pipeKSamplerSDXL¶
Documentation¶
- Class name:
ttN pipeKSamplerSDXL_v2 - Category:
🌏 tinyterra/pipe - Output node:
True
This node is designed to facilitate the sampling process within a specific pipeline, focusing on handling and transforming data through various stages of sampling, including initialization, processing, and output generation. It abstracts the complexities involved in managing state, handling image outputs, and integrating with other components like models and samplers, aiming to streamline the workflow for generating and refining samples based on given inputs.
Input types¶
Required¶
sdxl_pipe- Represents the pipeline state or configuration that is being passed through the sampling process. It is crucial for maintaining continuity and consistency across different stages of sampling.
- Comfy dtype:
PIPE_LINE_SDXL - Python dtype:
Dict
lora_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
lora_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
upscale_method- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
upscale_model_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
factor- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
rescale- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
percent- unknown
- Comfy dtype:
INT - Python dtype:
unknown
width- unknown
- Comfy dtype:
INT - Python dtype:
unknown
height- unknown
- Comfy dtype:
INT - Python dtype:
unknown
longer_side- unknown
- Comfy dtype:
INT - Python dtype:
unknown
crop- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
base_steps- unknown
- Comfy dtype:
INT - Python dtype:
unknown
cfg- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
denoise- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
refiner_steps- unknown
- Comfy dtype:
INT - Python dtype:
unknown
refiner_cfg- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
refiner_denoise- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
sampler_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
scheduler- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
image_output- Specifies the desired output handling for images, such as whether to hide or save them, affecting how the node processes and returns image data.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
save_prefix- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
file_type- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
embed_workflow- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
Optional¶
seed- unknown
- Comfy dtype:
INT - Python dtype:
unknown
optional_model- unknown
- Comfy dtype:
MODEL - Python dtype:
unknown
optional_positive- unknown
- Comfy dtype:
CONDITIONING - Python dtype:
unknown
optional_negative- unknown
- Comfy dtype:
CONDITIONING - Python dtype:
unknown
optional_latent- unknown
- Comfy dtype:
LATENT - Python dtype:
unknown
optional_vae- unknown
- Comfy dtype:
VAE - Python dtype:
unknown
optional_refiner_model- unknown
- Comfy dtype:
MODEL - Python dtype:
unknown
optional_refiner_positive- unknown
- Comfy dtype:
CONDITIONING - Python dtype:
unknown
optional_refiner_negative- unknown
- Comfy dtype:
CONDITIONING - Python dtype:
unknown
optional_clip- unknown
- Comfy dtype:
CLIP - Python dtype:
unknown
input_image_override- unknown
- Comfy dtype:
IMAGE - Python dtype:
unknown
adv_xyPlot- unknown
- Comfy dtype:
ADV_XYPLOT - Python dtype:
unknown
Output types¶
sdxl_pipe- Comfy dtype:
PIPE_LINE_SDXL - unknown
- Python dtype:
unknown
- Comfy dtype:
pipe- Comfy dtype:
PIPE_LINE - unknown
- Python dtype:
unknown
- Comfy dtype:
model- Comfy dtype:
MODEL - unknown
- Python dtype:
unknown
- Comfy dtype:
positive- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
negative- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_model- Comfy dtype:
MODEL - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_positive- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_negative- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
latent- Comfy dtype:
LATENT - unknown
- Python dtype:
unknown
- Comfy dtype:
vae- Comfy dtype:
VAE - unknown
- Python dtype:
unknown
- Comfy dtype:
clip- Comfy dtype:
CLIP - unknown
- Python dtype:
unknown
- Comfy dtype:
images- Comfy dtype:
IMAGE - unknown
- Python dtype:
unknown
- Comfy dtype:
seed- Comfy dtype:
INT - unknown
- Python dtype:
unknown
- Comfy dtype:
plot_image- Comfy dtype:
IMAGE - unknown
- Python dtype:
unknown
- Comfy dtype:
ui- Contains the UI elements or data to be displayed, typically including images or results from the sampling process.
