diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 768e729c7..7ddf86478 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -7,7 +7,7 @@ on:
 
 jobs:
   lint_and_typecheck:
-    if: ${{ github.event.name == 'push' || github.event.label.name == 'run-ci' }}
+    if: ${{ github.event_name == 'push' || github.event.label.name == 'run-ci' }}
     runs-on: ubuntu-latest
 
     steps:
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 000000000..6b8d160ac
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,20 @@
+name: Deploy docs to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    name: Deploy docs
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout main
+        uses: actions/checkout@v2
+
+      - name: Deploy MkDocs
+        uses: mhausenblas/mkdocs-deploy-gh-pages@master
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REQUIREMENTS: ./requirements.docs.txt
diff --git a/README.md b/README.md
index 620a37d72..dad2e4cf7 100644
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ from PIL import Image
 
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl import StableDiffusion_XL
 from refiners.foundationals.latent_diffusion import SDXLIPAdapter, SDXLT2IAdapter
-from refiners.fluxion.utils import manual_seed, image_to_tensor, load_from_safetensors
+from refiners.fluxion.utils import manual_seed, no_grad, image_to_tensor, load_from_safetensors
 
 # Load inputs
 init_image = Image.open("dropy_logo.png")
@@ -122,22 +122,13 @@ t2i_adapter.set_scale(0.8)
 sdxl.set_num_inference_steps(50)
 sdxl.set_self_attention_guidance(enable=True, scale=0.75)
 
-with torch.no_grad():
+with no_grad():
     # Note: default text prompts for IP-Adapter
     clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
         text="best quality, high quality", negative_text="monochrome, lowres, bad anatomy, worst quality, low quality"
     )
     clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(image_prompt))
-
-    negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
-    negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
-
-    clip_text_embedding = torch.cat(
-        (
-            torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
-            torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
-        )
-    )
+    ip_adapter.set_clip_image_embedding(clip_image_embedding)
     time_ids = sdxl.default_time_ids
 
