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import os
import random
import sys
from typing import Sequence, Mapping, Any, Union
import torch
import gradio as gr
from glob import glob
from pathlib import Path
import logging.config
LOGGING_CONFIG = {
'version': 1,
'formatters': {
'default': { # This is the formatter named 'default'
'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
},
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'formatter': 'default', # Reference to the 'default' formatter
},
},
'loggers': {
'': { # root logger
'handlers': ['console'],
'level': 'INFO',
},
},
}
# Assuming LOGGING_CONFIG is the dictionary defined above
logging.config.dictConfig(LOGGING_CONFIG)
def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
"""Returns the value at the given index of a sequence or mapping.
If the object is a sequence (like list or string), returns the value at the given index.
If the object is a mapping (like a dictionary), returns the value at the index-th key.
Some return a dictionary, in these cases, we look for the "results" key
Args:
obj (Union[Sequence, Mapping]): The object to retrieve the value from.
index (int): The index of the value to retrieve.
Returns:
Any: The value at the given index.
Raises:
IndexError: If the index is out of bounds for the object and the object is not a mapping.
"""
try:
return obj[index]
except KeyError:
return obj["result"][index]
def find_path(name: str, path: str = None) -> str:
"""
Recursively looks at parent folders starting from the given path until it finds the given name.
Returns the path as a Path object if found, or None otherwise.
"""
# If no path is given, use the current working directory
if path is None:
path = os.getcwd()
# Check if the current directory contains the name
if name in os.listdir(path):
path_name = os.path.join(path, name)
print(f"{name} found: {path_name}")
return path_name
# Get the parent directory
parent_directory = os.path.dirname(path)
# If the parent directory is the same as the current directory, we've reached the root and stop the search
if parent_directory == path:
return None
# Recursively call the function with the parent directory
return find_path(name, parent_directory)
def add_comfyui_directory_to_sys_path() -> None:
"""
Add 'ComfyUI' to the sys.path
"""
comfyui_path = find_path("ComfyUI")
if comfyui_path is not None and os.path.isdir(comfyui_path):
sys.path.append(comfyui_path)
print(f"'{comfyui_path}' added to sys.path")
def add_extra_model_paths() -> None:
"""
Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
"""
from main import load_extra_path_config
extra_model_paths = find_path("extra_model_paths.yaml")
if extra_model_paths is not None:
load_extra_path_config(extra_model_paths)
else:
print("Could not find the extra_model_paths config file.")
add_comfyui_directory_to_sys_path()
add_extra_model_paths()
def import_custom_nodes() -> None:
"""Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
This function sets up a new asyncio event loop, initializes the PromptServer,
creates a PromptQueue, and initializes the custom nodes.
"""
import asyncio
import execution
from nodes import init_custom_nodes
import server
# Creating a new event loop and setting it as the default loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# Creating an instance of PromptServer with the loop
server_instance = server.PromptServer(loop)
execution.PromptQueue(server_instance)
# Initializing custom nodes
init_custom_nodes()
from nodes import (
EmptyLatentImage,
CheckpointLoaderSimple,
NODE_CLASS_MAPPINGS,
KSamplerAdvanced,
MagicAlbum3DGaussianNoise,
CLIPTextEncode,
VAELoader,
VAEDecode,
)
