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feat(intel): add diffusers/transformers support (#1746)

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* feat(intel): add diffusers support

* try to consume upstream container image

* Debug

* Manually install deps

* Map transformers/hf cache dir to modelpath if not specified

* fix(compel): update initialization, pass by all gRPC options

* fix: add dependencies, implement transformers for xpu

* base it from the oneapi image

* Add pillow

* set threads if specified when launching the API

* Skip conda install if intel

* defaults to non-intel

* ci: add to pipelines

* prepare compel only if enabled

* Skip conda install if intel

* fix cleanup

* Disable compel by default

* Install torch 2.1.0 with Intel

* Skip conda on some setups

* Detect python

* Quiet output

* Do not override system python with conda

* Prefer python3

* Fixups

* exllama2: do not install without conda (overrides pytorch version)

* exllama/exllama2: do not install if not using cuda

* Add missing dataset dependency

* Small fixups, symlink to python, add requirements

* Add neural_speed to the deps

* correctly handle model offloading

* fix: device_map == xpu

* go back at calling python, fixed at dockerfile level

* Exllama2 restricted to only nvidia gpus

* Tokenizer to xpu
This commit is contained in:
Ettore Di Giacinto 2024-03-07 14:37:45 +01:00 • committed by GitHub
parent ad6fd7a991
commit 5d1018495f
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23 changed files with 250 additions and 81 deletions
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23
core/backend/image.go
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@ -8,27 +8,18 @@ import (
)
func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negative_prompt, src, dst string, loader *model.ModelLoader, backendConfig config.BackendConfig, appConfig *config.ApplicationConfig) (func() error, error) {
threads := backendConfig.Threads
if threads == 0 && appConfig.Threads != 0 {
threads = appConfig.Threads
}
gRPCOpts := gRPCModelOpts(backendConfig)
opts := modelOpts(backendConfig, appConfig, []model.Option{
model.WithBackendString(backendConfig.Backend),
model.WithAssetDir(appConfig.AssetsDestination),
model.WithThreads(uint32(backendConfig.Threads)),
model.WithThreads(uint32(threads)),
model.WithContext(appConfig.Context),
model.WithModel(backendConfig.Model),
model.WithLoadGRPCLoadModelOpts(&proto.ModelOptions{
CUDA: backendConfig.CUDA || backendConfig.Diffusers.CUDA,
SchedulerType: backendConfig.Diffusers.SchedulerType,
PipelineType: backendConfig.Diffusers.PipelineType,
CFGScale: backendConfig.Diffusers.CFGScale,
LoraAdapter: backendConfig.LoraAdapter,
LoraScale: backendConfig.LoraScale,
LoraBase: backendConfig.LoraBase,
IMG2IMG: backendConfig.Diffusers.IMG2IMG,
CLIPModel: backendConfig.Diffusers.ClipModel,
CLIPSubfolder: backendConfig.Diffusers.ClipSubFolder,
CLIPSkip: int32(backendConfig.Diffusers.ClipSkip),
ControlNet: backendConfig.Diffusers.ControlNet,
}),
model.WithLoadGRPCLoadModelOpts(gRPCOpts),
})
inferenceModel, err := loader.BackendLoader(