347 lines
19 KiB
Python
347 lines
19 KiB
Python
import random
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import nodes
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import torch
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import folder_paths
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from ..components import latentnoise
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from ..components import utility
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import comfy_extras.nodes_align_your_steps as nodes_align_your_steps
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import comfy.samplers
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import comfy_extras.nodes_custom_sampler as nodes_custom_sampler
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import comfy_extras.nodes_stable_cascade as nodes_stable_cascade
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import comfy_extras.nodes_flux as nodes_flux
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import comfy_extras.nodes_model_advanced as nodes_model_advanced
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from comfy import model_management
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import gc
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import os
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from diffusers import (
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DPMSolverMultistepScheduler,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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DEISMultistepScheduler,
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UniPCMultistepScheduler
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)
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def PKSampler(self, device, seed, model,
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steps, cfg, sampler_name, scheduler_name,
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positive, negative,
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latent_image, denoise,
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variation_extender, variation_batch_step_original, batch_counter, variation_extender_original, variation_batch_step, variation_level, variation_limit, align_your_steps, noise_extender, model_sampling=None, control_data=None):
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if model_sampling is not None and model_sampling > 0:
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model = nodes_model_advanced.ModelSamplingSD3.patch(self, model, model_sampling, 1.0)[0]
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samples = None
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if scheduler_name == 'beta' and control_data is not None:
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beta_alpha = float(control_data.get('beta_alpha', 0.6))
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beta_beta = float(control_data.get('beta_beta', 0.6))
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try:
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sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=beta_alpha, beta=beta_beta)
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sampler = comfy.samplers.sampler_object(sampler_name)
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samples = (nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas, latent_image)[0],)
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except Exception as e:
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print(f"Primere: BetaSamplingScheduler failed: {e}")
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if samples is None:
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if variation_level == True:
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samples = latentnoise.noisy_samples(model, device, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, seed, noise_extender)
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else:
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if variation_extender_original > 0 or device != 'DEFAULT' or variation_batch_step_original > 0:
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samples = latentnoise.noisy_samples(model, device, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, seed, noise_extender)
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else:
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if align_your_steps == True:
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modelname_only = model
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model_version = utility.get_value_from_cache('model_version', modelname_only)
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match model_version:
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case 'SDXL':
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model_type = 'SDXL'
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case _:
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model_type = 'SD1'
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sigmas = nodes_align_your_steps.AlignYourStepsScheduler.get_sigmas(self, model_type, steps, denoise)
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sampler = comfy.samplers.sampler_object(sampler_name)
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AYS_samples = nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
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samples = (AYS_samples[0],)
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else:
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samples = nodes.KSampler.sample(self, model, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise=denoise)
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if control_data and control_data.get('refiner') == True:
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refiner_model_name = control_data.get('refiner_model', None)
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if refiner_model_name and refiner_model_name != 'None':
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try:
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REFINER_CHECKPOINT = nodes.CheckpointLoaderSimple.load_checkpoint(self, refiner_model_name)
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RAW_IMAGE = nodes.VAEDecode.decode(self, REFINER_CHECKPOINT[2], samples[0])[0]
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RAW_IMAGE_ENCODED = nodes.VAEEncode.encode(self, REFINER_CHECKPOINT[2], RAW_IMAGE)[0]
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REFINER_SAMPLER = control_data.get('refiner_sampler', 'dpmpp_2m')
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REFINER_SCHEDULER = control_data.get('refiner_scheduler', 'normal')
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REFINER_CFG = float(control_data.get('refiner_cfg', 2.0))
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REFINER_STEPS = int(control_data.get('refiner_steps', 22))
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REFINER_DENOISE = float(control_data.get('refiner_denoise', 0.9))
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REFINER_START = int(control_data.get('refiner_start', 12))
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sigmas_refiner = nodes_custom_sampler.BasicScheduler.execute(REFINER_CHECKPOINT[0], REFINER_SCHEDULER, REFINER_STEPS, REFINER_DENOISE)[0]
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splitted_sigma = nodes_custom_sampler.SplitSigmas.execute(sigmas_refiner, REFINER_START)[1]
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sampler_refiner = comfy.samplers.sampler_object(REFINER_SAMPLER)
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if control_data.get('refiner_ignore_prompt', True):
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empty_cond = nodes.CLIPTextEncode.encode(self, REFINER_CHECKPOINT[1], "")[0]
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refiner_pos = empty_cond
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refiner_neg = empty_cond
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else:
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refiner_pos = positive
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refiner_neg = negative
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samples = (nodes_custom_sampler.SamplerCustom.execute(REFINER_CHECKPOINT[0], True, seed, REFINER_CFG, refiner_pos, refiner_neg, sampler_refiner, splitted_sigma, RAW_IMAGE_ENCODED)[0],)
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except Exception as e:
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print(f"Primere: Refiner sampling failed: {e}")
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return samples
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def PTurboSampler(model, seed, cfg, positive, negative, latent_image, steps, denoise, sampler_name):
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sigmas = nodes_custom_sampler.SDTurboScheduler.execute(model, steps, denoise)
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sampler = comfy.samplers.sampler_object(sampler_name)
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turbo_samples = nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
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samples = (turbo_samples[0],)
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return samples
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def PCascadeSampler(self, model, seed, steps, cfg, sampler_name, scheduler_name,
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positive, negative,
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latent_image, denoise, device,
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variation_level, variation_limit, variation_extender_original, variation_batch_step_original, variation_extender, variation_batch_step, batch_counter, noise_extender):
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samples = latent_image
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if type(model).__name__ == 'list':
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latent_size = utility.getLatentSize(latent_image)
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if (latent_size[0] < latent_size[1]):
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orientation = 'Vertical'
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else:
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orientation = 'Horizontal'
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dimensions = utility.get_dimensions_by_shape(self, 'Square [1:1]', 1024, orientation, True, True, latent_size[0], latent_size[1], 'CASCADE')
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dimension_x = dimensions[0]
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dimension_y = dimensions[1]
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height = dimension_y
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width = dimension_x
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compression = 42
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if type(model[0]).__name__ == 'ModelPatcher' and type(model[1]).__name__ == 'ModelPatcher':
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c_latent = {"samples": torch.zeros([1, 16, height // compression, width // compression])}
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b_latent = {"samples": torch.zeros([1, 4, height // 4, width // 4])}
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if variation_level == True:
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samples_c = latentnoise.noisy_samples(model[1], device, steps, cfg, sampler_name, scheduler_name, positive, negative, c_latent, denoise, seed, noise_extender)[0]
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else:
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if variation_extender_original > 0 or device != 'DEFAULT' or variation_batch_step_original > 0:
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samples_c = latentnoise.noisy_samples(model[1], device, steps, cfg, sampler_name, scheduler_name, positive, negative, c_latent, denoise, seed, noise_extender)[0]
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else:
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samples_c = nodes.KSampler.sample(self, model[1], seed, steps, cfg, sampler_name, scheduler_name, positive, negative, c_latent, denoise=denoise)[0]
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conditining_c = nodes_stable_cascade.StableCascade_StageB_Conditioning.execute(positive, samples_c)[0]
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samples = nodes.KSampler.sample(self, model[0], seed, 10, 1.00, sampler_name, scheduler_name, conditining_c, negative, b_latent, denoise=denoise)
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return samples
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def PSamplerHyper(self, extra_pnginfo, model, seed, steps, cfg, positive, negative, sampler_name, scheduler_name, latent_image, denoise, prompt, control_data):
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# WORKFLOWDATA = extra_pnginfo['workflow']['nodes']
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OriginalBaseModel = control_data['model_name'] # OriginalBaseModel = utility.getDataFromWorkflowByName(WORKFLOWDATA, 'PrimereVisualCKPT', 'base_model', prompt)
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fullpathFile = folder_paths.get_full_path('checkpoints', OriginalBaseModel)
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is_link = os.path.islink(str(fullpathFile))
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HyperSDSelector = None
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if is_link == True:
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HyperSDSelector = 'UNET'
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if HyperSDSelector == 'UNET':
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sigmas = utility.get_hypersd_sigmas(model)
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sampler = comfy.samplers.sampler_object(sampler_name)
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hyper_samples = nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
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samples = (hyper_samples[0],)
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else:
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SamplingDiscreteResults = utility.TCDModelSamplingDiscrete(self, model, steps, scheduler_name, denoise, eta=0.8)
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model = SamplingDiscreteResults[0]
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sampler = SamplingDiscreteResults[1]
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sigmas = SamplingDiscreteResults[2]
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hyper_lora_samples = nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas, latent_image)
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samples = (hyper_lora_samples[0],)
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return samples
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def PSamplerSana(self, device, seed, model,
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steps, cfg, sampler_name, scheduler_name,
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positive, negative,
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latent_image, denoise,
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variation_extender, variation_batch_step_original, batch_counter, variation_extender_original, variation_batch_step, variation_level, variation_limit, align_your_steps, noise_extender, WORKFLOWDATA, prompt):
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if device == 'DEFAULT':
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device = model_management.get_torch_device()
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pag_applied_layers = None if 'pag_applied_layers' not in model.keys() else model['pag_applied_layers']
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latent_out = model['pipe'](
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cond = positive,