class MagicMeController:
def __init__(self):
self.id_embed_dir = "models/embeddings"
self.save_dir = "output"
self.base_model_dir = "models/checkpoints"
self.base_model_list = []
self.selected_base_model = "realisticVision_v51.safetensors"
self.motion_lora_dir = "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora"
self.motion_lora_list = []
self.selected_motion_lora = "v2_lora_ZoomIn.ckpt"
self.id_embed_list = []
self.woman_id_embed_list = ["beyonce", "hermione", "lifeifei", "lisa", "mona", "monroe", "taylor", "scarlett"]
self.refresh_id_embed_list()
self.refresh_base_model_list()
self.refresh_motion_lora_list()
with torch.inference_mode():
vaeloader = VAELoader()
self.vaeloader_2 = vaeloader.load_vae(
vae_name="vae-ft-mse-840000-ema-pruned.safetensors"
)
checkpointloadersimple = CheckpointLoaderSimple()
self.checkpointloadersimple_32 = checkpointloadersimple.load_checkpoint(
ckpt_name=self.selected_base_model
)
ultralyticsdetectorprovider = NODE_CLASS_MAPPINGS[
"UltralyticsDetectorProvider"
]()
self.ultralyticsdetectorprovider_75 = ultralyticsdetectorprovider.doit(
model_name="bbox/face_yolov8m.pt"
)
samloader = NODE_CLASS_MAPPINGS["SAMLoader"]()
self.samloader_78 = samloader.load_model(
model_name="sam_vit_b_01ec64.pth", device_mode="AUTO"
)
ade_animatediffuniformcontextoptions = NODE_CLASS_MAPPINGS[
"ADE_AnimateDiffUniformContextOptions"
]()
self.ade_animatediffuniformcontextoptions_102 = (
ade_animatediffuniformcontextoptions.create_options(
context_length=16, context_stride=1, context_overlap=2, closed_loop=False,
context_schedule="uniform", fuse_method="flat"
)
)
upscalemodelloader = NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
self.upscalemodelloader_157 = upscalemodelloader.load_model(
model_name="4xUltrasharpV10.pt"
)
ade_animatediffloraloader = NODE_CLASS_MAPPINGS["ADE_AnimateDiffLoRALoader"]()
self.ade_animatediffloraloader_196 = ade_animatediffloraloader.load_motion_lora(
lora_name=self.selected_motion_lora, strength=0.6
)
impactint = NODE_CLASS_MAPPINGS["ImpactInt"]()
self.impactint_204 = impactint.doit(value=16)
self.ade_animatediffloaderwithcontext = NODE_CLASS_MAPPINGS[
"ADE_AnimateDiffLoaderWithContext"
]()
self.freeu_v2 = NODE_CLASS_MAPPINGS["FreeU_V2"]()
self.tobasicpipe = NODE_CLASS_MAPPINGS["ToBasicPipe"]()
self.frombasicpipe = NODE_CLASS_MAPPINGS["FromBasicPipe"]()
self.bnk_getsigma = NODE_CLASS_MAPPINGS["BNK_GetSigma"]()
self.emptylatentimage = EmptyLatentImage()
self.magicalbum3dgaussiannoise = MagicAlbum3DGaussianNoise()
self.bnk_injectnoise = NODE_CLASS_MAPPINGS["BNK_InjectNoise"]()
self.ksampleradvanced = KSamplerAdvanced()
self.vaedecode = VAEDecode()
self.vhs_videocombine = NODE_CLASS_MAPPINGS["VHS_VideoCombine"]()
self.impactsimpledetectorsegs_for_ad = NODE_CLASS_MAPPINGS[
"ImpactSimpleDetectorSEGS_for_AD"
]()
self.segsdetailerforanimatediff = NODE_CLASS_MAPPINGS["SEGSDetailerForAnimateDiff"]()
self.segspaste = NODE_CLASS_MAPPINGS["SEGSPaste"]()
self.segspreview = NODE_CLASS_MAPPINGS["SEGSPreview"]()
self.ultimatesdupscale = NODE_CLASS_MAPPINGS["UltimateSDUpscale"]()
self.imagecasharpening = NODE_CLASS_MAPPINGS["ImageCASharpening+"]()
def refresh_id_embed_list(self):
id_embed_list = glob(os.path.join(self.id_embed_dir, "*.pt"))
self.id_embed_list = [Path(p).stem for p in id_embed_list]
def refresh_motion_lora_list(self):
motion_lora_list = glob(os.path.join(self.motion_lora_dir, "*.ckpt"))
self.motion_lora_list = [os.path.basename(p)for p in motion_lora_list]
def refresh_base_model_list(self):
base_model_list = glob(os.path.join(self.base_model_dir, "*.safetensors"))
self.base_model_list = [os.path.basename(p)for p in base_model_list]
