* Update the stable diffusion example with inpainting support for 1.5, 2 and XL.
* Apply cargo fmt.
* Clippy fixes.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* onnx: fix pad, unsqueeze
both implementations have off-by-one errors:
- Pad 'reflect' cycle for eg `dim==3` is `[0,1,2,1]` which has length of
4 (or `dim*2 - 2`) not 5 (current code `dim*2 - 1`)
- Unsqueeze(-1) for tensor with `dim==3` should be 3 (ie `dim+index+1`)
not 2 (ie currently `dim+index`)
in addition, Pad is incorrectly calculating the starting padding.
If we want to pad out 2 elements to the start, and we have this cycle
of indices of length 6, then we should skip 4 elements, but currently
we skip 2. A more visual representation of what's going on is below:
```
pad_start: 2
data: [a,b,c,d]
indices: [0, 1, 2, 3, 2, 1, 0, 1, 2, 3, 2, 1, 0, ..] // zigzag between 0..4
actual: skip [ c d| c b a b]
expected: ~ skip ~ [ c b| a b c d]
```
The values between `[` and `|` are padding and the values between
`|` and `]` in the example should match the original data being padded.
* Fix clippy lints.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Utilize batches in Stable Diffusion that were already there, but unutilized.
Also refactor out the `save_image` function.
* Clippy + cosmetic fixes.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Add a --seed argument to the stable-diffusion example.
* Make the case when no seed is specified, that it will not be set, but use the engine's default. This will make the CPU engine work again when no --seed is given, and will cause a bailout when a seed is there, as the engine does not currently support it.
---------
Co-authored-by: niklas <niklas@appli.se>
* Add support for SD Turbo
* Set Leading as default in euler_ancestral discrete
* Use the appropriate default values for n_steps and guidance_scale.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* img2img pipeline for stable diffusion.
* Rename the arguments + fix.
* Fix for zero strength.
* Another fix.
* Another fix.
* Revert.
* Include the backtrace.
* Noise scaling.
* Fix the height/width.
* Add a custom softmax implementation.
* Add softmaxlastdim to the benchmarks.
* And add a test.
* Support more dtypes.
* Polish the code.
* Use the slow implementation on cuda.
* Add a todo for the cuda kernel.
* Add the dilation parameter.
* Restore the basic optimizer example.
* Dilation support in cudnn.
* Use the dilation parameter in the cpu backend.
* More dilation support.
* No support for dilation in transposed convolutions.
* Add dilation to a test.
* Remove a print.
* Helper function.
* Add some group parameter to convolutions.
* Avoid some unnecessary groups checks.
* Move the tensor convolution bits.
* Properh handling of groups.
* Bump the crate version.
* And add a changelog.
* Skeleton files for neon support of quantization.
* SIMD version for q4 vecdot.
* Also simdify the q6k multiplication.
* Add some timings to stable-diffusion.
* Add flash-attention for the stable-diffusion example.
* Change the dtype.
* Silly fix.
* Another fix.
* Revert the dtype back to the query dtype after apply flash-attn.
* Track the conv2d operations in stable-diffusion.
* Add more tracing to stable-diffusion.
* Also trace the resnet bits.
* Trace the attention blocks.
* Also trace the attention inner part.
* Small tweak.
* Start adding a stable-diffusion example.
* Proper computation of the causal mask.
* Add the chunk operation.
* Work in progress: port the attention module.
* Add some dummy modules for conv2d and group-norm, get the attention module to compile.
* Re-enable the 2d convolution.
* Add the embeddings module.
* Add the resnet module.
* Add the unet blocks.
* Add the unet.
* And add the variational auto-encoder.
* Use the pad function from utils.
