Update base.py

This commit is contained in:
VALADI K JAGANATHAN
2023-11-26 14:51:44 +05:30
committed by GitHub
parent fc9556199f
commit 456439903a
+108
View File
@@ -44,3 +44,111 @@ class SeedContext():
random.seed(self.seed)
def __exit__(self, exc_type, exc_val, exc_tb):
random.setstate(self.state)
# import comfy.model_base as BaseModel
class xy_Tiling_KSampler:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"seed": ("SEED", ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
# here is custom config
"step_range": ("INT", {
"startStep": 0, # Start tiling from step N
"stopStep": -1 # Stop tiling after step N (-1: Don't stop)
}),
"tileX": (["enable", "disable"],),
"tileY": (["enable", "disable"],)
},
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "Alsritter/Sampling"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
step_range, tileX, tileY, denoise=1.0):
if tileX != "enable" and tileY != "enable":
return nodes.common_ksampler(model, seed['seed'], steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
else:
return self.tile_ksampler(model, seed['seed'], steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
# Self-determined
def tile_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
# ========================== Base code ==========================
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(
), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = latent_preview.get_previewer(
device, model.model.latent_format)
# Arrangement progress clause
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
preview_bytes = None
self.print_object_info(step)
self.print_object_info(x0)
self.print_object_info(x)
self.print_object_info(total_steps)
#Generation guide
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(
preview_format, x0)
# Update progress clause
pbar.update_absolute(step + 1, total_steps, preview_bytes)
# ========================== Custom code ==========================
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, seed=seed)
out = latent.copy()
out["samples"] = samples
return (out, )
def print_object_info(self, obj):
print("Type:", type(obj))
print("Attributes and methods:", end=" ")
for item in dir(obj):
print(item, end=" ")