nodes/mrm987-ComfyUI_Multi_Prompt_Generator
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mrm987
2025-12-28 04:53:09 +09:00
committed by GitHub
parent 6c53c0f28f
commit bca08a020b
2 changed files with 99 additions and 67 deletions
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README.md
+6
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@@ -55,6 +55,8 @@ crying, tears
| seed | INT | Seed |
| steps | INT | Sampling steps (default: 30) |
| cfg | FLOAT | CFG scale (default: 5.0) |
| sampler_name | COMBO | Sampler (euler_ancestral, etc.) |
| scheduler | COMBO | Scheduler (normal, karras, etc.) |
| enable_upscale | BOOL | Enable upscale (default: True) |
| save_prefix | STRING | Output folder name (default: "MultiPrompt") |
@@ -100,6 +102,7 @@ For each prompt:
- **size_alignment**: 64 is safest. Use 64 if you see white edges
- **LUT files**: Auto-detected from `comfyui-propost/LUTs/` or `models/luts/` folder
- **Preview**: Check 1st pass result, cancel with Cancel button if not satisfied
- **Metadata**: Workflow info saved in PNG, can drag back into ComfyUI
---
@@ -154,6 +157,8 @@ crying, tears
| seed | INT | 시드 |
| steps | INT | 샘플링 스텝 (기본: 30) |
| cfg | FLOAT | CFG 스케일 (기본: 5.0) |
| sampler_name | COMBO | 샘플러 (euler_ancestral 등) |
| scheduler | COMBO | 스케줄러 (normal, karras 등) |
| enable_upscale | BOOL | 업스케일 활성화 (기본: True) |
| save_prefix | STRING | 저장 폴더명 (기본: "MultiPrompt") |
@@ -199,6 +204,7 @@ output/[save_prefix]/03_sad_00001.png
- **size_alignment**: 64가 가장 안전. 흰 테두리 생기면 64 사용
- **LUT 파일**: `comfyui-propost/LUTs/` 또는 `models/luts/` 폴더의 .cube 파일 자동 인식
- **프리뷰**: 1차 결과 확인 후 마음에 안 들면 Cancel로 중단 가능
- **메타데이터**: 워크플로우 정보가 PNG에 저장되어 ComfyUI에 다시 드래그 가능
---
__init__.py
+93 -67
View File
@@ -93,67 +93,79 @@ def parse_cube_lut(lut_path):
if lut_size == 0 or len(lut_data) == 0:
return None, 0
return np.array(lut_data, dtype=np.float32), lut_size
# cube 파일은 R이 가장 빠르게 변함 -> Fortran order로 reshape
lut_array = np.array(lut_data, dtype=np.float32)
lut_3d = np.reshape(lut_array, (lut_size, lut_size, lut_size, 3), order='F')
return lut_3d, lut_size
def apply_lut(image, lut_data, lut_size, strength=1.0):
"""Apply 3D LUT to image"""
if lut_data is None:
def apply_lut(image, lut_3d, lut_size, strength=1.0):
"""Apply 3D LUT to image using trilinear interpolation"""
if lut_3d is None or strength <= 0:
return image
# image: [B, H, W, C] tensor, 0-1 range
img_np = image.cpu().numpy()
result = np.zeros_like(img_np)
img_np = image.cpu().to(torch.float32).numpy()
result = []
for b in range(img_np.shape[0]):
img = img_np[b]
h, w, c = img.shape
img = img_np[b][:, :, :3].copy()
img = np.clip(img, 0, 1)
# Reshape LUT
lut_3d = lut_data.reshape((lut_size, lut_size, lut_size, 3))
# 이미지를 LUT 인덱스로 스케일
h, w, _ = img.shape
img_flat = img.reshape(-1, 3)
# Scale image to LUT indices
img_scaled = img * (lut_size - 1)
# 스케일링
coords = img_flat * (lut_size - 1)
# Get integer indices and fractions for trilinear interpolation
idx_low = np.floor(img_scaled).astype(np.int32)
idx_high = np.ceil(img_scaled).astype(np.int32)
frac = img_scaled - idx_low
# 정수 인덱스와 소수 부분
coords_floor = np.floor(coords).astype(np.int32)
coords_ceil = np.minimum(coords_floor + 1, lut_size - 1)
frac = coords - coords_floor
# Clamp indices
idx_low = np.clip(idx_low, 0, lut_size - 1)
idx_high = np.clip(idx_high, 0, lut_size - 1)
