Add the Llama and Stable Diffusion examples

stable_diffusion/README.md

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+Stable Diffusion
+================
+
+Stable Diffusion in MLX. The implementation was ported from Hugginface's
+[diffusers](https://huggingface.co/docs/diffusers/index) and we are fetching
+and using the weights available on the Huggingface Hub by Stability AI at
+[stabilitiai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1).
+
+![out](generated-mlx.png)    
+*Image generated using Stable Diffusion in MLX and the prompt 'A big red sign saying MLX in capital letters.'*
+
+Installation
+------------
+
+The dependencies are minimal, namely:
+
+- `safetensors` and `huggingface-hub` to load the checkpoints.
+- `regex` for the tokenization
+- `numpy` because safetensors needs to return some form of array
+- `tqdm` and `PIL` for the `txt2image.py` script
+
+You can install all of the above with the `requirements.txt` as follows:
+
+    pip install -r requirements.txt
+
+Usage
+------
+
+Although each component in this repository can be used by itsself, the fastest
+way to get started is by using the `StableDiffusion` class from the `diffusion`
+module.
+
+```python
+from stable_diffusion import StableDiffusion
+
+# This will download all the weights from HF hub and load the models in
+# memory
+sd = StableDiffusion()
+
+# This creates a python generator that returns the latent produced by the
+# reverse diffusion process.
+#
+# Because MLX is lazily evaluated iterating over this generator doesn't
+# actually perform the computation until mx.eval() is called.
+latent_generator = sd.generate_latents("A photo of an astronaut riding a horse on Mars.")
+
+# Here we are evaluating each diffusion step but we could also evaluate
+# once at the end.
+for x_t in latent_generator:
+    mx.simplify(x_t) # remove possible redundant computation eg reuse
+                     # scalars etc
+    mx.eval(x_t)
+
+# Now x_t is the last latent from the reverse process aka x_0. We can
+# decode it into an image using the stable diffusion VAE.
+im = sd.decode(x_t)
+```
+
+The above is almost line for line the implementation of the `txt2image.py`
+script in the root of the repository. You can use the script as follows:
+
+    python txt2image.py "A photo of an astronaut riding a horse on Mars." --n_images 4 --n_rows 2
+
+Performance
+-----------
+
+The following table compares the performance of the UNet in stable diffusion.
+We report throughput in images per second for the provided `txt2image.py`
+script and the `diffusers` library using the MPS PyTorch backend.
+
+At the time of writing this comparison convolutions are still some of the least
+optimized operations in MLX. Despite that, MLX still achieves **~40% higher
+throughput** than PyTorch with a batch size of 16 and ~15% higher when
+comparing the optimal batch sizes.
+
+Notably, PyTorch achieves almost ~50% higher throughput for the batch size of 1
+which is unfortunate as that means that a single image can be computed faster.
+However, when starting with the models not loaded in memory and PyTorch's MPS
+graph kernels not cached, the compilation time more than accounts for this
+speed difference.
+
+| Batch size |   PyTorch   |     MLX     |
+| ---------- | ----------- | ----------- |
+| 1          |  6.25 im/s  |  4.17 im/s  |
+| 2          |  7.14 im/s  |  5.88 im/s  |
+| 4          |**7.69 im/s**|  7.14 im/s  |
+| 6          |  7.22 im/s  |  8.00 im/s  |
+| 8          |  6.89 im/s  |  8.42 im/s  |
+| 12         |  6.62 im/s  |  8.51 im/s  |
+| 16         |  6.32 im/s  |**8.79 im/s**|
+
+The above experiments were made on an M2 Ultra with PyTorch version 2.1,
+diffusers version 0.21.4 and transformers version 4.33.3. For the generation we
+used classifier free guidance which means that the above batch sizes result
+double the images processed by the UNet.

stable_diffusion/requirements.txt

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+safetensors
+huggingface-hub
+regex
+numpy
+tqdm
+Pillow

stable_diffusion/stable_diffusion/__init__.py

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+import time
+from typing import Tuple
+
+import mlx.core as mx
+
+from .model_io import (
+    load_unet,
+    load_text_encoder,
+    load_autoencoder,
+    load_diffusion_config,
+    load_tokenizer,
+    _DEFAULT_MODEL,
+)
+from .sampler import SimpleEulerSampler
+
+
+def _repeat(x, n, axis):
+    # Make the expanded shape
+    s = x.shape
+    s.insert(axis + 1, n)
+
+    # Expand
+    x = mx.broadcast_to(mx.expand_dims(x, axis + 1), s)
+
+    # Make the flattened shape
+    s.pop(axis + 1)
+    s[axis] *= n
+
+    return x.reshape(s)
+
+
+class StableDiffusion:
+    def __init__(self, model: str = _DEFAULT_MODEL, float16: bool = False):
+        self.dtype = mx.float16 if float16 else mx.float32
+        self.diffusion_config = load_diffusion_config(model)
+        self.unet = load_unet(model, float16)
+        self.text_encoder = load_text_encoder(model, float16)
+        self.autoencoder = load_autoencoder(model, float16)
+        self.sampler = SimpleEulerSampler(self.diffusion_config)
+        self.tokenizer = load_tokenizer(model)
+
+    def generate_latents(
+        self,
+        text: str,
+        n_images: int = 1,
+        num_steps: int = 50,
+        cfg_weight: float = 7.5,
+        negative_text: str = "",
+        latent_size: Tuple[int] = (64, 64),
+        seed=None,
+    ):
+        # Set the PRNG state
+        seed = seed or int(time.time())
+        mx.random.seed(seed)
+
+        # Tokenize the text
+        tokens = [self.tokenizer.tokenize(text)]
+        if cfg_weight > 1:
+            tokens += [self.tokenizer.tokenize(negative_text)]
+        lengths = [len(t) for t in tokens]
+        N = max(lengths)
+        tokens = [t + [0] * (N - len(t)) for t in tokens]
+        tokens = mx.array(tokens)
+
+        # Compute the features
+        conditioning = self.text_encoder(tokens)
+
+        # Repeat the conditioning for each of the generated images
+        if n_images > 1:
+            conditioning = _repeat(conditioning, n_images, axis=0)
+
+        # Create the latent variables
+        x_T = self.sampler.sample_prior(
+            (n_images, *latent_size, self.autoencoder.latent_channels),
+            dtype=self.dtype
+        )
+
+        # Perform the denoising loop
+        x_t = x_T
+        for t, t_prev in self.sampler.timesteps(num_steps, dtype=self.dtype):
+            x_t_unet = mx.concatenate([x_t] * 2, axis=0) if cfg_weight > 1 else x_t
+            t_unet = mx.broadcast_to(t, [len(x_t_unet)])
+            eps_pred = self.unet(x_t_unet, t_unet, encoder_x=conditioning)
+
+            if cfg_weight > 1:
+                eps_text, eps_neg = eps_pred.split(2)
+                eps_pred = eps_neg + cfg_weight * (eps_text - eps_neg)
+
+            x_t_prev = self.sampler.step(eps_pred, x_t, t, t_prev)
+            x_t = x_t_prev
+            yield x_t
+
+    def decode(self, x_t):
+        x = self.autoencoder.decode(x_t / self.autoencoder.scaling_factor)
+        x = mx.minimum(1, mx.maximum(0, x / 2 + 0.5))
+        return x