def update_motion_lora(self, base_model_dropdown):
self.selected_base_model = base_model_dropdown
checkpointloadersimple = CheckpointLoaderSimple()
self.checkpointloadersimple_32 = checkpointloadersimple.load_checkpoint(
ckpt_name=self.selected_base_model
)
return gr.Dropdown.update()
def update_base_model(self, base_model_dropdown):
self.selected_base_model = base_model_dropdown
checkpointloadersimple = CheckpointLoaderSimple()
self.checkpointloadersimple_32 = checkpointloadersimple.load_checkpoint(
ckpt_name=self.selected_base_model
)
return gr.Dropdown.update()
def update_motion_lora(self, motion_lora_dropdown):
self.selected_motion_lora = motion_lora_dropdown
ade_animatediffloraloader = NODE_CLASS_MAPPINGS["ADE_AnimateDiffLoRALoader"]()
self.ade_animatediffloraloader_196 = ade_animatediffloraloader.load_motion_lora(
lora_name=self.selected_motion_lora, strength=0.6
)
return gr.Dropdown.update()
def run_t2v_face_tiled(self, base_model_dropdown, motion_lora_dropdown, prompt_text_box, negative_prompt_text_box, id_embed_dropdown, gaussian_slider, seed_text_box):
if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
if self.selected_motion_lora != motion_lora_dropdown: self.update_motion_lora(motion_lora_dropdown)
category = "woman" if id_embed_dropdown in self.woman_id_embed_list else "man"
prompt = f"a photo of embedding:{id_embed_dropdown} {category} " + prompt_text_box
print("prompt:", prompt)
print("negative_prompt_text_box:", negative_prompt_text_box)
print("id_embed_dropdown:", id_embed_dropdown)
print("gaussian_slider:", gaussian_slider)
print("seed_text_box:", seed_text_box)
seed_text_box = int(seed_text_box)
with torch.inference_mode():
cliptextencode = CLIPTextEncode()
cliptextencode_6 = cliptextencode.encode(
text=negative_prompt_text_box,
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
)
cliptextencode_274 = cliptextencode.encode(
text=prompt,
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
)
ade_animatediffloaderwithcontext_261 = (
self.ade_animatediffloaderwithcontext.load_mm_and_inject_params(
model_name="mm_sd_v15_v2.ckpt",
beta_schedule="autoselect",
motion_scale=1,
apply_v2_models_properly=True,
model=get_value_at_index(self.checkpointloadersimple_32, 0),
context_options=get_value_at_index(
self.ade_animatediffuniformcontextoptions_102, 0
),
motion_lora=get_value_at_index(self.ade_animatediffloraloader_196, 0),
)
)
freeu_v2_151 = self.freeu_v2.patch(
b1=1.1,
b2=1.2,
s1=0.9,
s2=0.4,
model=get_value_at_index(ade_animatediffloaderwithcontext_261, 0),
)
tobasicpipe_42 = self.tobasicpipe.doit(
model=get_value_at_index(freeu_v2_151, 0),
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
vae=get_value_at_index(self.vaeloader_2, 0),
positive=get_value_at_index(cliptextencode_274, 0),
negative=get_value_at_index(cliptextencode_6, 0),
)
frombasicpipe_52 = self.frombasicpipe.doit(
basic_pipe=get_value_at_index(tobasicpipe_42, 0)
)
bnk_getsigma_254 = self.bnk_getsigma.calc_sigma(
sampler_name="dpmpp_2m",
scheduler="karras",
steps=20,
start_at_step=0,
end_at_step=20,
model=get_value_at_index(frombasicpipe_52, 0),
)
emptylatentimage_223 = self.emptylatentimage.generate(
width=512, height=512, batch_size=get_value_at_index(self.impactint_204, 0)
)
magicalbum3dgaussiannoise_262 = self.magicalbum3dgaussiannoise.generate(
width=512,
height=512,
batch_size=get_value_at_index(self.impactint_204, 0),
seed=seed_text_box,
cov_factor=gaussian_slider,
)
bnk_injectnoise_253 = self.bnk_injectnoise.inject_noise(
strength=get_value_at_index(bnk_getsigma_254, 0),
latents=get_value_at_index(emptylatentimage_223, 0),
noise=get_value_at_index(magicalbum3dgaussiannoise_262, 0),
)
ksampleradvanced_248 = self.ksampleradvanced.sample(
