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sync (#658)

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lllyasviel
2023-10-12 04:23:10 -07:00
committed by GitHub
parent 4c867c1b8b
commit e61aac34ca
147 changed files with 523 additions and 642 deletions
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backend/headless/nodes.py
+46 -46
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@@ -14,20 +14,20 @@ from PIL.PngImagePlugin import PngInfo
import numpy as np
import safetensors.torch
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "fcbh"))
import comfy.diffusers_load
import comfy.samplers
import comfy.sample
import comfy.sd
import comfy.utils
import comfy.controlnet
import fcbh.diffusers_load
import fcbh.samplers
import fcbh.sample
import fcbh.sd
import fcbh.utils
import fcbh.controlnet
import comfy.clip_vision
import fcbh.clip_vision
import comfy.model_management
from comfy.cli_args import args
import fcbh.model_management
from fcbh.cli_args import args
import importlib
@@ -35,10 +35,10 @@ import folder_paths
import latent_preview
def before_node_execution():
comfy.model_management.throw_exception_if_processing_interrupted()
fcbh.model_management.throw_exception_if_processing_interrupted()
def interrupt_processing(value=True):
comfy.model_management.interrupt_current_processing(value)
fcbh.model_management.interrupt_current_processing(value)
MAX_RESOLUTION=8192
@@ -366,7 +366,7 @@ class SaveLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT", ),
"filename_prefix": ("STRING", {"default": "latents/ComfyUI"})},
"filename_prefix": ("STRING", {"default": "latents/fcbh_backend"})},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
@@ -376,7 +376,7 @@ class SaveLatent:
CATEGORY = "_for_testing"
def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
def save(self, samples, filename_prefix="fcbh_backend", prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
# support save metadata for latent sharing
@@ -406,7 +406,7 @@ class SaveLatent:
output["latent_tensor"] = samples["samples"]
output["latent_format_version_0"] = torch.tensor([])
comfy.utils.save_torch_file(output, file, metadata=metadata)
fcbh.utils.save_torch_file(output, file, metadata=metadata)
return { "ui": { "latents": results } }
@@ -459,7 +459,7 @@ class CheckpointLoader:
def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
config_path = folder_paths.get_full_path("configs", config_name)
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return fcbh.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
class CheckpointLoaderSimple:
@classmethod
@@ -473,7 +473,7 @@ class CheckpointLoaderSimple:
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
out = fcbh.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return out[:3]
class DiffusersLoader:
@@ -500,7 +500,7 @@ class DiffusersLoader:
model_path = path
break
return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return fcbh.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
class unCLIPCheckpointLoader:
@@ -515,7 +515,7 @@ class unCLIPCheckpointLoader:
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
out = fcbh.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return out
class CLIPSetLastLayer:
@@ -566,10 +566,10 @@ class LoraLoader:
del temp
if lora is None:
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
lora = fcbh.utils.load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
model_lora, clip_lora = fcbh.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
return (model_lora, clip_lora)
class VAELoader:
@@ -584,7 +584,7 @@ class VAELoader:
#TODO: scale factor?
def load_vae(self, vae_name):
vae_path = folder_paths.get_full_path("vae", vae_name)
vae = comfy.sd.VAE(ckpt_path=vae_path)
vae = fcbh.sd.VAE(ckpt_path=vae_path)
return (vae,)
class ControlNetLoader:
@@ -599,7 +599,7 @@ class ControlNetLoader:
def load_controlnet(self, control_net_name):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet = comfy.controlnet.load_controlnet(controlnet_path)
controlnet = fcbh.controlnet.load_controlnet(controlnet_path)
return (controlnet,)
class DiffControlNetLoader:
@@ -615,7 +615,7 @@ class DiffControlNetLoader:
def load_controlnet(self, model, control_net_name):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet = comfy.controlnet.load_controlnet(controlnet_path, model)
controlnet = fcbh.controlnet.load_controlnet(controlnet_path, model)
return (controlnet,)
@@ -708,7 +708,7 @@ class UNETLoader:
def load_unet(self, unet_name):
unet_path = folder_paths.get_full_path("unet", unet_name)
model = comfy.sd.load_unet(unet_path)
model = fcbh.sd.load_unet(unet_path)
return (model,)
class CLIPLoader:
@@ -723,7 +723,7 @@ class CLIPLoader:
def load_clip(self, clip_name):
clip_path = folder_paths.get_full_path("clip", clip_name)
clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
clip = fcbh.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
return (clip,)
class DualCLIPLoader:
@@ -739,7 +739,7 @@ class DualCLIPLoader:
def load_clip(self, clip_name1, clip_name2):
clip_path1 = folder_paths.get_full_path("clip", clip_name1)
clip_path2 = folder_paths.get_full_path("clip", clip_name2)
clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
clip = fcbh.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
return (clip,)
class CLIPVisionLoader:
@@ -754,7 +754,7 @@ class CLIPVisionLoader:
def load_clip(self, clip_name):
clip_path = folder_paths.get_full_path("clip_vision", clip_name)
clip_vision = comfy.clip_vision.load(clip_path)
clip_vision = fcbh.clip_vision.load(clip_path)
return (clip_vision,)
class CLIPVisionEncode:
@@ -784,7 +784,7 @@ class StyleModelLoader:
def load_style_model(self, style_model_name):
style_model_path = folder_paths.get_full_path("style_models", style_model_name)
style_model = comfy.sd.load_style_model(style_model_path)
style_model = fcbh.sd.load_style_model(style_model_path)
return (style_model,)
@@ -849,7 +849,7 @@ class GLIGENLoader:
def load_gligen(self, gligen_name):
gligen_path = folder_paths.get_full_path("gligen", gligen_name)
gligen = comfy.sd.load_gligen(gligen_path)
gligen = fcbh.sd.load_gligen(gligen_path)
return (gligen,)
class GLIGENTextBoxApply:
@@ -991,7 +991,7 @@ class LatentUpscale:
width = max(64, width)
height = max(64, height)
s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
s["samples"] = fcbh.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
return (s,)
class LatentUpscaleBy:
@@ -1010,7 +1010,7 @@ class LatentUpscaleBy:
s = samples.copy()
width = round(samples["samples"].shape[3] * scale_by)
height = round(samples["samples"].shape[2] * scale_by)
s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
s["samples"] = fcbh.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
return (s,)
class LatentRotate:
@@ -1126,7 +1126,7 @@ class LatentBlend:
if samples1.shape != samples2.shape:
samples2.permute(0, 3, 1, 2)
samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')
samples2 = fcbh.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')
samples2.permute(0, 2, 3, 1)
samples_blended = self.blend_mode(samples1, samples2, blend_mode)
@@ -1195,15 +1195,15 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
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 = fcbh.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
disable_pbar = not fcbh.utils.PROGRESS_BAR_ENABLED
samples = fcbh.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, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
@@ -1218,8 +1218,8 @@ class KSampler:
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"sampler_name": (fcbh.samplers.KSampler.SAMPLERS, ),
"scheduler": (fcbh.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
@@ -1244,8 +1244,8 @@ class KSamplerAdvanced:
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"sampler_name": (fcbh.samplers.KSampler.SAMPLERS, ),
"scheduler": (fcbh.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
@@ -1279,7 +1279,7 @@ class SaveImage:
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"})},
"filename_prefix": ("STRING", {"default": "fcbh_backend"})},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
@@ -1290,7 +1290,7 @@ class SaveImage:
CATEGORY = "image"
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
def save_images(self, images, filename_prefix="fcbh_backend", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
results = list()
@@ -1448,7 +1448,7 @@ class ImageScale:
elif height == 0:
height = max(1, round(samples.shape[2] * width / samples.shape[3]))
s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop)
s = fcbh.utils.common_upscale(samples, width, height, upscale_method, crop)
s = s.movedim(1,-1)
return (s,)
@@ -1468,7 +1468,7 @@ class ImageScaleBy:
samples = image.movedim(-1,1)
width = round(samples.shape[3] * scale_by)
height = round(samples.shape[2] * scale_by)
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
s = fcbh.utils.common_upscale(samples, width, height, upscale_method, "disabled")
s = s.movedim(1,-1)
return (s,)
@@ -1500,7 +1500,7 @@ class ImageBatch:
def batch(self, image1, image2):
if image1.shape[1:] != image2.shape[1:]:
image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
image2 = fcbh.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
s = torch.cat((image1, image2), dim=0)
return (s,)
@@ -1781,7 +1781,7 @@ def load_custom_nodes():
print()
def init_custom_nodes():
extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras")
extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fcbh_extras")
extras_files = [
"nodes_latent.py",
"nodes_hypernetwork.py",