stable_diffusion/stable_diffusion/clip.py

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+import mlx.core as mx
+import mlx.nn as nn
+
+from .config import CLIPTextModelConfig
+
+
+class CLIPEncoderLayer(nn.Module):
+    """The transformer encoder layer from CLIP."""
+
+    def __init__(self, model_dims: int, num_heads: int):
+        super().__init__()
+
+        self.layer_norm1 = nn.LayerNorm(model_dims)
+        self.layer_norm2 = nn.LayerNorm(model_dims)
+
+        self.attention = nn.MultiHeadAttention(model_dims, num_heads)
+        # Add biases to the attention projections to match CLIP
+        self.attention.query_proj.bias = mx.zeros(model_dims)
+        self.attention.key_proj.bias = mx.zeros(model_dims)
+        self.attention.value_proj.bias = mx.zeros(model_dims)
+        self.attention.out_proj.bias = mx.zeros(model_dims)
+
+        self.linear1 = nn.Linear(model_dims, 4 * model_dims)
+        self.linear2 = nn.Linear(4 * model_dims, model_dims)
+
+    def __call__(self, x, attn_mask=None):
+        y = self.layer_norm1(x)
+        y = self.attention(y, y, y, attn_mask)
+        x = y + x
+
+        y = self.layer_norm2(x)
+        y = self.linear1(y)
+        y = nn.gelu_approx(y)
+        y = self.linear2(y)
+        x = y + x
+
+        return x
+
+
+class CLIPTextModel(nn.Module):
+    """Implements the text encoder transformer from CLIP."""
+
+    def __init__(self, config: CLIPTextModelConfig):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(config.vocab_size, config.model_dims)
+        self.position_embedding = nn.Embedding(config.max_length, config.model_dims)
+        self.layers = [
+            CLIPEncoderLayer(config.model_dims, config.num_heads)
+            for i in range(config.num_layers)
+        ]
+        self.final_layer_norm = nn.LayerNorm(config.model_dims)
+
+    def __call__(self, x):
+        # Extract some shapes
+        B, N = x.shape
+
+        # Compute the embeddings
+        x = self.token_embedding(x)
+        x = x + self.position_embedding.weight[:N]
+
+        # Compute the features from the transformer
+        mask = nn.MultiHeadAttention.create_additive_causal_mask(N, x.dtype)
+        for l in self.layers:
+            x = l(x, mask)
+
+        # Apply the final layernorm and return
+        return self.final_layer_norm(x)

stable_diffusion/stable_diffusion/config.py

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+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+
+@dataclass
+class AutoencoderConfig:
+    in_channels: int = 3
+    out_channels: int = 3
+    latent_channels_out: int = 8
+    latent_channels_in: int = 4
+    block_out_channels: Tuple[int] = (128, 256, 512, 512)
+    layers_per_block: int = 2
+    norm_num_groups: int = 32
+    scaling_factor: float = 0.18215
+
+
+@dataclass
+class CLIPTextModelConfig:
+    num_layers: int = 23
+    model_dims: int = 1024
+    num_heads: int = 16
+    max_length: int = 77
+    vocab_size: int = 49408
+
+
+@dataclass
+class UNetConfig:
+    in_channels: int = 4
+    out_channels: int = 4
+    conv_in_kernel: int = 3
+    conv_out_kernel: int = 3
+    block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
+    layers_per_block: Tuple[int] = (2, 2, 2, 2)
+    mid_block_layers: int = 2
+    transformer_layers_per_block: Tuple[int] = (1, 1, 1, 1)
+    num_attention_heads: Tuple[int] = (5, 10, 20, 20)
+    cross_attention_dim: Tuple[int] = (1024,) * 4
+    norm_num_groups: int = 32
+
+
+@dataclass
+class DiffusionConfig:
+    beta_schedule: str = "scaled_linear"
+    beta_start: float = 0.00085
+    beta_end: float = 0.012
+    num_train_steps: int = 1000