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class ttN_pipeKSamplerSDXL_v2:
version = '2.3.1'
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"sdxl_pipe": ("PIPE_LINE_SDXL",),
"lora_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"upscale_method": (UPSCALE_METHODS, {"default": "None"}),
"upscale_model_name": (UPSCALE_MODELS,),
"factor": ("FLOAT", {"default": 2, "min": 0.0, "max": 10.0, "step": 0.25}),
"rescale": (["by percentage", "to Width/Height", 'to longer side - maintain aspect', 'None'],),
"percent": ("INT", {"default": 50, "min": 0, "max": 1000, "step": 1}),
"width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"longer_side": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"crop": (CROP_METHODS,),
"base_steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"refiner_steps": ("INT", {"default": 20, "min": 0, "max": 10000}),
"refiner_cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"refiner_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS + CUSTOM_SCHEDULERS,),
"image_output": (["Hide", "Preview", "Save", "Hide/Save", "Disabled"],),
"save_prefix": ("STRING", {"default": "ComfyUI"}),
"file_type": (OUTPUT_FILETYPES, {"default": "png"}),
"embed_workflow": ("BOOLEAN", {"default": True}),
},
"optional":
{"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"optional_model": ("MODEL",),
"optional_positive": ("CONDITIONING",),
"optional_negative": ("CONDITIONING",),
"optional_latent": ("LATENT",),
"optional_vae": ("VAE",),
"optional_refiner_model": ("MODEL",),
"optional_refiner_positive": ("CONDITIONING",),
"optional_refiner_negative": ("CONDITIONING",),
"optional_latent": ("LATENT",),
"optional_clip": ("CLIP",),
"input_image_override": ("IMAGE",),
"adv_xyPlot": ("ADV_XYPLOT",),
},
"hidden":
{"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",
"ttNnodeVersion": ttN_pipeKSamplerSDXL_v2.version},
}
RETURN_TYPES = ("PIPE_LINE_SDXL", "PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE", "INT", "IMAGE")
RETURN_NAMES = ("sdxl_pipe", "pipe","model", "positive", "negative" , "refiner_model", "refiner_positive", "refiner_negative", "latent", "vae", "clip", "images", "seed", "plot_image")
OUTPUT_NODE = True
FUNCTION = "sample"
CATEGORY = "🌏 tinyterra/pipe"
def sample(self, sdxl_pipe,
lora_name, lora_strength,
base_steps, refiner_steps, cfg, denoise, refiner_cfg, refiner_denoise, sampler_name, scheduler, image_output, save_prefix, file_type, embed_workflow,
optional_model=None, optional_positive=None, optional_negative=None, optional_latent=None, optional_vae=None, optional_clip=None, input_image_override=None, adv_xyPlot=None,
seed=None, upscale_model_name=None, upscale_method=None, factor=None, rescale=None, percent=None, width=None, height=None, longer_side=None, crop=None,
prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False,
optional_refiner_model=None, optional_refiner_positive=None, optional_refiner_negative=None):
my_unique_id = int(my_unique_id)
ttN_save = ttNsave(my_unique_id, prompt, extra_pnginfo)
sdxl_model = optional_model if optional_model is not None else sdxl_pipe["model"]
sdxl_positive = optional_positive if optional_positive is not None else sdxl_pipe["positive"]
sdxl_negative = optional_negative if optional_negative is not None else sdxl_pipe["negative"]
sdxl_samples = optional_latent if optional_latent is not None else sdxl_pipe["samples"]
sdxl_images = input_image_override if input_image_override is not None else sdxl_pipe["images"]
sdxl_vae = optional_vae if optional_vae is not None else sdxl_pipe["vae"]
