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instruct-pix2pix AND fix

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Cleans up the implementation of instruct-pix2pix to reduce unnecessary transformations and allows AND and over 77 tokens.
This commit is contained in:
Kyle 2023-02-08 12:53:10 -05:00
parent ea9bd9fc74
commit da24466d87
1 changed files with 15 additions and 34 deletions
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49
modules/sd_samplers_kdiffusion.py
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@ -57,7 +57,7 @@ class CFGDenoiser(torch.nn.Module):
self.nmask = None
self.init_latent = None
self.step = 0
self.image_cfg_scale = None
self.image_cfg_scale = 1
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@ -65,40 +65,27 @@ class CFGDenoiser(torch.nn.Module):
for i, conds in enumerate(conds_list):
for cond_index, weight in conds:
denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
denoised[i] += (weight * cond_scale) * (x_out[cond_index] - denoised_uncond[i]) * self.image_cfg_scale
return denoised
def combine_denoised_for_edit_model(self, x_out, cond_scale):
out_cond, out_img_cond, out_uncond = x_out.chunk(3)
denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
return denoised
def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
# so is_edit_model is set to False to support AND composition.
is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
else:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
cfg_denoiser_callback(denoiser_params)
@ -107,10 +94,9 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = denoiser_params.sigma
if tensor.shape[1] == uncond.shape[1]:
if not is_edit_model:
cond_in = torch.cat([tensor, uncond])
else:
cond_in = torch.cat([tensor, uncond, uncond])
cond_in = torch.cat([tensor, uncond])
if shared.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
@ -127,10 +113,7 @@ class CFGDenoiser(torch.nn.Module):
a = batch_offset
b = min(a + batch_size, tensor.shape[0])
if not is_edit_model:
c_crossattn = [tensor[a:b]]
else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
c_crossattn = [tensor[a:b]]
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
@ -143,10 +126,8 @@ class CFGDenoiser(torch.nn.Module):
elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
if not is_edit_model:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
else:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
@ -229,7 +210,7 @@ class KDiffusionSampler:
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.step = 0
self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', 1)
self.eta = p.eta if p.eta is not None else opts.eta_ancestral
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
@ -296,7 +277,7 @@ class KDiffusionSampler:
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
'cond_scale': p.cfg_scale
}
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))