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uncond = negative,
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guidance_scale = cfg,
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pag_guidance_scale = 2,
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num_inference_steps = (steps + 1),
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generator = torch.Generator(device=device).manual_seed(seed),
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latents = latent_image['samples'],
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noise_scheduler = scheduler_name,
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output_type = True,
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pag_applied_layers = pag_applied_layers,
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)
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model['unet'].to(comfy.model_management.unet_offload_device())
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comfy.model_management.soft_empty_cache(True)
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return (latent_out,)
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def _run_refiner_pass(self, refiner_model, refiner_cond_pos, refiner_cond_neg, samples_main, control_data, seed):
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main_vae = utility.vae_loader_class.load_vae(control_data.get('vae'))[0]
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raw_image = nodes.VAEDecode.decode(self, main_vae, samples_main)[0]
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refiner_ckpt = nodes.CheckpointLoaderSimple.load_checkpoint(self, control_data.get('refiner_model'))
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encoded_image = nodes.VAEEncode.encode(self, refiner_ckpt[2], raw_image)[0]
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refiner_sampler = control_data.get('refiner_sampler', 'dpmpp_2m')
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refiner_scheduler = control_data.get('refiner_scheduler', 'normal')
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refiner_cfg = float(control_data.get('refiner_cfg', 2.0))
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refiner_steps = int(control_data.get('refiner_steps', 22))
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refiner_denoise = float(control_data.get('refiner_denoise', 0.9))
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refiner_start = int(control_data.get('refiner_start', 12))
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sigmas = nodes_custom_sampler.BasicScheduler.execute(refiner_model, refiner_scheduler, refiner_steps, refiner_denoise)[0]
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low_sigmas = nodes_custom_sampler.SplitSigmas.execute(sigmas, refiner_start)[1]
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sampler = comfy.samplers.sampler_object(refiner_sampler)
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if control_data.get('refiner_ignore_prompt', True):
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empty_cond = nodes.CLIPTextEncode.encode(self, refiner_ckpt[1], "")[0]
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pos_cond = empty_cond
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neg_cond = empty_cond
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else:
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pos_cond = refiner_cond_pos if refiner_cond_pos is not None else nodes.CLIPTextEncode.encode(self, refiner_ckpt[1], "")[0]
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neg_cond = refiner_cond_neg if refiner_cond_neg is not None else nodes.CLIPTextEncode.encode(self, refiner_ckpt[1], "")[0]
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return nodes_custom_sampler.SamplerCustom.execute(refiner_model, True, seed, refiner_cfg, pos_cond, neg_cond, sampler, low_sigmas, encoded_image)[0]
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def PSamplerPixart(self, device, seed, model,
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steps, cfg, sampler_name, scheduler_name,
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positive, negative,
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latent_image, denoise,
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variation_extender, variation_batch_step_original, batch_counter, variation_extender_original, variation_batch_step, variation_level, variation_limit, align_your_steps, noise_extender, control_data):
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if control_data:
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sampler_name = control_data.get('sampler_name', sampler_name)
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scheduler_name = control_data.get('scheduler_name', scheduler_name)
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steps = control_data.get('steps', steps)
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cfg = control_data.get('cfg', cfg)
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if control_data and control_data.get('refiner') == True:
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denoise = float(control_data.get('refiner_sampling_denoise', denoise))
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sigmas_main = nodes_custom_sampler.BasicScheduler.execute(model, scheduler_name, steps, denoise=denoise)[0]
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sampler = comfy.samplers.sampler_object(sampler_name)
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if variation_level == True:
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samples_main = latentnoise.noisy_samples(model, device, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, seed, noise_extender)[0]
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else:
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if variation_extender_original > 0 or device != 'DEFAULT' or variation_batch_step_original > 0:
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samples_main = latentnoise.noisy_samples(model, device, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, seed, noise_extender)[0]
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else:
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if align_your_steps == True:
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model_type = 'SDXL'
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sigmas = nodes_align_your_steps.AlignYourStepsScheduler.get_sigmas(self, model_type, steps, denoise)
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sampler = comfy.samplers.sampler_object(sampler_name)
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AYS_samples = nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
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samples_main = AYS_samples[0]
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else:
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samples_main = nodes_custom_sampler.SamplerCustom.execute(model, True, seed, cfg, positive, negative, sampler, sigmas_main, latent_image)[0]
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return (samples_main,)
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def PSamplerAdvanced(self, model, seed, guidance, positive, scheduler_name, sampler_name, steps, denoise, latent_image):
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if guidance <= 0:
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CONDITIONING_POS = positive
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else:
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CONDITIONING_POS = nodes_flux.FluxGuidance.execute(positive, guidance)[0]
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FLUX_GUIDER = nodes_custom_sampler.BasicGuider.execute(model, CONDITIONING_POS)[0]
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FLUX_SIGMAS = nodes_custom_sampler.BasicScheduler.execute(model, scheduler_name, steps, denoise=denoise)[0]
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FLUX_NOISE = nodes_custom_sampler.RandomNoise.execute(seed)[0]
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sampler_object = comfy.samplers.sampler_object(sampler_name)