     condition = image_to_tensor(condition_image.convert("RGB"), device=sdxl.device, dtype=sdxl.dtype)
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 000000000..9a1ce1c92
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,3 @@
+# Refiners - Docs
+
+WIP
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 000000000..ced975126
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,4 @@
+site_name: Refiners
+
+theme:
+  name: material
diff --git a/notebooks/basics.ipynb b/notebooks/basics.ipynb
new file mode 100644
index 000000000..04aca2524
--- /dev/null
+++ b/notebooks/basics.ipynb
@@ -0,0 +1,1574 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Refiners Demo\n",
+    "\n",
+    "This notebook aims to demonstrate the basics of using the [Refiners](https://github.com/finegrain-ai/refiners) micro-framework.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to run you need to have `Refiners` installed (uncomment the line below)\n",
+    "# %pip install git+https://github.com/finegrain-ai/refiners.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from refiners.fluxion import layers as fl, manual_seed\n",
+    "from torch import nn\n",
+    "\n",
+    "torch.set_grad_enabled(mode=False)\n",
+    "manual_seed(82570858)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The core idea of Refiners is to improve on the `Sequential` API of PyTorch.\n",
+    "\n",
+    "A `Sequential` is defined by:\n",
+    "\n",
+    "`Sequential([layer1, layer2, layer3])(x) = layer3(layer2(layer1(x)))`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "  (1): ReLU()\n",
+       "  (2): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "  (3): ReLU()\n",
+       "  (4): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "  (5): ReLU()\n",
+       "  (6): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       ")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Native PyTorch sequential\n",
+    "sequential = nn.Sequential(\n",
+    "    fl.Conv2d(3, 32, 3, padding=1),\n",
+    "    nn.ReLU(),\n",
+    "    fl.Conv2d(32, 32, 3, padding=1),\n",
+    "    nn.ReLU(),\n",
+    "    fl.Conv2d(32, 32, 3, padding=1),\n",
+    "    nn.ReLU(),\n",
+    "    fl.Conv2d(32, 32, 3, padding=1),\n",
+    ")\n",
+    "\n",
+    "sequential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(CHAIN)\n",
+       "    ├── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #1\n",
+       "    ├── ReLU() #1\n",
+       "    ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #2\n",
+       "    ├── ReLU() #2\n",
+       "    ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #3\n",
+       "    ├── ReLU() #3\n",
+       "    └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #4"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Same as above, but with a Fluxion Chain\n",
+    "chain = fl.Chain(\n",
+    "    fl.Conv2d(3, 32, 3, padding=1),\n",
+    "    fl.ReLU(),\n",
+    "    fl.Conv2d(32, 32, 3, padding=1),\n",
+    "    fl.ReLU(),\n",
+    "    fl.Conv2d(32, 32, 3, padding=1),\n",
+    "    fl.ReLU(),\n",
+    "    fl.Conv2d(32, 32, 3, padding=1),\n",
+    ")\n",
+    "\n",
+    "chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note here that the keys of the Chain are the names of the layers, whereas in PyTorch Sequential API, the keys are the indices of the layers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sequential keys:\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "\n",
+      "Chain keys:\n",
+      "Conv2d_1\n",
+      "ReLU_1\n",
+      "Conv2d_2\n",
+      "ReLU_2\n",
+      "Conv2d_3\n",
+      "ReLU_3\n",
+      "Conv2d_4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Sequential keys:\")\n",
+    "for key, _ in sequential.named_children():\n",
+    "    print(key)\n",
+    "\n",
+    "print(\"\\nChain keys:\")\n",
+    "for key, _ in chain.named_children():\n",
+    "    print(key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This choice is made because when a model is simple, it is easy to remember the indices of the layers, but when a model is complex, it is hard to remember the indices of the layers.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also improved on the Errors to showcase exactly where the error is coming from.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.randn(1, 4, 32, 32)\n",
+    "# uncomment to run\n",
+    "# sequential(x)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to run\n",
+    "# chain(x)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sequential is excellent for building basic and straightforward models, but most models don't have such a simple linear structure. \n",
+    "\n",
+    "Let's say you want to add a skip connection to the ConvNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 32, 32, 32])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ConvNet(\n",
+       "  (sequential): Sequential(\n",
+       "    (0): Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "    (1): ReLU()\n",
+       "    (2): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "    (3): ReLU()\n",
+       "    (4): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "    (5): ReLU()\n",
+       "    (6): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       "  )\n",
+       "  (skip): Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
+       ")"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ConvNet with a residual connection in PyTorch\n",
+    "\n",
+    "\n",
+    "class ConvNet(nn.Module):\n",
+    "    def __init__(self) -> None:\n",
+    "        super().__init__()\n",
+    "        self.sequential = nn.Sequential(\n",
+    "            fl.Conv2d(3, 32, 3, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "        )\n",
+    "        self.skip = fl.Conv2d(3, 32, 3, padding=1)\n",
+    "\n",
+    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+    "        return self.sequential(x) + self.skip(x)\n",
+    "\n",
+    "\n",
+    "convnet = ConvNet()\n",
+    "x = torch.randn(1, 3, 32, 32)\n",
+    "print(convnet(x).shape)\n",
+    "convnet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `repr` of this PyTorch is not declarative anymore: you cannot know how the model works.\n",
+    "\n",
+    "You can use Refiners' predefined `Chain` subclasses to handle such cases and build more complex models.  \n",
+    "\n",
+    "Let's start with the `Sum` class.\n",
+    "\n",
+    "`fl.Sum([layer1, layer2, layer3])(x) = layer1(x) + layer2(x) + layer3(x)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 32, 32, 32])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(SUM)\n",
+       "    ├── (CHAIN)\n",
+       "    │   ├── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #1\n",
+       "    │   ├── ReLU() #1\n",
+       "    │   ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #2\n",
+       "    │   ├── ReLU() #2\n",
+       "    │   ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #3\n",
+       "    │   ├── ReLU() #3\n",
+       "    │   └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #4\n",
+       "    └── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ConvNet with a residual connection in Refiners\n",
+    "convnet = fl.Sum(\n",
+    "    fl.Chain(\n",
+    "        fl.Conv2d(3, 32, 3, padding=1),\n",
+    "        fl.ReLU(),\n",
+    "        fl.Conv2d(32, 32, 3, padding=1),\n",
+    "        fl.ReLU(),\n",
+    "        fl.Conv2d(32, 32, 3, padding=1),\n",
+    "        fl.ReLU(),\n",
+    "        fl.Conv2d(32, 32, 3, padding=1),\n",
+    "    ),\n",
+    "    fl.Conv2d(3, 32, 3, padding=1),\n",
+    ")\n",
+    "\n",
+    "x = torch.randn(1, 3, 32, 32)\n",
+    "print(convnet(x).shape)\n",
+    "convnet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can subclass the basics `Chain` to give a name to improve declarativity. The `repr` will still tell you which kind of `Chain` it is and the name of the `Chain`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(SUM) ResidualNet()\n",
+       "    ├── (CHAIN) ConvNet()\n",
+       "    │   ├── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #1\n",
+       "    │   ├── ReLU() #1\n",
+       "    │   ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #2\n",
+       "    │   ├── ReLU() #2\n",
+       "    │   ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #3\n",
+       "    │   ├── ReLU() #3\n",
+       "    │   └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #4\n",
+       "    └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class ConvNet(fl.Chain):\n",
+    "    def __init__(self) -> None:\n",
+    "        super().__init__(\n",
+    "            fl.Conv2d(3, 32, 3, padding=1),\n",
+    "            fl.ReLU(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "            fl.ReLU(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "            fl.ReLU(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "class ResidualNet(fl.Sum):\n",
+    "    def __init__(self) -> None:\n",
+    "        super().__init__(\n",
+    "            ConvNet(),\n",
+    "            fl.Conv2d(32, 32, 3, padding=1),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "ResidualNet()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are some examples of `Chain` subclasses:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[-0.4723, -0.2809]]), tensor([[-0.5384,  0.6123,  0.2659,  0.0916]]))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run layers in parallel to output a tuple\n",
+    "par = fl.Parallel(\n",
+    "    fl.Linear(2, 2),\n",
+    "    fl.Linear(2, 4),\n",
+    ")\n",
+    "\n",
+    "x = torch.randn(1, 2)\n",
+    "par(x)  # (Linear_1(x), Linear_2(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.0949,  0.0749,  0.2607,  0.4013]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run layers in parallel and then concatenate the outputs\n",
+    "cat = fl.Concatenate(\n",
+    "    fl.Linear(2, 2),\n",
+    "    fl.Linear(2, 2),\n",
+    "    dim=-1,\n",
+    ")\n",
+    "\n",
+    "x = torch.randn(1, 2)\n",
+    "cat(x)  # Concatenate((Linear_1(x), Linear_2(x)), dim=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.1487, -1.1180]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run sequentially layers and then add the input\n",
+    "residual = fl.Residual(\n",
+    "    fl.Linear(2, 2),\n",
+    "    fl.Linear(2, 2),\n",
+    ")\n",
+    "\n",
+    "x = torch.randn(1, 2)\n",
+    "residual(x)  # Linear_2(Linear_1(x)) + x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now build something more complex such as a Vision Transformer. \n",
+    "\n",
+    "Let's start with the heart of a transformer layer: the Multi-Head Attention."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 197, 128])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(RES) Attention()\n",
+       "    ├── (PAR)\n",
+       "    │   └── Linear(in_features=128, out_features=128) (x3)\n",
+       "    ├── ScaledDotProductAttention(num_heads=8)\n",
+       "    └── Linear(in_features=128, out_features=128)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from refiners.fluxion.layers.attentions import ScaledDotProductAttention\n",
+    "\n",
+    "\n",
+    "class Attention(fl.Residual):\n",
+    "    def __init__(self, dim: int = 128, num_heads: int = 8) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.num_heads = num_heads\n",
+    "        super().__init__(\n",
+    "            fl.Parallel(\n",
+    "                fl.Linear(dim, dim),\n",
+    "                fl.Linear(dim, dim),\n",
+    "                fl.Linear(dim, dim),\n",
+    "            ),\n",
+    "            ScaledDotProductAttention(num_heads=num_heads),\n",
+    "            fl.Linear(dim, dim),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "x = torch.randn(1, 197, 128)\n",
+    "attention = Attention()\n",
+    "print(attention(x).shape)\n",
+    "attention"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(RES) FeedForward()\n",
+       "    ├── Linear(in_features=128, out_features=512) #1\n",
+       "    ├── SiLU()\n",
+       "    └── Linear(in_features=512, out_features=128) #2"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class FeedForward(fl.Residual):\n",
+    "    def __init__(self, dim: int = 128, inner_dim: int = 512) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.inner_dim = inner_dim\n",
+    "        super().__init__(\n",
+    "            fl.Linear(dim, inner_dim),\n",
+    "            fl.SiLU(),\n",
+    "            fl.Linear(inner_dim, dim),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "FeedForward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(CHAIN) TranformerLayer()\n",
+       "    ├── LayerNorm(normalized_shape=(128,)) #1\n",
+       "    ├── (RES) Attention()\n",
+       "    │   ├── (PAR)\n",
+       "    │   │   └── Linear(in_features=128, out_features=128) (x3)\n",
+       "    │   ├── ScaledDotProductAttention(num_heads=8)\n",
+       "    │   └── Linear(in_features=128, out_features=128)\n",
+       "    ├── LayerNorm(normalized_shape=(128,)) #2\n",
+       "    └── (RES) FeedForward()\n",
+       "        ├── Linear(in_features=128, out_features=512) #1\n",
+       "        ├── SiLU()\n",
+       "        └── Linear(in_features=512, out_features=128) #2"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class TranformerLayer(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self, dim: int = 128, num_heads: int = 8, inner_dim: int = 512\n",
+    "    ) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.num_heads = num_heads\n",
+    "        self.inner_dim = inner_dim\n",
+    "        super().__init__(\n",
+    "            fl.LayerNorm(dim),\n",
+    "            Attention(dim, num_heads),\n",
+    "            fl.LayerNorm(dim),\n",
+    "            FeedForward(dim, inner_dim),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "TranformerLayer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 196, 128])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class PatchEncoder(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self, in_channels: int = 3, dim: int = 128, patch_size: int = 16\n",
+    "    ) -> None:\n",
+    "        self.in_channels = in_channels\n",
+    "        self.dim = dim\n",
+    "        self.patch_size = patch_size\n",
+    "        super().__init__(\n",
+    "            fl.Conv2d(\n",
+    "                in_channels=in_channels,\n",
+    "                out_channels=dim,\n",
+    "                kernel_size=patch_size,\n",
+    "                stride=patch_size,\n",
+    "            ),\n",
+    "            fl.Reshape(-1, dim),  # Reshape always preserves the batch dimension\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "x = torch.randn(1, 3, 224, 224)\n",
+    "PatchEncoder()(x).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PositionalToken(fl.Residual):\n",
+    "    def __init__(self, num_patches: int = 196) -> None:\n",
+    "        self.num_patches = num_patches\n",
+    "        super().__init__(fl.Parameter(num_patches, 128))\n",
+    "\n",
+    "\n",
+    "class ClassToken(fl.Chain):\n",
+    "    def __init__(self, dim: int = 128) -> None:\n",
+    "        self.dim = dim\n",
+    "        super().__init__(fl.Parameter(1, dim))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have every bit to build a full Vision Transformer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(CHAIN) ViT()\n",
+      "    ├── (CAT)\n",
+      "    │   ├── (CHAIN) PatchEncoder()\n",
+      "    │   │   ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
+      "    │   │   └── Reshape(shape=(-1, 128))\n",
+      "    │   └── (CHAIN) ClassToken()\n",
+      "    │       └── Parameter(dims=(1, 128))\n",
+      "    ├── (RES) PositionalToken(num_patches=197)\n",
+      "    │   └── Parameter(dims=(197, 128))\n",
+      "    └── (CHAIN) Transformer()\n",
+      "        └── (CHAIN) TranformerLayer() (x4)\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #1\n",
+      "            ├── (RES) Attention()\n",
+      "            │   ├── (PAR)\n",
+      "            │   │   └── Linear(in_features=128, out_features=128) (x3)\n",
+      "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+      "            │   └── Linear(in_features=128, out_features=128)\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #2\n",
+      "            └── (RES) FeedForward()\n",
+      "                ├── Linear(in_features=128, out_features=512) #1\n",
+      "                ├── SiLU()\n",
+      "                └── Linear(in_features=512, out_features=128) #2\n",
+      "torch.Size([1, 197, 128])\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Transformer(fl.Chain):\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "class ViT(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        dim: int = 128,\n",
+    "        patch_size: int = 16,\n",
+    "        image_size: int = 224,\n",
+    "        num_layers: int = 4,\n",
+    "    ) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.patch_size = patch_size\n",
+    "        self.image_size = image_size\n",
+    "        self.num_layers = num_layers\n",
+    "        self.num_patches = (image_size // patch_size) ** 2 + 1\n",
+    "        super().__init__(\n",
+    "            fl.Concatenate(\n",
+    "                PatchEncoder(in_channels=3, dim=dim, patch_size=patch_size),\n",
+    "                ClassToken(dim=dim),\n",
+    "                dim=1,\n",
+    "            ),\n",
+    "            PositionalToken(num_patches=self.num_patches),\n",
+    "            Transformer(TranformerLayer(dim=dim) for _ in range(num_layers)),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "x = torch.randn(1, 3, 224, 224)\n",
+    "vit = ViT()\n",
+    "print(repr(vit))\n",
+    "print(vit(x).shape)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Advanced - Context API\n",
+    "\n",
+    "This ViT is still rudimentary and linear: we have one input in and one output out. But often, you want to use multiple inputs/modalities in the flow of your model.\n",
+    "\n",
+    "Let's take, for example, the `MaskDecoder` of the Segment Anything model by Meta; it's a Transformer that takes as input an image and a prompt and outputs a segmentation mask. You can prompt it with points to guide the segmentation. [Here is a link](https://github.com/finegrain-ai/refiners/blob/main/src/refiners/foundationals/segment_anything/mask_decoder.py) to the complete implementation in Refiners\n",
+    "\n",
+    "So the inputs are:\n",
+    "\n",
+    "    - an image of shape (3, 224, 224)\n",
+    "    - several points of shape (N, 2) \n",
+    "\n",
+    "One way to consider the points is to add a `CrossAttention` layer that will attend to the points from the image. Cross attention is a standard `Attention` layer, but the key and value come from a source different from the query. In our case, the query is the image, the key and value are the points embeddings.\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "\n",
+    "Let's start by building a point encoder (to simplify that all points have the same \"meaning\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 5, 128])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class PointEncoder(fl.Chain):\n",
+    "    def __init__(self, dim: int = 128) -> None:\n",
+    "        self.dim = dim\n",
+    "        super().__init__(\n",
+    "            fl.Linear(2, dim),\n",
+    "            fl.SiLU(),\n",
+    "            fl.Linear(dim, dim),\n",
+    "            fl.SiLU(),\n",
+    "            fl.Linear(dim, dim),\n",
+    "            fl.Unsqueeze(0),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "points = torch.randn(5, 2)\n",
+    "PointEncoder()(points).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 197, 128])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Chains can handle multiple inputs\n",
+    "class CrossAttention(fl.Chain):\n",
+    "    def __init__(self, dim: int = 128, num_heads: int = 8) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.num_heads = num_heads\n",
+    "        super().__init__(\n",
+    "            fl.Parallel(\n",
+    "                fl.GetArg(0),\n",
+    "                fl.GetArg(1),\n",
+    "                fl.GetArg(1),\n",
+    "            ),\n",
+    "            fl.Distribute(\n",
+    "                fl.Linear(dim, dim),\n",
+    "                fl.Linear(dim, dim),\n",
+    "                fl.Linear(dim, dim),\n",
+    "            ),\n",
+    "            ScaledDotProductAttention(num_heads=num_heads),\n",
+    "            fl.Linear(dim, dim),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "points_embedding = torch.randn(1, 5, 128)\n",
+    "patch_embedding = torch.randn(1, 197, 128)\n",
+    "CrossAttention()(patch_embedding, points_embedding).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now ideally, I would like to insert this `CrossAttention` layer in the middle of the `Transformer` like this:\n",
+    "\n",
+    "```python\n",
+    "class TranformerLayer(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self, dim: int = 128, num_heads: int = 8, inner_dim: int = 512\n",
+    "    ) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.num_heads = num_heads\n",
+    "        self.inner_dim = inner_dim\n",
+    "        super().__init__(\n",
+    "            fl.LayerNorm(dim),\n",
+    "            Attention(dim, num_heads),\n",
+    "            fl.LayerNorm(dim),\n",
+    "            CrossAttention(dim, num_heads),\n",
+    "            fl.LayerNorm(dim),\n",
+    "            FeedForward(dim, inner_dim),\n",
+    "        )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "But how do the `point_embedding` get into the `CrossAttention` layer? That's where the `Context` API comes into play."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = fl.Chain(\n",
+    "    fl.Linear(2, 2),\n",
+    "    fl.Concatenate(\n",
+    "        fl.Linear(2, 2),\n",
+    "        fl.UseContext(\"embedding\", \"value\"),\n",
+    "        dim=-1,\n",
+    "    ),\n",
+    "    fl.Linear(5, 2),\n",
+    ")\n",
+    "\n",
+    "chain.set_context(\"embedding\", {\"value\": torch.randn(1, 3)})\n",
+    "print(f\"Current embedding context: {chain.use_context('embedding')}\")\n",
+    "\n",
+    "x = torch.randn(1, 2)\n",
+    "chain(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the context is recursive, so you can access the context of an outer `Chain` from an inner `Chain`.\n",
+    "\n",
