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# Diffusers for vision

## Direct image generation

#### **Example image generation with PNDM**

```python
from diffusers import PNDM, UNetModel, PNDMScheduler
import PIL.Image
import numpy as np
import torch

model_id = "fusing/ddim-celeba-hq"

model = UNetModel.from_pretrained(model_id)
scheduler = PNDMScheduler()

# load model and scheduler
pndm = PNDM(unet=model, noise_scheduler=scheduler)

# run pipeline in inference (sample random noise and denoise)
with torch.no_grad():
    image = pndm()

# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) / 2
image_processed = torch.clamp(image_processed, 0.0, 1.0)
image_processed = image_processed * 255
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# save image
image_pil.save("test.png")
```

#### **Example 1024x1024 image generation with SDE VE**

See [paper](https://arxiv.org/abs/2011.13456) for more information on SDE VE.

```python
from diffusers import DiffusionPipeline
import torch
import PIL.Image
import numpy as np

torch.manual_seed(32)

score_sde_sv = DiffusionPipeline.from_pretrained("fusing/ffhq_ncsnpp")

# Note this might take up to 3 minutes on a GPU
image = score_sde_sv(num_inference_steps=2000)

image = image.permute(0, 2, 3, 1).cpu().numpy()
image = np.clip(image * 255, 0, 255).astype(np.uint8)
image_pil = PIL.Image.fromarray(image[0])

# save image
image_pil.save("test.png")
```
#### **Example 32x32 image generation with SDE VP**

See [paper](https://arxiv.org/abs/2011.13456) for more information on SDE VE.

```python
from diffusers import DiffusionPipeline
import torch
import PIL.Image
import numpy as np

torch.manual_seed(32)

score_sde_sv = DiffusionPipeline.from_pretrained("fusing/cifar10-ddpmpp-deep-vp")

# Note this might take up to 3 minutes on a GPU
image = score_sde_sv(num_inference_steps=1000)

image = image.permute(0, 2, 3, 1).cpu().numpy()
image = np.clip(image * 255, 0, 255).astype(np.uint8)
image_pil = PIL.Image.fromarray(image[0])

# save image
image_pil.save("test.png")
```


#### **Text to Image generation with Latent Diffusion**

_Note: To use latent diffusion install transformers from [this branch](https://github.com/patil-suraj/transformers/tree/ldm-bert)._

```python
from diffusers import DiffusionPipeline

ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")

generator = torch.manual_seed(42)

prompt = "A painting of a squirrel eating a burger"
image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)

image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = image_processed * 255.0
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# save image
image_pil.save("test.png")
```


## Text to image generation

```python
import torch
from diffusers import BDDMPipeline, GradTTSPipeline

torch_device = "cuda"

# load grad tts and bddm pipelines
grad_tts = GradTTSPipeline.from_pretrained("fusing/grad-tts-libri-tts")
bddm = BDDMPipeline.from_pretrained("fusing/diffwave-vocoder-ljspeech")

text = "Hello world, I missed you so much."

# generate mel spectograms using text
mel_spec = grad_tts(text, torch_device=torch_device)

#  generate the speech by passing mel spectograms to BDDMPipeline pipeline
generator = torch.manual_seed(42)
audio = bddm(mel_spec, generator, torch_device=torch_device)

# save generated audio
from scipy.io.wavfile import write as wavwrite

sampling_rate = 22050
wavwrite("generated_audio.wav", sampling_rate, audio.squeeze().cpu().numpy())
```