Initial Olive implementation.

modules/processing_diffusers.py

@@ -7,6 +7,7 @@ import torch
 import torchvision.transforms.functional as TF
 import diffusers
 from modules import shared, devices, processing, sd_samplers, sd_models, images, errors, masking, prompt_parser_diffusers, sd_hijack_hypertile, processing_correction, processing_vae
+from modules.olive import OlivePipeline
 
 
 debug = shared.log.trace if os.environ.get('SD_DIFFUSERS_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -20,6 +21,9 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
     orig_pipeline = shared.sd_model
     results = []
 
+    if isinstance(shared.sd_model, OlivePipeline):
+        shared.sd_model = shared.sd_model.optimize(p.width, p.height)
+
     def is_txt2img():
         return sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE
 
@@ -219,7 +223,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
             generator = [torch.Generator(generator_device).manual_seed(s) for s in p.seeds]
         prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
         parser = 'Fixed attention'
-        if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__:
+        if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__ and not isinstance(model, diffusers.OnnxStableDiffusionPipeline):
             try:
                 prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, kwargs.get("num_inference_steps", 1), kwargs.pop("clip_skip", None))
                 parser = shared.opts.prompt_attention