+    "We can rewrite the `CrossAttention` layer using context instead of passing the `point_embedding` as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 197, 128])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(CHAIN) PointsCrossAttention()\n",
+       "    ├── (PAR)\n",
+       "    │   ├── Identity()\n",
+       "    │   └── UseContext(context=vit, key=points_embedding) (x2)\n",
+       "    ├── (DISTR)\n",
+       "    │   └── Linear(in_features=128, out_features=128) (x3)\n",
+       "    ├── ScaledDotProductAttention(num_heads=8)\n",
+       "    └── Linear(in_features=128, out_features=128)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class PointsCrossAttention(fl.Chain):\n",
+    "    def __init__(self, dim: int = 128, num_heads: int = 8) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.num_heads = num_heads\n",
+    "        super().__init__(\n",
+    "            fl.Parallel(\n",
+    "                fl.Identity(),\n",
+    "                fl.UseContext(\"vit\", \"points_embedding\"),\n",
+    "                fl.UseContext(\"vit\", \"points_embedding\"),\n",
+    "            ),\n",
+    "            fl.Distribute(\n",
+    "                fl.Linear(dim, dim),\n",
+    "                fl.Linear(dim, dim),\n",
+    "                fl.Linear(dim, dim),\n",
+    "            ),\n",
+    "            ScaledDotProductAttention(num_heads=num_heads),\n",
+    "            fl.Linear(dim, dim),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "points_cross_attention = PointsCrossAttention()\n",
+    "\n",
+    "# If the context is not set, the layer will raise an error\n",
+    "points_embedding = torch.randn(1, 5, 128)\n",
+    "points_cross_attention.set_context(\"vit\", {\"points_embedding\": points_embedding})\n",
+    "\n",
+    "x = torch.randn(1, 197, 128)\n",
+    "\n",
+    "print(points_cross_attention(x).shape)\n",
+    "points_cross_attention"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's rewrite the `TransformerLayer` using the `PointsCrossAttention` layer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 197, 128])\n"
+     ]
+    }
+   ],
+   "source": [
+    "class TranformerLayer(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self, dim: int = 128, num_heads: int = 8, inner_dim: int = 512\n",
+    "    ) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.num_heads = num_heads\n",
+    "        self.inner_dim = inner_dim\n",
+    "        super().__init__(\n",
+    "            fl.LayerNorm(dim),\n",
+    "            Attention(dim, num_heads),\n",
+    "            fl.LayerNorm(dim),\n",
+    "            PointsCrossAttention(dim, num_heads),\n",
+    "            fl.LayerNorm(dim),\n",
+    "            FeedForward(dim, inner_dim),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "layer = TranformerLayer()\n",
+    "x = torch.randn(1, 197, 128)\n",
+    "points_embedding = torch.randn(1, 5, 128)\n",
+    "layer.set_context(\"vit\", {\"points_embedding\": points_embedding})\n",
+    "print(layer(x).shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ViT is still valid as is, but we might want to add the `PointEncoder` directly into the model to not have to deal with multiple models separately. To do that, we can wrap the `PointEncoder` into a `Passthrough` layer that will let the main arguments pass through, but will also add the `point_embedding` to the context using a `SetContext` layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 197, 128])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(CHAIN) ViT()\n",
+       "    ├── (PASS) PointEncoder()\n",
+       "    │   ├── UseContext(context=vit, key=points_tensor)\n",
+       "    │   ├── Linear(in_features=2, out_features=128) #1\n",
+       "    │   ├── SiLU() #1\n",
+       "    │   ├── Linear(in_features=128, out_features=128) #2\n",
+       "    │   ├── SiLU() #2\n",
+       "    │   ├── Linear(in_features=128, out_features=128) #3\n",
+       "    │   ├── Unsqueeze(dim=0)\n",
+       "    │   └── SetContext(context=vit, key=points_embedding)\n",
+       "    ├── (CAT)\n",
+       "    │   ├── (CHAIN) PatchEncoder()\n",
+       "    │   │   ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
+       "    │   │   └── Reshape(shape=(-1, 128))\n",
+       "    │   └── (CHAIN) ClassToken()\n",
+       "    │       └── Parameter(dims=(1, 128))\n",
+       "    ├── (RES) PositionalToken(num_patches=197)\n",
+       "    │   └── Parameter(dims=(197, 128))\n",
+       "    └── (CHAIN) Transformer()\n",
+       "        └── (CHAIN) TranformerLayer() (x4)\n",
+       "            ├── LayerNorm(normalized_shape=(128,)) #1\n",
+       "            ├── (RES) Attention()\n",
+       "            │   ├── (PAR)\n",
+       "            │   │   └── Linear(in_features=128, out_features=128) (x3)\n",
+       "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+       "            │   └── Linear(in_features=128, out_features=128)\n",
+       "            ├── LayerNorm(normalized_shape=(128,)) #2\n",
+       "            ├── (CHAIN) PointsCrossAttention()\n",
+       "            │   ├── (PAR)\n",
+       "            │   │   ├── Identity()\n",
+       "            │   │   └── UseContext(context=vit, key=points_embedding) (x2)\n",
+       "            │   ├── (DISTR)\n",
+       "            │   │   └── Linear(in_features=128, out_features=128) (x3)\n",
+       "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+       "            │   └── Linear(in_features=128, out_features=128)\n",
+       "            ├── LayerNorm(normalized_shape=(128,)) #3\n",
+       "            └── (RES) FeedForward()\n",
+       "                ├── Linear(in_features=128, out_features=512) #1\n",
+       "                ├── SiLU()\n",
+       "                └── Linear(in_features=512, out_features=128) #2"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class PointEncoder(fl.Passthrough):\n",
+    "    def __init__(self, dim: int = 128) -> None:\n",
+    "        self.dim = dim\n",
+    "        super().__init__(\n",
+    "            fl.UseContext(\"vit\", \"points_tensor\"),\n",
+    "            fl.Linear(2, dim),\n",
+    "            fl.SiLU(),\n",
+    "            fl.Linear(dim, dim),\n",
+    "            fl.SiLU(),\n",
+    "            fl.Linear(dim, dim),\n",
+    "            fl.Unsqueeze(0),\n",
+    "            fl.SetContext(\"vit\", \"points_embedding\"),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "class ViT(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        dim: int = 128,\n",
+    "        patch_size: int = 16,\n",
+    "        image_size: int = 224,\n",
+    "        num_layers: int = 4,\n",
+    "    ) -> None:\n",
+    "        self.dim = dim\n",
+    "        self.patch_size = patch_size\n",
+    "        self.image_size = image_size\n",
+    "        self.num_layers = num_layers\n",
+    "        self.num_patches = (image_size // patch_size) ** 2 + 1\n",
+    "        super().__init__(\n",
+    "            PointEncoder(dim=dim),\n",
+    "            fl.Concatenate(\n",
+    "                PatchEncoder(in_channels=3, dim=dim, patch_size=patch_size),\n",
+    "                ClassToken(dim=dim),\n",
+    "                dim=1,\n",
+    "            ),\n",
+    "            PositionalToken(num_patches=self.num_patches),\n",
+    "            Transformer(TranformerLayer(dim=dim) for _ in range(num_layers)),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "vit = ViT()\n",
+    "x = torch.randn(1, 3, 224, 224)\n",
+    "points = torch.randn(5, 2)\n",
+    "vit.set_context(\"vit\", {\"points_tensor\": points})\n",
+    "print(vit(x).shape)\n",
+    "vit"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adaptation\n",
+    "\n",
+    "I think to have a very explicit and declarative model like we showcased here is an interesting property, but the place where it shines the most is when you want to adapt a model to a new task.\n",
+    "\n",
+    "Let's demonstrate on a simple example how to create a LoRA adaptation on the ViT without having to rewrite the whole model.\n",
+    "\n",
+    "The low-rank adaptation technique adds lighter new layers on top of the model. The rank is the inner_dim of the new layers. The outer layer is zero-initialized, so the model's output is the same before training.\n",
+    "![image.png](attachment:image.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 1, 128])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(CHAIN) Lora(in_features=128, out_features=128)\n",
+       "    ├── Linear(in_features=128, out_features=16) #1\n",
+       "    └── Linear(in_features=16, out_features=128) #2"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Lora(fl.Chain):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        in_features: int,\n",
+    "        out_features: int,\n",
+    "        rank: int = 16,\n",
+    "    ) -> None:\n",
+    "        self.in_features = in_features\n",
+    "        self.out_features = out_features\n",
+    "        self.rank = rank\n",
+    "        self.scale: float = 1.0\n",
+    "\n",
+    "        super().__init__(\n",
+    "            fl.Linear(in_features=in_features, out_features=rank, bias=False),\n",
+    "            fl.Linear(in_features=rank, out_features=out_features),\n",
+    "        )\n",
+    "\n",
+    "        nn.init.normal_(tensor=self.Linear_1.weight, std=1 / self.rank)\n",
+    "        nn.init.zeros_(tensor=self.Linear_2.weight)\n",
+    "\n",
+    "\n",
+    "lora = Lora(128, 128)\n",
+    "x = torch.randn(1, 1, 128)\n",
+    "print(lora(x).shape)\n",
+    "lora"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to be able to insert this into any `Linear` layer of the Model. To do that, we can use the `Adapter` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Adapter:\n",
+      "(SUM) LoraAdapter()\n",
+      "    ├── Linear(in_features=128, out_features=128)\n",
+      "    └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "        ├── Linear(in_features=128, out_features=16) #1\n",
+      "        └── Linear(in_features=16, out_features=128) #2\n",
+      "\n",
+      "Note that the original attention is not modified:\n",
+      "(RES) Attention()\n",
+      "    ├── (PAR)\n",
+      "    │   └── Linear(in_features=128, out_features=128) (x3)\n",
+      "    ├── ScaledDotProductAttention(num_heads=8)\n",
+      "    └── Linear(in_features=128, out_features=128)  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from refiners.fluxion.adapters import Adapter\n",
+    "\n",
+    "\n",
+    "class LoraAdapter(fl.Sum, Adapter[fl.Linear]):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        target: fl.Linear,\n",
+    "        rank: int = 16,\n",
+    "    ) -> None:\n",
+    "        self.in_features = target.in_features\n",
+    "        self.out_features = target.out_features\n",
+    "        self.rank = rank\n",
+    "        # the setup_adapter method is used to remove boilerplate code\n",
+    "        with self.setup_adapter(target):\n",
+    "            super().__init__(\n",
+    "                target,\n",
+    "                Lora(\n",
+    "                    in_features=target.in_features,\n",
+    "                    out_features=target.out_features,\n",
+    "                    rank=rank,\n",
+    "                ),\n",
+    "            )\n",
+    "\n",
+    "\n",
+    "attention = Attention()\n",
+    "linear = attention.ensure_find(fl.Linear)\n",
+    "adapter = LoraAdapter(linear)\n",
+    "print(\n",
+    "    f\"\"\"\n",
+    "Adapter:\n",
+    "{repr(adapter)}\n",
+    "\n",
+    "Note that the original attention is not modified:\n",
+    "{repr(attention)}  \n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now `inject` the `Adapter` into the `FeedForward` layer of the `TransformerLayer`. One subtlety is that the `Linear` layer is considered a `WeightedModule` and as such can belong to multiple `Chain` at the same time. So we need to specify which `Chain` we want to inject the `Adapter` into."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(RES) Attention()\n",
+      "    ├── (PAR)\n",
+      "    │   ├── (SUM) LoraAdapter()\n",
+      "    │   │   ├── Linear(in_features=128, out_features=128)\n",
+      "    │   │   └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "    │   │       ├── Linear(in_features=128, out_features=16) #1\n",
+      "    │   │       └── Linear(in_features=16, out_features=128) #2\n",
+      "    │   └── Linear(in_features=128, out_features=128) (x2)\n",
+      "    ├── ScaledDotProductAttention(num_heads=8)\n",
+      "    └── Linear(in_features=128, out_features=128)\n",
+      "(RES) Attention()\n",
+      "    ├── (PAR)\n",
+      "    │   └── Linear(in_features=128, out_features=128) (x3)\n",
+      "    ├── ScaledDotProductAttention(num_heads=8)\n",
+      "    └── Linear(in_features=128, out_features=128)\n"
+     ]
+    }
+   ],
+   "source": [
+    "adapter.inject(parent=attention.Parallel)\n",
+    "print(repr(attention))\n",
+    "\n",
+    "# we can also `eject` the adapter to get back to normal\n",
+    "adapter.eject()\n",
+    "print(repr(attention))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally let's write a top-level adapter that will inject the `Adapter` into all the `Linear` layers of the `ViT`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(CHAIN) ViT()\n",
+      "    ├── (PASS) PointEncoder()\n",
+      "    │   ├── UseContext(context=vit, key=points_tensor)\n",
+      "    │   ├── (SUM) LoraAdapter() #1\n",
+      "    │   │   ├── Linear(in_features=2, out_features=128)\n",
+      "    │   │   └── (CHAIN) Lora(in_features=2, out_features=128)\n",
+      "    │   │       ├── Linear(in_features=2, out_features=16) #1\n",
+      "    │   │       └── Linear(in_features=16, out_features=128) #2\n",
+      "    │   ├── SiLU() #1\n",
+      "    │   ├── (SUM) LoraAdapter() #2\n",
+      "    │   │   ├── Linear(in_features=128, out_features=128)\n",
+      "    │   │   └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "    │   │       ├── Linear(in_features=128, out_features=16) #1\n",
+      "    │   │       └── Linear(in_features=16, out_features=128) #2\n",
+      "    │   ├── SiLU() #2\n",
+      "    │   ├── (SUM) LoraAdapter() #3\n",
+      "    │   │   ├── Linear(in_features=128, out_features=128)\n",
+      "    │   │   └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "    │   │       ├── Linear(in_features=128, out_features=16) #1\n",
+      "    │   │       └── Linear(in_features=16, out_features=128) #2\n",
+      "    │   ├── Unsqueeze(dim=0)\n",
+      "    │   └── SetContext(context=vit, key=points_embedding)\n",
+      "    ├── (CAT)\n",
+      "    │   ├── (CHAIN) PatchEncoder()\n",
+      "    │   │   ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
+      "    │   │   └── Reshape(shape=(-1, 128))\n",
+      "    │   └── (CHAIN) ClassToken()\n",
+      "    │       └── Parameter(dims=(1, 128))\n",
+      "    ├── (RES) PositionalToken(num_patches=197)\n",
+      "    │   └── Parameter(dims=(197, 128))\n",
+      "    └── (CHAIN) Transformer()\n",
+      "        └── (CHAIN) TranformerLayer() (x4)\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #1\n",
+      "            ├── (RES) Attention()\n",
+      "            │   ├── (PAR)\n",
+      "            │   │   └── (SUM) LoraAdapter() (x3)\n",
+      "            │   │       ├── Linear(in_features=128, out_features=128)\n",
+      "            │   │       └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "            │   │           ├── Linear(in_features=128, out_features=16) #1\n",
+      "            │   │           └── Linear(in_features=16, out_features=128) #2\n",
+      "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+      "            │   └── (SUM) LoraAdapter()\n",
+      "            │       ├── Linear(in_features=128, out_features=128)\n",
+      "            │       └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "            │           ├── Linear(in_features=128, out_features=16) #1\n",
+      "            │           └── Linear(in_features=16, out_features=128) #2\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #2\n",
+      "            ├── (CHAIN) PointsCrossAttention()\n",
+      "            │   ├── (PAR)\n",
+      "            │   │   ├── Identity()\n",
+      "            │   │   └── UseContext(context=vit, key=points_embedding) (x2)\n",
+      "            │   ├── (DISTR)\n",
+      "            │   │   └── (SUM) LoraAdapter() (x3)\n",
+      "            │   │       ├── Linear(in_features=128, out_features=128)\n",
+      "            │   │       └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "            │   │           ├── Linear(in_features=128, out_features=16) #1\n",
+      "            │   │           └── Linear(in_features=16, out_features=128) #2\n",
+      "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+      "            │   └── (SUM) LoraAdapter()\n",
+      "            │       ├── Linear(in_features=128, out_features=128)\n",
+      "            │       └── (CHAIN) Lora(in_features=128, out_features=128)\n",
+      "            │           ├── Linear(in_features=128, out_features=16) #1\n",
+      "            │           └── Linear(in_features=16, out_features=128) #2\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #3\n",
+      "            └── (RES) FeedForward()\n",
+      "                ├── (SUM) LoraAdapter() #1\n",
+      "                │   ├── Linear(in_features=128, out_features=512)\n",
+      "                │   └── (CHAIN) Lora(in_features=128, out_features=512)\n",
+      "                │       ├── Linear(in_features=128, out_features=16) #1\n",
+      "                │       └── Linear(in_features=16, out_features=512) #2\n",
+      "                ├── SiLU()\n",
+      "                └── (SUM) LoraAdapter() #2\n",
+      "                    ├── Linear(in_features=512, out_features=128)\n",
+      "                    └── (CHAIN) Lora(in_features=512, out_features=128)\n",
+      "                        ├── Linear(in_features=512, out_features=16) #1\n",
+      "                        └── Linear(in_features=16, out_features=128) #2\n",
+      "torch.Size([1, 197, 128])\n",
+      "(CHAIN) ViT()\n",
+      "    ├── (PASS) PointEncoder()\n",
+      "    │   ├── UseContext(context=vit, key=points_tensor)\n",
+      "    │   ├── Linear(in_features=2, out_features=128) #1\n",
+      "    │   ├── SiLU() #1\n",
+      "    │   ├── Linear(in_features=128, out_features=128) #2\n",
+      "    │   ├── SiLU() #2\n",
+      "    │   ├── Linear(in_features=128, out_features=128) #3\n",
+      "    │   ├── Unsqueeze(dim=0)\n",
+      "    │   └── SetContext(context=vit, key=points_embedding)\n",
+      "    ├── (CAT)\n",
+      "    │   ├── (CHAIN) PatchEncoder()\n",
+      "    │   │   ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
+      "    │   │   └── Reshape(shape=(-1, 128))\n",
+      "    │   └── (CHAIN) ClassToken()\n",
+      "    │       └── Parameter(dims=(1, 128))\n",
+      "    ├── (RES) PositionalToken(num_patches=197)\n",
+      "    │   └── Parameter(dims=(197, 128))\n",
+      "    └── (CHAIN) Transformer()\n",
+      "        └── (CHAIN) TranformerLayer() (x4)\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #1\n",
+      "            ├── (RES) Attention()\n",
+      "            │   ├── (PAR)\n",
+      "            │   │   └── Linear(in_features=128, out_features=128) (x3)\n",
+      "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+      "            │   └── Linear(in_features=128, out_features=128)\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #2\n",
+      "            ├── (CHAIN) PointsCrossAttention()\n",
+      "            │   ├── (PAR)\n",
+      "            │   │   ├── Identity()\n",
+      "            │   │   └── UseContext(context=vit, key=points_embedding) (x2)\n",
+      "            │   ├── (DISTR)\n",
+      "            │   │   └── Linear(in_features=128, out_features=128) (x3)\n",
+      "            │   ├── ScaledDotProductAttention(num_heads=8)\n",
+      "            │   └── Linear(in_features=128, out_features=128)\n",
+      "            ├── LayerNorm(normalized_shape=(128,)) #3\n",
+      "            └── (RES) FeedForward()\n",
+      "                ├── Linear(in_features=128, out_features=512) #1\n",
+      "                ├── SiLU()\n",
+      "                └── Linear(in_features=512, out_features=128) #2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import Self\n",
+    "\n",
+    "\n",
+    "class ViTLoraAdapter(fl.Chain, Adapter[ViT]):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        target: ViT,\n",
+    "        rank: int = 16,\n",
+    "    ) -> None:\n",
+    "        self.rank = rank\n",
+    "        with self.setup_adapter(target):\n",
+    "            super().__init__(target)\n",
+    "\n",
+    "        # Let's wrap all the Linear layers in the ViT model into LoraAdapters\n",
+    "        self.sub_adapters: list[tuple[LoraAdapter, fl.Chain]] = []\n",
+    "        for linear, parent in self.target.walk(fl.Linear):\n",
+    "            self.sub_adapters.append((LoraAdapter(target=linear, rank=rank), parent))\n",
+    "\n",
+    "    def inject(self, parent: fl.Chain | None = None) -> Self:\n",
+    "        for adapter, adapter_parent in self.sub_adapters:\n",
+    "            adapter.inject(adapter_parent)\n",
+    "        return super().inject(parent)\n",
+    "\n",
+    "    def eject(self) -> None:\n",
+    "        for adapter, _ in self.sub_adapters:\n",
+    "            adapter.eject()\n",
+    "        super().eject()\n",
+    "\n",
+    "\n",
+    "vit = ViT()\n",
+    "x = torch.randn(1, 3, 224, 224)\n",
+    "points = torch.randn(5, 2)\n",
+    "vit.set_context(\"vit\", {\"points_tensor\": points})\n",
+    "adapter = ViTLoraAdapter(vit)\n",
+    "adapter.inject()  # since `ViT` has no parent, no need to pass it to `inject`\n",
+    "print(repr(vit))\n",
+    "print(vit(x).shape)\n",
+    "\n",
+    "# we can also `eject` the adapter to get back to normal\n",
+    "adapter.eject()\n",
+    "print(repr(vit))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 17d2e93c5..0ca1f8b4e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,10 +54,11 @@ build-backend = "hatchling.build"
 [tool.rye]
 managed = true
 dev-dependencies = [
-    "pyright == 1.1.333",
+    "pyright == 1.1.342",
     "ruff>=0.0.292",
     "docformatter>=1.7.5",
     "pytest>=7.4.2",
+    "mkdocs-material>=9.5.3",
 ]
 