add_noise="disable",
noise_seed=seed_text_box,
steps=20,
cfg=8,
sampler_name="dpmpp_2m",
scheduler="karras",
start_at_step=0,
end_at_step=20,
return_with_leftover_noise="disable",
model=get_value_at_index(frombasicpipe_52, 0),
positive=get_value_at_index(frombasicpipe_52, 3),
negative=get_value_at_index(frombasicpipe_52, 4),
latent_image=get_value_at_index(bnk_injectnoise_253, 0),
)
vaedecode_10 = self.vaedecode.decode(
samples=get_value_at_index(ksampleradvanced_248, 0),
vae=get_value_at_index(frombasicpipe_52, 2),
)
vhs_videocombine_35 = self.vhs_videocombine.combine_video(
frame_rate=8,
loop_count=0,
filename_prefix="orig",
format="video/h264-mp4",
pingpong=False,
save_output=True,
images=get_value_at_index(vaedecode_10, 0),
unique_id=2001771405939721385,
)
impactsimpledetectorsegs_for_ad_156 = self.impactsimpledetectorsegs_for_ad.doit(
bbox_threshold=0.5,
bbox_dilation=0,
crop_factor=3,
drop_size=10,
sub_threshold=0.5,
sub_dilation=0,
sub_bbox_expansion=0,
sam_mask_hint_threshold=0.7,
masking_mode="Pivot SEGS",
segs_pivot="Combined mask",
bbox_detector=get_value_at_index(self.ultralyticsdetectorprovider_75, 0),
image_frames=get_value_at_index(vaedecode_10, 0),
sam_model_opt=get_value_at_index(self.samloader_78, 0),
)
segsdetailerforanimatediff_41 = self.segsdetailerforanimatediff.doit(
guide_size=512,
guide_size_for=False,
max_size=512,
seed=seed_text_box,
steps=20,
cfg=8,
sampler_name="euler",
scheduler="normal",
denoise=0.8,
refiner_ratio=0.2,
image_frames=get_value_at_index(vaedecode_10, 0),
segs=get_value_at_index(impactsimpledetectorsegs_for_ad_156, 0),
basic_pipe=get_value_at_index(tobasicpipe_42, 0),
)
segspaste_49 = self.segspaste.doit(
feather=5,
alpha=255,
image=get_value_at_index(vaedecode_10, 0),
segs=get_value_at_index(segsdetailerforanimatediff_41, 0),
)
vhs_videocombine_51 = self.vhs_videocombine.combine_video(
frame_rate=8,
loop_count=0,
filename_prefix="face_detailer",
format="video/h264-mp4",
pingpong=False,
save_output=True,
images=get_value_at_index(segspaste_49, 0),
unique_id=7104489750160636615,
)
# segspreview_101 = self.segspreview.doit(
# alpha_mode=True,
# min_alpha=0.2,
# segs=get_value_at_index(impactsimpledetectorsegs_for_ad_156, 0),
# )
frombasicpipe_175 = self.frombasicpipe.doit(
basic_pipe=get_value_at_index(tobasicpipe_42, 0)
)
ultimatesdupscale_172 = self.ultimatesdupscale.upscale(
upscale_by=2,
seed=seed_text_box,
steps=20,
cfg=8,
sampler_name="euler",
scheduler="normal",
denoise=0.2,
mode_type="Linear",
tile_width=512,
tile_height=512,
mask_blur=8,
tile_padding=32,
seam_fix_mode="None",
seam_fix_denoise=1,
seam_fix_width=64,
seam_fix_mask_blur=8,
seam_fix_padding=16,
force_uniform_tiles=True,
tiled_decode=False,
image=get_value_at_index(segspaste_49, 0),
model=get_value_at_index(frombasicpipe_175, 0),
positive=get_value_at_index(frombasicpipe_175, 3),
negative=get_value_at_index(frombasicpipe_175, 4),
vae=get_value_at_index(frombasicpipe_175, 2),
upscale_model=get_value_at_index(self.upscalemodelloader_157, 0),
)
imagecasharpening_183 = self.imagecasharpening.execute(
amount=0.2, image=get_value_at_index(ultimatesdupscale_172, 0)
)
vhs_videocombine_176 = self.vhs_videocombine.combine_video(
frame_rate=8,
loop_count=0,
filename_prefix="SR",
format="video/h265-mp4",
pingpong=False,
save_output=True,
images=get_value_at_index(imagecasharpening_183, 0),
unique_id=5059112282155244564,
)
orig_video_path = sorted(glob(os.path.join(self.save_dir, 'orig*.mp4')))[-1]
face_detailer_video_path = sorted(glob(os.path.join(self.save_dir, 'face_detailer*.mp4')))[-1]
sr_video_path = sorted(glob(os.path.join(self.save_dir, 'SR*.mp4')))[-1]