# 인덱스 클램프
coords_floor = np.clip(coords_floor, 0, lut_size - 1)
# Trilinear interpolation
r_low, g_low, b_low = idx_low[:,:,0], idx_low[:,:,1], idx_low[:,:,2]
r_high, g_high, b_high = idx_high[:,:,0], idx_high[:,:,1], idx_high[:,:,2]
r_frac, g_frac, b_frac = frac[:,:,0:1], frac[:,:,1:2], frac[:,:,2:3]
r0, g0, b0 = coords_floor[:, 0], coords_floor[:, 1], coords_floor[:, 2]
r1, g1, b1 = coords_ceil[:, 0], coords_ceil[:, 1], coords_ceil[:, 2]
fr, fg, fb = frac[:, 0], frac[:, 1], frac[:, 2]
# 8 corners of the cube
c000 = lut_3d[r_low, g_low, b_low]
c001 = lut_3d[r_low, g_low, b_high]
c010 = lut_3d[r_low, g_high, b_low]
c011 = lut_3d[r_low, g_high, b_high]
c100 = lut_3d[r_high, g_low, b_low]
c101 = lut_3d[r_high, g_low, b_high]
c110 = lut_3d[r_high, g_high, b_low]
c111 = lut_3d[r_high, g_high, b_high]
# 8개 꼭짓점 값
c000 = lut_3d[r0, g0, b0]
c001 = lut_3d[r0, g0, b1]
c010 = lut_3d[r0, g1, b0]
c011 = lut_3d[r0, g1, b1]
c100 = lut_3d[r1, g0, b0]
c101 = lut_3d[r1, g0, b1]
c110 = lut_3d[r1, g1, b0]
c111 = lut_3d[r1, g1, b1]
# Interpolate
c00 = c000 * (1 - r_frac) + c100 * r_frac
c01 = c001 * (1 - r_frac) + c101 * r_frac
c10 = c010 * (1 - r_frac) + c110 * r_frac
c11 = c011 * (1 - r_frac) + c111 * r_frac
# 8개 가중치 (colour-science 방식)
w000 = ((1 - fr) * (1 - fg) * (1 - fb))[:, None]
w001 = ((1 - fr) * (1 - fg) * fb)[:, None]
w010 = ((1 - fr) * fg * (1 - fb))[:, None]
w011 = ((1 - fr) * fg * fb)[:, None]
w100 = (fr * (1 - fg) * (1 - fb))[:, None]
w101 = (fr * (1 - fg) * fb)[:, None]
w110 = (fr * fg * (1 - fb))[:, None]
w111 = (fr * fg * fb)[:, None]
c0 = c00 * (1 - g_frac) + c10 * g_frac
c1 = c01 * (1 - g_frac) + c11 * g_frac
# trilinear interpolation
lut_result = (c000 * w000 + c001 * w001 + c010 * w010 + c011 * w011 +
c100 * w100 + c101 * w101 + c110 * w110 + c111 * w111)
lut_result = c0 * (1 - b_frac) + c1 * b_frac
lut_result = lut_result.reshape(h, w, 3)
# Blend with original based on strength
result[b] = img * (1 - strength) + lut_result * strength
# strength 블렌딩
if strength < 1.0:
lut_result = img * (1 - strength) + lut_result * strength
lut_result = np.clip(lut_result, 0, 1).astype(np.float32)
result.append(lut_result)
result = np.clip(result, 0, 1)
result = np.stack(result, axis=0)
return torch.from_numpy(result).to(image.device)
@@ -180,6 +192,8 @@ class MultiPromptGenerator:
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 30, "min": 1, "max": 200}),
"cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.1}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"enable_upscale": ("BOOLEAN", {"default": True}),
"save_prefix": ("STRING", {"default": "MultiPrompt"}),
},
@@ -195,6 +209,8 @@ class MultiPromptGenerator:
},
"hidden": {
"unique_id": "UNIQUE_ID",
"prompt": "PROMPT",
"extra_pnginfo": "EXTRA_PNGINFO",
}
}
@@ -211,7 +227,7 @@ class MultiPromptGenerator:
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return [[cond, {"pooled_output": pooled}]]
def do_sample(self, model, seed, steps, cfg, positive, negative, latent_image, denoise=1.0):
def do_sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
"""샘플링 실행"""
device = comfy.model_management.get_torch_device()
latent = latent_image.clone().to(device)
@@ -223,8 +239,8 @@ class MultiPromptGenerator:
noise,
steps,
cfg,
"euler_ancestral",
"normal",
sampler_name,
scheduler,
positive,
negative,
latent,
@@ -312,8 +328,11 @@ class MultiPromptGenerator:
