Merge pull request #10357 from catboxanon/sag
Add/modify CFG callbacks for Self-Attention Guidance extension
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cb9a3a7809
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@ -53,6 +53,21 @@ class CFGDenoiserParams:
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class CFGDenoisedParams:
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class CFGDenoisedParams:
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def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
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self.x = x
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"""Latent image representation in the process of being denoised"""
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self.sampling_step = sampling_step
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"""Current Sampling step number"""
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self.total_sampling_steps = total_sampling_steps
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"""Total number of sampling steps planned"""
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self.inner_model = inner_model
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"""Inner model reference used for denoising"""
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class AfterCFGCallbackParams:
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def __init__(self, x, sampling_step, total_sampling_steps):
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def __init__(self, x, sampling_step, total_sampling_steps):
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self.x = x
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self.x = x
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"""Latent image representation in the process of being denoised"""
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"""Latent image representation in the process of being denoised"""
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@ -63,6 +78,9 @@ class CFGDenoisedParams:
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self.total_sampling_steps = total_sampling_steps
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self.total_sampling_steps = total_sampling_steps
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"""Total number of sampling steps planned"""
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"""Total number of sampling steps planned"""
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self.output_altered = False
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"""A flag for CFGDenoiser indicating whether the output has been altered by the callback"""
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class UiTrainTabParams:
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class UiTrainTabParams:
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def __init__(self, txt2img_preview_params):
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def __init__(self, txt2img_preview_params):
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@ -87,6 +105,7 @@ callback_map = dict(
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callbacks_image_saved=[],
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callbacks_image_saved=[],
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callbacks_cfg_denoiser=[],
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callbacks_cfg_denoiser=[],
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callbacks_cfg_denoised=[],
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callbacks_cfg_denoised=[],
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callbacks_cfg_after_cfg=[],
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callbacks_before_component=[],
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callbacks_before_component=[],
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callbacks_after_component=[],
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callbacks_after_component=[],
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callbacks_image_grid=[],
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callbacks_image_grid=[],
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@ -186,6 +205,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams):
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report_exception(c, 'cfg_denoised_callback')
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report_exception(c, 'cfg_denoised_callback')
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def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
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for c in callback_map['callbacks_cfg_after_cfg']:
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try:
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c.callback(params)
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except Exception:
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report_exception(c, 'cfg_after_cfg_callback')
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def before_component_callback(component, **kwargs):
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def before_component_callback(component, **kwargs):
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for c in callback_map['callbacks_before_component']:
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for c in callback_map['callbacks_before_component']:
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try:
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try:
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@ -332,6 +359,14 @@ def on_cfg_denoised(callback):
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add_callback(callback_map['callbacks_cfg_denoised'], callback)
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add_callback(callback_map['callbacks_cfg_denoised'], callback)
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def on_cfg_after_cfg(callback):
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"""register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
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The callback is called with one argument:
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- params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
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"""
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add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
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def on_before_component(callback):
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def on_before_component(callback):
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"""register a function to be called before a component is created.
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"""register a function to be called before a component is created.
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The callback is called with arguments:
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The callback is called with arguments:
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@ -8,6 +8,7 @@ from modules.shared import opts, state
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import modules.shared as shared
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import modules.shared as shared
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from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
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from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
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from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
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from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
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from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
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samplers_k_diffusion = [
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samplers_k_diffusion = [
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('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
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('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
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@ -160,7 +161,7 @@ class CFGDenoiser(torch.nn.Module):
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fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
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fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
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x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
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x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
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denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
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denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
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cfg_denoised_callback(denoised_params)
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cfg_denoised_callback(denoised_params)
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devices.test_for_nans(x_out, "unet")
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devices.test_for_nans(x_out, "unet")
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@ -180,6 +181,11 @@ class CFGDenoiser(torch.nn.Module):
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if self.mask is not None:
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if self.mask is not None:
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denoised = self.init_latent * self.mask + self.nmask * denoised
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denoised = self.init_latent * self.mask + self.nmask * denoised
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after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
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cfg_after_cfg_callback(after_cfg_callback_params)
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if after_cfg_callback_params.output_altered:
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denoised = after_cfg_callback_params.x
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self.step += 1
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self.step += 1
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return denoised
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return denoised
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