stable_diffusion/stable_diffusion/model_io.py

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+import json
+from functools import partial
+
+import numpy as np
+from huggingface_hub import hf_hub_download
+from safetensors import safe_open as safetensor_open
+
+import mlx.core as mx
+from mlx.utils import tree_unflatten
+
+from .clip import CLIPTextModel
+from .config import UNetConfig, CLIPTextModelConfig, AutoencoderConfig, DiffusionConfig
+from .tokenizer import Tokenizer
+from .unet import UNetModel
+from .vae import Autoencoder
+
+
+_DEFAULT_MODEL = "stabilityai/stable-diffusion-2-1-base"
+_MODELS = {
+    # See https://huggingface.co/stabilityai/stable-diffusion-2-1-base for the model details and license
+    "stabilityai/stable-diffusion-2-1-base": {
+        "unet_config": "unet/config.json",
+        "unet": "unet/diffusion_pytorch_model.safetensors",
+        "text_encoder_config": "text_encoder/config.json",
+        "text_encoder": "text_encoder/model.safetensors",
+        "vae_config": "vae/config.json",
+        "vae": "vae/diffusion_pytorch_model.safetensors",
+        "diffusion_config": "scheduler/scheduler_config.json",
+        "tokenizer_vocab": "tokenizer/vocab.json",
+        "tokenizer_merges": "tokenizer/merges.txt",
+    }
+}
+
+
+def _from_numpy(x):
+    return mx.array(np.ascontiguousarray(x))
+
+
+def map_unet_weights(key, value):
+    # Map up/downsampling
+    if "downsamplers" in key:
+        key = key.replace("downsamplers.0.conv", "downsample")
+    if "upsamplers" in key:
+        key = key.replace("upsamplers.0.conv", "upsample")
+
+    # Map the mid block
+    if "mid_block.resnets.0" in key:
+        key = key.replace("mid_block.resnets.0", "mid_blocks.0")
+    if "mid_block.attentions.0" in key:
+        key = key.replace("mid_block.attentions.0", "mid_blocks.1")
+    if "mid_block.resnets.1" in key:
+        key = key.replace("mid_block.resnets.1", "mid_blocks.2")
+
+    # Map attention layers
+    if "to_k" in key:
+        key = key.replace("to_k", "key_proj")
+    if "to_out.0" in key:
+        key = key.replace("to_out.0", "out_proj")
+    if "to_q" in key:
+        key = key.replace("to_q", "query_proj")
+    if "to_v" in key:
+        key = key.replace("to_v", "value_proj")
+
+    # Map transformer ffn
+    if "ff.net.2" in key:
+        key = key.replace("ff.net.2", "linear3")
+    if "ff.net.0" in key:
+        k1 = key.replace("ff.net.0.proj", "linear1")
+        k2 = key.replace("ff.net.0.proj", "linear2")
+        v1, v2 = np.split(value, 2)
+
+        return [(k1, _from_numpy(v1)), (k2, _from_numpy(v2))]
+
+    if "conv_shortcut.weight" in key:
+        value = value.squeeze()
+
+    if len(value.shape) == 4:
+        value = value.transpose(0, 2, 3, 1)
+
+    return [(key, _from_numpy(value))]
+
+
+def map_clip_text_encoder_weights(key, value):
+    # Remove prefixes
+    if key.startswith("text_model."):
+        key = key[11:]
+    if key.startswith("embeddings."):
+        key = key[11:]
+    if key.startswith("encoder."):
+        key = key[8:]
+
+    # Map attention layers
+    if "self_attn." in key:
+        key = key.replace("self_attn.", "attention.")
+    if "q_proj." in key:
+        key = key.replace("q_proj.", "query_proj.")
+    if "k_proj." in key:
+        key = key.replace("k_proj.", "key_proj.")
+    if "v_proj." in key:
+        key = key.replace("v_proj.", "value_proj.")
+
+    # Map ffn layers
+    if "mlp.fc1" in key:
+        key = key.replace("mlp.fc1", "linear1")
+    if "mlp.fc2" in key:
+        key = key.replace("mlp.fc2", "linear2")
+
+    return [(key, _from_numpy(value))]
+
+
+def map_vae_weights(key, value):
+    # Map up/downsampling
+    if "downsamplers" in key:
+        key = key.replace("downsamplers.0.conv", "downsample")
+    if "upsamplers" in key:
+        key = key.replace("upsamplers.0.conv", "upsample")
+
+    # Map attention layers
+    if "to_k" in key:
+        key = key.replace("to_k", "key_proj")
+    if "to_out.0" in key:
+        key = key.replace("to_out.0", "out_proj")
+    if "to_q" in key:
+        key = key.replace("to_q", "query_proj")
+    if "to_v" in key:
+        key = key.replace("to_v", "value_proj")
+
+    # Map the mid block
+    if "mid_block.resnets.0" in key:
+        key = key.replace("mid_block.resnets.0", "mid_blocks.0")
+    if "mid_block.attentions.0" in key:
+        key = key.replace("mid_block.attentions.0", "mid_blocks.1")
+    if "mid_block.resnets.1" in key:
+        key = key.replace("mid_block.resnets.1", "mid_blocks.2")
+
+    # Map the quant/post_quant layers
+    if "quant_conv" in key:
+        key = key.replace("quant_conv", "quant_proj")
+        value = value.squeeze()
+
+    # Map the conv_shortcut to linear
+    if "conv_shortcut.weight" in key:
+        value = value.squeeze()
+
+    if len(value.shape) == 4:
+        value = value.transpose(0, 2, 3, 1)
+
+    return [(key, _from_numpy(value))]
+
+
+def _flatten(params):
+    return [(k, v) for p in params for (k, v) in p]
+
+
+def _load_safetensor_weights(mapper, model, weight_file, float16: bool = False):
+    dtype = np.float16 if float16 else np.float32
+    with safetensor_open(weight_file, framework="numpy") as f:
+        weights = _flatten([mapper(k, f.get_tensor(k).astype(dtype)) for k in f.keys()])
+    model.update(tree_unflatten(weights))
+
+
+def _check_key(key: str, part: str):
+    if key not in _MODELS:
+        raise ValueError(
+            f"[{part}] '{key}' model not found, choose one of {{{','.join(_MODELS.keys())}}}"
+        )
+
+
+def load_unet(key: str = _DEFAULT_MODEL, float16: bool = False):
+    """Load the stable diffusion UNet from Huggingface Hub."""
+    _check_key(key, "load_unet")
+
+    # Download the config and create the model
+    unet_config = _MODELS[key]["unet_config"]
+    with open(hf_hub_download(key, unet_config)) as f:
+        config = json.load(f)
+
+    n_blocks = len(config["block_out_channels"])
+    model = UNetModel(
+        UNetConfig(
+            in_channels=config["in_channels"],
+            out_channels=config["out_channels"],
+            block_out_channels=config["block_out_channels"],
+            layers_per_block=[config["layers_per_block"]] * n_blocks,
+            num_attention_heads=config["attention_head_dim"],
+            cross_attention_dim=[config["cross_attention_dim"]] * n_blocks,
+            norm_num_groups=config["norm_num_groups"],
+        )
+    )
+
+    # Download the weights and map them into the model
+    unet_weights = _MODELS[key]["unet"]
+    weight_file = hf_hub_download(key, unet_weights)
+    _load_safetensor_weights(map_unet_weights, model, weight_file, float16)
+
+    return model
+
+
+def load_text_encoder(key: str = _DEFAULT_MODEL, float16: bool = False):
+    """Load the stable diffusion text encoder from Huggingface Hub."""
+    _check_key(key, "load_text_encoder")
+
+    # Download the config and create the model
+    text_encoder_config = _MODELS[key]["text_encoder_config"]
+    with open(hf_hub_download(key, text_encoder_config)) as f:
+        config = json.load(f)
+
+    model = CLIPTextModel(
+        CLIPTextModelConfig(
+            num_layers=config["num_hidden_layers"],
+            model_dims=config["hidden_size"],
+            num_heads=config["num_attention_heads"],
+            max_length=config["max_position_embeddings"],
+            vocab_size=config["vocab_size"],
+        )
+    )
+
+    # Download the weights and map them into the model
+    text_encoder_weights = _MODELS[key]["text_encoder"]
+    weight_file = hf_hub_download(key, text_encoder_weights)
+    _load_safetensor_weights(map_clip_text_encoder_weights, model, weight_file, float16)
+
+    return model
+
+
+def load_autoencoder(key: str = _DEFAULT_MODEL, float16: bool = False):
+    """Load the stable diffusion autoencoder from Huggingface Hub."""
+    _check_key(key, "load_autoencoder")
+
+    # Download the config and create the model
+    vae_config = _MODELS[key]["vae_config"]
+    with open(hf_hub_download(key, vae_config)) as f:
+        config = json.load(f)
+
+    model = Autoencoder(
+        AutoencoderConfig(
+            in_channels=config["in_channels"],
+            out_channels=config["out_channels"],
+            latent_channels_out=2 * config["latent_channels"],
+            latent_channels_in=config["latent_channels"],
+            block_out_channels=config["block_out_channels"],
+            layers_per_block=config["layers_per_block"],
+            norm_num_groups=config["norm_num_groups"],
+        )
+    )
+
+    # Download the weights and map them into the model
+    vae_weights = _MODELS[key]["vae"]
+    weight_file = hf_hub_download(key, vae_weights)
+    _load_safetensor_weights(map_vae_weights, model, weight_file, float16)
+
+    return model
+
+
+def load_diffusion_config(key: str = _DEFAULT_MODEL):
+    """Load the stable diffusion config from Huggingface Hub."""
+    _check_key(key, "load_diffusion_config")
+
+    diffusion_config = _MODELS[key]["diffusion_config"]
+    with open(hf_hub_download(key, diffusion_config)) as f:
+        config = json.load(f)
+
+    return DiffusionConfig(
+        beta_start=config["beta_start"],
+        beta_end=config["beta_end"],
+        beta_schedule=config["beta_schedule"],
+        num_train_steps=config["num_train_timesteps"],
+    )
+
+
+def load_tokenizer(key: str = _DEFAULT_MODEL):
+    _check_key(key, "load_tokenizer")
+
+    vocab_file = hf_hub_download(key, _MODELS[key]["tokenizer_vocab"])
+    with open(vocab_file, encoding="utf-8") as f:
+        vocab = json.load(f)
+
+    merges_file = hf_hub_download(key, _MODELS[key]["tokenizer_merges"])
+    with open(merges_file, encoding="utf-8") as f:
+        bpe_merges = f.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1]
+    bpe_merges = [tuple(m.split()) for m in bpe_merges]
+    bpe_ranks = dict(map(reversed, enumerate(bpe_merges)))
+
+    return Tokenizer(bpe_ranks, vocab)

