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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-27 07:39:53 +08:00
512 lines
24 KiB
Python
512 lines
24 KiB
Python
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.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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('Euler', 'sample_euler', ['k_euler'], {}),
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('LMS', 'sample_lms', ['k_lms'], {}),
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('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
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('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
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('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
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('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
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('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
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('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
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('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
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('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
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('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
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('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
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('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
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('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
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('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
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('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
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('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
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]
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samplers_data_k_diffusion = [
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sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
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for label, funcname, aliases, options in samplers_k_diffusion
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if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
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]
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sampler_extra_params = {
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'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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}
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k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
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k_diffusion_scheduler = {
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'Automatic': None,
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'karras': k_diffusion.sampling.get_sigmas_karras,
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'exponential': k_diffusion.sampling.get_sigmas_exponential,
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'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
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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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# implementation.
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self.sampler_noises = deque(sampler_noises)
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def __getattr__(self, item):
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if item == 'randn_like':
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return self.randn_like
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if hasattr(torch, item):
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return getattr(torch, item)
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raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
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def randn_like(self, x):
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if self.sampler_noises:
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noise = self.sampler_noises.popleft()
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if noise.shape == x.shape:
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return noise
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return devices.randn_like(x)
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class KDiffusionSampler:
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def __init__(self, funcname, sd_model):
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denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
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self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
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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.sampler_noises = None
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self.stop_at = None
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self.eta = None
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self.config = None # set by the function calling the constructor
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self.last_latent = None
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self.s_min_uncond = None
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# NOTE: These are also defined in the StableDiffusionProcessing class.
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# They should have been here to begin with but we're going to
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# leave that class __init__ signature alone.
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self.s_churn = 0.0
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self.s_tmin = 0.0
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self.s_tmax = float('inf')
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self.s_noise = 1.0
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self.conditioning_key = sd_model.model.conditioning_key
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def callback_state(self, d):
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step = d['i']
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latent = d["denoised"]
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if opts.live_preview_content == "Combined":
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sd_samplers_common.store_latent(latent)
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self.last_latent = latent
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if self.stop_at is not None and step > self.stop_at:
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raise sd_samplers_common.InterruptedException
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state.sampling_step = step
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shared.total_tqdm.update()
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def launch_sampling(self, steps, func):
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state.sampling_steps = steps
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state.sampling_step = 0
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try:
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return func()
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except RecursionError:
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print(
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'Encountered RecursionError during sampling, returning last latent. '
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'rho >5 with a polyexponential scheduler may cause this error. '
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'You should try to use a smaller rho value instead.'
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)
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return self.last_latent
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except sd_samplers_common.InterruptedException:
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return self.last_latent
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def number_of_needed_noises(self, p):
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return p.steps
