Implementing Stable Diffusion From Its Components

Creating a Diffuser that Diffuses Stable-y

Implementing stable diffusion from the 🤗 🧨 library.

This notebook follows the fastai style guide.

In this notebook, we’ll implement stable diffusion from its various components through the Hugging Face Diffusers library.

At the end, we’ll have our own custom stable diffusion class, from which we can generate images as simply as diffuser.diffuse().

If you would like a refresher, I’ve summarized at a high level how a diffuser is trained in this post. Though this notebook focuses on inference and not the training aspect, the linked summary may be helpful.

Let’s begin.

Overview

Before we get hands on with the code, let’s refresh how inference works for a diffuser.

1. We input a prompt to the diffuser.
2. This prompt is given a mathematical representation (an embedding) through the text encoder.
3. A latent comprised of noise is produced.
4. The U-Net predicts the noise in the latent in conjunction with the prompt.
5. The predicted noise is subtracted from the latent in conjunction with the scheduler.
6. After many iterations, the denoised latent is decompressed to produce our final generated image.

The main components in use are:

• a text encoder,
• a U-Net,
• and a VAE decoder.

Setup

! pip install -Uqq fastcore transformers diffusers

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1import logging; logging.disable(logging.WARNING)
from fastcore.all import *
from fastai.imports import *
from fastai.vision.all import *

1

Hugging Face can be very verbose.

Get Components

CLIP Components

To process the prompt, we need to download a tokenizer and a text encoder. The tokenizer will split the prompt into tokens while the text encoder will convert the tokens into a numerical representation (an embedding).

from transformers import CLIPTokenizer, CLIPTextModel

tokz = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14', torch_dtype=torch.float16)
txt_enc = CLIPTextModel.from_pretrained('openai/clip-vit-large-patch14', torch_dtype=torch.float16).to('cuda')

float16 is used for faster performance.

U-Net and VAE

The U-Net will predict the noise in the image, while the VAE will decompress the generated image.

from diffusers import AutoencoderKL, UNet2DConditionModel

vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-ema', torch_dtype=torch.float16).to('cuda')
unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet", torch_dtype=torch.float16).to("cuda")

Scheduler

The scheduler will control how much noise is intially added to the image, and will also control how much of the noise predicted from the U-Net will be subtracted from the image.

from diffusers import LMSDiscreteScheduler

sched = LMSDiscreteScheduler(
    beta_start = 0.00085,
    beta_end = 0.012,
    beta_schedule = 'scaled_linear',
    num_train_timesteps = 1000
); sched

LMSDiscreteScheduler {
  "_class_name": "LMSDiscreteScheduler",
  "_diffusers_version": "0.16.1",
  "beta_end": 0.012,
  "beta_schedule": "scaled_linear",
  "beta_start": 0.00085,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "trained_betas": null
}

Define Generation Parameters

The six main parameters needed for generation are:

• The prompt
• The width and height of the image
• A number describing how noisy the output image is to be (the number of inference steps)
• A number describing how much the diffuser should stick to the prompt (the guidance scale)
• The batch size
• The seed

prompt = ['a photograph of an astronaut riding a horse']
w, h = 512, 512
n_inf_steps = 70
g_scale = 7.5
bs = 1
seed = 77

Encode Prompt

Now we need to parse the prompt. To do so, we’ll first tokenize it, and then encode the tokens to produce an embedding.

First, let’s tokenize.

txt_inp = tokz(
    prompt,
    padding = 'max_length',
    max_length = tokz.model_max_length,
    truncation = True,
    return_tensors = 'pt'
); txt_inp

{'input_ids': tensor([[49406,   320,  8853,   539,   550, 18376,  6765,   320,  4558, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0]])}

The token 49407 is a padding token and represents '<|endoftext|>'. These tokens have been given an attention mask of 0.

tokz.decode(49407)

'<|endoftext|>'

Now using the text encoder, we’ll create an embedding out of these tokens.

