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control set task args

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Vladimir Mandic
2023-12-21 10:57:11 -05:00
parent 787e9b0b22
commit aeccc28d60
7 changed files with 104 additions and 25 deletions
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modules/control/adapters.py
+1 -1
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@@ -1,6 +1,6 @@
import os
import time
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, MultiAdapter, StableDiffusionAdapterPipeline, StableDiffusionXLAdapterPipeline
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, MultiAdapter, StableDiffusionAdapterPipeline, StableDiffusionXLAdapterPipeline # pylint: disable=unused-import
from modules.shared import log
from modules import errors
modules/control/reference.py
+1 -1
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@@ -5,7 +5,7 @@ from modules.control.proc.reference_sd15 import StableDiffusionReferencePipeline
from modules.control.proc.reference_sdxl import StableDiffusionXLReferencePipeline
what = 'ControlNet-XS'
what = 'Reference'
def list_models():
modules/control/run.py
+19 -20
View File
@@ -193,22 +193,20 @@ def control_run(units: List[unit.Unit], inputs, unit_type: str, is_generator: bo
pass
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE) # reset current pipeline
if not has_models and (unit_type == 'reference' or unit_type == 'adapter' or unit_type == 'controlnet' or unit_type == 'xs'): # run in img2img mode
if len(active_strength) > 0:
p.strength = active_strength[0]
pipe = diffusers.AutoPipelineForImage2Image.from_pipe(shared.sd_model) # use set_diffuser_pipe
elif unit_type == 'adapter' and has_models:
debug(f'Control: run type={unit_type} models={has_models}')
if unit_type == 'adapter' and has_models:
p.extra_generation_params["Control mode"] = 'Adapter'
p.extra_generation_params["Control conditioning"] = use_conditioning
p.adapter_conditioning_scale = use_conditioning
p.task_args['adapter_conditioning_scale'] = use_conditioning
instance = adapters.AdapterPipeline(selected_models, shared.sd_model)
pipe = instance.pipeline
elif unit_type == 'controlnet' and has_models:
p.extra_generation_params["Control mode"] = 'ControlNet'
p.extra_generation_params["Control conditioning"] = use_conditioning
p.controlnet_conditioning_scale = use_conditioning
p.control_guidance_start = active_start[0] if len(active_start) == 1 else list(active_start)
p.control_guidance_end = active_end[0] if len(active_end) == 1 else list(active_end)
p.task_args['controlnet_conditioning_scale'] = use_conditioning
p.task_args['control_guidance_start'] = active_start[0] if len(active_start) == 1 else list(active_start)
p.task_args['control_guidance_end'] = active_end[0] if len(active_end) == 1 else list(active_end)
p.task_args['guess_mode'] = p.guess_mode
instance = controlnets.ControlNetPipeline(selected_models, shared.sd_model)
pipe = instance.pipeline
elif unit_type == 'xs' and has_models:
@@ -222,16 +220,17 @@ def control_run(units: List[unit.Unit], inputs, unit_type: str, is_generator: bo
elif unit_type == 'reference':
p.extra_generation_params["Control mode"] = 'Reference'
p.extra_generation_params["Control attention"] = p.attention
p.reference_attn = 'Attention' in p.attention
p.reference_adain = 'Adain' in p.attention
p.attention_auto_machine_weight = p.query_weight
p.gn_auto_machine_weight = p.adain_weight
p.style_fidelity = p.fidelity
p.task_args['reference_attn'] = 'Attention' in p.attention
p.task_args['reference_adain'] = 'Adain' in p.attention
p.task_args['attention_auto_machine_weight'] = p.query_weight
p.task_args['gn_auto_machine_weight'] = p.adain_weight
p.task_args['style_fidelity'] = p.fidelity
instance = reference.ReferencePipeline(shared.sd_model)
pipe = instance.pipeline
else:
shared.log.error(f'Control: unknown unit type: {unit_type}')
pipe = None
else: # run in img2img mode
if len(active_strength) > 0:
p.strength = active_strength[0]
pipe = diffusers.AutoPipelineForImage2Image.from_pipe(shared.sd_model) # use set_diffuser_pipe
debug(f'Control pipeline: class={pipe.__class__} args={vars(p)}')
t1, t2, t3 = time.time(), 0, 0
status = True
@@ -353,20 +352,20 @@ def control_run(units: List[unit.Unit], inputs, unit_type: str, is_generator: bo
# pipeline
output = None
if pipe is not None: # run new pipeline
if not has_models and (unit_type == 'reference' or unit_type == 'controlnet' or unit_type == 'adapter' or unit_type == 'xs'): # run in img2img mode
if not has_models and (unit_type == 'controlnet' or unit_type == 'adapter' or unit_type == 'xs'): # run in img2img mode
if p.image is None:
if hasattr(p, 'init_images'):
del p.init_images
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE) # reset current pipeline
else:
p.init_images = [processed_image] # pylint: disable=attribute-defined-outside-init
processed_image.save('/tmp/test.png')
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE) # reset current pipeline
else:
if hasattr(p, 'init_images'):
del p.init_images
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE) # reset current pipeline
debug(f'Control exec pipeline: class={pipe.__class__} args={vars(p)}')
debug(f'Control exec pipeline: class={pipe.__class__} p={vars(p)}')
debug(f'Control exec pipeline: class={pipe.__class__} args={p.task_args}')
processed: processing.Processed = processing.process_images(p) # run actual pipeline
output = processed.images if processed is not None else None
# output = pipe(**vars(p)).images # alternative direct pipe exec call