sdxl_clip = optional_clip if optional_clip is not None else sdxl_pipe["clip"]
sdxl_refiner_model = optional_refiner_model if optional_refiner_model is not None else sdxl_pipe["refiner_model"]
sdxl_refiner_positive = optional_refiner_positive if optional_refiner_positive is not None else sdxl_pipe["refiner_positive"]
#sdxl_refiner_positive = sdxl_positive if sdxl_refiner_positive is None else sdxl_refiner_positive
sdxl_refiner_negative = optional_refiner_negative if optional_refiner_negative is not None else sdxl_pipe["refiner_negative"]
#sdxl_refiner_negative = sdxl_negative if sdxl_refiner_negative is None else sdxl_refiner_negative
sdxl_refiner_clip = sdxl_pipe["refiner_clip"]
if seed in (None, 'undefined'):
sdxl_seed = sdxl_pipe["seed"]
else:
sdxl_seed = seed
del sdxl_pipe
def process_sample_state(sdxl_model, sdxl_images, sdxl_clip, sdxl_samples, sdxl_vae, sdxl_seed, sdxl_positive, sdxl_negative, lora_name, lora_model_strength, lora_clip_strength,
sdxl_refiner_model, sdxl_refiner_positive, sdxl_refiner_negative, sdxl_refiner_clip,
upscale_model_name, upscale_method, factor, rescale, percent, width, height, longer_side, crop,
base_steps, refiner_steps, cfg, sampler_name, scheduler, denoise, refiner_denoise,
image_output, save_prefix, file_type, embed_workflow, prompt, my_unique_id, preview_latent, force_full_denoise=force_full_denoise, disable_noise=disable_noise):
# Load Lora
if lora_name not in (None, "None"):
sdxl_model, sdxl_clip = loader.load_lora(lora_name, sdxl_model, sdxl_clip, lora_model_strength, lora_clip_strength)
total_steps = base_steps + refiner_steps
# Upscale samples if enabled
if upscale_method != "None":
sdxl_samples = sampler.handle_upscale(sdxl_samples, upscale_method, factor, crop, upscale_model_name, sdxl_vae, sdxl_images, rescale, percent, width, height, longer_side,)
if (refiner_steps > 0) and (sdxl_refiner_model not in [None, "None"]):
# Base Sample
sdxl_samples = sampler.common_ksampler(sdxl_model, sdxl_seed, total_steps, cfg, sampler_name, scheduler, sdxl_positive, sdxl_negative, sdxl_samples,
denoise=denoise, preview_latent=preview_latent, start_step=0, last_step=base_steps, force_full_denoise=force_full_denoise, disable_noise=disable_noise)
# Refiner Sample
sdxl_samples = sampler.common_ksampler(sdxl_refiner_model, sdxl_seed, total_steps, refiner_cfg, sampler_name, scheduler, sdxl_refiner_positive, sdxl_refiner_negative, sdxl_samples,
denoise=refiner_denoise, preview_latent=preview_latent, start_step=base_steps, last_step=10000, force_full_denoise=True, disable_noise=True)
else:
sdxl_samples = sampler.common_ksampler(sdxl_model, sdxl_seed, base_steps, cfg, sampler_name, scheduler, sdxl_positive, sdxl_negative, sdxl_samples,
denoise=denoise, preview_latent=preview_latent, start_step=0, last_step=base_steps, force_full_denoise=True, disable_noise=disable_noise)
results = list()
if (image_output != "Disabled"):
latent = sdxl_samples["samples"]
sdxl_images = sdxl_vae.decode(latent)
results = ttN_save.images(sdxl_images, save_prefix, image_output, embed_workflow, file_type)
new_sdxl_pipe = {
"model": sdxl_model,
"positive": sdxl_positive,
"negative": sdxl_negative,
"vae": sdxl_vae,
"clip": sdxl_clip,
"refiner_model": sdxl_refiner_model,
"refiner_positive": sdxl_refiner_positive,
"refiner_negative": sdxl_refiner_negative,
"refiner_clip": sdxl_refiner_clip,
"samples": sdxl_samples,
"images": sdxl_images,
"seed": sdxl_seed,
"loader_settings": None,
}
pipe = {"model": sdxl_model,
"positive": sdxl_positive,
