   └── SetContext(context=range_adapter, key=timestep_embedding_tile)\n",
            "    │   ├── Slicing(dim=1, end=4)\n",
            "    │   ├── (CHAIN) DownBlocks(in_channels=4)\n",
            "    │   │   ├── (CHAIN) #1 ...\n",
            "    │   │   ├── (CHAIN) (x2) #2 ...\n",
            "    │   │   ├── (CHAIN) #3 ...\n",
            "0: Tensor(shape=(2, 4, 144, 112), dtype=bfloat16, device=cuda:0, min=-3.75, max=4.19, mean=0.03, std=0.87, norm=311.07, grad=False)\n",
            "Keyboard interruption in main thread... closing server.\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 2709, in block_thread\n",
            "    time.sleep(0.1)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/app.py\", line 289, in <module>\n",
            "    demo.launch(share=True)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 2614, in launch\n",
            "    self.block_thread()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 2713, in block_thread\n",
            "    self.server.close()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/http_server.py\", line 68, in close\n",
            "    self.thread.join(timeout=5)\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 1100, in join\n",
            "    self._wait_for_tstate_lock(timeout=max(timeout, 0))\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 1116, in _wait_for_tstate_lock\n",
            "    if lock.acquire(block, timeout):\n",
            "KeyboardInterrupt\n",
            "Killing tunnel 127.0.0.1:7860 <> https://4950554fb9c46ee6e3.gradio.live\n",
            "^C\n"
          ]
        }
      ]
    }
  ]
}