Add UniPC sampler settings
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@ -3,6 +3,7 @@
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import torch
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from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
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from modules import shared
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class UniPCSampler(object):
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def __init__(self, model, **kwargs):
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@ -89,7 +90,7 @@ class UniPCSampler(object):
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guidance_scale=unconditional_guidance_scale,
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)
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uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
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x = uni_pc.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=3, lower_order_final=True)
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uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=shared.opts.uni_pc_thresholding, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
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x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
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return x.to(device), None
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@ -750,7 +750,7 @@ class UniPC:
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if method == 'multistep':
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assert steps >= order, "UniPC order must be < sampling steps"
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timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
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print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps")
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print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
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assert timesteps.shape[0] - 1 == steps
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with torch.no_grad():
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vec_t = timesteps[0].expand((x.shape[0]))
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@ -480,6 +480,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
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'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
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'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "vary_coeff"]}),
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'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
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'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 150 - 1, "step": 1}),
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'uni_pc_thresholding': OptionInfo(False, "UniPC thresholding"),
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'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
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}))
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options_templates.update(options_section(('postprocessing', "Postprocessing"), {
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@ -126,6 +126,10 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
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p.styles.extend(x.split(','))
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def apply_uni_pc_order(p, x, xs):
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opts.data["uni_pc_order"] = min(x, p.steps - 1)
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def format_value_add_label(p, opt, x):
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if type(x) == float:
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x = round(x, 8)
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@ -202,6 +206,7 @@ axis_options = [
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AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
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AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
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AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
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AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
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]
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@ -310,9 +315,11 @@ class SharedSettingsStackHelper(object):
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def __enter__(self):
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self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
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self.vae = opts.sd_vae
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self.uni_pc_order = opts.uni_pc_order
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def __exit__(self, exc_type, exc_value, tb):
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opts.data["sd_vae"] = self.vae
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opts.data["uni_pc_order"] = self.uni_pc_order
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modules.sd_models.reload_model_weights()
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modules.sd_vae.reload_vae_weights()
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