json_config = {
"prompt": prompt,
"n_prompt": negative_prompt_text_box,
"id_embed_dropdown": id_embed_dropdown,
"gaussian_slider": gaussian_slider,
"seed_text_box": seed_text_box,
"motion_lora_dropdown": motion_lora_dropdown,
"base_model_dropdown": base_model_dropdown
}
return gr.Video.update(value=orig_video_path), gr.Video.update(value=face_detailer_video_path),gr.Video.update(value=sr_video_path), gr.Json.update(value=json_config)
def run_t2v_face(self, base_model_dropdown, motion_lora_dropdown, prompt_text_box, negative_prompt_text_box, id_embed_dropdown, gaussian_slider, seed_text_box):
if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
if self.selected_motion_lora != motion_lora_dropdown: self.update_motion_lora(motion_lora_dropdown)
category = "woman" if id_embed_dropdown in self.woman_id_embed_list else "man"
prompt = f"a photo of embedding:{id_embed_dropdown} {category} " + prompt_text_box
print("prompt:", prompt)
print("negative_prompt_text_box:", negative_prompt_text_box)
print("id_embed_dropdown:", id_embed_dropdown)
print("gaussian_slider:", gaussian_slider)
print("seed_text_box:", seed_text_box)
seed_text_box = int(seed_text_box)
with torch.inference_mode():
cliptextencode = CLIPTextEncode()
cliptextencode_6 = cliptextencode.encode(
text=negative_prompt_text_box,
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
)
cliptextencode_274 = cliptextencode.encode(
text=prompt,
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
)
ade_animatediffloaderwithcontext_261 = (
self.ade_animatediffloaderwithcontext.load_mm_and_inject_params(
model_name="mm_sd_v15_v2.ckpt",
beta_schedule="autoselect",
motion_scale=1,
apply_v2_models_properly=True,
model=get_value_at_index(self.checkpointloadersimple_32, 0),
context_options=get_value_at_index(
self.ade_animatediffuniformcontextoptions_102, 0
),
motion_lora=get_value_at_index(self.ade_animatediffloraloader_196, 0),
)
)
freeu_v2_151 = self.freeu_v2.patch(
b1=1.1,
b2=1.2,
s1=0.9,
s2=0.4,
model=get_value_at_index(ade_animatediffloaderwithcontext_261, 0),
)
tobasicpipe_42 = self.tobasicpipe.doit(
model=get_value_at_index(freeu_v2_151, 0),
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
vae=get_value_at_index(self.vaeloader_2, 0),
positive=get_value_at_index(cliptextencode_274, 0),
negative=get_value_at_index(cliptextencode_6, 0),
)
frombasicpipe_52 = self.frombasicpipe.doit(
basic_pipe=get_value_at_index(tobasicpipe_42, 0)
)
bnk_getsigma_254 = self.bnk_getsigma.calc_sigma(
sampler_name="dpmpp_2m",
scheduler="karras",
steps=20,
start_at_step=0,
end_at_step=20,
model=get_value_at_index(frombasicpipe_52, 0),
)
emptylatentimage_223 = self.emptylatentimage.generate(
width=512, height=512, batch_size=get_value_at_index(self.impactint_204, 0)
)
magicalbum3dgaussiannoise_262 = self.magicalbum3dgaussiannoise.generate(
width=512,
height=512,
batch_size=get_value_at_index(self.impactint_204, 0),
seed=seed_text_box,
cov_factor=gaussian_slider,
)
bnk_injectnoise_253 = self.bnk_injectnoise.inject_noise(
strength=get_value_at_index(bnk_getsigma_254, 0),
latents=get_value_at_index(emptylatentimage_223, 0),
noise=get_value_at_index(magicalbum3dgaussiannoise_262, 0),
)
ksampleradvanced_248 = self.ksampleradvanced.sample(
add_noise="disable",
noise_seed=seed_text_box,
steps=20,
cfg=8,
sampler_name="dpmpp_2m",
scheduler="karras",
start_at_step=0,
end_at_step=20,
return_with_leftover_noise="disable",
model=get_value_at_index(frombasicpipe_52, 0),
positive=get_value_at_index(frombasicpipe_52, 3),
negative=get_value_at_index(frombasicpipe_52, 4),
latent_image=get_value_at_index(bnk_injectnoise_253, 0),
)
vaedecode_10 = self.vaedecode.decode(
