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multiple control fixes

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This commit is contained in:
Vladimir Mandic
2024-02-03 11:22:18 -05:00
parent dbb79c9669
commit 01d77ffbde
5 changed files with 29 additions and 23 deletions
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CHANGELOG.md
+8 -3
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@@ -2,15 +2,20 @@
## Future
- wuerstchen v3 [pr](https://github.com/huggingface/diffusers/pull/6487)
- ipadapter multi image
- control second pass
- control api
- masking api
- preprocess api
- wuerstchen v3 [pr](https://github.com/huggingface/diffusers/pull/6487)
## TODO for Dev merge
- updated docs
- control fixes
- update docs
- face apply style
- control reference mode
- control init image same as control, separate init image
- control t2i-adapter with ip-adapter
## Update for 2023-02-02
extensions-builtin/sd-extension-system-info
+1 -1
Submodule extensions-builtin/sd-extension-system-info updated: 72d871b456...363d441747
modules/control/run.py
+19 -17
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@@ -44,7 +44,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
*input_script_args
):
global pipe, original_pipeline # pylint: disable=global-statement
debug(f'Control {unit_type}: input={inputs} init={inits} type={input_type}')
debug(f'Control: type={unit_type} input={inputs} init={inits} type={input_type}')
if inputs is None or (type(inputs) is list and len(inputs) == 0):
inputs = [None]
output_images: List[Image.Image] = [] # output images
@@ -134,7 +134,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
active_process.append(u.process)
active_model.append(u.controlnet)
active_strength.append(float(u.strength))
shared.log.debug(f'Control ControlNet-XS unit: i={num_units} process={u.process.processor_id} model={u.controlnet.model_id} strength={u.strength} guess={u.guess} start={u.start} end={u.end}')
shared.log.debug(f'Control ControlLLite unit: i={num_units} process={u.process.processor_id} model={u.controlnet.model_id} strength={u.strength} guess={u.guess} start={u.start} end={u.end}')
elif unit_type == 'reference':
p.override = u.override
p.attention = u.attention
@@ -161,9 +161,9 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
selected_models = active_model[0].model if active_model[0].model is not None else None
p.extra_generation_params["Control model"] = (active_model[0].model_id or '') if active_model[0].model is not None else None
has_models = selected_models is not None
control_conditioning = active_strength[0]
control_guidance_start = active_start[0]
control_guidance_end = active_end[0]
control_conditioning = active_strength[0] if len(active_strength) > 0 else 1 # strength or list[strength]
control_guidance_start = active_start[0] if len(active_start) > 0 else 0
control_guidance_end = active_end[0] if len(active_end) > 0 else 1
else:
selected_models = [m.model for m in active_model if m.model is not None]
p.extra_generation_params["Control model"] = ', '.join([(m.model_id or '') for m in active_model if m.model is not None])
@@ -233,7 +233,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
"""
debug(f'Control pipeline: class={pipe.__class__} args={vars(p)}')
debug(f'Control pipeline: class={pipe.__class__.__name__} args={vars(p)}')
t1, t2, t3 = time.time(), 0, 0
status = True
frame = None
@@ -369,9 +369,12 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
shared.log.error(f'{msg}: {processed_images}')
restore_pipeline()
return msg
processed_image = [np.array(i) for i in processed_images]
processed_image = util.blend(processed_image) # blend all processed images into one
processed_image = Image.fromarray(processed_image)
if len(processed_images) > 1:
processed_image = [np.array(i) for i in processed_images]
processed_image = util.blend(processed_image) # blend all processed images into one
processed_image = Image.fromarray(processed_image)
else:
processed_image = processed_images[0]
if isinstance(selected_models, list) and len(processed_images) == len(selected_models):
debug(f'Control: inputs match: input={len(processed_images)} models={len(selected_models)}')
p.init_images = processed_images
@@ -381,7 +384,8 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
restore_pipeline()
return msg
elif selected_models is not None:
debug('Control: single model - blending images')
if len(processed_images) > 1:
debug('Control: using blended image for single model')
p.init_images = [processed_image]
else:
debug('Control processed: using input direct')
@@ -399,16 +403,14 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
return msg
elif unit_type == 'controlnet' and input_type == 1: # Init image same as control
p.init_images = input_image
p.task_args['control_image'] = p.image
p.task_args['control_image'] = p.override or input_image
p.task_args['strength'] = p.denoising_strength
elif unit_type == 'controlnet' and input_type == 2: # Separate init image
p.task_args['control_image'] = p.image
p.task_args['strength'] = p.denoising_strength
p.task_args['control_image'] = init_image
p.task_args['strength'] = init_image
if init_image is None:
shared.log.warning('Control: separate init image not provided')
p.init_images = input_image
else:
p.init_images = init_image
p.init_images = input_image if init_image is None else init_image
if is_generator:
image_txt = f'{processed_image.width}x{processed_image.height}' if processed_image is not None else 'None'
@@ -447,7 +449,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
if hasattr(p, 'init_images') and p.init_images is not None:
p.task_args['image'] = p.init_images # need to set explicitly for txt2img
if unit_type == 'lite':
instance.apply(selected_models, p.image, control_conditioning)
instance.apply(selected_models, p.init_images, control_conditioning)
if hasattr(p, 'init_images') and p.init_images is None:
del p.init_images
modules/images.py
+1 -1
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@@ -248,7 +248,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None, output_type
if upscaler is not None:
im = latent(im, w, h, upscaler)
else:
shared.log.warning(f"Could not find upscaler: {upscaler_name or '<empty string>'} using fallback: {upscaler.name}")
shared.log.warning(f"Resize upscaler: invalid={upscaler_name} fallback={upscaler.name}")
if im.width != w or im.height != h:
im = im.resize((w, h), resample=Image.Resampling.LANCZOS)
return im
modules/processing_diffusers.py
-1
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@@ -418,7 +418,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
shared.log.debug(f'Profile: pipeline call: {t1-t0:.2f}')
if not hasattr(output, 'images') and hasattr(output, 'frames'):
if hasattr(output.frames[0], 'shape'):
print('HERE', output.frames[0].shape)
shared.log.debug(f'Generated: frames={output.frames[0].shape[1]}')
else:
shared.log.debug(f'Generated: frames={len(output.frames[0])}')