txt_emb = txt_enc(txt_inp['input_ids'].to('cuda'))[0].half(); txt_emb

tensor([[[-0.3884,  0.0229, -0.0523,  ..., -0.4902, -0.3066,  0.0674],
         [ 0.0292, -1.3242,  0.3076,  ..., -0.5254,  0.9766,  0.6655],
         [ 0.4609,  0.5610,  1.6689,  ..., -1.9502, -1.2266,  0.0093],
         ...,
         [-3.0410, -0.0674, -0.1777,  ...,  0.3950, -0.0174,  0.7671],
         [-3.0566, -0.1058, -0.1936,  ...,  0.4258, -0.0184,  0.7588],
         [-2.9844, -0.0850, -0.1726,  ...,  0.4373,  0.0092,  0.7490]]],
       device='cuda:0', dtype=torch.float16, grad_fn=<NativeLayerNormBackward0>)

txt_emb.shape

torch.Size([1, 77, 768])

Embeddings for CFG

We also need to create an embedding for an empty prompt, also known as the uncondtional prompt. This embedding is what is used to control the guidance.

txt_inp['input_ids'].shape

torch.Size([1, 77])

1max_len = txt_inp['input_ids'].shape[-1]
uncond_inp = tokz(
2    [''] * bs,
    padding = 'max_length',
    max_length = max_len,
    return_tensors = 'pt',
); uncond_inp

1

We use the maximum length of the prompt so the unconditional prompt embedding matches the size of the text prmpt embedding.

2

We also multiply the list containing the empty prompt with the batch size so we have an empty prompt for each text prompt.

{'input_ids': tensor([[49406, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
         49407, 49407, 49407, 49407, 49407, 49407, 49407]]), 'attention_mask': tensor([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0]])}

uncond_inp['input_ids'].shape

torch.Size([1, 77])

uncond_emb = txt_enc(uncond_inp['input_ids'].to('cuda'))[0].half()
uncond_emb.shape

torch.Size([1, 77, 768])

We can then concatenate both the unconditonal embedding and the text embedding together. This allows images to be generated from each prompt without having to go through the U-Net twice.

embs = torch.cat([uncond_emb, txt_emb])

Create Noisy Image

It’s now time to create our noisy image, which will be the starting point for generation.

We’ll create a single latent that is 64 by 64 pixels, and that also has 4 channels. After the latent is denoised, we’ll decompress it to a 512 by 512 pixel image with 3 channels.

bs, unet.config.in_channels, h//8, w//8

(1, 4, 64, 64)

print(torch.randn((2, 3, 4)))
print(torch.randn((2, 3, 4)).shape)

tensor([[[ 0.0800, -1.3597, -0.2033, -0.5647],
         [-1.6066,  0.8178,  1.0832,  0.0638],
         [ 0.3133,  1.8516,  0.4320, -0.9295]],

        [[-1.0798,  3.2928,  0.7443,  1.2190],
         [-0.4984,  0.3551, -0.6012, -0.5856],
         [-0.3988, -1.2950, -1.6061, -0.0207]]])
torch.Size([2, 3, 4])

torch.manual_seed(seed)
lats = torch.randn((bs, unet.config.in_channels, h//8, w//8)); lats.shape

torch.Size([1, 4, 64, 64])

The latent is a rank 4 tensor. 1 refers to the batch size, which is the number of images being generated. 4 is the number of channels, and 64 is the number of pixel with regard to both height and width.

lats = lats.to('cuda').half(); lats

tensor([[[[-0.5044, -0.4163, -0.1365,  ..., -1.6104,  0.1381,  1.7676],
          [ 0.7017,  1.5947, -1.4434,  ..., -1.5859, -0.4089, -2.8164],
          [ 1.0664, -0.0923,  0.3462,  ..., -0.2390, -1.0947,  0.7554],
          ...,
          [-1.0283,  0.2433,  0.3337,  ...,  0.6641,  0.4219,  0.7065],
          [ 0.4280, -1.5439,  0.1409,  ...,  0.8989, -1.0049,  0.0482],
          [-1.8682,  0.4988,  0.4668,  ..., -0.5874, -0.4019, -0.2856]],

         [[ 0.5688, -1.2715, -1.4980,  ...,  0.2230,  1.4785, -0.6821],
          [ 1.8418, -0.5117,  1.1934,  ..., -0.7222, -0.7417,  1.0479],
          [-0.6558,  0.1201,  1.4971,  ...,  0.1454,  0.4714,  0.2441],
          ...,
          [ 0.9492,  0.1953, -2.4141,  ..., -0.5176,  1.1191,  0.5879],
          [ 0.2129,  1.8643, -1.8506,  ...,  0.8096, -1.5264,  0.3191],
          [-0.3640, -0.9189,  0.8931,  ..., -0.4944,  0.3916, -0.1406]],