samples=get_value_at_index(ksampleradvanced_248, 0),
vae=get_value_at_index(frombasicpipe_52, 2),
)
vhs_videocombine_35 = self.vhs_videocombine.combine_video(
frame_rate=8,
loop_count=0,
filename_prefix="orig",
format="video/h264-mp4",
pingpong=False,
save_output=True,
images=get_value_at_index(vaedecode_10, 0),
unique_id=2001771405939721385,
)
impactsimpledetectorsegs_for_ad_156 = self.impactsimpledetectorsegs_for_ad.doit(
bbox_threshold=0.5,
bbox_dilation=0,
crop_factor=3,
drop_size=10,
sub_threshold=0.5,
sub_dilation=0,
sub_bbox_expansion=0,
sam_mask_hint_threshold=0.7,
masking_mode="Pivot SEGS",
segs_pivot="Combined mask",
bbox_detector=get_value_at_index(self.ultralyticsdetectorprovider_75, 0),
image_frames=get_value_at_index(vaedecode_10, 0),
sam_model_opt=get_value_at_index(self.samloader_78, 0),
)
segsdetailerforanimatediff_41 = self.segsdetailerforanimatediff.doit(
guide_size=512,
guide_size_for=False,
max_size=512,
seed=seed_text_box,
steps=20,
cfg=8,
sampler_name="euler",
scheduler="normal",
denoise=0.8,
refiner_ratio=0.2,
image_frames=get_value_at_index(vaedecode_10, 0),
segs=get_value_at_index(impactsimpledetectorsegs_for_ad_156, 0),
basic_pipe=get_value_at_index(tobasicpipe_42, 0),
)
segspaste_49 = self.segspaste.doit(
feather=5,
alpha=255,
image=get_value_at_index(vaedecode_10, 0),
segs=get_value_at_index(segsdetailerforanimatediff_41, 0),
)
vhs_videocombine_51 = self.vhs_videocombine.combine_video(
frame_rate=8,
loop_count=0,
filename_prefix="face_detailer",
format="video/h264-mp4",
pingpong=False,
save_output=True,
images=get_value_at_index(segspaste_49, 0),
unique_id=7104489750160636615,
)
orig_video_path = sorted(glob(os.path.join(self.save_dir, 'orig*.mp4')))[-1]
face_detailer_video_path = sorted(glob(os.path.join(self.save_dir, 'face_detailer*.mp4')))[-1]
json_config = {
"prompt": prompt,
"n_prompt": negative_prompt_text_box,
"id_embed_dropdown": id_embed_dropdown,
"gaussian_slider": gaussian_slider,
"seed_text_box": seed_text_box,
"motion_lora_dropdown": motion_lora_dropdown,
"base_model_dropdown": base_model_dropdown
}
return gr.Video.update(value=orig_video_path), gr.Video.update(value=face_detailer_video_path), gr.Json.update(value=json_config)
def run_t2v(self, base_model_dropdown, motion_lora_dropdown, prompt_text_box, negative_prompt_text_box, id_embed_dropdown, gaussian_slider, seed_text_box):
if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
if self.selected_motion_lora != motion_lora_dropdown: self.update_motion_lora(motion_lora_dropdown)
category = "woman" if id_embed_dropdown in self.woman_id_embed_list else "man"
prompt = f"a photo of embedding:{id_embed_dropdown} {category} " + prompt_text_box
print("prompt:", prompt)
print("negative_prompt_text_box:", negative_prompt_text_box)
print("id_embed_dropdown:", id_embed_dropdown)
print("gaussian_slider:", gaussian_slider)
print("seed_text_box:", seed_text_box)
seed_text_box = int(seed_text_box)
with torch.inference_mode():
cliptextencode = CLIPTextEncode()
cliptextencode_6 = cliptextencode.encode(
text=negative_prompt_text_box,
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
)
cliptextencode_274 = cliptextencode.encode(
text=prompt,
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
)
ade_animatediffloaderwithcontext_261 = (
self.ade_animatediffloaderwithcontext.load_mm_and_inject_params(
model_name="mm_sd_v15_v2.ckpt",
beta_schedule="autoselect",
motion_scale=1,
apply_v2_models_properly=True,
model=get_value_at_index(self.checkpointloadersimple_32, 0),
context_options=get_value_at_index(
self.ade_animatediffuniformcontextoptions_102, 0
),
motion_lora=get_value_at_index(self.ade_animatediffloraloader_196, 0),