 
@@ -66,6 +67,7 @@ allow-direct-references = true
 
 [tool.rye.scripts]
 lint = { chain = ["ruff format .", "ruff --fix ."] }
+serve-docs = "mkdocs serve"
 
 [tool.black]
 line-length = 120
diff --git a/requirements.docs.txt b/requirements.docs.txt
new file mode 100644
index 000000000..00707e027
--- /dev/null
+++ b/requirements.docs.txt
@@ -0,0 +1 @@
+mkdocs-material==9.5.3
diff --git a/scripts/conversion/convert_diffusers_controlnet.py b/scripts/conversion/convert_diffusers_controlnet.py
index cacdfdd54..5185193db 100644
--- a/scripts/conversion/convert_diffusers_controlnet.py
+++ b/scripts/conversion/convert_diffusers_controlnet.py
@@ -7,7 +7,7 @@
 from torch import nn
 
 from refiners.fluxion.model_converter import ModelConverter
-from refiners.fluxion.utils import save_to_safetensors
+from refiners.fluxion.utils import no_grad, save_to_safetensors
 from refiners.foundationals.latent_diffusion import (
     DPMSolver,
     SD1ControlnetAdapter,
@@ -20,7 +20,7 @@ class Args(argparse.Namespace):
     output_path: str | None
 
 
-@torch.no_grad()
+@no_grad()
 def convert(args: Args) -> dict[str, torch.Tensor]:
     # low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
     controlnet_src: nn.Module = ControlNetModel.from_pretrained(  # type: ignore
diff --git a/scripts/conversion/convert_diffusers_ip_adapter.py b/scripts/conversion/convert_diffusers_ip_adapter.py
index 8fd33be03..8282db1af 100644
--- a/scripts/conversion/convert_diffusers_ip_adapter.py
+++ b/scripts/conversion/convert_diffusers_ip_adapter.py
@@ -133,24 +133,14 @@ def main() -> None:
     ip_adapter_weights: dict[str, torch.Tensor] = weights["ip_adapter"]
     assert len(ip_adapter.sub_adapters) == len(ip_adapter_weights.keys()) // 2
 
-    for i, cross_attn in enumerate(ip_adapter.sub_adapters):
+    for i, _ in enumerate(ip_adapter.sub_adapters):
         cross_attn_index = cross_attn_mapping[i]
         k_ip = f"{cross_attn_index}.to_k_ip.weight"
         v_ip = f"{cross_attn_index}.to_v_ip.weight"
 
-        # Ignore Wq, Wk, Wv and Proj (hence strict=False): at runtime, they will be part of the UNet original weights
-
-        names = [k for k, _ in cross_attn.named_parameters()]
-        assert len(names) == 2
-
-        cross_attn_state_dict: dict[str, Any] = {
-            names[0]: ip_adapter_weights[k_ip],
-            names[1]: ip_adapter_weights[v_ip],
-        }
-        cross_attn.load_state_dict(state_dict=cross_attn_state_dict, strict=False)
-
-        for k, v in cross_attn_state_dict.items():
-            state_dict[f"ip_adapter.{i:03d}.{k}"] = v
+        # the name of the key is not checked at runtime, so we keep the original name
+        state_dict[f"ip_adapter.{i:03d}.to_k_ip.weight"] = ip_adapter_weights[k_ip]
+        state_dict[f"ip_adapter.{i:03d}.to_v_ip.weight"] = ip_adapter_weights[v_ip]
 
     if args.half:
         state_dict = {key: value.half() for key, value in state_dict.items()}
diff --git a/scripts/conversion/convert_diffusers_lora.py b/scripts/conversion/convert_diffusers_lora.py
index 9abffd872..1c37d8dbd 100644
--- a/scripts/conversion/convert_diffusers_lora.py
+++ b/scripts/conversion/convert_diffusers_lora.py
@@ -11,7 +11,7 @@
 import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.lora import Lora, LoraAdapter
 from refiners.fluxion.model_converter import ModelConverter
-from refiners.fluxion.utils import save_to_safetensors
+from refiners.fluxion.utils import no_grad, save_to_safetensors
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget, lora_targets
 
@@ -37,7 +37,7 @@ class Args(argparse.Namespace):
     verbose: bool
 
 
-@torch.no_grad()
+@no_grad()
 def process(args: Args) -> None:
     diffusers_state_dict = cast(dict[str, Tensor], torch.load(args.source_path, map_location="cpu"))  # type: ignore
     # low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
diff --git a/scripts/conversion/convert_segment_anything.py b/scripts/conversion/convert_segment_anything.py
index 9057cbb33..14ba2ef2b 100644
--- a/scripts/conversion/convert_segment_anything.py
+++ b/scripts/conversion/convert_segment_anything.py
@@ -37,13 +37,36 @@ class Args(argparse.Namespace):
 
 
 def convert_mask_encoder(prompt_encoder: nn.Module) -> dict[str, Tensor]:
+    manual_seed(seed=0)
+    refiners_mask_encoder = MaskEncoder()
+
+    converter = ModelConverter(
+        source_model=prompt_encoder.mask_downscaling,
+        target_model=refiners_mask_encoder,
+        custom_layer_mapping=custom_layers,  # type: ignore
+    )
+
+    x = torch.randn(1, 256, 256)
+    mapping = converter.map_state_dicts(source_args=(x,))
+    assert mapping
+
+    source_state_dict = prompt_encoder.mask_downscaling.state_dict()
+    target_state_dict = refiners_mask_encoder.state_dict()
+
+    # Mapping handled manually (see below) because nn.Parameter is a special case
+    del target_state_dict["no_mask_embedding"]
+
+    converted_source = converter._convert_state_dict(  # pyright: ignore[reportPrivateUsage]
+        source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping
+    )
+
     state_dict: dict[str, Tensor] = {
         "no_mask_embedding": nn.Parameter(data=prompt_encoder.no_mask_embed.weight.clone()),  # type: ignore
     }
 
-    refiners_mask_encoder = MaskEncoder()
-    # TODO: handle other weights
-    refiners_mask_encoder.load_state_dict(state_dict=state_dict, strict=False)
+    state_dict.update(converted_source)
+
+    refiners_mask_encoder.load_state_dict(state_dict=state_dict)
 
     return state_dict
 
diff --git a/src/refiners/fluxion/layers/sampling.py b/src/refiners/fluxion/layers/sampling.py
index d6368e3b1..69d1412e3 100644
--- a/src/refiners/fluxion/layers/sampling.py
+++ b/src/refiners/fluxion/layers/sampling.py
@@ -1,3 +1,5 @@
+from typing import Callable
+
 from torch import Size, Tensor, device as Device, dtype as DType
 from torch.nn.functional import pad
 
@@ -40,7 +42,8 @@ def __init__(
             ),
         )
         if padding == 0:
-            self.insert(0, Lambda(lambda x: pad(x, (0, 1, 0, 1))))
+            zero_pad: Callable[[Tensor], Tensor] = lambda x: pad(x, (0, 1, 0, 1))
+            self.insert(0, Lambda(zero_pad))
         if register_shape:
             self.insert(0, SetContext(context="sampling", key="shapes", callback=self.register_shape))
 
diff --git a/src/refiners/fluxion/model_converter.py b/src/refiners/fluxion/model_converter.py
index ef14d0238..8e47ebb49 100644
--- a/src/refiners/fluxion/model_converter.py
+++ b/src/refiners/fluxion/model_converter.py
@@ -7,7 +7,7 @@
 from torch import Tensor, nn
 from torch.utils.hooks import RemovableHandle
 
-from refiners.fluxion.utils import norm, save_to_safetensors
+from refiners.fluxion.utils import no_grad, norm, save_to_safetensors
 
 TORCH_BASIC_LAYERS: list[type[nn.Module]] = [
     nn.Conv1d,
@@ -512,7 +512,7 @@ def _verify_missing_basic_layers(self) -> bool:
 
         return True
 
-    @torch.no_grad()
+    @no_grad()
     def _trace_module_execution_order(
         self,
         module: nn.Module,
@@ -603,7 +603,7 @@ def _convert_state_dict(
 
         return converted_state_dict
 
-    @torch.no_grad()
+    @no_grad()
     def _collect_layers_outputs(
         self, module: nn.Module, args: ModuleArgs, keys_to_skip: list[str]
     ) -> list[tuple[str, Tensor]]:
diff --git a/src/refiners/fluxion/utils.py b/src/refiners/fluxion/utils.py
index 7c4f5e06d..deb0d4693 100644
--- a/src/refiners/fluxion/utils.py
+++ b/src/refiners/fluxion/utils.py
@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Iterable, Literal, TypeVar
+from typing import Any, Iterable, Literal, TypeVar
 
 import torch
 from jaxtyping import Float
@@ -7,7 +7,14 @@
 from PIL import Image
 from safetensors import safe_open as _safe_open  # type: ignore
 from safetensors.torch import save_file as _save_file  # type: ignore
-from torch import Tensor, device as Device, dtype as DType, manual_seed as _manual_seed, norm as _norm  # type: ignore
+from torch import (
+    Tensor,
+    device as Device,
+    dtype as DType,
+    manual_seed as _manual_seed,  # type: ignore
+    no_grad as _no_grad,  # type: ignore
+    norm as _norm,  # type: ignore
+)
 from torch.nn.functional import conv2d, interpolate as _interpolate, pad as _pad  # type: ignore
 
 T = TypeVar("T")
@@ -22,6 +29,11 @@ def manual_seed(seed: int) -> None:
     _manual_seed(seed)
 
 
+class no_grad(_no_grad):
+    def __new__(cls, orig_func: Any | None = None) -> "no_grad":  # type: ignore
+        return object.__new__(cls)
+
+
 def pad(x: Tensor, pad: Iterable[int], value: float = 0.0, mode: str = "constant") -> Tensor:
     return _pad(input=x, pad=pad, value=value, mode=mode)  # type: ignore
 
diff --git a/src/refiners/foundationals/clip/image_encoder.py b/src/refiners/foundationals/clip/image_encoder.py
index ed6db3d7b..270d1beb9 100644
--- a/src/refiners/foundationals/clip/image_encoder.py
+++ b/src/refiners/foundationals/clip/image_encoder.py
@@ -1,4 +1,6 @@
-from torch import device as Device, dtype as DType
+from typing import Callable
+
+from torch import Tensor, device as Device, dtype as DType
 
 import refiners.fluxion.layers as fl
 from refiners.foundationals.clip.common import FeedForward, PositionalEncoder
@@ -126,6 +128,7 @@ def __init__(
         self.num_layers = num_layers
         self.num_attention_heads = num_attention_heads
         self.feedforward_dim = feedforward_dim
+        cls_token_pooling: Callable[[Tensor], Tensor] = lambda x: x[:, 0, :]
         super().__init__(
             ViTEmbeddings(
                 image_size=image_size, embedding_dim=embedding_dim, patch_size=patch_size, device=device, dtype=dtype
@@ -142,7 +145,7 @@ def __init__(
                 )
                 for _ in range(num_layers)
             ),
-            fl.Lambda(func=lambda x: x[:, 0, :]),
+            fl.Lambda(func=cls_token_pooling),
             fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype),
             fl.Linear(in_features=embedding_dim, out_features=output_dim, bias=False, device=device, dtype=dtype),
         )
diff --git a/src/refiners/foundationals/latent_diffusion/freeu.py b/src/refiners/foundationals/latent_diffusion/freeu.py
index 61726bd81..3e2580f2e 100644
--- a/src/refiners/foundationals/latent_diffusion/freeu.py
+++ b/src/refiners/foundationals/latent_diffusion/freeu.py
@@ -1,5 +1,5 @@
 import math
-from typing import Any, Generic, TypeVar
+from typing import Any, Callable, Generic, TypeVar
 
 import torch
 from torch import Tensor
@@ -54,9 +54,10 @@ def forward(self, x: Tensor) -> Tensor:
 
 class FreeUSkipFeatures(fl.Chain):
     def __init__(self, n: int, skip_scale: float) -> None:
+        apply_filter: Callable[[Tensor], Tensor] = lambda x: fourier_filter(x, scale=skip_scale)
         super().__init__(
             fl.UseContext(context="unet", key="residuals").compose(lambda residuals: residuals[n]),
-            fl.Lambda(lambda x: fourier_filter(x, scale=skip_scale)),
+            fl.Lambda(apply_filter),
         )
 
 
diff --git a/src/refiners/foundationals/latent_diffusion/image_prompt.py b/src/refiners/foundationals/latent_diffusion/image_prompt.py
index 9c4bac019..b0cdfdd21 100644
--- a/src/refiners/foundationals/latent_diffusion/image_prompt.py
+++ b/src/refiners/foundationals/latent_diffusion/image_prompt.py
@@ -1,17 +1,15 @@
 import math
-from enum import IntEnum
-from functools import partial
-from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar
+from typing import TYPE_CHECKING, Any, Generic, TypeVar
 
 from jaxtyping import Float
 from PIL import Image
-from torch import Tensor, cat, device as Device, dtype as DType, softmax, zeros_like
+from torch import Tensor, cat, device as Device, dtype as DType, nn, softmax, zeros_like
 
 import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
-from refiners.fluxion.adapters.lora import Lora
 from refiners.fluxion.context import Contexts
 from refiners.fluxion.layers.attentions import ScaledDotProductAttention
+from refiners.fluxion.layers.chain import Distribute
 from refiners.fluxion.utils import image_to_tensor, normalize
 from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 
@@ -236,120 +234,89 @@ def init_context(self) -> Contexts:
         return {"perceiver_resampler": {"x": None}}
 
