109 lines
5.9 KiB
Python
109 lines
5.9 KiB
Python
import gradio as gr
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import torch
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import math
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import traceback
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from modules import shared
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from modules.models.diffusion import uni_pc
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######################### UniPC Implementation logic #########################
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# The majority of this is straight from modules.models/diffusion/uni_pc/sampler.py
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# Unfortunately that's not an easy middle-injection point, so, just copypasta'd it all
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# It's likely they designed it to intentionally be as difficult to inject into as possible :(
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# (It has hooks but not in useful locations)
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# I stripped the original comments for brevity.
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# Some never-used code (scheduler modes, noise modes, guidance modes) have been removed as well for brevity.
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# The actual impl comes down to just the last line in particular, and the `beforeSample` insert to track step count.
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class CustomUniPCSampler(uni_pc.sampler.UniPCSampler):
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def __init__(self, model, **kwargs):
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super().__init__(model, *kwargs)
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@torch.no_grad()
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def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None,
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quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None,
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corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1.,
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unconditional_conditioning=None, **kwargs):
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if conditioning is not None:
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if isinstance(conditioning, dict):
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ctmp = conditioning[list(conditioning.keys())[0]]
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while isinstance(ctmp, list): ctmp = ctmp[0]
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cbs = ctmp.shape[0]
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if cbs != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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elif isinstance(conditioning, list):
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for ctmp in conditioning:
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if ctmp.shape[0] != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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C, H, W = shape
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size = (batch_size, C, H, W)
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device = self.model.betas.device
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if x_T is None:
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img = torch.randn(size, device=device)
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else:
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img = x_T
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ns = uni_pc.uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
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model_type = "v" if self.model.parameterization == "v" else "noise"
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model_fn = CustomUniPC_model_wrapper(lambda x, t, c: self.model.apply_model(x, t, c), ns, model_type=model_type, guidance_scale=unconditional_guidance_scale, dtData=self.main_class)
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self.main_class.step = 0
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def beforeSample(x, t, cond, uncond):
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self.main_class.step += 1
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return self.before_sample(x, t, cond, uncond)
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uni_pc_inst = uni_pc.uni_pc.UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=beforeSample, after_sample=self.after_sample, after_update=self.after_update)
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x = uni_pc_inst.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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def CustomUniPC_model_wrapper(model, noise_schedule, model_type="noise", model_kwargs={}, guidance_scale=1.0, dtData=None):
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def expand_dims(v, dims):
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return v[(...,) + (None,)*(dims - 1)]
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def get_model_input_time(t_continuous):
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return (t_continuous - 1. / noise_schedule.total_N) * 1000.
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def noise_pred_fn(x, t_continuous, cond=None):
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if t_continuous.reshape((-1,)).shape[0] == 1:
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t_continuous = t_continuous.expand((x.shape[0]))
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t_input = get_model_input_time(t_continuous)
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if cond is None:
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output = model(x, t_input, None, **model_kwargs)
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else:
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output = model(x, t_input, cond, **model_kwargs)
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if model_type == "noise":
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return output
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elif model_type == "v":
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alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
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dims = x.dim()
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return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
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def model_fn(x, t_continuous, condition, unconditional_condition):
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if t_continuous.reshape((-1,)).shape[0] == 1:
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t_continuous = t_continuous.expand((x.shape[0]))
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if guidance_scale == 1. or unconditional_condition is None:
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return noise_pred_fn(x, t_continuous, cond=condition)
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else:
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t_continuous] * 2)
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if isinstance(condition, dict):
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assert isinstance(unconditional_condition, dict)
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c_in = dict()
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for k in condition:
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if isinstance(condition[k], list):
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c_in[k] = [torch.cat([
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unconditional_condition[k][i],
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condition[k][i]]) for i in range(len(condition[k]))]
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else:
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c_in[k] = torch.cat([
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unconditional_condition[k],
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condition[k]])
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elif isinstance(condition, list):
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c_in = list()
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assert isinstance(unconditional_condition, list)
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for i in range(len(condition)):
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c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
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else:
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c_in = torch.cat([unconditional_condition, condition])
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noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
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#return noise_uncond + guidance_scale * (noise - noise_uncond)
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return dtData.dynthresh(noise, noise_uncond, guidance_scale, None)
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return model_fn
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