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-rw-r--r--modules/infotext_versions.py5
-rw-r--r--modules/sd_samplers_cfg_denoiser.py70
-rw-r--r--modules/sd_samplers_common.py7
-rw-r--r--modules/sd_samplers_kdiffusion.py6
-rw-r--r--modules/sd_samplers_timesteps.py6
-rw-r--r--modules/shared_options.py3
6 files changed, 78 insertions, 19 deletions
diff --git a/modules/infotext_versions.py b/modules/infotext_versions.py
index a5afeebf..23b45c3f 100644
--- a/modules/infotext_versions.py
+++ b/modules/infotext_versions.py
@@ -31,9 +31,12 @@ def backcompat(d):
if ver is None:
return
- if ver < v160:
+ if ver < v160 and '[' in d.get('Prompt', ''):
d["Old prompt editing timelines"] = True
+ if ver < v160 and d.get('Sampler', '') in ('DDIM', 'PLMS'):
+ d["Pad conds v0"] = True
+
if ver < v170_tsnr:
d["Downcast alphas_cumprod"] = True
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py
index 6d76aa96..ef237396 100644
--- a/modules/sd_samplers_cfg_denoiser.py
+++ b/modules/sd_samplers_cfg_denoiser.py
@@ -53,6 +53,7 @@ class CFGDenoiser(torch.nn.Module):
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
+ self.padded_cond_uncond_v0 = False
self.sampler = sampler
self.model_wrap = None
self.p = None
@@ -91,6 +92,62 @@ class CFGDenoiser(torch.nn.Module):
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
+ def pad_cond_uncond(self, cond, uncond):
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (cond.shape[1] - cond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ cond = pad_cond(cond, -num_repeats, empty)
+ self.padded_cond_uncond = True
+ elif num_repeats > 0:
+ uncond = pad_cond(uncond, num_repeats, empty)
+ self.padded_cond_uncond = True
+
+ return cond, uncond
+
+ def pad_cond_uncond_v0(self, cond, uncond):
+ """
+ Pads the 'uncond' tensor to match the shape of the 'cond' tensor.
+
+ If 'uncond' is a dictionary, it is assumed that the 'crossattn' key holds the tensor to be padded.
+ If 'uncond' is a tensor, it is padded directly.
+
+ If the number of columns in 'uncond' is less than the number of columns in 'cond', the last column of 'uncond'
+ is repeated to match the number of columns in 'cond'.
+
+ If the number of columns in 'uncond' is greater than the number of columns in 'cond', 'uncond' is truncated
+ to match the number of columns in 'cond'.
+
+ Args:
+ cond (torch.Tensor or DictWithShape): The condition tensor to match the shape of 'uncond'.
+ uncond (torch.Tensor or DictWithShape): The tensor to be padded, or a dictionary containing the tensor to be padded.
+
+ Returns:
+ tuple: A tuple containing the 'cond' tensor and the padded 'uncond' tensor.
+
+ Note:
+ This is the padding that was always used in DDIM before version 1.6.0
+ """
+
+ is_dict_cond = isinstance(uncond, dict)
+ uncond_vec = uncond['crossattn'] if is_dict_cond else uncond
+
+ if uncond_vec.shape[1] < cond.shape[1]:
+ last_vector = uncond_vec[:, -1:]
+ last_vector_repeated = last_vector.repeat([1, cond.shape[1] - uncond_vec.shape[1], 1])
+ uncond_vec = torch.hstack([uncond_vec, last_vector_repeated])
+ self.padded_cond_uncond_v0 = True
+ elif uncond_vec.shape[1] > cond.shape[1]:
+ uncond_vec = uncond_vec[:, :cond.shape[1]]
+ self.padded_cond_uncond_v0 = True
+
+ if is_dict_cond:
+ uncond['crossattn'] = uncond_vec
+ else:
+ uncond = uncond_vec
+
+ return cond, uncond
+
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -162,16 +219,11 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
+ self.padded_cond_uncond_v0 = False
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
- empty = shared.sd_model.cond_stage_model_empty_prompt
- num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
- if num_repeats < 0:
- tensor = pad_cond(tensor, -num_repeats, empty)
- self.padded_cond_uncond = True
- elif num_repeats > 0:
- uncond = pad_cond(uncond, num_repeats, empty)
- self.padded_cond_uncond = True
+ tensor, uncond = self.pad_cond_uncond(tensor, uncond)
+ elif shared.opts.pad_cond_uncond_v0 and tensor.shape[1] != uncond.shape[1]:
+ tensor, uncond = self.pad_cond_uncond_v0(tensor, uncond)
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 58efcad2..6bd38e12 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -335,3 +335,10 @@ class Sampler:
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
raise NotImplementedError()
+
+ def add_infotext(self, p):
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ if self.model_wrap_cfg.padded_cond_uncond_v0:
+ p.extra_generation_params["Pad conds v0"] = True
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 8a8c87e0..337106c0 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -187,8 +187,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
@@ -234,8 +233,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py
index 777dd8d0..8cc7d384 100644
--- a/modules/sd_samplers_timesteps.py
+++ b/modules/sd_samplers_timesteps.py
@@ -133,8 +133,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
@@ -158,8 +157,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
}
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
diff --git a/modules/shared_options.py b/modules/shared_options.py
index fef1fb83..bdd066c4 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -210,7 +210,8 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
- "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "pad_cond_uncond_v0": OptionInfo(False, "Pad prompt/negative prompt (v0)", infotext='Pad conds v0').info("alternative implementation for the above; used prior to 1.6.0 for DDIM sampler; ignored if the above is set; changes seeds"),
"persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
"fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 06:57:14 +0000