 
-class _CrossAttnIndex(IntEnum):
-    TXT_CROSS_ATTN = 0  # text cross-attention
-    IMG_CROSS_ATTN = 1  # image cross-attention
-
-
 class InjectionPoint(fl.Chain):
     pass
 
 
+class ImageCrossAttention(fl.Chain):
+    def __init__(self, text_cross_attention: fl.Attention, scale: float = 1.0) -> None:
+        self.scale = scale
+        super().__init__(
+            fl.Distribute(
+                fl.UseContext(context="ip_adapter", key="query_projection"),
+                fl.Chain(
+                    fl.UseContext(context="ip_adapter", key="clip_image_embedding"),
+                    fl.Linear(
+                        in_features=text_cross_attention.key_embedding_dim,
+                        out_features=text_cross_attention.inner_dim,
+                        bias=text_cross_attention.use_bias,
+                        device=text_cross_attention.device,
+                        dtype=text_cross_attention.dtype,
+                    ),
+                ),
+                fl.Chain(
+                    fl.UseContext(context="ip_adapter", key="clip_image_embedding"),
+                    fl.Linear(
+                        in_features=text_cross_attention.key_embedding_dim,
+                        out_features=text_cross_attention.inner_dim,
+                        bias=text_cross_attention.use_bias,
+                        device=text_cross_attention.device,
+                        dtype=text_cross_attention.dtype,
+                    ),
+                ),
+            ),
+            ScaledDotProductAttention(
+                num_heads=text_cross_attention.num_heads, is_causal=text_cross_attention.is_causal
+            ),
+            fl.Multiply(self.scale),
+        )
+
+
+class SetQueryProjection(fl.Passthrough):
+    def __init__(self) -> None:
+        super().__init__(fl.GetArg(index=0), fl.SetContext(context="ip_adapter", key="query_projection"))
+
+
 class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
     def __init__(
         self,
         target: fl.Attention,
-        text_sequence_length: int = 77,
-        image_sequence_length: int = 4,
         scale: float = 1.0,
     ) -> None:
-        self.text_sequence_length = text_sequence_length
-        self.image_sequence_length = image_sequence_length
-        self.scale = scale
-
         with self.setup_adapter(target):
             super().__init__(
-                fl.Distribute(
-                    # Note: the same query is used for image cross-attention as for text cross-attention
-                    InjectionPoint(),  # Wq
-                    fl.Parallel(
-                        fl.Chain(
-                            fl.Slicing(dim=1, end=text_sequence_length),
-                            InjectionPoint(),  # Wk
-                        ),
-                        fl.Chain(
-                            fl.Slicing(dim=1, start=text_sequence_length),
-                            fl.Linear(
-                                in_features=self.target.key_embedding_dim,
-                                out_features=self.target.inner_dim,
-                                bias=self.target.use_bias,
-                                device=target.device,
-                                dtype=target.dtype,
-                            ),  # Wk'
-                        ),
-                    ),
-                    fl.Parallel(
-                        fl.Chain(
-                            fl.Slicing(dim=1, end=text_sequence_length),
-                            InjectionPoint(),  # Wv
-                        ),
-                        fl.Chain(
-                            fl.Slicing(dim=1, start=text_sequence_length),
-                            fl.Linear(
-                                in_features=self.target.key_embedding_dim,
-                                out_features=self.target.inner_dim,
-                                bias=self.target.use_bias,
-                                device=target.device,
-                                dtype=target.dtype,
-                            ),  # Wv'
-                        ),
-                    ),
-                ),
                 fl.Sum(
-                    fl.Chain(
-                        fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.TXT_CROSS_ATTN)),
-                        ScaledDotProductAttention(num_heads=target.num_heads, is_causal=target.is_causal),
-                    ),
-                    fl.Chain(
-                        fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.IMG_CROSS_ATTN)),
-                        ScaledDotProductAttention(num_heads=target.num_heads, is_causal=target.is_causal),
-                        fl.Lambda(func=self.scale_outputs),
-                    ),
+                    target[:-1],  # original text cross attention
+                    ImageCrossAttention(text_cross_attention=target, scale=scale),
                 ),
-                InjectionPoint(),  # proj
+                target[-1],  # projection
             )
+            self.ensure_find(fl.Attention).insert_after_type(Distribute, SetQueryProjection())
 
-    def select_qkv(
-        self, query: Tensor, keys: tuple[Tensor, Tensor], values: tuple[Tensor, Tensor], index: _CrossAttnIndex
-    ) -> tuple[Tensor, Tensor, Tensor]:
-        return (query, keys[index.value], values[index.value])
-
-    def scale_outputs(self, x: Tensor) -> Tensor:
-        return x * self.scale
-
-    def _predicate(self, k: type[fl.Module]) -> Callable[[fl.Module, fl.Chain], bool]:
-        def f(m: fl.Module, _: fl.Chain) -> bool:
-            if isinstance(m, Lora):  # do not adapt LoRAs
-                raise StopIteration
-            return isinstance(m, k)
-
-        return f
-
-    def _target_linears(self) -> list[fl.Linear]:
-        return [m for m, _ in self.target.walk(self._predicate(fl.Linear)) if isinstance(m, fl.Linear)]
-
-    def inject(self: "CrossAttentionAdapter", parent: fl.Chain | None = None) -> "CrossAttentionAdapter":
-        linears = self._target_linears()
-        assert len(linears) == 4  # Wq, Wk, Wv and Proj
-
-        injection_points = list(self.layers(InjectionPoint))
-        assert len(injection_points) == 4
+    @property
+    def image_cross_attention(self) -> ImageCrossAttention:
+        return self.ensure_find(ImageCrossAttention)
 
-        for linear, ip in zip(linears, injection_points):
-            ip.append(linear)
-            assert len(ip) == 1
+    @property
+    def image_key_projection(self) -> fl.Linear:
+        return self.image_cross_attention.Distribute[1].Linear
 
-        return super().inject(parent)
+    @property
+    def image_value_projection(self) -> fl.Linear:
+        return self.image_cross_attention.Distribute[2].Linear
 
-    def eject(self) -> None:
-        injection_points = list(self.layers(InjectionPoint))
-        assert len(injection_points) == 4
+    @property
+    def scale(self) -> float:
+        return self.image_cross_attention.scale
 
-        for ip in injection_points:
-            ip.pop()
-            assert len(ip) == 0
+    @scale.setter
+    def scale(self, value: float) -> None:
+        self.image_cross_attention.scale = value
 
-        super().eject()
+    def load_weights(self, key_tensor: Tensor, value_tensor: Tensor) -> None:
+        self.image_key_projection.weight = nn.Parameter(key_tensor)
+        self.image_value_projection.weight = nn.Parameter(value_tensor)
+        self.image_cross_attention.to(self.device, self.dtype)
 
 
 class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
@@ -377,7 +344,7 @@ def __init__(
         self._image_proj = [image_proj]
 
         self.sub_adapters = [
-            CrossAttentionAdapter(target=cross_attn, scale=scale, image_sequence_length=self.image_proj.num_tokens)
+            CrossAttentionAdapter(target=cross_attn, scale=scale)
             for cross_attn in filter(lambda attn: type(attn) != fl.SelfAttention, target.layers(fl.Attention))
         ]
 
@@ -388,14 +355,15 @@ def __init__(
             self.image_proj.load_state_dict(image_proj_state_dict)
 
             for i, cross_attn in enumerate(self.sub_adapters):
-                cross_attn_state_dict: dict[str, Tensor] = {}
+                cross_attention_weights: list[Tensor] = []
                 for k, v in weights.items():
                     prefix = f"ip_adapter.{i:03d}."
                     if not k.startswith(prefix):
                         continue
-                    cross_attn_state_dict[k.removeprefix(prefix)] = v
+                    cross_attention_weights.append(v)
 
-                cross_attn.load_state_dict(state_dict=cross_attn_state_dict)
+                assert len(cross_attention_weights) == 2
+                cross_attn.load_weights(*cross_attention_weights)
 
     @property
     def clip_image_encoder(self) -> CLIPImageEncoderH:
@@ -420,10 +388,22 @@ def eject(self) -> None:
             adapter.eject()
         super().eject()
 
+    @property
+    def scale(self) -> float:
+        return self.sub_adapters[0].scale
+
+    @scale.setter
+    def scale(self, value: float) -> None:
+        for cross_attn in self.sub_adapters:
+            cross_attn.scale = value
+
     def set_scale(self, scale: float) -> None:
         for cross_attn in self.sub_adapters:
             cross_attn.scale = scale
 
+    def set_clip_image_embedding(self, image_embedding: Tensor) -> None:
+        self.set_context("ip_adapter", {"clip_image_embedding": image_embedding})
+
     # These should be concatenated to the CLIP text embedding before setting the UNet context
     def compute_clip_image_embedding(self, image_prompt: Tensor) -> Tensor:
         image_encoder = self.clip_image_encoder if not self.fine_grained else self.grid_image_encoder
diff --git a/src/refiners/foundationals/latent_diffusion/lora.py b/src/refiners/foundationals/latent_diffusion/lora.py
index bc041c8b1..d8820ab6f 100644
--- a/src/refiners/foundationals/latent_diffusion/lora.py
+++ b/src/refiners/foundationals/latent_diffusion/lora.py
@@ -122,7 +122,7 @@ def from_safetensors(
         assert metadata is not None, "Invalid safetensors checkpoint: missing metadata"
         tensors = load_from_safetensors(checkpoint_path, device=target.device)
 
-        sub_targets = {}
+        sub_targets: dict[str, list[LoraTarget]] = {}
         for model_name in MODELS:
             if not (v := metadata.get(f"{model_name}_targets", "")):
                 continue
diff --git a/src/refiners/foundationals/latent_diffusion/reference_only_control.py b/src/refiners/foundationals/latent_diffusion/reference_only_control.py
index bf17bc724..1f0e049ca 100644
--- a/src/refiners/foundationals/latent_diffusion/reference_only_control.py
+++ b/src/refiners/foundationals/latent_diffusion/reference_only_control.py
@@ -1,3 +1,5 @@
+from typing import Callable
+
 from torch import Tensor
 
 from refiners.fluxion.adapters.adapter import Adapter
@@ -45,8 +47,9 @@ def __init__(
         )
 
         with self.setup_adapter(target):
+            slice_tensor: Callable[[Tensor], Tensor] = lambda x: x[:1]
             super().__init__(
-                Parallel(sa_guided, Chain(Lambda(lambda x: x[:1]), target)),
+                Parallel(sa_guided, Chain(Lambda(slice_tensor), target)),
                 Lambda(self.compute_averaged_unconditioned_x),
             )
 
diff --git a/src/refiners/foundationals/segment_anything/model.py b/src/refiners/foundationals/segment_anything/model.py
index f8abfb71e..905c4b67f 100644
--- a/src/refiners/foundationals/segment_anything/model.py
+++ b/src/refiners/foundationals/segment_anything/model.py
@@ -3,11 +3,12 @@
 
 import numpy as np
 import torch
+from jaxtyping import Float
 from PIL import Image
 from torch import Tensor, device as Device, dtype as DType
 
 import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import image_to_tensor, interpolate, normalize, pad
+from refiners.fluxion.utils import interpolate, no_grad, normalize, pad
 from refiners.foundationals.segment_anything.image_encoder import SAMViT, SAMViTH
 from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
 from refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder
@@ -39,7 +40,7 @@ def __init__(
         self.mask_encoder = mask_encoder.to(device=self.device, dtype=self.dtype)
         self.mask_decoder = mask_decoder.to(device=self.device, dtype=self.dtype)
 
-    @torch.no_grad()
+    @no_grad()
     def compute_image_embedding(self, image: Image.Image) -> ImageEmbedding:
         original_size = (image.height, image.width)
         target_size = self.compute_target_size(original_size)
@@ -48,14 +49,14 @@ def compute_image_embedding(self, image: Image.Image) -> ImageEmbedding:
             original_image_size=original_size,
         )
 
-    @torch.no_grad()
+    @no_grad()
     def predict(
         self,
         input: Image.Image | ImageEmbedding,
         foreground_points: Sequence[tuple[float, float]] | None = None,
         background_points: Sequence[tuple[float, float]] | None = None,
         box_points: Sequence[Sequence[tuple[float, float]]] | None = None,
-        masks: Sequence[Image.Image] | None = None,
+        low_res_mask: Float[Tensor, "1 1 256 256"] | None = None,
         binarize: bool = True,
     ) -> tuple[Tensor, Tensor, Tensor]:
         if isinstance(input, ImageEmbedding):
@@ -74,15 +75,13 @@ def predict(
         )
         self.point_encoder.set_type_mask(type_mask=type_mask)
 
