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runtime eval pipeline type

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This commit is contained in:
Vladimir Mandic
2023-12-21 08:09:12 -05:00
parent 10c3671276
commit 787e9b0b22
2 changed files with 33 additions and 25 deletions
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modules/processing_diffusers.py
+32 -24
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@@ -19,9 +19,20 @@ from modules.sd_hijack_hypertile import hypertile_set
from modules.processing_correction import correction_callback
debug = shared.log.trace if os.environ.get('SD_DIFFUSERS_DEBUG', None) is not None else lambda *args, **kwargs: None
debug('Trace: DIFFUSERS')
debug_steps = shared.log.trace if os.environ.get('SD_STEPS_DEBUG', None) is not None else lambda *args, **kwargs: None
debug_steps('Trace: STEPS')
def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_prompts):
results = []
is_refiner_enabled = 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 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
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)
@@ -293,6 +304,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
'width': p.width if hasattr(p, 'width') else None,
'height': p.height if hasattr(p, 'height') else None,
}
debug(f'Diffusers task args: {task_args}')
return task_args
def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: typing.Optional[list]=None, negative_prompts_2: typing.Optional[list]=None, desc:str='', **kwargs):
@@ -302,6 +314,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args = {}
signature = inspect.signature(type(model).__call__)
possible = signature.parameters.keys()
debug(f'Diffusers pipeline possible: {possible}')
generator_device = devices.cpu if shared.opts.diffusers_generator_device == "cpu" else shared.device
generator = [torch.Generator(generator_device).manual_seed(s) for s in seeds]
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
@@ -407,6 +420,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.cmd_opts.profile:
t1 = time.time()
shared.log.debug(f'Profile: pipeline args: {t1-t0:.2f}')
debug(f'Diffusers pipeline args: {args}')
return args
def recompile_model(hires=False):
@@ -423,7 +437,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
shared.log.info("OpenVINO: Recompiling base model")
sd_models.unload_model_weights(op='model')
sd_models.reload_model_weights(op='model')
if is_refiner_enabled:
if is_refiner_enabled():
shared.log.info("OpenVINO: Recompiling refiner")
sd_models.unload_model_weights(op='refiner')
sd_models.reload_model_weights(op='refiner')
@@ -457,12 +471,19 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.opts.diffusers_move_base and not getattr(shared.sd_model, 'has_accelerate', False):
shared.sd_model.to(devices.device)
is_img2img = bool(sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
use_refiner_start = bool(is_refiner_enabled and not p.is_hr_pass and not is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
use_denoise_start = bool(is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
# pipeline type is set earlier in processing, but check for sanity
if sd_models.get_diffusers_task(shared.sd_model) != sd_models.DiffusersTaskType.TEXT_2_IMAGE and len(getattr(p, 'init_images' ,[])) == 0:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE) # reset pipeline
if hasattr(shared.sd_model, 'unet') and hasattr(shared.sd_model.unet, 'config') and hasattr(shared.sd_model.unet.config, 'in_channels') and shared.sd_model.unet.config.in_channels == 9:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.INPAINTING) # force pipeline
if len(getattr(p, 'init_images' ,[])) == 0:
p.init_images = [TF.to_pil_image(torch.rand((3, getattr(p, 'height', 512), getattr(p, 'width', 512))))]
use_refiner_start = is_txt2img() and is_refiner_enabled() and not p.is_hr_pass and p.refiner_start > 0 and p.refiner_start < 1
use_denoise_start = not is_txt2img() and p.refiner_start > 0 and p.refiner_start < 1
def calculate_base_steps():
if is_img2img:
if not is_txt2img():
if use_denoise_start and shared.sd_model_type == 'sdxl':
steps = p.steps // (1 - p.refiner_start)
elif p.denoising_strength > 0:
@@ -473,9 +494,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
steps = (p.steps // p.refiner_start) + 1
else:
steps = p.steps
if os.environ.get('SD_STEPS_DEBUG', None) is not None:
shared.log.debug(f'Steps: type=base input={p.steps} output={steps} refiner={use_refiner_start}')
debug_steps(f'Steps: type=base input={p.steps} output={steps} task={sd_models.get_diffusers_task(shared.sd_model)} refiner={use_refiner_start} denoise={p.denoising_strength} model={shared.sd_model_type}')
return max(2, int(steps))
def calculate_hires_steps():
@@ -485,9 +504,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
steps = (p.steps // p.denoising_strength) + 1
else:
steps = 0
if os.environ.get('SD_STEPS_DEBUG', None) is not None:
shared.log.debug(f'Steps: type=hires input={p.hr_second_pass_steps} output={steps} denoise={p.denoising_strength}')
debug_steps(f'Steps: type=hires input={p.hr_second_pass_steps} output={steps} denoise={p.denoising_strength} model={shared.sd_model_type}')
return max(2, int(steps))
def calculate_refiner_steps():
@@ -502,18 +519,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
else:
#steps = p.refiner_steps # SD 1.5 with denoise strenght
steps = (p.refiner_steps * 1.25) + 1
if os.environ.get('SD_STEPS_DEBUG', None) is not None:
shared.log.debug(f'Steps: type=refiner input={p.refiner_steps} output={steps} start={p.refiner_start} denoise={p.denoising_strength}')
debug_steps(f'Steps: type=refiner input={p.refiner_steps} output={steps} start={p.refiner_start} denoise={p.denoising_strength}')
return max(2, int(steps))
# pipeline type is set earlier in processing, but check for sanity
if sd_models.get_diffusers_task(shared.sd_model) != sd_models.DiffusersTaskType.TEXT_2_IMAGE and len(getattr(p, 'init_images' ,[])) == 0:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE) # reset pipeline
if hasattr(shared.sd_model, 'unet') and hasattr(shared.sd_model.unet, 'config') and hasattr(shared.sd_model.unet.config, 'in_channels') and shared.sd_model.unet.config.in_channels == 9:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.INPAINTING) # force pipeline
if len(getattr(p, 'init_images' ,[])) == 0:
p.init_images = [TF.to_pil_image(torch.rand((3, getattr(p, 'height', 512), getattr(p, 'width', 512))))]
base_args = set_pipeline_args(
model=shared.sd_model,
prompts=prompts,
@@ -610,7 +618,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
p.is_hr_pass = False
# optional refiner pass or decode
if is_refiner_enabled:
if is_refiner_enabled():
prev_job = shared.state.job
shared.state.job = 'refine'
shared.state.job_count +=1
@@ -678,7 +686,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
p.is_refiner_pass = False
# final decode since there is no refiner
if not is_refiner_enabled:
if not is_refiner_enabled():
if output is not None:
if not hasattr(output, 'images') and hasattr(output, 'frames'):
shared.log.debug(f'Generated: frames={len(output.frames[0])}')