Stable Diffusion on CPU: Battling the "RuntimeError: expected scalar type BFloat16 but found Float"
The Problem:
You're excited to run Stable Diffusion on your CPU, but when you fire it up, you encounter the error message "RuntimeError: expected scalar type BFloat16 but found Float". This frustrating error means your CPU doesn't support the BFloat16 data type required by Stable Diffusion's PyTorch implementation.
In simpler terms: Stable Diffusion is trying to use a special kind of number format (BFloat16) to run faster, but your CPU doesn't understand that format.
The Scenario:
from diffusers import StableDiffusionPipeline
from diffusers import EulerDiscreteScheduler
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
pipe = pipe.to("cpu") # Move model to CPU
# Generate an image
image = pipe("a cute cat wearing a hat", num_inference_steps=50).images[0]
# Save the image
image.save("cat_with_hat.png")
This is a common code snippet for generating images with Stable Diffusion. However, running this on a CPU often results in the "RuntimeError: expected scalar type BFloat16 but found Float" error.
The Solution:
The solution is straightforward: use the standard float32 data type instead of bfloat16. Here's how you modify your code:
from diffusers import StableDiffusionPipeline
from diffusers import EulerDiscreteScheduler
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float32)
pipe = pipe.to("cpu") # Move model to CPU
# Generate an image
image = pipe("a cute cat wearing a hat", num_inference_steps=50).images[0]
# Save the image
image.save("cat_with_hat.png")
By changing torch_dtype to torch.float32, you tell Stable Diffusion to use the standard float format which your CPU understands.
Understanding BFloat16 and Float:
- BFloat16 is a specialized data type that utilizes a smaller memory footprint than
float32while still offering relatively high precision. It's often used in specialized hardware like TPUs for faster processing. - Float32 is the standard floating-point data type widely supported by CPUs. It provides a balance of precision and performance.
Since most CPUs don't have dedicated hardware for BFloat16 calculations, using float32 ensures compatibility and avoids the error.
The Trade-Off:
Using float32 instead of bfloat16 will generally result in slightly slower processing times, but you won't experience the error and can successfully run Stable Diffusion on your CPU.
Additional Considerations:
- Performance Impact: The difference in speed between
float32andbfloat16can vary depending on your CPU and the complexity of the model. It's best to test and see the impact for yourself. - Memory Usage: Using
float32might require slightly more memory due to its larger size compared tobfloat16. - GPU Support: If you have a GPU with BFloat16 support, you can potentially achieve faster performance by using
torch_dtype=torch.bfloat16and running the model on your GPU.
Conclusion:
Running Stable Diffusion on a CPU is possible without BFloat16 support by simply switching to the float32 data type. While this might introduce a slight performance difference, it guarantees compatibility and allows you to enjoy the power of Stable Diffusion on your machine.
