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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-04 03:10:21 +00:00
split sd_samplers_kdiffusion into two
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AUTOMATIC1111
@@ -2,14 +2,11 @@ from collections import deque
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import torch
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import inspect
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import k_diffusion.sampling
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from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
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from modules import devices, sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
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from modules.processing import StableDiffusionProcessing
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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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samplers_k_diffusion = [
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('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
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@@ -57,190 +54,6 @@ k_diffusion_scheduler = {
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}
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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, model):
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super().__init__()
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self.inner_model = model
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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.step = 0
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self.image_cfg_scale = None
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self.padded_cond_uncond = False
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def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
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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 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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# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
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# so is_edit_model is set to False to support AND composition.
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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
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conds_list, tensor = 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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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)"
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batch_size = len(conds_list)
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repeats = [len(conds_list[i]) for i in range(batch_size)]
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if shared.sd_model.model.conditioning_key == "crossattn-adm":
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image_uncond = torch.zeros_like(image_cond)
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make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
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else:
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image_uncond = image_cond
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if isinstance(uncond, dict):
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make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
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else:
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make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
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if not is_edit_model:
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
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else:
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
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denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
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cfg_denoiser_callback(denoiser_params)
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x_in = denoiser_params.x
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image_cond_in = denoiser_params.image_cond
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sigma_in = denoiser_params.sigma
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tensor = denoiser_params.text_cond
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uncond = denoiser_params.text_uncond
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skip_uncond = False
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# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
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if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
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skip_uncond = True
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x_in = x_in[:-batch_size]
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sigma_in = sigma_in[:-batch_size]
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self.padded_cond_uncond = False
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if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
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empty = shared.sd_model.cond_stage_model_empty_prompt
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num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
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if num_repeats < 0:
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tensor = pad_cond(tensor, -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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if tensor.shape[1] == uncond.shape[1] or skip_uncond:
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if is_edit_model:
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cond_in = catenate_conds([tensor, uncond, uncond])
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elif skip_uncond:
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cond_in = tensor
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else:
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cond_in = catenate_conds([tensor, uncond])
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if shared.batch_cond_uncond:
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x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
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else:
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x_out = torch.zeros_like(x_in)
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for batch_offset in range(0, x_out.shape[0], batch_size):
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a = batch_offset
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b = a + batch_size
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
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else:
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x_out = torch.zeros_like(x_in)
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batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
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for batch_offset in range(0, tensor.shape[0], batch_size):
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a = batch_offset
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b = min(a + batch_size, tensor.shape[0])
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if not is_edit_model:
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c_crossattn = subscript_cond(tensor, a, b)
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else:
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c_crossattn = torch.cat([tensor[a:b]], uncond)
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
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if not skip_uncond:
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
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denoised_image_indexes = [x[0][0] for x in conds_list]
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if skip_uncond:
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fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
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x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
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denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
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cfg_denoised_callback(denoised_params)
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devices.test_for_nans(x_out, "unet")
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if opts.live_preview_content == "Prompt":
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sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
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elif opts.live_preview_content == "Negative prompt":
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sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
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if is_edit_model:
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denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
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elif skip_uncond:
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denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
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else:
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denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
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if self.mask is not None:
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denoised = self.init_latent * self.mask + self.nmask * denoised
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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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class TorchHijack:
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def __init__(self, sampler_noises):
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# Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
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@@ -273,7 +86,7 @@ class KDiffusionSampler:
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self.funcname = funcname
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self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
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self.extra_params = sampler_extra_params.get(funcname, [])
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self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
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self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap)
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self.sampler_noises = None
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self.stop_at = None
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self.eta = None
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