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def initialize(self, p: StableDiffusionProcessing):
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self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
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self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
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self.model_wrap_cfg.step = 0
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self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
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self.eta = p.eta if p.eta is not None else opts.eta_ancestral
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self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
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k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
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extra_params_kwargs = {}
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for param_name in self.extra_params:
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if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
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extra_params_kwargs[param_name] = getattr(p, param_name)
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if 'eta' in inspect.signature(self.func).parameters:
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if self.eta != 1.0:
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p.extra_generation_params["Eta"] = self.eta
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extra_params_kwargs['eta'] = self.eta
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if len(self.extra_params) > 0:
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s_churn = getattr(opts, 's_churn', p.s_churn)
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s_tmin = getattr(opts, 's_tmin', p.s_tmin)
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s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
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s_noise = getattr(opts, 's_noise', p.s_noise)
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if s_churn != self.s_churn:
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extra_params_kwargs['s_churn'] = s_churn
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p.s_churn = s_churn
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p.extra_generation_params['Sigma churn'] = s_churn
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if s_tmin != self.s_tmin:
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extra_params_kwargs['s_tmin'] = s_tmin
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p.s_tmin = s_tmin
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p.extra_generation_params['Sigma tmin'] = s_tmin
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if s_tmax != self.s_tmax:
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extra_params_kwargs['s_tmax'] = s_tmax
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p.s_tmax = s_tmax
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p.extra_generation_params['Sigma tmax'] = s_tmax
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if s_noise != self.s_noise:
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extra_params_kwargs['s_noise'] = s_noise
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p.s_noise = s_noise
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p.extra_generation_params['Sigma noise'] = s_noise
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return extra_params_kwargs
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def get_sigmas(self, p, steps):
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discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
|
|
if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
|
|
discard_next_to_last_sigma = True
|
|
p.extra_generation_params["Discard penultimate sigma"] = True
|
|
|
|
steps += 1 if discard_next_to_last_sigma else 0
|
|
|
|
if p.sampler_noise_scheduler_override:
|
|
sigmas = p.sampler_noise_scheduler_override(steps)
|
|
elif opts.k_sched_type != "Automatic":
|
|
m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
|
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
|
|
sigmas_kwargs = {
|
|
'sigma_min': sigma_min,
|
|
'sigma_max': sigma_max,
|
|
}
|
|
|
|
sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
|
|
p.extra_generation_params["Schedule type"] = opts.k_sched_type
|
|
|
|
if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
|
|
sigmas_kwargs['sigma_min'] = opts.sigma_min
|
|
p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
|
|
if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
|
|
sigmas_kwargs['sigma_max'] = opts.sigma_max
|
|
p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
|
|
|
|
default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
|
|
|
|
if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
|
|
sigmas_kwargs['rho'] = opts.rho
|
|
p.extra_generation_params["Schedule rho"] = opts.rho
|
|
|
|
sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
|
|
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
|
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
|
|
|
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
|
|
elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
|
|
m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
|
sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
|
|
else:
|
|
sigmas = self.model_wrap.get_sigmas(steps)
|
|
|
|
if discard_next_to_last_sigma:
|
|
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
|
|
|
|
return sigmas
|
|
|
|
def create_noise_sampler(self, x, sigmas, p):
|
|
"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
|
|
if shared.opts.no_dpmpp_sde_batch_determinism:
|
|
return None
|
|
|
|
from k_diffusion.sampling import BrownianTreeNoiseSampler
|
|
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
|
current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
|
|
return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
|
|
|
|
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
|
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
|
|
|
|
sigmas = self.get_sigmas(p, steps)
|
|
|
|
sigma_sched = sigmas[steps - t_enc - 1:]
|
|
xi = x + noise * sigma_sched[0]
|
|
|
|
extra_params_kwargs = self.initialize(p)
|
|
parameters = inspect.signature(self.func).parameters
|
|
|
|
if 'sigma_min' in parameters:
|
|
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
|
|
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
|
|
if 'sigma_max' in parameters:
|
|
extra_params_kwargs['sigma_max'] = sigma_sched[0]
|
|
if 'n' in parameters:
|
|
extra_params_kwargs['n'] = len(sigma_sched) - 1
|
|
if 'sigma_sched' in parameters:
|
|
extra_params_kwargs['sigma_sched'] = sigma_sched
|
|
if 'sigmas' in parameters:
|
|
extra_params_kwargs['sigmas'] = sigma_sched
|
|
|
|
if self.config.options.get('brownian_noise', False):
|
|
noise_sampler = self.create_noise_sampler(x, sigmas, p)
|
|
extra_params_kwargs['noise_sampler'] = noise_sampler
|
|
|
|
self.model_wrap_cfg.init_latent = x
|
|
self.last_latent = x
|
|
extra_args = {
|
|
'cond': conditioning,
|
|
'image_cond': image_conditioning,
|
|
'uncond': unconditional_conditioning,
|
|
'cond_scale': p.cfg_scale,
|
|
's_min_uncond': self.s_min_uncond
|
|
}
|
|
|
|
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))
|
|
|
|
if self.model_wrap_cfg.padded_cond_uncond:
|
|
p.extra_generation_params["Pad conds"] = True
|
|
|
|
return samples
|
|
|
|
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
|
steps = steps or p.steps
|
|
|
|
sigmas = self.get_sigmas(p, steps)
|
|
|
|
x = x * sigmas[0]
|
|
|
|
extra_params_kwargs = self.initialize(p)
|
|
parameters = inspect.signature(self.func).parameters
|
|
|
|
if 'sigma_min' in parameters:
|
|
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
|
|
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
|
|
if 'n' in parameters:
|
|
extra_params_kwargs['n'] = steps
|
|
else:
|
|
extra_params_kwargs['sigmas'] = sigmas
|
|
|
|
if self.config.options.get('brownian_noise', False):
|
|
noise_sampler = self.create_noise_sampler(x, sigmas, p)
|
|
extra_params_kwargs['noise_sampler'] = noise_sampler
|
|
|
|
self.last_latent = x
|
|
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
|
|
'cond': conditioning,
|
|
'image_cond': image_conditioning,
|
|
'uncond': unconditional_conditioning,
|
|
'cond_scale': p.cfg_scale,
|
|
's_min_uncond': self.s_min_uncond
|
|
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
|
|
|
if self.model_wrap_cfg.padded_cond_uncond:
|
|
p.extra_generation_params["Pad conds"] = True
|
|
|
|
return samples
|
|
|