"negative": sdxl_negative,
"vae": sdxl_vae,
"clip": sdxl_clip,
"samples": sdxl_samples,
"images": sdxl_images,
"seed": sdxl_seed,
"loader_settings": None,
}
if image_output in ("Hide", "Hide/Save", "Disabled"):
return (*sampler.get_output_sdxl(new_sdxl_pipe, pipe), None)
return {"ui": {"images": results},
"result": (*sampler.get_output_sdxl(new_sdxl_pipe, pipe), None)}
def process_xyPlot(sdxl_model, sdxl_clip, sdxl_samples, sdxl_vae, sdxl_seed, sdxl_positive, sdxl_negative, lora_name, lora_model_strength, lora_clip_strength,
base_steps, refiner_steps, cfg, sampler_name, scheduler, denoise,
image_output, save_prefix, file_type, embed_workflow, prompt, extra_pnginfo, my_unique_id, preview_latent, adv_xyPlot):
random.seed(seed)
executor = xyExecutor()
plotter = ttNadv_xyPlot(adv_xyPlot, my_unique_id, prompt, extra_pnginfo, save_prefix, image_output, executor)
plot_image, images, samples = plotter.xy_plot_process()
plotter.reset()
del executor, plotter
if samples is None and images is None:
return process_sample_state(sdxl_model, sdxl_images, sdxl_clip, sdxl_samples, sdxl_vae, sdxl_seed, sdxl_positive, sdxl_negative, lora_name, lora_model_strength, lora_clip_strength,
sdxl_refiner_model, sdxl_refiner_positive, sdxl_refiner_negative, sdxl_refiner_clip,
upscale_model_name, upscale_method, factor, rescale, percent, width, height, longer_side, crop,
base_steps, refiner_steps, cfg, sampler_name, scheduler, denoise, refiner_denoise,
image_output, save_prefix, prompt, my_unique_id, preview_latent, force_full_denoise=force_full_denoise, disable_noise=disable_noise)
plot_result = ttN_save.images(plot_image, save_prefix, image_output, embed_workflow, file_type)
#plot_result.extend(ui_results)
new_sdxl_pipe = {
"model": sdxl_model,
"positive": sdxl_positive,
"negative": sdxl_negative,
"vae": sdxl_vae,
"clip": sdxl_clip,
"refiner_model": sdxl_refiner_model,
"refiner_positive": sdxl_refiner_positive,
"refiner_negative": sdxl_refiner_negative,
"refiner_clip": sdxl_refiner_clip,
"samples": samples,
"images": images,
"seed": sdxl_seed,
"loader_settings": None,
}
pipe = {"model": sdxl_model,
"positive": sdxl_positive,
"negative": sdxl_negative,
"vae": sdxl_vae,
"clip": sdxl_clip,
"samples": samples,
"images": images,
"seed": sdxl_seed,
"loader_settings": None,
}
if image_output in ("Hide", "Hide/Save", "Disabled"):
return (*sampler.get_output_sdxl(new_sdxl_pipe, pipe), plot_image)
return {"ui": {"images": plot_result},
"result": (*sampler.get_output_sdxl(new_sdxl_pipe, pipe), plot_image)}
preview_latent = True
if image_output in ("Hide", "Hide/Save", "Disabled"):
preview_latent = False
if adv_xyPlot is None:
return process_sample_state(sdxl_model, sdxl_images, sdxl_clip, sdxl_samples, sdxl_vae, sdxl_seed, sdxl_positive, sdxl_negative,
lora_name, lora_strength, lora_strength,
sdxl_refiner_model, sdxl_refiner_positive, sdxl_refiner_negative, sdxl_refiner_clip,
upscale_model_name, upscale_method, factor, rescale, percent, width, height, longer_side, crop,
base_steps, refiner_steps, cfg, sampler_name, scheduler, denoise, refiner_denoise, image_output, save_prefix, file_type, embed_workflow, prompt, my_unique_id, preview_latent)
else:
return process_xyPlot(sdxl_model, sdxl_clip, sdxl_samples, sdxl_vae, sdxl_seed, sdxl_positive, sdxl_negative, lora_name, lora_strength, lora_strength,
base_steps, refiner_steps, cfg, sampler_name, scheduler, denoise,
image_output, save_prefix, file_type, embed_workflow, prompt, extra_pnginfo, my_unique_id, preview_latent, adv_xyPlot)