-        if masks is not None:
-            mask_tensor = torch.stack(
-                tensors=[image_to_tensor(image=mask, device=self.device, dtype=self.dtype) for mask in masks]
-            )
-            mask_embedding = self.mask_encoder(mask_tensor)
+        if low_res_mask is not None:
+            mask_embedding = self.mask_encoder(low_res_mask)
         else:
             mask_embedding = self.mask_encoder.get_no_mask_dense_embedding(
                 image_embedding_size=self.image_encoder.image_embedding_size
             )
+
         point_embedding = self.point_encoder(
             self.normalize(coordinates, target_size=target_size, original_size=original_size)
         )
diff --git a/src/refiners/training_utils/latent_diffusion.py b/src/refiners/training_utils/latent_diffusion.py
index dff85fca7..f4f8ccf0a 100644
--- a/src/refiners/training_utils/latent_diffusion.py
+++ b/src/refiners/training_utils/latent_diffusion.py
@@ -250,7 +250,7 @@ def on_compute_loss_end(self, trainer: LatentDiffusionTrainer[Any]) -> None:
         self.timestep_bins[bin_index].append(loss_value)
 
     def on_epoch_end(self, trainer: LatentDiffusionTrainer[Any]) -> None:
-        log_data = {}
+        log_data: dict[str, WandbLoggable] = {}
         for bin_index, losses in self.timestep_bins.items():
             if losses:
                 avg_loss = sum(losses) / len(losses)
diff --git a/src/refiners/training_utils/trainer.py b/src/refiners/training_utils/trainer.py
index 87276d8ca..730bb8ae7 100644
--- a/src/refiners/training_utils/trainer.py
+++ b/src/refiners/training_utils/trainer.py
@@ -6,7 +6,7 @@
 
 import numpy as np
 from loguru import logger
-from torch import Tensor, cuda, device as Device, get_rng_state, no_grad, set_rng_state, stack
+from torch import Tensor, cuda, device as Device, get_rng_state, set_rng_state, stack
 from torch.autograd import backward
 from torch.nn import Parameter
 from torch.optim import Optimizer
@@ -26,7 +26,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 from refiners.fluxion import layers as fl
-from refiners.fluxion.utils import manual_seed
+from refiners.fluxion.utils import manual_seed, no_grad
 from refiners.training_utils.callback import (
     Callback,
     ClockCallback,
diff --git a/tests/e2e/test_diffusion.py b/tests/e2e/test_diffusion.py
index 201c1b0e4..4850e79c3 100644
--- a/tests/e2e/test_diffusion.py
+++ b/tests/e2e/test_diffusion.py
@@ -6,7 +6,7 @@
 import torch
 from PIL import Image
 
-from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, manual_seed
+from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, manual_seed, no_grad
 from refiners.foundationals.clip.concepts import ConceptExtender
 from refiners.foundationals.latent_diffusion import (
     SD1ControlnetAdapter,
@@ -501,7 +501,7 @@ def sdxl_ddim(
     return sdxl
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_std_random_init(
     sd15_std: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device
 ):
@@ -529,7 +529,7 @@ def test_diffusion_std_random_init(
     ensure_similar_images(predicted_image, expected_image_std_random_init)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_karras_random_init(
     sd15_ddim_karras: StableDiffusion_1, expected_karras_random_init: Image.Image, test_device: torch.device
 ):
@@ -554,7 +554,7 @@ def test_diffusion_karras_random_init(
     ensure_similar_images(predicted_image, expected_karras_random_init, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_std_random_init_float16(
     sd15_std_float16: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device
 ):
@@ -583,7 +583,7 @@ def test_diffusion_std_random_init_float16(
     ensure_similar_images(predicted_image, expected_image_std_random_init, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_std_random_init_sag(
     sd15_std: StableDiffusion_1, expected_image_std_random_init_sag: Image.Image, test_device: torch.device
 ):
@@ -612,7 +612,7 @@ def test_diffusion_std_random_init_sag(
     ensure_similar_images(predicted_image, expected_image_std_random_init_sag)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_std_init_image(
     sd15_std: StableDiffusion_1,
     cutecat_init: Image.Image,
@@ -643,7 +643,7 @@ def test_diffusion_std_init_image(
     ensure_similar_images(predicted_image, expected_image_std_init_image)
 
 
-@torch.no_grad()
+@no_grad()
 def test_rectangular_init_latents(
     sd15_std: StableDiffusion_1,
     cutecat_init: Image.Image,
@@ -658,7 +658,7 @@ def test_rectangular_init_latents(
     assert sd15.lda.decode_latents(x).size == (width, height)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_inpainting(
     sd15_inpainting: StableDiffusion_1_Inpainting,
     kitchen_dog: Image.Image,
@@ -692,7 +692,7 @@ def test_diffusion_inpainting(
     ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=25, min_ssim=0.95)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_inpainting_float16(
     sd15_inpainting_float16: StableDiffusion_1_Inpainting,
     kitchen_dog: Image.Image,
@@ -727,7 +727,7 @@ def test_diffusion_inpainting_float16(
     ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=20, min_ssim=0.92)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_controlnet(
     sd15_std: StableDiffusion_1,
     controlnet_data: tuple[str, Image.Image, Image.Image, Path],
@@ -770,7 +770,7 @@ def test_diffusion_controlnet(
     ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_controlnet_structural_copy(
     sd15_std: StableDiffusion_1,
     controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path],
@@ -814,7 +814,7 @@ def test_diffusion_controlnet_structural_copy(
     ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_controlnet_float16(
     sd15_std_float16: StableDiffusion_1,
     controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path],
@@ -857,7 +857,7 @@ def test_diffusion_controlnet_float16(
     ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_controlnet_stack(
     sd15_std: StableDiffusion_1,
     controlnet_data_depth: tuple[str, Image.Image, Image.Image, Path],
@@ -912,7 +912,7 @@ def test_diffusion_controlnet_stack(
     ensure_similar_images(predicted_image, expected_image_controlnet_stack, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_lora(
     sd15_std: StableDiffusion_1,
     lora_data_pokemon: tuple[Image.Image, Path],
@@ -949,7 +949,7 @@ def test_diffusion_lora(
     ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_lora_float16(
     sd15_std_float16: StableDiffusion_1,
     lora_data_pokemon: tuple[Image.Image, Path],
@@ -986,7 +986,7 @@ def test_diffusion_lora_float16(
     ensure_similar_images(predicted_image, expected_image, min_psnr=33, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_lora_twice(
     sd15_std: StableDiffusion_1,
     lora_data_pokemon: tuple[Image.Image, Path],
@@ -1025,7 +1025,7 @@ def test_diffusion_lora_twice(
     ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_refonly(
     sd15_ddim: StableDiffusion_1,
     condition_image_refonly: Image.Image,
@@ -1061,7 +1061,7 @@ def test_diffusion_refonly(
     ensure_similar_images(predicted_image, expected_image_refonly, min_psnr=35, min_ssim=0.99)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_inpainting_refonly(
     sd15_inpainting: StableDiffusion_1_Inpainting,
     scene_image_inpainting_refonly: Image.Image,
@@ -1106,7 +1106,7 @@ def test_diffusion_inpainting_refonly(
     ensure_similar_images(predicted_image, expected_image_inpainting_refonly, min_psnr=35, min_ssim=0.99)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_textual_inversion_random_init(
     sd15_std: StableDiffusion_1,
     expected_image_textual_inversion_random_init: Image.Image,
@@ -1141,7 +1141,7 @@ def test_diffusion_textual_inversion_random_init(
     ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_ip_adapter(
     sd15_ddim_lda_ft_mse: StableDiffusion_1,
     ip_adapter_weights: Path,
@@ -1168,16 +1168,7 @@ def test_diffusion_ip_adapter(
 
     clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
     clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
-
-    negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
-    negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
-
-    clip_text_embedding = torch.cat(
-        (
-            torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
-            torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
-        )
-    )
+    ip_adapter.set_clip_image_embedding(clip_image_embedding)
 
     sd15.set_num_inference_steps(n_steps)
 
@@ -1196,7 +1187,7 @@ def test_diffusion_ip_adapter(
     ensure_similar_images(predicted_image, expected_image_ip_adapter_woman)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_sdxl_ip_adapter(
     sdxl_ddim: StableDiffusion_XL,
     sdxl_ip_adapter_weights: Path,
@@ -1215,28 +1206,20 @@ def test_diffusion_sdxl_ip_adapter(
     ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
     ip_adapter.inject()
 
-    with torch.no_grad():
+    with no_grad():
         clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
             text=prompt, negative_text=negative_prompt
         )
         clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
+        ip_adapter.set_clip_image_embedding(clip_image_embedding)
 
-        negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
-        negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
-
-        clip_text_embedding = torch.cat(
-            (
-                torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
-                torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
-            )
-        )
     time_ids = sdxl.default_time_ids
     sdxl.set_num_inference_steps(n_steps)
 
     manual_seed(2)
     x = torch.randn(1, 4, 128, 128, device=test_device, dtype=torch.float16)
 
-    with torch.no_grad():
+    with no_grad():
         for step in sdxl.steps:
             x = sdxl(
                 x,
@@ -1254,7 +1237,7 @@ def test_diffusion_sdxl_ip_adapter(
     ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_woman)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_ip_adapter_controlnet(
     sd15_ddim: StableDiffusion_1,
     ip_adapter_weights: Path,
@@ -1285,16 +1268,7 @@ def test_diffusion_ip_adapter_controlnet(
 
     clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
     clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(input_image))
-
-    negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
-    negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
-
-    clip_text_embedding = torch.cat(
-        (
-            torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
-            torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
-        )
-    )
+    ip_adapter.set_clip_image_embedding(clip_image_embedding)
 
     depth_cn_condition = image_to_tensor(
         depth_condition_image.convert("RGB"),
@@ -1320,7 +1294,7 @@ def test_diffusion_ip_adapter_controlnet(
     ensure_similar_images(predicted_image, expected_image_ip_adapter_controlnet)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_ip_adapter_plus(
     sd15_ddim_lda_ft_mse: StableDiffusion_1,
     ip_adapter_plus_weights: Path,
@@ -1343,16 +1317,7 @@ def test_diffusion_ip_adapter_plus(
 
     clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
     clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(statue_image))
-
-    negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
-    negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
-
-    clip_text_embedding = torch.cat(
-        (
-            torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
-            torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
-        )
-    )
+    ip_adapter.set_clip_image_embedding(clip_image_embedding)
 
     sd15.set_num_inference_steps(n_steps)
 
@@ -1371,7 +1336,7 @@ def test_diffusion_ip_adapter_plus(
     ensure_similar_images(predicted_image, expected_image_ip_adapter_plus_statue, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_diffusion_sdxl_ip_adapter_plus(
     sdxl_ddim: StableDiffusion_XL,
     sdxl_ip_adapter_plus_weights: Path,
@@ -1396,16 +1361,8 @@ def test_diffusion_sdxl_ip_adapter_plus(
         text=prompt, negative_text=negative_prompt
     )
     clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
+    ip_adapter.set_clip_image_embedding(clip_image_embedding)
 
-    negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
-    negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
-
-    clip_text_embedding = torch.cat(
-        (
-            torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
-            torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
-        )
-    )
     time_ids = sdxl.default_time_ids
     sdxl.set_num_inference_steps(n_steps)
 
@@ -1427,7 +1384,7 @@ def test_diffusion_sdxl_ip_adapter_plus(
     ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_plus_woman)
 
 
-@torch.no_grad()
+@no_grad()
 def test_sdxl_random_init(
     sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init: Image.Image, test_device: torch.device
 ) -> None:
@@ -1462,7 +1419,7 @@ def test_sdxl_random_init(
     ensure_similar_images(img_1=predicted_image, img_2=expected_image, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_sdxl_random_init_sag(
     sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init_sag: Image.Image, test_device: torch.device
 ) -> None:
@@ -1498,7 +1455,7 @@ def test_sdxl_random_init_sag(
     ensure_similar_images(img_1=predicted_image, img_2=expected_image)
 
 
-@torch.no_grad()
+@no_grad()
 def test_multi_diffusion(sd15_ddim: StableDiffusion_1, expected_multi_diffusion: Image.Image) -> None:
     manual_seed(seed=2)
     sd = sd15_ddim
@@ -1529,7 +1486,7 @@ def test_multi_diffusion(sd15_ddim: StableDiffusion_1, expected_multi_diffusion:
     ensure_similar_images(img_1=result, img_2=expected_multi_diffusion, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_t2i_adapter_depth(
     sd15_std: StableDiffusion_1,
     t2i_adapter_data_depth: tuple[str, Image.Image, Image.Image, Path],
@@ -1570,7 +1527,7 @@ def test_t2i_adapter_depth(
     ensure_similar_images(predicted_image, expected_image)
 
 
-@torch.no_grad()
+@no_grad()
 def test_t2i_adapter_xl_canny(
     sdxl_ddim: StableDiffusion_XL,
     t2i_adapter_xl_data_canny: tuple[str, Image.Image, Image.Image, Path],
@@ -1619,7 +1576,7 @@ def test_t2i_adapter_xl_canny(
     ensure_similar_images(predicted_image, expected_image)
 
 
-@torch.no_grad()
+@no_grad()
 def test_restart(
     sd15_ddim: StableDiffusion_1,
     expected_restart: Image.Image,
@@ -1659,7 +1616,7 @@ def test_restart(
     ensure_similar_images(predicted_image, expected_restart, min_psnr=35, min_ssim=0.98)
 
 
-@torch.no_grad()
+@no_grad()
 def test_freeu(
     sd15_std: StableDiffusion_1,
     expected_freeu: Image.Image,
diff --git a/tests/e2e/test_diffusion_ref/expected_ip_adapter_controlnet.png b/tests/e2e/test_diffusion_ref/expected_ip_adapter_controlnet.png
index e838df167..41a90cc4e 100644
Binary files a/tests/e2e/test_diffusion_ref/expected_ip_adapter_controlnet.png and b/tests/e2e/test_diffusion_ref/expected_ip_adapter_controlnet.png differ
diff --git a/tests/e2e/test_preprocessors.py b/tests/e2e/test_preprocessors.py
index 69131b21c..4492638d0 100644
--- a/tests/e2e/test_preprocessors.py
+++ b/tests/e2e/test_preprocessors.py
@@ -5,7 +5,7 @@
 import torch
 from PIL import Image
 
-from refiners.fluxion.utils import image_to_tensor, tensor_to_image
+from refiners.fluxion.utils import image_to_tensor, no_grad, tensor_to_image
 from refiners.foundationals.latent_diffusion.preprocessors.informative_drawings import InformativeDrawings
 from tests.utils import ensure_similar_images
 
@@ -41,7 +41,7 @@ def informative_drawings_model(informative_drawings_weights: Path, test_device:
     return model
 