stable_diffusion/stable_diffusion/sampler.py

@@ -0,0 +1,70 @@
+from .config import DiffusionConfig
+
+import mlx.core as mx
+
+
+def _linspace(a, b, num):
+    x = mx.arange(0, num) / (num - 1)
+    return (b - a) * x + a
+
+
+def _interp(y, x_new):
+    """Interpolate the function defined by (arange(0, len(y)), y) at positions x_new."""
+    x_low = x_new.astype(mx.int32)
+    x_high = mx.minimum(x_low + 1, len(y) - 1)
+
+    y_low = y[x_low]
+    y_high = y[x_high]
+    delta_x = x_new - x_low
+    y_new = y_low * (1 - delta_x) + delta_x * y_high
+
+    return y_new
+
+
+class SimpleEulerSampler:
+    """A simple Euler integrator that can be used to sample from our diffusion models.
+
+    The method ``step()`` performs one Euler step from x_t to x_t_prev.
+    """
+
+    def __init__(self, config: DiffusionConfig):
+        # Compute the noise schedule
+        if config.beta_schedule == "linear":
+            betas = _linspace(
+                config.beta_start, config.beta_end, config.num_train_steps
+            )
+        elif config.beta_schedule == "scaled_linear":
+            betas = _linspace(
+                config.beta_start**0.5, config.beta_end**0.5, config.num_train_steps
+            ).square()
+        else:
+            raise NotImplementedError(f"{config.beta_schedule} is not implemented.")
+
+        alphas = 1 - betas
+        alphas_cumprod = mx.cumprod(alphas)
+
+        self._sigmas = mx.concatenate(
+            [mx.zeros(1), ((1 - alphas_cumprod) / alphas_cumprod).sqrt()]
+        )
+
+    def sample_prior(self, shape, dtype=mx.float32, key=None):
+        noise = mx.random.normal(shape, key=key)
+        return (noise * self._sigmas[-1] * (self._sigmas[-1].square() + 1).rsqrt()).astype(dtype)
+
+    def sigmas(self, t):
+        return _interp(self._sigmas, t)
+
+    def timesteps(self, num_steps: int, dtype=mx.float32):
+        steps = _linspace(len(self._sigmas) - 1, 0, num_steps + 1).astype(dtype)
+        return list(zip(steps, steps[1:]))
+
+    def step(self, eps_pred, x_t, t, t_prev):
+        sigma = self.sigmas(t).astype(eps_pred.dtype)
+        sigma_prev = self.sigmas(t_prev).astype(eps_pred.dtype)
+
+        dt = sigma_prev - sigma
+        x_t_prev = (sigma.square() + 1).sqrt() * x_t + eps_pred * dt
+
+        x_t_prev = x_t_prev * (sigma_prev.square() + 1).rsqrt()
+
+        return x_t_prev