         [[-0.5259,  1.5059, -0.3413,  ...,  1.2539,  0.3669, -0.1593],
          [-0.2957, -0.1169, -2.0078,  ...,  1.9268,  0.3833, -0.0992],
          [ 0.5020,  1.0068, -0.9907,  ..., -0.3008,  0.7324, -1.1963],
          ...,
          [-0.7437, -1.1250,  0.1349,  ..., -0.6714, -0.6753, -0.7920],
          [ 0.5415, -0.5269, -1.0166,  ...,  1.1270, -1.7637, -1.5156],
          [-0.2319,  0.9165,  1.6318,  ...,  0.6602, -1.2871,  1.7568]],

         [[ 0.7100,  0.4133,  0.5513,  ...,  0.0326,  0.9175,  1.4922],
          [ 0.8862,  1.3760,  0.8599,  ..., -2.1172, -1.6533,  0.8955],
          [-0.7783, -0.0246,  1.4717,  ...,  0.0328,  0.4316, -0.6416],
          ...,
          [ 0.0855, -0.1279, -0.0319,  ..., -0.2817,  1.2744, -0.5854],
          [ 0.2402,  1.3945, -2.4062,  ...,  0.3435, -0.5254,  1.2441],
          [ 1.6377,  1.2539,  0.6099,  ...,  1.5391, -0.6304,  0.9092]]]],
       device='cuda:0', dtype=torch.float16)

Our latent has random values which represent noise. This noise needs to be scaled so it can work with the scheduler.

sched.set_timesteps(n_inf_steps); sched

LMSDiscreteScheduler {
  "_class_name": "LMSDiscreteScheduler",
  "_diffusers_version": "0.16.1",
  "beta_end": 0.012,
  "beta_schedule": "scaled_linear",
  "beta_start": 0.00085,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "trained_betas": null
}

lats *= sched.init_noise_sigma; sched.init_noise_sigma

tensor(14.6146)

sched.sigmas

tensor([14.6146, 13.3974, 12.3033, 11.3184, 10.4301,  9.6279,  8.9020,  8.2443,
         7.6472,  7.1044,  6.6102,  6.1594,  5.7477,  5.3709,  5.0258,  4.7090,
         4.4178,  4.1497,  3.9026,  3.6744,  3.4634,  3.2680,  3.0867,  2.9183,
         2.7616,  2.6157,  2.4794,  2.3521,  2.2330,  2.1213,  2.0165,  1.9180,
         1.8252,  1.7378,  1.6552,  1.5771,  1.5031,  1.4330,  1.3664,  1.3030,
         1.2427,  1.1852,  1.1302,  1.0776,  1.0272,  0.9788,  0.9324,  0.8876,
         0.8445,  0.8029,  0.7626,  0.7236,  0.6858,  0.6490,  0.6131,  0.5781,
         0.5438,  0.5102,  0.4770,  0.4443,  0.4118,  0.3795,  0.3470,  0.3141,
         0.2805,  0.2455,  0.2084,  0.1672,  0.1174,  0.0292,  0.0000])

sched.timesteps

tensor([999.0000, 984.5217, 970.0435, 955.5652, 941.0870, 926.6087, 912.1304,
        897.6522, 883.1739, 868.6957, 854.2174, 839.7391, 825.2609, 810.7826,
        796.3043, 781.8261, 767.3478, 752.8696, 738.3913, 723.9130, 709.4348,
        694.9565, 680.4783, 666.0000, 651.5217, 637.0435, 622.5652, 608.0870,
        593.6087, 579.1304, 564.6522, 550.1739, 535.6957, 521.2174, 506.7391,
        492.2609, 477.7826, 463.3043, 448.8261, 434.3478, 419.8696, 405.3913,
        390.9130, 376.4348, 361.9565, 347.4783, 333.0000, 318.5217, 304.0435,
        289.5652, 275.0870, 260.6087, 246.1304, 231.6522, 217.1739, 202.6957,
        188.2174, 173.7391, 159.2609, 144.7826, 130.3043, 115.8261, 101.3478,
         86.8696,  72.3913,  57.9130,  43.4348,  28.9565,  14.4783,   0.0000],
       dtype=torch.float64)

plt.plot(sched.timesteps, sched.sigmas[:-1])

