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Integrate Olive into compile backend.

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This commit is contained in:
Seunghoon Lee
2023-12-23 18:53:07 +09:00
parent 3cad532d81
commit 286ec8e753
6 changed files with 68 additions and 44 deletions
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modules/processing_diffusers.py
+16 -27
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@@ -7,7 +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.onnx_pipelines import OnnxStableDiffusionPipeline
from modules.onnx import optimize_pipeline as onnx_optimize_pipeline
debug = shared.log.trace if os.environ.get('SD_DIFFUSERS_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -21,37 +21,24 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
orig_pipeline = shared.sd_model
results = []
if hasattr(shared.sd_model, 'preprocess'):
shared.sd_model = shared.sd_model.preprocess(p)
if hasattr(shared.sd_model, 'override_processing'):
shared.sd_model.override_processing(p)
def is_txt2img():
return sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE
def is_refiner_enabled():
return p.enable_hr and p.refiner_steps > 0 and p.refiner_start > 0 and p.refiner_start < 1 and shared.sd_refiner is not None
def resize_images():
if getattr(p, 'image', None) is not None and getattr(p, 'init_images', None) is None:
p.init_images = [p.image]
if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
tgt_width, tgt_height = 8 * math.ceil(p.init_images[0].width / 8), 8 * math.ceil(p.init_images[0].height / 8)
if p.init_images[0].size != (tgt_width, tgt_height):
shared.log.debug(f'Resizing init images: original={p.init_images[0].width}x{p.init_images[0].height} target={tgt_width}x{tgt_height}')
p.init_images = [images.resize_image(1, image, tgt_width, tgt_height, upscaler_name=None) for image in p.init_images]
p.height = tgt_height
p.width = tgt_width
sd_hijack_hypertile.hypertile_set(p)
if getattr(p, 'mask', None) is not None and p.mask.size != (tgt_width, tgt_height):
p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'init_mask', None) is not None and p.init_mask.size != (tgt_width, tgt_height):
p.init_mask = images.resize_image(1, p.init_mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
return tgt_width, tgt_height
return p.width, p.height
if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
tgt_width, tgt_height = 8 * math.ceil(p.init_images[0].width / 8), 8 * math.ceil(p.init_images[0].height / 8)
if p.init_images[0].width != tgt_width or p.init_images[0].height != tgt_height:
shared.log.debug(f'Resizing init images: original={p.init_images[0].width}x{p.init_images[0].height} target={tgt_width}x{tgt_height}')
p.init_images = [images.resize_image(1, image, tgt_width, tgt_height, upscaler_name=None) for image in p.init_images]
p.height = tgt_height
p.width = tgt_width
hypertile_set(p)
if getattr(p, 'mask', None) is not None and p.mask.size != (tgt_width, tgt_height):
p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
def hires_resize(latents): # input=latents output=pil
if not torch.is_tensor(latents):
@@ -226,7 +213,7 @@ 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__ and not isinstance(model, OnnxStableDiffusionPipeline):
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__ and 'Onnx' not in model.__class__.__name__:
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
@@ -477,6 +464,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
return max(1, int(steps))
shared.sd_model = update_pipeline(shared.sd_model, p)
onnx_optimize_pipeline(p, is_refiner_enabled())
base_args = set_pipeline_args(
model=shared.sd_model,
prompts=p.prompts,
@@ -548,6 +536,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
if (latent_scale_mode is not None or p.hr_force) and p.denoising_strength > 0:
p.ops.append('hires')
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
onnx_optimize_pipeline(p, is_refiner_enabled())
recompile_model(hires=True)
update_sampler(shared.sd_model, second_pass=True)
hires_args = set_pipeline_args(