stable_diffusion/stable_diffusion/tokenizer.py

@@ -0,0 +1,95 @@
+import regex
+
+
+class Tokenizer:
+    """A simple port of CLIPTokenizer from https://github.com/huggingface/transformers/ ."""
+    def __init__(self, bpe_ranks, vocab):
+        self.bpe_ranks = bpe_ranks
+        self.vocab = vocab
+        self.pat = regex.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+            regex.IGNORECASE,
+        )
+
+        self._cache = {self.bos: self.bos, self.eos: self.eos}
+
+    @property
+    def bos(self):
+        return "<|startoftext|>"
+
+    @property
+    def bos_token(self):
+        return self.vocab[self.bos]
+
+    @property
+    def eos(self):
+        return "<|endoftext|>"
+
+    @property
+    def eos_token(self):
+        return self.vocab[self.eos]
+
+    def bpe(self, text):
+        if text in self._cache:
+            return self._cache[text]
+
+        unigrams = list(text[:-1]) + [text[-1] + "</w>"]
+        unique_bigrams = set(zip(unigrams, unigrams[1:]))
+
+        if not unique_bigrams:
+            return unigrams
+
+        # In every iteration try to merge the two most likely bigrams. If none
+        # was merged we are done.
+        #
+        # Ported from https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py
+        while unique_bigrams:
+            bigram = min(unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+
+            new_unigrams = []
+            skip = False
+            for a, b in zip(unigrams, unigrams[1:]):
+                if skip:
+                    skip = False
+                    continue
+
+                if (a, b) == bigram:
+                    new_unigrams.append(a + b)
+                    skip = True
+
+                else:
+                    new_unigrams.append(a)
+
+            if not skip:
+                new_unigrams.append(b)
+
+            unigrams = new_unigrams
+            unique_bigrams = set(zip(unigrams, unigrams[1:]))
+
+        self._cache[text] = unigrams
+
+        return unigrams
+
+    def tokenize(self, text, prepend_bos=True, append_eos=True):
+        if isinstance(text, list):
+            return [self.tokenize(t, prepend_bos, append_eos) for t in text]
+
+        # Lower case cleanup and split according to self.pat. Huggingface does
+        # a much more thorough job here but this should suffice for 95% of
+        # cases.
+        clean_text = regex.sub(r"\s+", " ", text.lower())
+        tokens = regex.findall(self.pat, clean_text)
+
+        # Split the tokens according to the byte-pair merge file
+        bpe_tokens = [ti for t in tokens for ti in self.bpe(t)]
+
+        # Map to token ids and return
+        tokens = [self.vocab[t] for t in bpe_tokens]
+        if prepend_bos:
+            tokens = [self.bos_token] + tokens
+        if append_eos:
+            tokens.append(self.eos_token)
+
+        return tokens