)
)
freeu_v2_151 = self.freeu_v2.patch(
b1=1.1,
b2=1.2,
s1=0.9,
s2=0.4,
model=get_value_at_index(ade_animatediffloaderwithcontext_261, 0),
)
tobasicpipe_42 = self.tobasicpipe.doit(
model=get_value_at_index(freeu_v2_151, 0),
clip=get_value_at_index(self.checkpointloadersimple_32, 1),
vae=get_value_at_index(self.vaeloader_2, 0),
positive=get_value_at_index(cliptextencode_274, 0),
negative=get_value_at_index(cliptextencode_6, 0),
)
frombasicpipe_52 = self.frombasicpipe.doit(
basic_pipe=get_value_at_index(tobasicpipe_42, 0)
)
bnk_getsigma_254 = self.bnk_getsigma.calc_sigma(
sampler_name="dpmpp_2m",
scheduler="karras",
steps=20,
start_at_step=0,
end_at_step=20,
model=get_value_at_index(frombasicpipe_52, 0),
)
emptylatentimage_223 = self.emptylatentimage.generate(
width=512, height=512, batch_size=get_value_at_index(self.impactint_204, 0)
)
magicalbum3dgaussiannoise_262 = self.magicalbum3dgaussiannoise.generate(
width=512,
height=512,
batch_size=get_value_at_index(self.impactint_204, 0),
seed=seed_text_box,
cov_factor=gaussian_slider,
)
bnk_injectnoise_253 = self.bnk_injectnoise.inject_noise(
strength=get_value_at_index(bnk_getsigma_254, 0),
latents=get_value_at_index(emptylatentimage_223, 0),
noise=get_value_at_index(magicalbum3dgaussiannoise_262, 0),
)
ksampleradvanced_248 = self.ksampleradvanced.sample(
add_noise="disable",
noise_seed=seed_text_box,
steps=20,
cfg=8,
sampler_name="dpmpp_2m",
scheduler="karras",
start_at_step=0,
end_at_step=20,
return_with_leftover_noise="disable",
model=get_value_at_index(frombasicpipe_52, 0),
positive=get_value_at_index(frombasicpipe_52, 3),
negative=get_value_at_index(frombasicpipe_52, 4),
latent_image=get_value_at_index(bnk_injectnoise_253, 0),
)
vaedecode_10 = self.vaedecode.decode(
samples=get_value_at_index(ksampleradvanced_248, 0),
vae=get_value_at_index(frombasicpipe_52, 2),
)
vhs_videocombine_35 = self.vhs_videocombine.combine_video(
frame_rate=8,
loop_count=0,
filename_prefix="orig",
format="video/h264-mp4",
pingpong=False,
save_output=True,
images=get_value_at_index(vaedecode_10, 0),
unique_id=2001771405939721385,
)
orig_video_path = sorted(glob(os.path.join(self.save_dir, 'orig*.mp4')))[-1]
json_config = {
"base_model_dropdown": base_model_dropdown,
"motion_lora_dropdown": motion_lora_dropdown,
"prompt": prompt,
"n_prompt": negative_prompt_text_box,
"id_embed_dropdown": id_embed_dropdown,
"gaussian_slider": gaussian_slider,
"seed_text_box": seed_text_box,
}
return gr.Video.update(value=orig_video_path), gr.Json.update(value=json_config)
import_custom_nodes()
c = MagicMeController()
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
examples = [
# 1-Realistic Vision
[
"realisticVision_v51.safetensors",
"v2_lora_ZoomIn.ckpt",
"a photo of embedding:altman man in superman costume in the outer space, stars in the background",
"(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, UnrealisticDream",
"altman",
0.2,
3323153235
],
# 2-RCNZ
[
"rcnzCartoon3d_v10.safetensors",
"v2_lora_ZoomIn.ckpt",
"a photo of embedding:altman man in superman costume in the outer space, stars in the background",
"(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, UnrealisticDream",
"altman",
0.2,
4164379572666061
],
]
def ui():
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Magic-Me: Identity-Specific Video Customized Diffusion
Ze Ma*, Daquan Zhou* †, Chun-Hsiao Yeh, Xue-She Wang, Xiuyu Li, Huanrui Yang, Zhen Dong †, Kurt Keutzer, Jiashi Feng (*Joint First Author, † Corresponding Author)
[Arxiv Report](https://arxiv.org/abs/2402.09368) | [Project Page](https://magic-me-webpage.github.io/) | [Github](https://github.com/Zhen-Dong/Magic-Me)
"""
)
gr.Markdown(
"""