 
-@torch.no_grad()
+@no_grad()
 def test_preprocessor_informative_drawing(
     informative_drawings_model: InformativeDrawings,
     cutecat_init: Image.Image,
diff --git a/tests/fluxion/layers/test_converter.py b/tests/fluxion/layers/test_converter.py
index 8a3318838..356cdb1fd 100644
--- a/tests/fluxion/layers/test_converter.py
+++ b/tests/fluxion/layers/test_converter.py
@@ -1,3 +1,4 @@
+from typing import Any, Callable
 from warnings import warn
 
 import pytest
@@ -60,8 +61,9 @@ def test_converter_multiple_tensors(test_device: torch.device) -> None:
 
 
 def test_converter_no_parent_device_or_dtype() -> None:
+    identity: Callable[[Any], Any] = lambda x: x
     chain = fl.Chain(
-        fl.Lambda(func=(lambda x: x)),
+        fl.Lambda(func=identity),
         fl.Converter(set_device=True, set_dtype=False),
     )
 
diff --git a/tests/fluxion/test_utils.py b/tests/fluxion/test_utils.py
index 8811c4716..883755089 100644
--- a/tests/fluxion/test_utils.py
+++ b/tests/fluxion/test_utils.py
@@ -7,7 +7,7 @@
 from torch import device as Device, dtype as DType
 from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur  # type: ignore
 
-from refiners.fluxion.utils import gaussian_blur, image_to_tensor, manual_seed, tensor_to_image
+from refiners.fluxion.utils import gaussian_blur, image_to_tensor, manual_seed, no_grad, tensor_to_image
 
 
 @dataclass
@@ -62,3 +62,18 @@ def test_tensor_to_image() -> None:
     assert tensor_to_image(torch.zeros(1, 3, 512, 512)).mode == "RGB"
     assert tensor_to_image(torch.zeros(1, 1, 512, 512)).mode == "L"
     assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"
+
+
+def test_no_grad() -> None:
+    x = torch.randn(1, 1, requires_grad=True)
+
+    with torch.no_grad():
+        y = x + 1
+        assert not y.requires_grad
+
+    with no_grad():
+        z = x + 1
+        assert not z.requires_grad
+
+    w = x + 1
+    assert w.requires_grad
diff --git a/tests/foundationals/clip/test_concepts.py b/tests/foundationals/clip/test_concepts.py
index ed8656145..9c4ed749f 100644
--- a/tests/foundationals/clip/test_concepts.py
+++ b/tests/foundationals/clip/test_concepts.py
@@ -7,7 +7,7 @@
 from diffusers import StableDiffusionPipeline  # type: ignore
 
 import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import load_from_safetensors
+from refiners.fluxion.utils import load_from_safetensors, no_grad
 from refiners.foundationals.clip.concepts import ConceptExtender, TokenExtender
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
@@ -124,7 +124,7 @@ def test_encoder(
     our_tokens = tokenizer(prompt)
     assert torch.equal(our_tokens, ref_tokens)
 
-    with torch.no_grad():
+    with no_grad():
         ref_embeddings = ref_encoder_with_new_concepts(ref_tokens.to(test_device))[0]
         our_embeddings = our_encoder_with_new_concepts(prompt)
 
diff --git a/tests/foundationals/clip/test_image_encoder.py b/tests/foundationals/clip/test_image_encoder.py
index 3aac6684e..ff990bda5 100644
--- a/tests/foundationals/clip/test_image_encoder.py
+++ b/tests/foundationals/clip/test_image_encoder.py
@@ -5,7 +5,7 @@
 import torch
 from transformers import CLIPVisionModelWithProjection  # type: ignore
 
-from refiners.fluxion.utils import load_from_safetensors
+from refiners.fluxion.utils import load_from_safetensors, no_grad
 from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 
 
@@ -44,7 +44,7 @@ def test_encoder(
 ):
     x = torch.randn(1, 3, 224, 224).to(test_device)
 
-    with torch.no_grad():
+    with no_grad():
         ref_embeddings = ref_encoder(x).image_embeds
         our_embeddings = our_encoder(x)
 
diff --git a/tests/foundationals/clip/test_text_encoder.py b/tests/foundationals/clip/test_text_encoder.py
index 0e108b7f8..f1b6f07c6 100644
--- a/tests/foundationals/clip/test_text_encoder.py
+++ b/tests/foundationals/clip/test_text_encoder.py
@@ -5,7 +5,7 @@
 import torch
 import transformers  # type: ignore
 
-from refiners.fluxion.utils import load_from_safetensors
+from refiners.fluxion.utils import load_from_safetensors, no_grad
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
@@ -89,7 +89,7 @@ def test_encoder(
     our_tokens = tokenizer(prompt)
     assert torch.equal(our_tokens, ref_tokens)
 
-    with torch.no_grad():
+    with no_grad():
         ref_embeddings = ref_encoder(ref_tokens.to(test_device))[0]
         our_embeddings = our_encoder(prompt)
 
diff --git a/tests/foundationals/dinov2/test_dinov2.py b/tests/foundationals/dinov2/test_dinov2.py
index 9b5f40e77..7bcf81863 100644
--- a/tests/foundationals/dinov2/test_dinov2.py
+++ b/tests/foundationals/dinov2/test_dinov2.py
@@ -7,7 +7,7 @@
 from transformers import AutoModel  # type: ignore
 from transformers.models.dinov2.modeling_dinov2 import Dinov2Model  # type: ignore
 
-from refiners.fluxion.utils import load_from_safetensors, manual_seed
+from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
 from refiners.foundationals.dinov2 import (
     DINOv2_base,
     DINOv2_base_reg,
@@ -124,7 +124,7 @@ def test_encoder(
 
     x = torch.randn(1, 3, 518, 518).to(test_device)
 
-    with torch.no_grad():
+    with no_grad():
         ref_features = ref_backbone(x).last_hidden_state
         our_features = our_backbone(x)
 
diff --git a/tests/foundationals/latent_diffusion/test_auto_encoder.py b/tests/foundationals/latent_diffusion/test_auto_encoder.py
index 2ddca248e..462c40776 100644
--- a/tests/foundationals/latent_diffusion/test_auto_encoder.py
+++ b/tests/foundationals/latent_diffusion/test_auto_encoder.py
@@ -6,7 +6,7 @@
 from PIL import Image
 from tests.utils import ensure_similar_images
 
-from refiners.fluxion.utils import load_from_safetensors
+from refiners.fluxion.utils import load_from_safetensors, no_grad
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 
 
@@ -38,7 +38,7 @@ def sample_image(ref_path: Path) -> Image.Image:
     return img
 
 
-@torch.no_grad()
+@no_grad()
 def test_encode_decode(encoder: LatentDiffusionAutoencoder, sample_image: Image.Image):
     encoded = encoder.encode_image(sample_image)
     decoded = encoder.decode_latents(encoded)
diff --git a/tests/foundationals/latent_diffusion/test_controlnet.py b/tests/foundationals/latent_diffusion/test_controlnet.py
index 36f3b04b7..4bfc5e600 100644
--- a/tests/foundationals/latent_diffusion/test_controlnet.py
+++ b/tests/foundationals/latent_diffusion/test_controlnet.py
@@ -1,10 +1,10 @@
 from typing import Iterator
 
 import pytest
-import torch
 
 import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import lookup_top_adapter
+from refiners.fluxion.utils import no_grad
 from refiners.foundationals.latent_diffusion import SD1ControlnetAdapter, SD1UNet
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import Controlnet
 
@@ -18,7 +18,7 @@ def unet(request: pytest.FixtureRequest) -> Iterator[SD1UNet]:
     yield unet
 
 
-@torch.no_grad()
+@no_grad()
 def test_single_controlnet(unet: SD1UNet) -> None:
     original_parent = unet.parent
     cn = SD1ControlnetAdapter(unet, name="cn")
@@ -43,7 +43,7 @@ def test_single_controlnet(unet: SD1UNet) -> None:
     assert len(list(unet.walk(Controlnet))) == 0
 
 
-@torch.no_grad()
+@no_grad()
 def test_two_controlnets_eject_bottom_up(unet: SD1UNet) -> None:
     original_parent = unet.parent
     cn1 = SD1ControlnetAdapter(unet, name="cn1").inject()
@@ -71,7 +71,7 @@ def test_two_controlnets_eject_bottom_up(unet: SD1UNet) -> None:
     assert len(list(unet.walk(Controlnet))) == 0
 
 
-@torch.no_grad()
+@no_grad()
 def test_two_controlnets_eject_top_down(unet: SD1UNet) -> None:
     original_parent = unet.parent
     cn1 = SD1ControlnetAdapter(unet, name="cn1").inject()
@@ -86,7 +86,7 @@ def test_two_controlnets_eject_top_down(unet: SD1UNet) -> None:
     assert len(list(unet.walk(Controlnet))) == 0
 
 
-@torch.no_grad()
+@no_grad()
 def test_two_controlnets_same_name(unet: SD1UNet) -> None:
     SD1ControlnetAdapter(unet, name="cnx").inject()
     cn2 = SD1ControlnetAdapter(unet, name="cnx")
diff --git a/tests/foundationals/latent_diffusion/test_freeu.py b/tests/foundationals/latent_diffusion/test_freeu.py
index 6b7001b24..3e4553ec2 100644
--- a/tests/foundationals/latent_diffusion/test_freeu.py
+++ b/tests/foundationals/latent_diffusion/test_freeu.py
@@ -4,6 +4,7 @@
 import torch
 
 from refiners.fluxion import manual_seed
+from refiners.fluxion.utils import no_grad
 from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
 from refiners.foundationals.latent_diffusion.freeu import FreeUResidualConcatenator, SDFreeUAdapter
 
@@ -52,14 +53,14 @@ def test_freeu_identity_scales() -> None:
     unet = SD1UNet(in_channels=4)
     unet.set_clip_text_embedding(clip_text_embedding=text_embedding)  # not flushed between forward-s
 
-    with torch.no_grad():
+    with no_grad():
         unet.set_timestep(timestep=timestep)
         y_1 = unet(x.clone())
 
     freeu = SDFreeUAdapter(unet, backbone_scales=[1.0, 1.0], skip_scales=[1.0, 1.0])
     freeu.inject()
 
-    with torch.no_grad():
+    with no_grad():
         unet.set_timestep(timestep=timestep)
         y_2 = unet(x.clone())
 
diff --git a/tests/foundationals/latent_diffusion/test_image_prompt.py b/tests/foundationals/latent_diffusion/test_image_prompt.py
deleted file mode 100644
index 612de2e58..000000000
--- a/tests/foundationals/latent_diffusion/test_image_prompt.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import refiners.fluxion.layers as fl
-from refiners.foundationals.latent_diffusion.image_prompt import CrossAttentionAdapter, InjectionPoint
-
-
-def test_cross_attention_adapter() -> None:
-    base = fl.Chain(fl.Attention(embedding_dim=4))
-    adapter = CrossAttentionAdapter(base.Attention).inject()
-
-    assert list(base) == [adapter]
-    assert len(list(adapter.layers(fl.Linear))) == 6
-    assert len(list(base.layers(fl.Linear))) == 6
-
-    injection_points = list(adapter.layers(InjectionPoint))
-    assert len(injection_points) == 4
-    for ip in injection_points:
-        assert len(ip) == 1
-        assert isinstance(ip[0], fl.Linear)
-
-    adapter.eject()
-
-    assert len(base) == 1
-    assert isinstance(base[0], fl.Attention)
-    assert len(list(adapter.layers(fl.Linear))) == 2
-    assert len(list(base.layers(fl.Linear))) == 4
-
-    injection_points = list(adapter.layers(InjectionPoint))
-    assert len(injection_points) == 4
-    for ip in injection_points:
-        assert len(ip) == 0
diff --git a/tests/foundationals/latent_diffusion/test_reference_only_control.py b/tests/foundationals/latent_diffusion/test_reference_only_control.py
index 68833b3ae..d0ed8a3f5 100644
--- a/tests/foundationals/latent_diffusion/test_reference_only_control.py
+++ b/tests/foundationals/latent_diffusion/test_reference_only_control.py
@@ -1,6 +1,6 @@
 import pytest
-import torch
 
+from refiners.fluxion.utils import no_grad
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock
 from refiners.foundationals.latent_diffusion.reference_only_control import (
@@ -11,7 +11,7 @@
 )
 
 
-@torch.no_grad()
+@no_grad()
 def test_refonly_inject_eject() -> None:
     unet = SD1UNet(in_channels=9)
     adapter = ReferenceOnlyControlAdapter(unet)
diff --git a/tests/foundationals/latent_diffusion/test_sdxl_double_encoder.py b/tests/foundationals/latent_diffusion/test_sdxl_double_encoder.py
index cb51253bb..9435b89da 100644
--- a/tests/foundationals/latent_diffusion/test_sdxl_double_encoder.py
+++ b/tests/foundationals/latent_diffusion/test_sdxl_double_encoder.py
@@ -7,7 +7,7 @@
 from torch import Tensor
 
 import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import manual_seed
+from refiners.fluxion.utils import manual_seed, no_grad
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
 
 
@@ -65,7 +65,7 @@ def double_text_encoder(double_text_encoder_weights: Path) -> DoubleTextEncoder:
     return double_text_encoder
 
 
-@torch.no_grad()
+@no_grad()
 def test_double_text_encoder(diffusers_sdxl: DiffusersSDXL, double_text_encoder: DoubleTextEncoder) -> None:
     manual_seed(seed=0)
     prompt = "A photo of a pizza."
diff --git a/tests/foundationals/latent_diffusion/test_sdxl_unet.py b/tests/foundationals/latent_diffusion/test_sdxl_unet.py
index 95b031bc9..c3d0f10a4 100644
--- a/tests/foundationals/latent_diffusion/test_sdxl_unet.py
+++ b/tests/foundationals/latent_diffusion/test_sdxl_unet.py
@@ -6,7 +6,7 @@
 import torch
 
 from refiners.fluxion.model_converter import ConversionStage, ModelConverter
-from refiners.fluxion.utils import manual_seed
+from refiners.fluxion.utils import manual_seed, no_grad
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl import SDXLUNet
 
