Metadata-Version: 2.1
Name: cjm-diffusers-utils
Version: 0.0.3
Summary: Some utility functions I frequently use with 🤗 diffusers.
Home-page: https://github.com/cj-mills/cjm-diffusers-utils
Author: cj-mills
Author-email: millscj.mills2@gmail.com
License: Apache Software License 2.0
Keywords: nbdev jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastcore
Requires-Dist: tqdm
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: diffusers
Requires-Dist: transformers
Requires-Dist: accelerate
Requires-Dist: cjm-pytorch-utils
Provides-Extra: dev

cjm-diffusers-utils
================

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

## Install

``` sh
pip install cjm_diffusers_utils
```

## How to use

``` python
import torch
from cjm_pytorch_utils.core import get_torch_device
device = get_torch_device()
dtype = torch.float16 if device == 'cuda' else torch.float16
device, dtype
```

    ('cuda', torch.float16)

### pil_to_latent

``` python
from cjm_diffusers_utils.core import pil_to_latent
from PIL import Image
from diffusers import AutoencoderKL
```

``` python
model_name = "stabilityai/stable-diffusion-2-1"
vae = AutoencoderKL.from_pretrained(model_name, subfolder="vae").to(device=device, dtype=dtype)
```

``` python
img_path = img_path = '../images/cat.jpg'
src_img = Image.open(img_path).convert('RGB')
print(f"Source Image Size: {src_img.size}")

img_latents = pil_to_latent(src_img, vae)
print(f"Latent Dimensions: {img_latents.shape}")
```

    Source Image Size: (768, 512)
    Latent Dimensions: torch.Size([1, 4, 64, 96])

### latent_to_pil

``` python
from cjm_diffusers_utils.core import latent_to_pil
```

``` python
decoded_img = latent_to_pil(img_latents, vae)
print(f"Decoded Image Size: {decoded_img.size}")
```

    Decoded Image Size: (768, 512)

### text_to_emb

``` python
from cjm_diffusers_utils.core import text_to_emb
from transformers import CLIPTextModel, CLIPTokenizer
```

``` python
# Load the tokenizer for the specified model
tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer")
# Load the text encoder for the specified model
text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder="text_encoder").to(device=device, dtype=dtype)
```

``` python
prompt = "A cat sitting on the floor."
text_emb = text_to_emb(prompt, tokenizer, text_encoder)
text_emb.shape
```

    torch.Size([2, 77, 1024])

### prepare_noise_scheduler

``` python
from cjm_diffusers_utils.core import prepare_noise_scheduler
from diffusers import DEISMultistepScheduler
```

``` python
noise_scheduler = DEISMultistepScheduler.from_pretrained(model_name, subfolder='scheduler')
print(f"Number of timesteps: {len(noise_scheduler.timesteps)}")
print(noise_scheduler.timesteps[:10])

noise_scheduler = prepare_noise_scheduler(noise_scheduler, 70, 1.0)
print(f"Number of timesteps: {len(noise_scheduler.timesteps)}")
print(noise_scheduler.timesteps[:10])
```

    Number of timesteps: 1000
    tensor([999., 998., 997., 996., 995., 994., 993., 992., 991., 990.])
    Number of timesteps: 70
    tensor([999, 985, 970, 956, 942, 928, 913, 899, 885, 871])

### prepare_depth_mask

``` python
from cjm_diffusers_utils.core import prepare_depth_mask
```

``` python
depth_map_path = '../images/depth-cat.png'
depth_map = Image.open(depth_map_path)
print(f"Depth map size: {depth_map.size}")

depth_mask = prepare_depth_mask(depth_map).to(device=device, dtype=dtype)
depth_mask.shape, depth_mask.min(), depth_mask.max()
```

    Depth map size: (768, 512)

    (torch.Size([1, 1, 64, 96]),
     tensor(-1., device='cuda:0', dtype=torch.float16),
     tensor(1., device='cuda:0', dtype=torch.float16))
