DDPM pipeline with black and white 1-channel image 'L" #1280
Description
I am trying to generate synthetic images starting from a black and white 1-channel training data set using the DDPMPipeline.
This is my UNet2D model
model = UNet2DModel(
sample_size=config.image_size, # the target image resolution
in_channels = 1, #3, # the number of input channels, 3 for RGB images
out_channels = 1, #3, # the number of output channels
layers_per_block=2, # how many ResNet layers to use per UNet block
block_out_channels=(128, 128, 256, 256, 512, 512), # the number of output channes for each UNet block
# block_out_channels=(132, 132, 256, 256, 512, 512), # the number of output channes for each UNet block
down_block_types=(
"DownBlock2D", # a regular ResNet downsampling block
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D", # a ResNet downsampling block with spatial self-attention
"DownBlock2D",
),
up_block_types=(
"UpBlock2D", # a regular ResNet upsampling block
"AttnUpBlock2D", # a ResNet upsampling block with spatial self-attention
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D"
),
)
However, during the evaluation of the model I get the following error:
Input In [134], in train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)
66 pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)
68 if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:
---> 69 evaluate(config, epoch, pipeline)
71 if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:
72 if config.push_to_hub:
Input In [133], in evaluate(config, epoch, pipeline)
12 def evaluate(config, epoch, pipeline):
13 # Sample some images from random noise (this is the backward diffusion process).
14 # The default pipeline output type is `List[PIL.Image]`
---> 15 images = pipeline(
16 batch_size = config.eval_batch_size,
17 generator=torch.manual_seed(config.seed),
18 )["sample"]
20 # Make a grid out of the images
21 image_grid = make_grid(images, rows=4, cols=4)
File /opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py:27, in _DecoratorContextManager.__call__.<locals>.decorate_context(*args, **kwargs)
24 @functools.wraps(func)
25 def decorate_context(*args, **kwargs):
26 with self.clone():
---> 27 return func(*args, **kwargs)
File /opt/conda/lib/python3.8/site-packages/diffusers/pipelines/ddpm/pipeline_ddpm.py:57, in DDPMPipeline.__call__(self, batch_size, generator, torch_device, output_type)
55 image = image.cpu().permute(0, 2, 3, 1).numpy()
56 if output_type == "pil":
---> 57 image = self.numpy_to_pil(image)
59 return {"sample": image}
File /opt/conda/lib/python3.8/site-packages/diffusers/pipeline_utils.py:202, in DiffusionPipeline.numpy_to_pil(images)
200 images = images[None, ...]
201 images = (images * 255).round().astype("uint8")
--> 202 pil_images = [Image.fromarray(image) for image in images]
204 return pil_images
File /opt/conda/lib/python3.8/site-packages/diffusers/pipeline_utils.py:202, in <listcomp>(.0)
200 images = images[None, ...]
201 images = (images * 255).round().astype("uint8")
--> 202 pil_images = [Image.fromarray(image) for image in images]
204 return pil_images
File /opt/conda/lib/python3.8/site-packages/PIL/Image.py:2815, in fromarray(obj, mode)
2813 mode, rawmode = _fromarray_typemap[typekey]
2814 except KeyError as e:
-> 2815 raise TypeError("Cannot handle this data type: %s, %s" % typekey) from e
2816 else:
2817 rawmode = mode
TypeError: Cannot handle this data type: (1, 1, 1), |u1
Any idea why this happens?
Thanks!