improves the FluxSamplerParams node
This commit is contained in:
cubiq
+25
-12
@@ -3,9 +3,11 @@ import comfy.samplers
|
||||
import comfy.sample
|
||||
import torch
|
||||
from nodes import common_ksampler, CLIPTextEncode
|
||||
from comfy.utils import ProgressBar
|
||||
from .utils import expand_mask, FONTS_DIR, parse_string_to_list
|
||||
import torchvision.transforms.v2 as T
|
||||
import torch.nn.functional as F
|
||||
import logging
|
||||
|
||||
class KSamplerVariationsWithNoise:
|
||||
@classmethod
|
||||
@@ -212,7 +214,7 @@ class FluxSamplerParams:
|
||||
"latent_image": ("LATENT", ),
|
||||
|
||||
"noise": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "?" }),
|
||||
"sampler": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "ipndm" }),
|
||||
"sampler": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "euler" }),
|
||||
"scheduler": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "simple" }),
|
||||
"steps": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "20" }),
|
||||
"guidance": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "3.5" }),
|
||||
@@ -283,15 +285,12 @@ class FluxSamplerParams:
|
||||
|
||||
if not is_schnell:
|
||||
max_shift = "1.15" if max_shift == "" else max_shift
|
||||
max_shift = parse_string_to_list(max_shift)
|
||||
base_shift = "0.5" if base_shift == "" else base_shift
|
||||
else:
|
||||
max_shift = [0]
|
||||
max_shift = "0"
|
||||
base_shift = "1.0" if base_shift == "" else base_shift
|
||||
|
||||
if base_shift == "":
|
||||
if is_schnell:
|
||||
base_shift = "1.0"
|
||||
else:
|
||||
base_shift = "0.5"
|
||||
max_shift = parse_string_to_list(max_shift)
|
||||
base_shift = parse_string_to_list(base_shift)
|
||||
|
||||
split_sigmas = "1.0" if split_sigmas == "" else split_sigmas
|
||||
@@ -316,6 +315,12 @@ class FluxSamplerParams:
|
||||
width = latent_image["samples"].shape[3]*8
|
||||
height = latent_image["samples"].shape[2]*8
|
||||
|
||||
# count total number of samples
|
||||
total_samples = len(cond_encoded) * len(noise) * len(max_shift) * len(base_shift) * len(guidance) * len(sampler) * len(scheduler) * len(steps) * len(denoise) * len(split_sigmas)
|
||||
current_sample = 0
|
||||
if total_samples > 1:
|
||||
pbar = ProgressBar(total_samples)
|
||||
|
||||
for i in range(len(cond_encoded)):
|
||||
conditioning = cond_encoded[i]
|
||||
ct = cond_text[i] if cond_text else None
|
||||
@@ -337,12 +342,16 @@ class FluxSamplerParams:
|
||||
for d in denoise:
|
||||
sigmas = basicschedueler.get_sigmas(work_model, sc, st, d)[0]
|
||||
for ss in split_sigmas:
|
||||
current_sample += 1
|
||||
logging.info(f"Sampling {current_sample}/{total_samples} with seed {n}, sampler {s}, scheduler {sc}, steps {st}, guidance {g}, max_shift {ms}, base_shift {bs}, denoise {d}, split_sigmas {ss}")
|
||||
sigmas = splitsigmadenoise.get_sigmas(sigmas, ss)[1]
|
||||
start_time = time.time()
|
||||
latent = samplercustomadvanced.sample(randnoise, guider, samplerobj, sigmas, latent_image)[1]
|
||||
elapsed_time = time.time() - start_time
|
||||
out_params.append({"time": elapsed_time,
|
||||
"seed": n,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"sampler": s,
|
||||
"scheduler": sc,
|
||||
"steps": st,
|
||||
@@ -357,6 +366,8 @@ class FluxSamplerParams:
|
||||
out_latent = latent
|
||||
else:
|
||||
out_latent = latentbatch.batch(out_latent, latent)[0]
|
||||
if total_samples > 1:
|
||||
pbar.update(1)
|
||||
|
||||
return (out_latent, out_params)
|
||||
|
||||
@@ -366,8 +377,8 @@ class PlotParameters:
|
||||
return {"required": {
|
||||
"images": ("IMAGE", ),
|
||||
"params": ("SAMPLER_PARAMS", ),
|
||||
"order_by": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler"], ),
|
||||
"cols_value": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler"], ),
|
||||
"order_by": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift", "split_sigmas"], ),
|
||||
"cols_value": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift", "split_sigmas"], ),
|
||||
"cols_num": ("INT", {"default": -1, "min": -1, "max": 1024 }),
|
||||
"add_prompt": (["false", "true", "excerpt"], ),
|
||||
}}
|
||||
@@ -406,7 +417,7 @@ class PlotParameters:
|
||||
text = f"time: {param['time']:.2f}s, seed: {param['seed']}, steps: {param['steps']}, denoise: {param['denoise']}\nsampler: {param['sampler']}, sched: {param['scheduler']}, sigmas at: {param['split_sigmas']}\nguidance: {param['guidance']}, max/base shift: {param['max_shift']}/{param['base_shift']}"
|
||||
lines = text.split("\n")
|
||||
text_height = line_height * len(lines)
|
||||
text_image = Image.new('RGB', (width, text_height), color=(0, 0, 0, 0))
|
||||
text_image = Image.new('RGB', (width, text_height), color=(0, 0, 0))
|
||||
|
||||
for i, line in enumerate(lines):
|
||||
draw = ImageDraw.Draw(text_image)
|
||||
@@ -424,7 +435,7 @@ class PlotParameters:
|
||||
cols = math.ceil(width / char_width)
|
||||
prompt_lines = textwrap.wrap(prompt, width=cols)
|
||||
prompt_height = line_height * len(prompt_lines)
|
||||
prompt_image = Image.new('RGB', (width, prompt_height), color=(0, 0, 0, 0))
|
||||
prompt_image = Image.new('RGB', (width, prompt_height), color=(0, 0, 0))
|
||||
|
||||
for i, line in enumerate(prompt_lines):
|
||||
draw = ImageDraw.Draw(prompt_image)
|
||||
@@ -433,6 +444,8 @@ class PlotParameters:
|
||||
prompt_image = T.ToTensor()(prompt_image).to(image.device)
|
||||
image = torch.cat([image, prompt_image], 1)
|
||||
|
||||
# a little cleanup
|
||||
image = torch.nan_to_num(image, nan=0.0).clamp(0.0, 1.0)
|
||||
out_image.append(image)
|
||||
|
||||
# ensure all images have the same height
|
||||
|
||||
Reference in New Issue
Block a user