195 lines
7.6 KiB
Python
195 lines
7.6 KiB
Python
import torch
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from modules import prompt_parser, devices, sd_samplers_common
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from modules.shared import opts, state
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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 CFGDenoisedParams, cfg_denoised_callback
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from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
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from modules_forge import forge_sampler
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def catenate_conds(conds):
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if not isinstance(conds[0], dict):
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return torch.cat(conds)
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return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
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def subscript_cond(cond, a, b):
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if not isinstance(cond, dict):
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return cond[a:b]
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return {key: vec[a:b] for key, vec in cond.items()}
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def pad_cond(tensor, repeats, empty):
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if not isinstance(tensor, dict):
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return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
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tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
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return tensor
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class CFGDenoiser(torch.nn.Module):
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"""
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Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
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that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
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instead of one. Originally, the second prompt is just an empty string, but we use non-empty
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negative prompt.
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"""
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def __init__(self, sampler):
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super().__init__()
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self.model_wrap = None
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self.mask = None
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self.nmask = None
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self.init_latent = None
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self.steps = None
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"""number of steps as specified by user in UI"""
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self.total_steps = None
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"""expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
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self.step = 0
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self.image_cfg_scale = None
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self.padded_cond_uncond = False
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self.padded_cond_uncond_v0 = False
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self.sampler = sampler
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self.model_wrap = None
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self.p = None
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# NOTE: masking before denoising can cause the original latents to be oversmoothed
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# as the original latents do not have noise
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self.mask_before_denoising = False
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@property
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def inner_model(self):
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raise NotImplementedError()
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def combine_denoised(self, x_out, conds_list, uncond, cond_scale, timestep, x_in, cond):
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denoised_uncond = x_out[-uncond.shape[0]:]
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denoised = torch.clone(denoised_uncond)
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for i, conds in enumerate(conds_list):
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for cond_index, weight in conds:
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denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
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return denoised
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def combine_denoised_for_edit_model(self, x_out, cond_scale):
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out_cond, out_img_cond, out_uncond = x_out.chunk(3)
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denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
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return denoised
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def get_pred_x0(self, x_in, x_out, sigma):
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return x_out
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def update_inner_model(self):
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self.model_wrap = None
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c, uc = self.p.get_conds()
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self.sampler.sampler_extra_args['cond'] = c
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self.sampler.sampler_extra_args['uncond'] = uc
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def pad_cond_uncond(self, cond, uncond):
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empty = shared.sd_model.cond_stage_model_empty_prompt
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num_repeats = (cond.shape[1] - uncond.shape[1]) // empty.shape[1]
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if num_repeats < 0:
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cond = pad_cond(cond, -num_repeats, empty)
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self.padded_cond_uncond = True
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elif num_repeats > 0:
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uncond = pad_cond(uncond, num_repeats, empty)
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self.padded_cond_uncond = True
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return cond, uncond
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def pad_cond_uncond_v0(self, cond, uncond):
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"""
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Pads the 'uncond' tensor to match the shape of the 'cond' tensor.
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If 'uncond' is a dictionary, it is assumed that the 'crossattn' key holds the tensor to be padded.
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If 'uncond' is a tensor, it is padded directly.
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If the number of columns in 'uncond' is less than the number of columns in 'cond', the last column of 'uncond'
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is repeated to match the number of columns in 'cond'.
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If the number of columns in 'uncond' is greater than the number of columns in 'cond', 'uncond' is truncated
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to match the number of columns in 'cond'.
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Args:
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cond (torch.Tensor or DictWithShape): The condition tensor to match the shape of 'uncond'.
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uncond (torch.Tensor or DictWithShape): The tensor to be padded, or a dictionary containing the tensor to be padded.
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Returns:
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tuple: A tuple containing the 'cond' tensor and the padded 'uncond' tensor.
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Note:
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This is the padding that was always used in DDIM before version 1.6.0
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"""
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is_dict_cond = isinstance(uncond, dict)
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uncond_vec = uncond['crossattn'] if is_dict_cond else uncond
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if uncond_vec.shape[1] < cond.shape[1]:
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last_vector = uncond_vec[:, -1:]
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last_vector_repeated = last_vector.repeat([1, cond.shape[1] - uncond_vec.shape[1], 1])
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uncond_vec = torch.hstack([uncond_vec, last_vector_repeated])
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self.padded_cond_uncond_v0 = True
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elif uncond_vec.shape[1] > cond.shape[1]:
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uncond_vec = uncond_vec[:, :cond.shape[1]]
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self.padded_cond_uncond_v0 = True
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if is_dict_cond:
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uncond['crossattn'] = uncond_vec
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else:
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uncond = uncond_vec
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return cond, uncond
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def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
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if state.interrupted or state.skipped:
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raise sd_samplers_common.InterruptedException
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if sd_samplers_common.apply_refiner(self, x):
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cond = self.sampler.sampler_extra_args['cond']
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uncond = self.sampler.sampler_extra_args['uncond']
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cond_composition, cond = prompt_parser.reconstruct_multicond_batch(cond, self.step)
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uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
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# If we use masks, blending between the denoised and original latent images occurs here.
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def apply_blend(current_latent):
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blended_latent = current_latent * self.nmask + self.init_latent * self.mask
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if self.p.scripts is not None:
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from modules import scripts
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mba = scripts.MaskBlendArgs(current_latent, self.nmask, self.init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
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self.p.scripts.on_mask_blend(self.p, mba)
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blended_latent = mba.blended_latent
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return blended_latent
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# Blend in the original latents (before)
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if self.mask_before_denoising and self.mask is not None:
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x = apply_blend(x)
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denoiser_params = CFGDenoiserParams(x, image_cond, sigma, state.sampling_step, state.sampling_steps, cond, uncond, self)
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cfg_denoiser_callback(denoiser_params)
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denoised = forge_sampler.forge_sample(self, denoiser_params=denoiser_params,
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cond_scale=cond_scale, cond_composition=cond_composition)
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preview = self.sampler.last_latent = denoised
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sd_samplers_common.store_latent(preview)
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after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
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cfg_after_cfg_callback(after_cfg_callback_params)
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denoised = after_cfg_callback_params.x
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self.step += 1
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return denoised
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