commit
3fcc087317
@ -46,6 +46,18 @@ class CFGDenoiserParams:
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"""Total number of sampling steps planned"""
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"""Total number of sampling steps planned"""
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class CFGDenoisedParams:
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def __init__(self, x, sampling_step, total_sampling_steps):
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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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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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self.txt2img_preview_params = txt2img_preview_params
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self.txt2img_preview_params = txt2img_preview_params
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@ -68,6 +80,7 @@ callback_map = dict(
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callbacks_before_image_saved=[],
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callbacks_before_image_saved=[],
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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_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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@ -150,6 +163,14 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
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report_exception(c, 'cfg_denoiser_callback')
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report_exception(c, 'cfg_denoiser_callback')
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def cfg_denoised_callback(params: CFGDenoisedParams):
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for c in callback_map['callbacks_cfg_denoised']:
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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_denoised_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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@ -283,6 +304,14 @@ def on_cfg_denoiser(callback):
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add_callback(callback_map['callbacks_cfg_denoiser'], callback)
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add_callback(callback_map['callbacks_cfg_denoiser'], callback)
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def on_cfg_denoised(callback):
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"""register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
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The callback is called with one argument:
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- params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
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"""
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add_callback(callback_map['callbacks_cfg_denoised'], 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 import prompt_parser, devices, sd_samplers_common
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from modules.shared import opts, state
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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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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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@ -136,6 +137,9 @@ class CFGDenoiser(torch.nn.Module):
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
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denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
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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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if opts.live_preview_content == "Prompt":
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if opts.live_preview_content == "Prompt":
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