stable_diffusion/stable_diffusion/unet.py

@@ -0,0 +1,423 @@
+import math
+from typing import Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .config import UNetConfig
+
+
+def upsample_nearest(x, scale: int = 2):
+    B, H, W, C = x.shape
+    x = mx.broadcast_to(x[:, :, None, :, None, :], (B, H, scale, W, scale, C))
+    x = x.reshape(B, H * scale, W * scale, C)
+
+    return x
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, in_channels: int, time_embed_dim: int):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(in_channels, time_embed_dim)
+        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
+
+    def __call__(self, x):
+        x = self.linear_1(x)
+        x = nn.silu(x)
+        x = self.linear_2(x)
+
+        return x
+
+
+class TransformerBlock(nn.Module):
+    def __init__(
+        self,
+        model_dims: int,
+        num_heads: int,
+        hidden_dims: Optional[int] = None,
+        memory_dims: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.norm1 = nn.LayerNorm(model_dims)
+        self.attn1 = nn.MultiHeadAttention(model_dims, num_heads)
+        self.attn1.out_proj.bias = mx.zeros(model_dims)
+
+        memory_dims = memory_dims or model_dims
+        self.norm2 = nn.LayerNorm(model_dims)
+        self.attn2 = nn.MultiHeadAttention(
+            model_dims, num_heads, key_input_dims=memory_dims
+        )
+        self.attn2.out_proj.bias = mx.zeros(model_dims)
+
+        hidden_dims = hidden_dims or 4 * model_dims
+        self.norm3 = nn.LayerNorm(model_dims)
+        self.linear1 = nn.Linear(model_dims, hidden_dims)
+        self.linear2 = nn.Linear(model_dims, hidden_dims)
+        self.linear3 = nn.Linear(hidden_dims, model_dims)
+
+    def __call__(self, x, memory, attn_mask, memory_mask):
+        # Self attention
+        y = self.norm1(x)
+        y = self.attn1(y, y, y, attn_mask)
+        x = x + y
+
+        # Cross attention
+        y = self.norm2(x)
+        y = self.attn2(y, memory, memory, memory_mask)
+        x = x + y
+
+        # FFN
+        y = self.norm3(x)
+        y_a = self.linear1(y)
+        y_b = self.linear2(y)
+        y = y_a * nn.gelu_approx(y_b)  # approximate gelu?
+        y = self.linear3(y)
+        x = x + y
+
+        return x
+
+
+class Transformer2D(nn.Module):
+    """A transformer model for inputs with 2 spatial dimensions."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        model_dims: int,
+        encoder_dims: int,
+        num_heads: int,
+        num_layers: int = 1,
+        norm_num_groups: int = 32,
+    ):
+        super().__init__()
+
+        self.norm = nn.GroupNorm(norm_num_groups, in_channels, pytorch_compatible=True)
+        self.proj_in = nn.Linear(in_channels, model_dims)
+        self.transformer_blocks = [
+            TransformerBlock(model_dims, num_heads, memory_dims=encoder_dims)
+            for i in range(num_layers)
+        ]
+        self.proj_out = nn.Linear(model_dims, in_channels)
+
+    def __call__(self, x, encoder_x, attn_mask, encoder_attn_mask):
+        # Save the input to add to the output
+        input_x = x
+
+        # Perform the input norm and projection
+        B, H, W, C = x.shape
+        x = self.norm(x).reshape(B, -1, C)
+        x = self.proj_in(x)
+
+        # Apply the transformer
+        for block in self.transformer_blocks:
+            x = block(x, encoder_x, attn_mask, encoder_attn_mask)
+
+        # Apply the output projection and reshape
+        x = self.proj_out(x)
+        x = x.reshape(B, H, W, C)
+
+        return x + input_x
+
+
+class ResnetBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        groups: int = 32,
+        temb_channels: Optional[int] = None,
+    ):
+        super().__init__()
+
+        out_channels = out_channels or in_channels
+
+        self.norm1 = nn.GroupNorm(groups, in_channels, pytorch_compatible=True)
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if temb_channels is not None:
+            self.time_emb_proj = nn.Linear(temb_channels, out_channels)
+        self.norm2 = nn.GroupNorm(groups, out_channels, pytorch_compatible=True)
+        self.conv2 = nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        if in_channels != out_channels:
+            self.conv_shortcut = nn.Linear(in_channels, out_channels)
+
+    def __call__(self, x, temb=None):
+        if temb is not None:
+            temb = self.time_emb_proj(nn.silu(temb))
+
+        y = self.norm1(x)
+        y = nn.silu(y)
+        y = self.conv1(y)
+        if temb is not None:
+            y = y + temb[:, None, None, :]
+        y = self.norm2(y)
+        y = nn.silu(y)
+        y = self.conv2(y)
+
+        x = y + (x if "conv_shortcut" not in self else self.conv_shortcut(x))
+
+        return x
+
+
+class UNetBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        prev_out_channels: Optional[int] = None,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        num_attention_heads: int = 8,
+        cross_attention_dim=1280,
+        resnet_groups: int = 32,
+        add_downsample=True,
+        add_upsample=True,
+        add_cross_attention=True,
+    ):
+        super().__init__()
+
+        # Prepare the in channels list for the resnets
+        if prev_out_channels is None:
+            in_channels_list = [in_channels] + [out_channels] * (num_layers - 1)
+        else:
+            in_channels_list = [prev_out_channels] + [out_channels] * (num_layers - 1)
+            res_channels_list = [out_channels] * (num_layers - 1) + [in_channels]
+            in_channels_list = [
+                a + b for a, b in zip(in_channels_list, res_channels_list)
+            ]
+
+        # Add resnet blocks that also process the time embedding
+        self.resnets = [
+            ResnetBlock2D(
+                in_channels=ic,
+                out_channels=out_channels,
+                temb_channels=temb_channels,
+                groups=resnet_groups,
+            )
+            for ic in in_channels_list
+        ]
+
+        # Add optional cross attention layers
+        if add_cross_attention:
+            self.attentions = [
+                Transformer2D(
+                    in_channels=out_channels,
+                    model_dims=out_channels,
+                    num_heads=num_attention_heads,
+                    num_layers=transformer_layers_per_block,
+                    encoder_dims=cross_attention_dim,
+                )
+                for i in range(num_layers)
+            ]
+
+        # Add an optional downsampling layer
+        if add_downsample:
+            self.downsample = nn.Conv2d(
+                out_channels, out_channels, kernel_size=3, stride=2, padding=1
+            )
+
+        # or upsampling layer
+        if add_upsample:
+            self.upsample = nn.Conv2d(