Denoise

The denoising process can now begin!

from tqdm.auto import tqdm

for i, ts in enumerate(tqdm(sched.timesteps)):
1  inp = torch.cat([lats] * 2)
2  inp = sched.scale_model_input(inp, ts)

3  with torch.no_grad(): preds = unet(inp, ts, encoder_hidden_states=embs).sample

4  pred_uncond, pred_txt = preds.chunk(2)
  pred = pred_uncond + g_scale * (pred_txt - pred_uncond)

5  lats = sched.step(pred, ts, lats).prev_sample

1

We first create two latents: one for the text prompt, and one for the unconditional prompt.

2

We then further scale the noise on the latents.

3

We then predict noise.

4

We then perform guidance.

5

We then subtract the predicted, guided noise from the image.

Decompress

We can now decompress the latent and display it.

with torch.no_grad(): img = vae.decode(1/0.18215*lats).sample

img = (img / 2 + 0.5).clamp(0, 1)
img = img[0].detach().cpu().permute(1, 2, 0).numpy()
img = (img * 255).round().astype('uint8')
Image.fromarray(img)

And there you have it! We implemented stable diffusion using a text encoder, VAE, and U-Net!

Let’s encapsulate everything so it looks simpler.

Encapsulation

First we’ll encapsulate everything into functions, then we’ll encapsulate into a class.

Functions

The main steps that are happening are:

• We create embeddings.
• We create latents.
• We denoise the latents.
• We decompress the latents.

Create Embeddings

def set_embs():
  txt_inp = tok_seq(prompt)
  uncond_inp = tok_seq(['']*len(prompt), max_len=txt_inp['input_ids'].shape[-1])

  txt_emb = make_emb(txt_inp['input_ids'])
  uncond_emb = make_emb(uncond_inp['input_ids'])
  return torch.cat([uncond_emb, txt_emb])

def tok_seq(prompt, max_len=None):
  if max_len is None: max_len = tokz.model_max_length
  return tokz(
      prompt,
      padding = 'max_length',
      max_length = max_len,
      truncation = True,
      return_tensors = 'pt'
  )

def make_emb(input_ids):
  return txt_enc(input_ids.to('cuda'))[0].half()

Create Latents

def set_lat():
  torch.manual_seed(seed)
  lat = torch.randn((bs, unet.config.in_channels, h//8, w//8))
  sched.set_timesteps(n_inf_steps)
  return lat.to('cuda').half() * sched.init_noise_sigma

Denoise Latents

def denoise(latent, embeddings, timestep):
  inp = sched.scale_model_input(torch.cat([latent]*2), timestep)
  with torch.no_grad():
    pred_uncond, pred_txt = unet(inp, timestep, encoder_hidden_states=embeddings).sample.chunk(2)
  pred = pred_uncond + g_scale * (pred_txt - pred_uncond)
  return sched.step(pred, timestep, latent).prev_sample

Decompress Latents

def decompress_lat(latent):
  with torch.no_grad(): img = vae.decode(1/0.18215*latent).sample
  img = (img / 2 + 0.5).clamp(0, 1)
  img = img[0].detach().cpu().permute(1, 2, 0).numpy()
  return (img * 255).round().astype('uint8')

Putting it All Together

prompt = ['An antique 18th century painting of a gorilla eating a plate of chips.']
embs = set_embs()
lat = set_lat()
for i, ts in enumerate(tqdm(sched.timesteps)): lat = denoise(lat, embs, ts)
img = decompress_lat(lat)
Image.fromarray(img)

Negative Prompts.

To implement negative prompts, we can simply pass in a list containing the negative prompt. This will be used in place of the empty list used for the unconditional prompt.

def set_embs():
  txt_inp = tok_seq(prompt)
  uncond_inp = tok_seq(neg_prompt*len(prompt), max_len=txt_inp['input_ids'].shape[-1])

  txt_emb = make_emb(txt_inp['input_ids'])
  uncond_emb = make_emb(uncond_inp['input_ids'])
  return torch.cat([uncond_emb, txt_emb])

def tok_seq(prompt, max_len=None):
  if max_len is None: max_len = tokz.model_max_length
  return tokz(
      prompt,
      padding = 'max_length',
      max_length = max_len,
      truncation = True,
      return_tensors = 'pt'
  )

prompt = ['An antique 18th century painting of a gorilla eating a plate of chips.']
neg_prompt = ['plate']
embs = set_embs()
lat = set_lat()
for i, ts in enumerate(tqdm(sched.timesteps)): lat = denoise(lat, embs, ts)
img = decompress_lat(lat)
Image.fromarray(img)

Let’s now encapsulate everything into a class, so we can much more easily further iterate.

