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Patrick von Platen 2022-07-15 14:06:45 +00:00
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@ -82,105 +82,52 @@ For more examples see [schedulers](https://github.com/huggingface/diffusers/tree
```python
import torch
from diffusers import UNetModel, DDPMScheduler
import PIL
from diffusers import UNetUnconditionalModel, DDIMScheduler
import PIL.Image
import numpy as np
import tqdm
generator = torch.manual_seed(0)
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
# 1. Load models
noise_scheduler = DDPMScheduler.from_config("fusing/ddpm-lsun-church", tensor_format="pt")
unet = UNetModel.from_pretrained("fusing/ddpm-lsun-church").to(torch_device)
scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
unet = UNetUnconditionalModel.from_pretrained("fusing/ddpm-celeba-hq", ddpm=True).to(torch_device)
# 2. Sample gaussian noise
generator = torch.manual_seed(23)
unet.image_size = unet.resolution
image = torch.randn(
(1, unet.in_channels, unet.resolution, unet.resolution),
generator=generator,
)
image = image.to(torch_device)
# 3. Denoise
num_prediction_steps = len(noise_scheduler)
for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
# predict noise residual
with torch.no_grad():
residual = unet(image, t)
# predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.step(residual, image, t)
# optionally sample variance
variance = 0
if t > 0:
noise = torch.randn(image.shape, generator=generator).to(image.device)
variance = noise_scheduler.get_variance(t).sqrt() * noise
# set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
# 5. process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# 6. save image
image_pil.save("test.png")
```
#### **Example for Unconditonal Image generation [LDM](https://github.com/CompVis/latent-diffusion):**
```python
import torch
from diffusers import UNetModel, DDIMScheduler
import PIL
import numpy as np
import tqdm
generator = torch.manual_seed(0)
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
# 1. Load models
noise_scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
unet = UNetModel.from_pretrained("fusing/ddpm-celeba-hq").to(torch_device)
# 2. Sample gaussian noise
image = torch.randn(
(1, unet.in_channels, unet.resolution, unet.resolution),
(1, unet.in_channels, unet.image_size, unet.image_size),
generator=generator,
)
image = image.to(torch_device)
# 3. Denoise
# 3. Denoise
num_inference_steps = 50
eta = 0.0 # <- deterministic sampling
scheduler.set_timesteps(num_inference_steps)
for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
for t in tqdm.tqdm(scheduler.timesteps):
# 1. predict noise residual
orig_t = len(noise_scheduler) // num_inference_steps * t
with torch.no_grad():
residual = unet(image, orig_t)
residual = unet(image, t)["sample"]
# 2. predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.step(residual, image, t, num_inference_steps, eta)
prev_image = scheduler.step(residual, t, image, eta)["prev_sample"]
# 3. optionally sample variance
variance = 0
if eta > 0:
noise = torch.randn(image.shape, generator=generator).to(image.device)
variance = noise_scheduler.get_variance(t).sqrt() * eta * noise
# 3. set current image to prev_image: x_t -> x_t-1
image = prev_image
# 4. set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
# 5. process image to PIL
# 4. process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# 6. save image
image_pil.save("test.png")
# 5. save image
image_pil.save("generated_image.png")
```
#### **Example for Unconditonal Image generation [LDM](https://github.com/CompVis/latent-diffusion):**
```python
```