+                out_channels, out_channels, kernel_size=3, stride=1, padding=1
+            )
+
+    def __call__(
+        self,
+        x,
+        encoder_x=None,
+        temb=None,
+        attn_mask=None,
+        encoder_attn_mask=None,
+        residual_hidden_states=None,
+    ):
+        output_states = []
+
+        for i in range(len(self.resnets)):
+            if residual_hidden_states is not None:
+                x = mx.concatenate([x, residual_hidden_states.pop()], axis=-1)
+
+            x = self.resnets[i](x, temb)
+
+            if "attentions" in self:
+                x = self.attentions[i](x, encoder_x, attn_mask, encoder_attn_mask)
+
+            output_states.append(x)
+
+        if "downsample" in self:
+            x = self.downsample(x)
+            output_states.append(x)
+
+        if "upsample" in self:
+            x = self.upsample(upsample_nearest(x))
+            output_states.append(x)
+
+        return x, output_states
+
+
+class UNetModel(nn.Module):
+    """The conditional 2D UNet model that actually performs the denoising."""
+
+    def __init__(self, config: UNetConfig):
+        super().__init__()
+
+        self.conv_in = nn.Conv2d(
+            config.in_channels,
+            config.block_out_channels[0],
+            config.conv_in_kernel,
+            padding=(config.conv_in_kernel - 1) // 2,
+        )
+
+        self.timesteps = nn.SinusoidalPositionalEncoding(
+            config.block_out_channels[0],
+            max_freq=1,
+            min_freq=math.exp(
+                -math.log(10000) + 2 * math.log(10000) / config.block_out_channels[0]
+            ),
+            scale=1.0,
+            cos_first=True,
+            full_turns=False,
+        )
+        self.time_embedding = TimestepEmbedding(
+            config.block_out_channels[0],
+            config.block_out_channels[0] * 4,
+        )
+
+        # Make the downsampling blocks
+        block_channels = [config.block_out_channels[0]] + list(
+            config.block_out_channels
+        )
+        self.down_blocks = [
+            UNetBlock2D(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                temb_channels=config.block_out_channels[0] * 4,
+                num_layers=config.layers_per_block[i],
+                transformer_layers_per_block=config.transformer_layers_per_block[i],
+                num_attention_heads=config.num_attention_heads[i],
+                cross_attention_dim=config.cross_attention_dim[i],
+                resnet_groups=config.norm_num_groups,
+                add_downsample=(i < len(config.block_out_channels) - 1),
+                add_upsample=False,
+                add_cross_attention=(i < len(config.block_out_channels) - 1),
+            )
+            for i, (in_channels, out_channels) in enumerate(
+                zip(block_channels, block_channels[1:])
+            )
+        ]
+
+        # Make the middle block
+        self.mid_blocks = [
+            ResnetBlock2D(
+                in_channels=config.block_out_channels[-1],
+                out_channels=config.block_out_channels[-1],
+                temb_channels=config.block_out_channels[0] * 4,
+                groups=config.norm_num_groups,
+            ),
+            Transformer2D(
+                in_channels=config.block_out_channels[-1],
+                model_dims=config.block_out_channels[-1],
+                num_heads=config.num_attention_heads[-1],
+                num_layers=config.transformer_layers_per_block[-1],
+                encoder_dims=config.cross_attention_dim[-1],
+            ),
+            ResnetBlock2D(
+                in_channels=config.block_out_channels[-1],
+                out_channels=config.block_out_channels[-1],
+                temb_channels=config.block_out_channels[0] * 4,
+                groups=config.norm_num_groups,
+            ),
+        ]
+
+        # Make the upsampling blocks
+        block_channels = (
+            [config.block_out_channels[0]]
+            + list(config.block_out_channels)
+            + [config.block_out_channels[-1]]
+        )
+        self.up_blocks = [
+            UNetBlock2D(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                temb_channels=config.block_out_channels[0] * 4,
+                prev_out_channels=prev_out_channels,
+                num_layers=config.layers_per_block[i] + 1,
+                transformer_layers_per_block=config.transformer_layers_per_block[i],
+                num_attention_heads=config.num_attention_heads[i],
+                cross_attention_dim=config.cross_attention_dim[i],
+                resnet_groups=config.norm_num_groups,
+                add_downsample=False,
+                add_upsample=(i > 0),
+                add_cross_attention=(i < len(config.block_out_channels) - 1),
+            )
+            for i, (in_channels, out_channels, prev_out_channels) in reversed(
+                list(
+                    enumerate(
+                        zip(block_channels, block_channels[1:], block_channels[2:])
+                    )
+                )
+            )
+        ]
+
+        self.conv_norm_out = nn.GroupNorm(
+            config.norm_num_groups,
+            config.block_out_channels[0],
+            pytorch_compatible=True,
+        )
+        self.conv_out = nn.Conv2d(
+            config.block_out_channels[0],
+            config.out_channels,
+            config.conv_out_kernel,
+            padding=(config.conv_out_kernel - 1) // 2,
+        )
+
+    def __call__(self, x, timestep, encoder_x, attn_mask=None, encoder_attn_mask=None):
+
+        # Compute the time embeddings
+        temb = self.timesteps(timestep).astype(x.dtype)
+        temb = self.time_embedding(temb)
+
+        # Preprocess the input
+        x = self.conv_in(x)
+
+        # Run the downsampling part of the unet
+        residuals = [x]
+        for block in self.down_blocks:
+            x, res = block(
+                x,
+                encoder_x=encoder_x,
+                temb=temb,
+                attn_mask=attn_mask,
+                encoder_attn_mask=encoder_attn_mask,
+            )
+            residuals.extend(res)
+
+        # Run the middle part of the unet
+        x = self.mid_blocks[0](x, temb)
+        x = self.mid_blocks[1](x, encoder_x, attn_mask, encoder_attn_mask)
+        x = self.mid_blocks[2](x, temb)
+
+        # Run the upsampling part of the unet
+        for block in self.up_blocks:
+            x, _ = block(
+                x,
+                encoder_x=encoder_x,
+                temb=temb,
+                attn_mask=attn_mask,
+                encoder_attn_mask=encoder_attn_mask,
+                residual_hidden_states=residuals,
+            )
+
+        # Postprocess the output
+        x = self.conv_norm_out(x)
+        x = nn.silu(x)
+        x = self.conv_out(x)
+
+        return x