Class

I’ll be tweaking the code above so that multiple prompts can be input.

This is as simple as using the length of the list of prompts as the batch size (an image is generated for each prompt).

class Diffuser:
  def __init__(self, prompts, neg_prompt=[''], guidance=7.5, seed=100, steps=70, width=512, height=512):
    self.prompts = prompts
    self.bs = len(prompts)
    self.neg_prompt = neg_prompt
    self.g = guidance
    self.seed = seed
    self.steps = steps
    self.w = width
    self.h = height
  
  def diffuse(self, progress=0):
    embs = self.set_embs()
    lats = self.set_lats()
    for i, ts in enumerate(tqdm(sched.timesteps)): lats = self.denoise(lats, embs, ts)
    return self.decompress_lats(lats)
  
  def set_embs(self):
    txt_inp = self.tok_seq(self.prompts)
    neg_inp = self.tok_seq(self.neg_prompt * len(self.prompts))

    txt_embs = self.make_embs(txt_inp['input_ids'])
    neg_embs = self.make_embs(neg_inp['input_ids'])
    return torch.cat([neg_embs, txt_embs])
  
  def tok_seq(self, prompts, max_len=None):
    if max_len is None: max_len = tokz.model_max_length
    return tokz(prompts, padding='max_length', max_length=max_len, truncation=True, return_tensors='pt')    
  
  def make_embs(self, input_ids):
    return txt_enc(input_ids.to('cuda'))[0].half()

  def set_lats(self):
    torch.manual_seed(self.seed)
    lats = torch.randn((self.bs, unet.config.in_channels, self.h//8, self.w//8))
    sched.set_timesteps(self.steps)
    return lats.to('cuda').half() * sched.init_noise_sigma

  def denoise(self, latents, embeddings, timestep):
    inp = sched.scale_model_input(torch.cat([latents]*2), timestep)
    with torch.no_grad(): pred_neg, pred_txt = unet(inp, timestep, encoder_hidden_states=embeddings).sample.chunk(2)
    pred = pred_neg + self.g * (pred_txt - pred_neg)
    return sched.step(pred, timestep, latents).prev_sample

  def decompress_lats(self, latents):
    with torch.no_grad(): imgs = vae.decode(1/0.18215*latents).sample
    imgs = (imgs / 2 + 0.5).clamp(0, 1)
    imgs = [img.detach().cpu().permute(1, 2, 0).numpy() for img in imgs]
    return [(img*255).round().astype('uint8') for img in imgs]

  def update_params(self, **kwargs):
    allowed_params = ['prompts', 'neg_prompt', 'guidance', 'seed', 'steps', 'width', 'height']
    for k, v in kwargs.items():
      if k not in allowed_params:
        raise ValueError(f"Invalid parameter name: {k}")
      if k == 'prompts':
        self.prompts = v
        self.bs = len(v)
      else:
        setattr(self, k, v)

Now creating a diffuser is as simple as this!

prompts = [
    'A lightning bolt striking a jumbo jet; 4k; photorealistic',
    'A toaster in the style of Jony Ive; modern; different; apple; form over function'
]
diffuser = Diffuser(prompts, seed=42)
imgs = diffuser.diffuse()

Image.fromarray(imgs[0])

Image.fromarray(imgs[1])

Let’s remove the wooden background from the second image.

prompt = [prompts[1]]
diffuser.update_params(prompts=prompt, neg_prompt='wood')
Image.fromarray(diffuser.diffuse()[0])

Now that we have a class, we can easily add more functionality to our diffuser.

Extra Functionality

Callbacks

Let’s make the diffuser output how the generated image looks like at each step interval (e.g., every 5 steps), if specified so.