 
@@ -37,7 +37,7 @@ def refiners_sdxl_unet() -> SDXLUNet:
     return unet
 
 
-@torch.no_grad()
+@no_grad()
 def test_sdxl_unet(diffusers_sdxl_unet: Any, refiners_sdxl_unet: SDXLUNet) -> None:
     source = diffusers_sdxl_unet
     target = refiners_sdxl_unet
diff --git a/tests/foundationals/latent_diffusion/test_unet.py b/tests/foundationals/latent_diffusion/test_unet.py
index 4fe09e34a..210eca720 100644
--- a/tests/foundationals/latent_diffusion/test_unet.py
+++ b/tests/foundationals/latent_diffusion/test_unet.py
@@ -1,6 +1,7 @@
 import torch
 
 from refiners.fluxion import manual_seed
+from refiners.fluxion.utils import no_grad
 from refiners.foundationals.latent_diffusion import SD1UNet
 
 
@@ -13,11 +14,11 @@ def test_unet_context_flush():
     unet = SD1UNet(in_channels=4)
     unet.set_clip_text_embedding(clip_text_embedding=text_embedding)  # not flushed between forward-s
 
-    with torch.no_grad():
+    with no_grad():
         unet.set_timestep(timestep=timestep)
         y_1 = unet(x.clone())
 
-    with torch.no_grad():
+    with no_grad():
         unet.set_timestep(timestep=timestep)
         y_2 = unet(x.clone())
 
diff --git a/tests/foundationals/segment_anything/test_sam.py b/tests/foundationals/segment_anything/test_sam.py
index 0c5fbf978..1e5668542 100644
--- a/tests/foundationals/segment_anything/test_sam.py
+++ b/tests/foundationals/segment_anything/test_sam.py
@@ -18,12 +18,12 @@
 
 from refiners.fluxion import manual_seed
 from refiners.fluxion.model_converter import ModelConverter
-from refiners.fluxion.utils import image_to_tensor
+from refiners.fluxion.utils import image_to_tensor, no_grad
 from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention
 from refiners.foundationals.segment_anything.model import SegmentAnythingH
 from refiners.foundationals.segment_anything.transformer import TwoWayTranformerLayer
 
-# See predictor_example.ipynb official notebook (note: mask_input is not yet properly supported)
+# See predictor_example.ipynb official notebook
 PROMPTS: list[SAMPrompt] = [
     SAMPrompt(foreground_points=((500, 375),)),
     SAMPrompt(background_points=((500, 375),)),
@@ -41,7 +41,9 @@ def prompt(request: pytest.FixtureRequest) -> SAMPrompt:
 
 @pytest.fixture
 def one_prompt() -> SAMPrompt:
-    return PROMPTS[0]
+    # Using the third prompt of the PROMPTS list in order to strictly do the same test as the official notebook in the
+    # test_predictor_dense_mask test.
+    return PROMPTS[2]
 
 
 @pytest.fixture(scope="module")
@@ -83,8 +85,7 @@ def facebook_sam_h_predictor(facebook_sam_h: FacebookSAM) -> FacebookSAMPredicto
 @pytest.fixture(scope="module")
 def sam_h(sam_h_weights: Path, test_device: torch.device) -> SegmentAnythingH:
     sam_h = SegmentAnythingH(device=test_device)
-    # TODO: make strict=True when the MasKEncoder conversion is done
-    sam_h.load_from_safetensors(tensors_path=sam_h_weights, strict=False)
+    sam_h.load_from_safetensors(tensors_path=sam_h_weights)
     return sam_h
 
 
@@ -98,7 +99,7 @@ def truck(ref_path: Path) -> Image.Image:
     return Image.open(ref_path / "truck.jpg").convert("RGB")
 
 
-@torch.no_grad()
+@no_grad()
 def test_fused_self_attention(facebook_sam_h: FacebookSAM) -> None:
     manual_seed(seed=0)
     x = torch.randn(25, 14, 14, 1280, device=facebook_sam_h.device)
@@ -124,7 +125,7 @@ def test_fused_self_attention(facebook_sam_h: FacebookSAM) -> None:
     assert torch.equal(input=y_1, other=y_2)
 
 
-@torch.no_grad()
+@no_grad()
 def test_image_encoder(sam_h: SegmentAnythingH, facebook_sam_h: FacebookSAM, truck: Image.Image) -> None:
     image_tensor = image_to_tensor(image=truck.resize(size=(1024, 1024)), device=facebook_sam_h.device)
     y_1 = facebook_sam_h.image_encoder(image_tensor)
@@ -133,7 +134,7 @@ def test_image_encoder(sam_h: SegmentAnythingH, facebook_sam_h: FacebookSAM, tru
     assert torch.allclose(input=y_1, other=y_2, atol=1e-4)
 
 
-@torch.no_grad()
+@no_grad()
 def test_prompt_encoder_dense_positional_embedding(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
     facebook_prompt_encoder = facebook_sam_h.prompt_encoder
     refiners_prompt_encoder = sam_h.point_encoder
@@ -144,7 +145,7 @@ def test_prompt_encoder_dense_positional_embedding(facebook_sam_h: FacebookSAM,
     assert torch.equal(input=refiners_dense_pe, other=facebook_dense_pe)
 
 
-@torch.no_grad()
+@no_grad()
 def test_prompt_encoder_no_mask_dense_embedding(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
     facebook_prompt_encoder = facebook_sam_h.prompt_encoder
     refiners_prompt_encoder = sam_h.mask_encoder
@@ -155,7 +156,7 @@ def test_prompt_encoder_no_mask_dense_embedding(facebook_sam_h: FacebookSAM, sam
     assert torch.equal(input=refiners_dense_pe, other=facebook_dense_pe)
 
 
-@torch.no_grad()
+@no_grad()
 def test_point_encoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH, prompt: SAMPrompt) -> None:
     facebook_prompt_encoder = facebook_sam_h.prompt_encoder
     refiners_prompt_encoder = sam_h.point_encoder
@@ -164,7 +165,14 @@ def test_point_encoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH, pro
         **prompt.facebook_prompt_encoder_kwargs(device=facebook_sam_h.device)
     )
 
-    coordinates, type_mask = refiners_prompt_encoder.points_to_tensor(**prompt.__dict__)
+    prompt_dict = prompt.__dict__
+    # Skip mask prompt, if any, since the point encoder only consumes points and boxes
+    # TODO: split `SAMPrompt` and introduce a dedicated one for dense prompts
+    prompt_dict.pop("low_res_mask", None)
+
+    assert prompt_dict is not None, "`test_point_encoder` cannot be called with just a `low_res_mask`"
+
+    coordinates, type_mask = refiners_prompt_encoder.points_to_tensor(**prompt_dict)
     # Shift to center of pixel + normalize in [0, 1] (see `_embed_points` in segment-anything official repo)
     coordinates[:, :, 0] = (coordinates[:, :, 0] + 0.5) / 1024.0
     coordinates[:, :, 1] = (coordinates[:, :, 1] + 0.5) / 1024.0
@@ -174,7 +182,7 @@ def test_point_encoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH, pro
     assert torch.equal(input=refiners_sparse_pe, other=facebook_sparse_pe)
 
 
-@torch.no_grad()
+@no_grad()
 def test_two_way_transformer(facebook_sam_h: FacebookSAM) -> None:
     dense_embedding = torch.randn(1, 64 * 64, 256, device=facebook_sam_h.device)
     dense_positional_embedding = torch.randn(1, 64 * 64, 256, device=facebook_sam_h.device)
@@ -223,7 +231,7 @@ def test_two_way_transformer(facebook_sam_h: FacebookSAM) -> None:
     assert torch.equal(input=y_1, other=y_2)
 
 
-@torch.no_grad()
+@no_grad()
 def test_mask_decoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
     manual_seed(seed=0)
     facebook_mask_decoder = facebook_sam_h.mask_decoder
@@ -319,3 +327,91 @@ def test_predictor_image_embedding(sam_h: SegmentAnythingH, truck: Image.Image,
 
     assert torch.equal(masks, masks_ref)
     assert torch.equal(scores_ref, scores)
+
+
+def test_predictor_dense_mask(
+    facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt
+) -> None:
+    """
+    NOTE : Binarizing intermediate masks isn't necessary, as per SamPredictor.predict_torch docstring:
+    > mask_input (np.ndarray): A low resolution mask input to the model, typically
+    >         coming from a previous prediction iteration. Has form Bx1xHxW, where
+    >         for SAM, H=W=256. Masks returned by a previous iteration of the
+    >         predict method do not need further transformation.
+    """
+    predictor = facebook_sam_h_predictor
+    predictor.set_image(np.array(truck))
+    facebook_masks, facebook_scores, facebook_logits = predictor.predict(
+        **one_prompt.facebook_predict_kwargs(),  # type: ignore
+        multimask_output=True,
+    )
+
+    assert len(facebook_masks) == 3
+
+    facebook_mask_input = facebook_logits[np.argmax(facebook_scores)]  # shape: HxW
+
+    # Using the same mask coordinates inputs as the official notebook
+    facebook_prompt = SAMPrompt(
+        foreground_points=((500, 375),), background_points=((1125, 625),), low_res_mask=facebook_mask_input[None, ...]
+    )
+    facebook_dense_masks, _, _ = predictor.predict(**facebook_prompt.facebook_predict_kwargs(), multimask_output=True)  # type: ignore
+
+    assert len(facebook_dense_masks) == 3
+
+    masks, scores, logits = sam_h.predict(truck, **one_prompt.__dict__)
+    masks = masks.squeeze(0)
+    scores = scores.squeeze(0)
+
+    assert len(masks) == 3
+
+    mask_input = logits[:, scores.max(dim=0).indices, ...]  # shape: 1xHxW
+
+    assert np.allclose(
+        mask_input.cpu().numpy(), facebook_mask_input, atol=1e-1
+    )  # Lower doesn't pass, but it's close enough for logits
+
+    refiners_prompt = SAMPrompt(
+        foreground_points=((500, 375),), background_points=((1125, 625),), low_res_mask=mask_input.unsqueeze(0)
+    )
+    dense_masks, _, _ = sam_h.predict(truck, **refiners_prompt.__dict__)
+    dense_masks = dense_masks.squeeze(0)
+
+    assert len(dense_masks) == 3
+
+    for i in range(3):
+        dense_mask_prediction = dense_masks[i].cpu()
+        facebook_dense_mask = torch.as_tensor(facebook_dense_masks[i])
+        assert dense_mask_prediction.shape == facebook_dense_mask.shape
+        assert isclose(intersection_over_union(dense_mask_prediction, facebook_dense_mask), 1.0, rel_tol=5e-05)
+
+
+def test_mask_encoder(
+    facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt
+) -> None:
+    predictor = facebook_sam_h_predictor
+    predictor.set_image(np.array(truck))
+    _, facebook_scores, facebook_logits = predictor.predict(
+        **one_prompt.facebook_predict_kwargs(),  # type: ignore
+        multimask_output=True,
+    )
+    facebook_mask_input = facebook_logits[np.argmax(facebook_scores)]
+    facebook_mask_input = (
+        torch.from_numpy(facebook_mask_input)  # type: ignore
+        .to(device=predictor.model.device)
+        .unsqueeze(0)
+        .unsqueeze(0)  # shape: 1x1xHxW
+    )
+
+    _, fb_dense_embeddings = predictor.model.prompt_encoder(
+        points=None,
+        boxes=None,
+        masks=facebook_mask_input,
+    )
+
+    _, scores, logits = sam_h.predict(truck, **one_prompt.__dict__)
+    scores = scores.squeeze(0)
+    mask_input = logits[:, scores.max(dim=0).indices, ...].unsqueeze(0)  # shape: 1x1xHxW
+    dense_embeddings = sam_h.mask_encoder(mask_input)
+
+    assert facebook_mask_input.shape == mask_input.shape
+    assert torch.allclose(dense_embeddings, fb_dense_embeddings, atol=1e-4, rtol=1e-4)
diff --git a/tests/foundationals/segment_anything/utils.py b/tests/foundationals/segment_anything/utils.py
index ef73e36ae..fa18e8841 100644
--- a/tests/foundationals/segment_anything/utils.py
+++ b/tests/foundationals/segment_anything/utils.py
@@ -63,8 +63,7 @@ class SAMPrompt:
     foreground_points: Sequence[tuple[float, float]] | None = None
     background_points: Sequence[tuple[float, float]] | None = None
     box_points: Sequence[Sequence[tuple[float, float]]] | None = None
-    # TODO: support masks
-    # masks: Sequence[Image.Image] | None = None
+    low_res_mask: Tensor | None = None
 
     def facebook_predict_kwargs(self) -> dict[str, NDArray]:
         prompt: dict[str, NDArray] = {}
@@ -85,13 +84,18 @@ def facebook_predict_kwargs(self) -> dict[str, NDArray]:
             prompt["box"] = np.array([coord for batch in self.box_points for xy in batch for coord in xy]).reshape(
                 len(self.box_points), 4
             )
+        if self.low_res_mask is not None:
+            prompt["mask_input"] = np.array(self.low_res_mask)
         return prompt
 
-    def facebook_prompt_encoder_kwargs(self, device: torch.device | None = None):
+    def facebook_prompt_encoder_kwargs(
+        self, device: torch.device | None = None
+    ) -> dict[str, Tensor | tuple[Tensor, Tensor | None] | None]:
         prompt = self.facebook_predict_kwargs()
         coords: Tensor | None = None
         labels: Tensor | None = None
         boxes: Tensor | None = None
+        masks: Tensor | None = None
         if "point_coords" in prompt:
             coords = torch.as_tensor(prompt["point_coords"], dtype=torch.float, device=device).unsqueeze(0)
         if "point_labels" in prompt:
@@ -99,8 +103,9 @@ def facebook_prompt_encoder_kwargs(self, device: torch.device | None = None):
         if "box" in prompt:
             boxes = torch.as_tensor(prompt["box"], dtype=torch.float, device=device).unsqueeze(0)
         points = (coords, labels) if coords is not None else None
-        # TODO: support masks
-        return {"points": points, "boxes": boxes, "masks": None}
+        if "mask_input" in prompt:
+            masks = torch.as_tensor(prompt["mask_input"], dtype=torch.float, device=device).unsqueeze(0)
+        return {"points": points, "boxes": boxes, "masks": masks}
 
 
 def intersection_over_union(