stable_diffusion/stable_diffusion/vae.py

@@ -0,0 +1,266 @@
+import math
+from typing import List
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .config import AutoencoderConfig
+from .unet import ResnetBlock2D, upsample_nearest
+
+
+class Attention(nn.Module):
+    """A single head unmasked attention for use with the VAE."""
+
+    def __init__(self, dims: int, norm_groups: int = 32):
+        super().__init__()
+
+        self.group_norm = nn.GroupNorm(norm_groups, dims, pytorch_compatible=True)
+        self.query_proj = nn.Linear(dims, dims)
+        self.key_proj = nn.Linear(dims, dims)
+        self.value_proj = nn.Linear(dims, dims)
+        self.out_proj = nn.Linear(dims, dims)
+
+    def __call__(self, x):
+        B, H, W, C = x.shape
+
+        y = self.group_norm(x)
+
+        queries = self.query_proj(y).reshape(B, H * W, C)
+        keys = self.key_proj(y).reshape(B, H * W, C)
+        values = self.value_proj(y).reshape(B, H * W, C)
+
+        scale = 1 / math.sqrt(queries.shape[-1])
+        scores = (queries * scale) @ keys.transpose(0, 2, 1)
+        attn = mx.softmax(scores, axis=-1)
+        y = (attn @ values).reshape(B, H, W, C)
+
+        y = self.out_proj(y)
+        x = x + y
+
+        return x
+
+
+class EncoderDecoderBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        resnet_groups: int = 32,
+        add_downsample=True,
+        add_upsample=True,
+    ):
+        super().__init__()
+
+        # Add the resnet blocks
+        self.resnets = [
+            ResnetBlock2D(
+                in_channels=in_channels if i == 0 else out_channels,
+                out_channels=out_channels,
+                groups=resnet_groups,
+            )
+            for i in range(num_layers)
+        ]
+
+        # Add an optional downsampling layer
+        if add_downsample:
+            self.downsample = nn.Conv2d(
+                out_channels, out_channels, kernel_size=3, stride=2, padding=1
+            )
+
+        # or upsampling layer
+        if add_upsample:
+            self.upsample = nn.Conv2d(
+                out_channels, out_channels, kernel_size=3, stride=1, padding=1
+            )
+
+    def __call__(self, x):
+        for resnet in self.resnets:
+            x = resnet(x)
+
+        if "downsample" in self:
+            x = self.downsample(x)
+
+        if "upsample" in self:
+            x = self.upsample(upsample_nearest(x))
+
+        return x
+
+
+class Encoder(nn.Module):
+    """Implements the encoder side of the Autoencoder."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        block_out_channels: List[int] = [64],
+        layers_per_block: int = 2,
+        resnet_groups: int = 32,
+    ):
+        super().__init__()
+
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1
+        )
+
+        channels = [block_out_channels[0]] + list(block_out_channels)
+        self.down_blocks = [
+            EncoderDecoderBlock2D(
+                in_channels,
+                out_channels,
+                num_layers=layers_per_block,
+                resnet_groups=resnet_groups,
+                add_downsample=i < len(block_out_channels) - 1,
+                add_upsample=False,
+            )
+            for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:]))
+        ]
+
+        self.mid_blocks = [
+            ResnetBlock2D(
+                in_channels=block_out_channels[-1],
+                out_channels=block_out_channels[-1],
+                groups=resnet_groups,
+            ),
+            Attention(block_out_channels[-1], resnet_groups),
+            ResnetBlock2D(
+                in_channels=block_out_channels[-1],
+                out_channels=block_out_channels[-1],
+                groups=resnet_groups,
+            ),
+        ]
+
+        self.conv_norm_out = nn.GroupNorm(
+            resnet_groups, block_out_channels[-1], pytorch_compatible=True
+        )
+        self.conv_out = nn.Conv2d(block_out_channels[-1], out_channels, 3, padding=1)
+
+    def __call__(self, x):
+        x = self.conv_in(x)
+
+        for l in self.down_blocks:
+            x = l(x)
+
+        x = self.mid_blocks[0](x)
+        x = self.mid_blocks[1](x)
+        x = self.mid_blocks[2](x)
+
+        x = self.conv_norm_out(x)
+        x = nn.silu(x)
+        x = self.conv_out(x)
+
+        return x
+
+
+class Decoder(nn.Module):
+    """Implements the decoder side of the Autoencoder."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        block_out_channels: List[int] = [64],
+        layers_per_block: int = 2,
+        resnet_groups: int = 32,
+    ):
+        super().__init__()
+
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1
+        )
+
+        self.mid_blocks = [
+            ResnetBlock2D(
+                in_channels=block_out_channels[-1],
+                out_channels=block_out_channels[-1],
+                groups=resnet_groups,
+            ),
+            Attention(block_out_channels[-1], resnet_groups),
+            ResnetBlock2D(
+                in_channels=block_out_channels[-1],
+                out_channels=block_out_channels[-1],
+                groups=resnet_groups,
+            ),
+        ]
+
+        channels = list(reversed(block_out_channels))
+        channels = [channels[0]] + channels
+        self.up_blocks = [
+            EncoderDecoderBlock2D(
+                in_channels,
+                out_channels,
+                num_layers=layers_per_block,
+                resnet_groups=resnet_groups,
+                add_downsample=False,
+                add_upsample=i < len(block_out_channels) - 1,
+            )
+            for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:]))
+        ]
+
+        self.conv_norm_out = nn.GroupNorm(
+            resnet_groups, block_out_channels[0], pytorch_compatible=True
+        )
+        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
+
+    def __call__(self, x):
+        x = self.conv_in(x)
+
+        x = self.mid_blocks[0](x)
+        x = self.mid_blocks[1](x)
+        x = self.mid_blocks[2](x)
+
+        for l in self.up_blocks:
+            x = l(x)
+
+        x = self.conv_norm_out(x)
+        x = nn.silu(x)
+        x = self.conv_out(x)
+
+        return x
+
+
+class Autoencoder(nn.Module):
+    """The autoencoder that allows us to perform diffusion in the latent space."""
+
+    def __init__(self, config: AutoencoderConfig):
+        super().__init__()
+
+        self.latent_channels = config.latent_channels_in
+        self.scaling_factor = config.scaling_factor
+        self.encoder = Encoder(
+            config.in_channels,
+            config.latent_channels_out,
+            config.block_out_channels,
+            config.layers_per_block,
+            resnet_groups=config.norm_num_groups,
+        )
+        self.decoder = Decoder(
+            config.latent_channels_in,
+            config.out_channels,
+            config.block_out_channels,
+            config.layers_per_block + 1,
+            resnet_groups=config.norm_num_groups,
+        )
+
+        self.quant_proj = nn.Linear(
+            config.latent_channels_out, config.latent_channels_out
+        )
+        self.post_quant_proj = nn.Linear(
+            config.latent_channels_in, config.latent_channels_in
+        )
+
+    def decode(self, z):
+        return self.decoder(self.post_quant_proj(z))
+
+    def __call__(self, x, key=None):
+        x = self.encoder(x)
+        x = self.query_proj(x)
+
+        mean, logvar = x.split(2, axis=-1)
+        std = mx.exp(0.5 * logvar)
+        z = mx.random.normal(mean.shape, key=key) * std + mean
+
+        x_hat = self.decode(z)
+
+        return dict(x_hat=x_hat, z=z, mean=mean, logvar=logvar)

stable_diffusion/txt2image.py

@@ -0,0 +1,55 @@
+import argparse
+from PIL import Image
+from tqdm import tqdm
+
+import mlx.core as mx
+
+from stable_diffusion import StableDiffusion
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate images from a textual prompt using stable diffusion"
+    )
+    parser.add_argument("prompt")
+    parser.add_argument("--n_images", type=int, default=4)
+    parser.add_argument("--steps", type=int, default=50)
+    parser.add_argument("--cfg", type=float, default=7.5)
+    parser.add_argument("--negative_prompt", default="")
+    parser.add_argument("--n_rows", type=int, default=1)
+    parser.add_argument("--decoding_batch_size", type=int, default=1)
+    parser.add_argument("--output", default="out.png")
+    args = parser.parse_args()
+
+    sd = StableDiffusion()
+
+    # Generate the latent vectors using diffusion
+    latents = sd.generate_latents(
+        args.prompt,
+        n_images=args.n_images,
+        cfg_weight=args.cfg,
+        num_steps=args.steps,
+        negative_text=args.negative_prompt,
+    )
+    for x_t in tqdm(latents, total=args.steps):
+        mx.simplify(x_t)
+        mx.simplify(x_t)
+        mx.eval(x_t)
+
+    # Decode them into images
+    decoded = []
+    for i in tqdm(range(0, args.n_images, args.decoding_batch_size)):
+        decoded.append(sd.decode(x_t[i : i + args.decoding_batch_size]))
+        mx.eval(decoded[-1])
+
+    # Arrange them on a grid
+    x = mx.concatenate(decoded, axis=0)
+    x = mx.pad(x, [(0, 0), (8, 8), (8, 8), (0, 0)])
+    B, H, W, C = x.shape
+    x = x.reshape(args.n_rows, B // args.n_rows, H, W, C).transpose(0, 2, 1, 3, 4)
+    x = x.reshape(args.n_rows * H, B // args.n_rows * W, C)
+    x = (x * 255).astype(mx.uint8)
+
+    # Save them to disc
+    im = Image.fromarray(x.__array__())
+    im.save(args.output)