To do so, we can simply tweak the diffuser.diffuse() method by make it output the latent at each desired interval.

def diffuse(self, interval=0)
  embs = self.set_embs()
  lats = self.set_lats()
1  if interval > 0:
2    row = []
    for i, ts in enumerate(tqdm(sched.timesteps)):
      lats = self.denoise(lats, embs, ts)
3      if (i % progress) == 0:
        row.append(self.decompress_lats(lats)[0])
4    row = np.concatenate(row, axis=1)
5    display(Image.fromarray(row))
  else:
    for i, ts in enumerate(tqdm(sched.timesteps)): lats = self.denoise(lats, embs, ts)
  return self.decompress_lats(lats)

1

We first check if callbacks are desired (we can’t save how the latents looked like every 0 intervals).

2

An empty list is created to store the images.

3

We check if we have reached our desired interval. If the current loop number matches the interval, it should divide the interval cleanly.

4

We smoosh all images into one long line.

5

The image is displayed.

class Diffuser:
  def __init__(self, prompts, neg_prompt=[''], guidance=7.5, seed=100, steps=70, width=512, height=512):
    self.prompts = prompts
    self.bs = len(prompts)
    self.neg_prompt = neg_prompt
    self.g = guidance
    self.seed = seed
    self.steps = steps
    self.w = width
    self.h = height
  
  def diffuse(self, interval=0):
    embs = self.set_embs()
    lats = self.set_lats()
    if interval > 0:
      row = []
      for i, ts in enumerate(tqdm(sched.timesteps)):
        lats = self.denoise(lats, embs, ts)
        if (i % interval) == 0: row.append(self.decompress_lats(lats)[0])
      row = np.concatenate(row, axis=1)
      display(Image.fromarray(row))
    else: 
      for i, ts in enumerate(tqdm(sched.timesteps)): lats = self.denoise(lats, embs, ts)
    return self.decompress_lats(lats)
  
  def set_embs(self):
    txt_inp = self.tok_seq(self.prompts)
    neg_inp = self.tok_seq(self.neg_prompt * len(self.prompts))

    txt_embs = self.make_embs(txt_inp['input_ids'])
    neg_embs = self.make_embs(neg_inp['input_ids'])
    return torch.cat([neg_embs, txt_embs])
  
  def tok_seq(self, prompts, max_len=None):
    if max_len is None: max_len = tokz.model_max_length
    return tokz(prompts, padding='max_length', max_length=max_len, truncation=True, return_tensors='pt')    
  
  def make_embs(self, input_ids):
    return txt_enc(input_ids.to('cuda'))[0].half()

  def set_lats(self):
    torch.manual_seed(self.seed)
    lats = torch.randn((self.bs, unet.config.in_channels, self.h//8, self.w//8))
    sched.set_timesteps(self.steps)
    return lats.to('cuda').half() * sched.init_noise_sigma

  def denoise(self, latents, embeddings, timestep):
    inp = sched.scale_model_input(torch.cat([latents]*2), timestep)
    with torch.no_grad(): pred_neg, pred_txt = unet(inp, timestep, encoder_hidden_states=embeddings).sample.chunk(2)
    pred = pred_neg + self.g * (pred_txt - pred_neg)
    return sched.step(pred, timestep, latents).prev_sample

  def decompress_lats(self, latents):
    with torch.no_grad(): imgs = vae.decode(1/0.18215*latents).sample
    imgs = (imgs / 2 + 0.5).clamp(0, 1)
    imgs = [img.detach().cpu().permute(1, 2, 0).numpy() for img in imgs]
    return [(img*255).round().astype('uint8') for img in imgs]

  def update_params(self, **kwargs):
    allowed_params = ['prompts', 'neg_prompt', 'guidance', 'seed', 'steps', 'width', 'height']
    for key, value in kwargs.items():
      if key not in allowed_params:
        raise ValueError(f"Invalid parameter name: {key}")
      if key == 'prompts':
        self.prompts = value
        self.bs = len(value)
      else:
        setattr(self, key, value)

prompt = ['A toaster in the style of Jony Ive; modern; realistic; different; apple; form over function']
diffuser = Diffuser(prompts=prompt, neg_prompt=['wood'], seed=42)
Image.fromarray(diffuser.diffuse(interval=7)[0])

Conclusion

And there you have it! All that’s happening is:

• A compressed, noisy image is generated.
• The noise in the image is predicted.
• The predicted noise is subtracted.
• This is repeated until desired.
• The final image is decompressed.

If you have any comments, questions, suggestions, feedback, criticisms, or corrections, please do post them down in the comment section below!