### Quick Start
1. Select desired `ID embedding`. There are more advanced settings in the drop-down menu `Advanced`.
2. Provide `Prompt` and `Negative Prompt`. Please use propoer pronoun for the character's gender.
3. Click on one of three `Go` buttons. The fewer the running modules, the less time you need to wait. Enjoy!
"""
)
with gr.Row():
with gr.Column():
id_embed_dropdown = gr.Dropdown( label="ID Embedding", choices=c.id_embed_list, value=c.id_embed_list[0], interactive=True )
prompt_textbox = gr.Textbox( label="Prompt", info="a photo of <V*> man/woman ", lines=3, value="in superman costume in the outer space, stars in the background" )
negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, UnrealisticDream")
with gr.Row():
seed_textbox = gr.Textbox( label="Seed (change to get various videos)", value=random.randint(1, 2 ** 32))
seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox])
with gr.Column():
with gr.Accordion("Advance", open=False):
base_model_dropdown = gr.Dropdown( label="Base DreamBooth Model", choices=c.base_model_list, value=c.selected_base_model, interactive=True)
base_model_dropdown.change(fn=c.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])
motion_lora_dropdown = gr.Dropdown( label="Motion LoRA Model", choices=c.motion_lora_list, value=c.selected_motion_lora, interactive=True)
motion_lora_dropdown.change(fn=c.update_motion_lora, inputs=[motion_lora_dropdown], outputs=[motion_lora_dropdown])
gaussian_slider = gr.Slider( label="3D Gaussian Noise Covariance", value=0.2, minimum=0, maximum=1, step=0.05 )
json_config = gr.Json(label="Output Config", value=None )
with gr.Row():
generate_button_t2v = gr.Button( value="Go (T2V VCD)", variant='primary' )
generate_button_face = gr.Button( value="Go (T2V + Face VCD, 2X slower)", variant='primary' )
generate_button_tiled = gr.Button( value="Go (T2V + Face + Tiled VCD, 8X slower)", variant='primary' )
with gr.Row():
orig_video = gr.Video( label="Video after T2V VCD", interactive=False )
face_detailer_video = gr.Video( label="Video after Face VCD", interactive=False )
sr_video = gr.Video( label="Video after Tiled VCD", interactive=False )
inputs = [base_model_dropdown, motion_lora_dropdown, prompt_textbox, negative_prompt_textbox, id_embed_dropdown, gaussian_slider, seed_textbox]
outputs_t2v = [orig_video, json_config]
outputs_t2v_face = [orig_video, face_detailer_video, json_config]
outputs_t2v_face_tiled = [orig_video, face_detailer_video, sr_video, json_config]
generate_button_t2v.click( fn=c.run_t2v, inputs=inputs, outputs=outputs_t2v )
generate_button_face.click( fn=c.run_t2v_face, inputs=inputs, outputs=outputs_t2v_face )
generate_button_tiled.click( fn=c.run_t2v_face_tiled, inputs=inputs, outputs=outputs_t2v_face_tiled )
gr.Examples( fn=c.run_t2v_face_tiled, examples=examples, inputs=inputs, outputs=outputs_t2v_face_tiled, cache_examples=True )
return demo
if __name__ == "__main__":
demo = ui()
demo.queue(max_size=20)
demo.launch() |