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samples = (nodes_custom_sampler.SamplerCustomAdvanced.execute(FLUX_NOISE, FLUX_GUIDER, sampler_object, FLUX_SIGMAS, latent_image)[0],)
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return samples
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def PSamplerChroma(self, model, seed, cfg, positive, negative, scheduler_name, sampler_name, steps, denoise, latent_image):
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guider = nodes_custom_sampler.CFGGuider.execute(model, positive, negative, cfg)[0]
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sigmas = nodes_custom_sampler.BasicScheduler.execute(model, scheduler_name, steps, denoise=denoise)[0]
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sampler = nodes_custom_sampler.KSamplerSelect.execute(sampler_name)[0]
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noise = nodes_custom_sampler.RandomNoise.execute(seed)[0]
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samples = (nodes_custom_sampler.SamplerCustomAdvanced.execute(noise, guider, sampler, sigmas, latent_image)[0],)
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return samples
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def PSamplerSD3(self, model, seed, cfg, positive, negative, latent_image, steps, denoise, sampler_name, scheduler_name, model_sampling = 2.5, multiplier = 1000):
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sd3sampling = nodes_model_advanced.ModelSamplingSD3.patch(self, model, model_sampling, multiplier)[0]
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samples = nodes.KSampler.sample(self, sd3sampling, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise=denoise)
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return samples
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def PSamplerKOROLS(self, model, seed, cfg, positive, negative, latent_image, steps, denoise, sampler_name, scheduler_name, model_sampling = 0, multiplier = 1000):
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device = model_management.get_torch_device() #"cuda" if torch.cuda.is_available() else "cpu"
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offload_device = model_management.unet_offload_device()
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vae_scaling_factor = 0.13025
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try:
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model_management.soft_empty_cache()
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except Exception:
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print('Cannot clear cache...')
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gc.collect()
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pipeline = model['pipeline']
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scheduler_config = {
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"beta_end": 0.014,
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"dynamic_thresholding_ratio": 0.995,
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"num_train_timesteps": 1100,
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"prediction_type": "epsilon",
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"rescale_betas_zero_snr": False,
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"steps_offset": 1,
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"timestep_spacing": "leading",
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"trained_betas": None,
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}
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noise_scheduler = None
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if scheduler_name == "DPMSolverMultistepScheduler":
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noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
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elif scheduler_name == "DPMSolverMultistepScheduler_SDE_karras":
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scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
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scheduler_config.update({"use_karras_sigmas": True})
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noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
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elif scheduler_name == "DEISMultistepScheduler":
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scheduler_config.pop("rescale_betas_zero_snr")
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noise_scheduler = DEISMultistepScheduler(**scheduler_config)
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elif scheduler_name == "EulerDiscreteScheduler":
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scheduler_config.update({"interpolation_type": "linear"})
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scheduler_config.pop("dynamic_thresholding_ratio")
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noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
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elif scheduler_name == "EulerAncestralDiscreteScheduler":
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scheduler_config.pop("dynamic_thresholding_ratio")
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noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config)
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elif scheduler_name == "UniPCMultistepScheduler":
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scheduler_config.pop("rescale_betas_zero_snr")
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noise_scheduler = UniPCMultistepScheduler(**scheduler_config)
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if noise_scheduler == None:
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scheduler_config.update({"interpolation_type": "linear"})
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scheduler_config.pop("dynamic_thresholding_ratio")
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noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
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pipeline.scheduler = noise_scheduler
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generator = torch.Generator(device).manual_seed(seed)
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pipeline.unet.to(device)
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latentWidth, latentHeigth = utility.getLatentSize(latent_image)
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latent_out = pipeline(
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prompt = None,
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latents = None, #samples_in if latent_image is not None else None,
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prompt_embeds = positive['prompt_embeds'],
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pooled_prompt_embeds = positive['pooled_prompt_embeds'],
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negative_prompt_embeds = positive['negative_prompt_embeds'],
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negative_pooled_prompt_embeds = positive['negative_pooled_prompt_embeds'],
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height = latentHeigth * 8,
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width = latentWidth * 8,
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num_inference_steps = steps,
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guidance_scale = cfg,
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num_images_per_prompt = 1,
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generator = generator,
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strength = denoise,
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).images
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pipeline.unet.to(offload_device)
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latent_out = latent_out / vae_scaling_factor
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return ({'samples': latent_out},) |