220 lines
8.2 KiB
Python
220 lines
8.2 KiB
Python
import os
|
|
import json
|
|
import torch
|
|
import folder_paths
|
|
|
|
from comfy.model_management import get_torch_device, soft_empty_cache, text_encoder_offload_device
|
|
from comfy import utils
|
|
from .conf import sana_conf, sana_res
|
|
from .loader import load_sana
|
|
from ..utils.dtype import string_to_dtype
|
|
|
|
dtypes = [
|
|
"auto",
|
|
"FP32",
|
|
"FP16",
|
|
"BF16"
|
|
]
|
|
|
|
class SanaCheckpointLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
|
|
"model": (list(sana_conf.keys()),),
|
|
}
|
|
}
|
|
RETURN_TYPES = ("MODEL",)
|
|
RETURN_NAMES = ("model",)
|
|
FUNCTION = "load_checkpoint"
|
|
CATEGORY = "ExtraModels/Sana"
|
|
TITLE = "Sana Checkpoint Loader"
|
|
|
|
def load_checkpoint(self, ckpt_name, model):
|
|
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
|
model_conf = sana_conf[model]
|
|
model = load_sana(
|
|
model_path = ckpt_path,
|
|
model_conf = model_conf,
|
|
)
|
|
return (model,)
|
|
|
|
|
|
class SanaResolutionSelect():
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": (list(sana_res.keys()),),
|
|
"ratio": (list(sana_res["1024px"].keys()),{"default":"1.00"}),
|
|
}
|
|
}
|
|
RETURN_TYPES = ("INT","INT")
|
|
RETURN_NAMES = ("width","height")
|
|
FUNCTION = "get_res"
|
|
CATEGORY = "ExtraModels/Sana"
|
|
TITLE = "Sana Resolution Select"
|
|
|
|
def get_res(self, model, ratio):
|
|
width, height = sana_res[model][ratio]
|
|
return (width,height)
|
|
|
|
|
|
class SanaResolutionCond:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"cond": ("CONDITIONING", ),
|
|
"width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
|
|
"height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
RETURN_NAMES = ("cond",)
|
|
FUNCTION = "add_cond"
|
|
CATEGORY = "ExtraModels/Sana"
|
|
TITLE = "Sana Resolution Conditioning"
|
|
|
|
def add_cond(self, cond, width, height):
|
|
for c in range(len(cond)):
|
|
cond[c][1].update({
|
|
"img_hw": [[height, width]],
|
|
"aspect_ratio": [[height/width]],
|
|
})
|
|
return (cond,)
|
|
|
|
|
|
class SanaTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"text": ("STRING", {"multiline": True}),
|
|
"preset_styles": (STYLE_NAMES,),
|
|
"GEMMA": ("GEMMA",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
FUNCTION = "encode"
|
|
CATEGORY = "ExtraModels/Sana"
|
|
TITLE = "Sana Text Encode"
|
|
|
|
def encode(self, text, preset_styles, GEMMA=None):
|
|
tokenizer = GEMMA["tokenizer"]
|
|
text_encoder = GEMMA["text_encoder"]
|
|
|
|
# 应用预设样式 - 只使用正面提示词部分
|
|
text, _ = apply_style(preset_styles, text)
|
|
|
|
with torch.no_grad():
|
|
# 处理正面提示词
|
|
chi_prompt = "\n".join(preset_te_prompt)
|
|
full_prompt = chi_prompt + text
|
|
num_chi_tokens = len(tokenizer.encode(chi_prompt))
|
|
max_length = num_chi_tokens + 300 - 2 # 减去[bos]和[_]标记
|
|
|
|
tokens = tokenizer(
|
|
[full_prompt],
|
|
max_length=max_length,
|
|
padding="max_length",
|
|
truncation=True,
|
|
return_tensors="pt"
|
|
).to(text_encoder.device)
|
|
|
|
select_idx = [0] + list(range(-300 + 1, 0))
|
|
embs = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx]
|
|
emb_masks = tokens.attention_mask[:, select_idx]
|
|
# 利用emb_masks将有效的embs选出来，其他置零
|
|
embs = embs * emb_masks.unsqueeze(-1)
|
|
|
|
return ([[embs, {}]], )
|
|
|
|
# 需要添加style相关的辅助函数
|
|
style_list = [
|
|
{
|
|
"name": "(No style)",
|
|
"prompt": "{prompt}",
|
|
"negative_prompt": "",
|
|
},
|
|
{
|
|
"name": "Cinematic",
|
|
"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, "
|
|
"cinemascope, moody, epic, gorgeous, film grain, grainy",
|
|
"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
|
|
},
|
|
{
|
|
"name": "Photographic",
|
|
"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
|
|
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
|
|
},
|
|
{
|
|
"name": "Anime",
|
|
"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",
|
|
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
|
|
},
|
|
{
|
|
"name": "Manga",
|
|
"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
|
|
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
|
|
},
|
|
{
|
|
"name": "Digital Art",
|
|
"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
|
|
"negative_prompt": "photo, photorealistic, realism, ugly",
|
|
},
|
|
{
|
|
"name": "Pixel art",
|
|
"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
|
|
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
|
|
},
|
|
{
|
|
"name": "Fantasy art",
|
|
"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, "
|
|
"majestic, magical, fantasy art, cover art, dreamy",
|
|
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, "
|
|
"glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, "
|
|
"disfigured, sloppy, duplicate, mutated, black and white",
|
|
},
|
|
{
|
|
"name": "Neonpunk",
|
|
"prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, "
|
|
"detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, "
|
|
"ultra detailed, intricate, professional",
|
|
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
|
|
},
|
|
{
|
|
"name": "3D Model",
|
|
"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
|
|
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
|
|
},
|
|
]
|
|
|
|
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
|
|
STYLE_NAMES = list(styles.keys())
|
|
|
|
def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
|
|
p, n = styles.get(style_name, styles[style_name])
|
|
if not negative:
|
|
negative = ""
|
|
return p.replace("{prompt}", positive), n + negative
|
|
|
|
preset_te_prompt = ['Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:', '- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.', '- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.', 'Here are examples of how to transform or refine prompts:', '- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.', '- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.', 'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:', 'User Prompt: ']
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"SanaCheckpointLoader" : SanaCheckpointLoader,
|
|
"SanaResolutionSelect" : SanaResolutionSelect,
|
|
"SanaTextEncode" : SanaTextEncode,
|
|
"SanaResolutionCond" : SanaResolutionCond,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"Sana Checkpoint Loader": "SanaCheckpointLoader",
|
|
"Sana Resolution Select": "SanaResolutionSelect",
|
|
"Sana Text Encoder": "SanaTextEncode",
|
|
"Sana Resolution Cond": "SanaResolutionCond",
|
|
}
|