return s
def save_image(self, image, prefix, filename, counter):
"""이미지 저장"""
def save_image(self, image, prefix, filename, counter, prompt=None, extra_pnginfo=None):
"""이미지 저장 (메타데이터 포함)"""
import json
from PIL.PngImagePlugin import PngInfo
full_output_folder = os.path.join(self.output_dir, prefix)
os.makedirs(full_output_folder, exist_ok=True)
@@ -323,23 +342,34 @@ class MultiPromptGenerator:
img = image[0].cpu().numpy()
img = (img * 255).astype(np.uint8)
pil_img = Image.fromarray(img)
pil_img.save(filepath, compress_level=4)
# 메타데이터 추가
metadata = PngInfo()
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for key, value in extra_pnginfo.items():
metadata.add_text(key, json.dumps(value))
pil_img.save(filepath, pnginfo=metadata, compress_level=4)
print(f"[MultiPrompt] Saved: {filepath}")
return filepath
def send_preview(self, image, unique_id, idx):
def send_preview(self, image, unique_id, idx, stage=""):
"""생성 중 프리뷰를 UI로 전송"""
import time
temp_dir = folder_paths.get_temp_directory()
# temp 폴더에 프리뷰 저장
preview_filename = f"multiprompt_preview_{unique_id}_{idx}.png"
# temp 폴더에 프리뷰 저장 (타임스탬프로 캐시 우회)
timestamp = int(time.time() * 1000)
preview_filename = f"multiprompt_preview_{unique_id}_{idx}_{stage}_{timestamp}.png"
preview_path = os.path.join(temp_dir, preview_filename)
img = image[0].cpu().numpy()
img = (img * 255).astype(np.uint8)
pil_img = Image.fromarray(img)
pil_img.save(preview_path, compress_level=4)
pil_img.save(preview_path, compress_level=1)
# UI로 프리뷰 전송
PromptServer.instance.send_sync("executed", {
@@ -354,10 +384,11 @@ class MultiPromptGenerator:
})
def generate(self, model, clip, vae, latent, upscale_model, base_prompt, negative_prompt, prompt_list,
seed, steps, cfg, enable_upscale, save_prefix,
seed, steps, cfg, sampler_name, scheduler, enable_upscale, save_prefix,
downscale_ratio=0.7, upscale_steps=15,
upscale_cfg=5.0, upscale_denoise=0.5, size_alignment="64",
lut_name="None", lut_strength=0.3, enable_preview=True, unique_id=None):
lut_name="None", lut_strength=0.3, enable_preview=True,
unique_id=None, prompt=None, extra_pnginfo=None):
# prompt_list 파싱
lines = [line.strip() for line in prompt_list.strip().split("\n") if line.strip()]
@@ -396,7 +427,7 @@ class MultiPromptGenerator:
# 1단계: 기본 샘플링
samples = self.do_sample(
model, seed, steps, cfg,
model, seed, steps, cfg, sampler_name, scheduler,
positive_cond, negative_cond,
latent_samples, denoise=1.0
)
@@ -404,7 +435,7 @@ class MultiPromptGenerator:
# 1차 결과 프리뷰 전송
if enable_preview and unique_id is not None:
self.send_preview(image, unique_id, idx)
self.send_preview(image, unique_id, idx, "1st")
# 2단계: 업스케일
if enable_upscale and upscale_model is not None:
@@ -418,7 +449,7 @@ class MultiPromptGenerator:
latent_up = self.encode_vae(vae, resized)
samples_up = self.do_sample(
model, seed, upscale_steps, upscale_cfg,
model, seed, upscale_steps, upscale_cfg, sampler_name, scheduler,
positive_cond, negative_cond,
latent_up, denoise=upscale_denoise
)
@@ -430,21 +461,16 @@ class MultiPromptGenerator:
# 최종 결과 프리뷰 교체
if enable_preview and unique_id is not None:
self.send_preview(image, unique_id, idx)
self.send_preview(image, unique_id, idx, "final")
# 저장
self.save_image(image, save_prefix, filename, counter)
self.save_image(image, save_prefix, filename, counter, prompt, extra_pnginfo)
all_images.append(image)
print(f"[MultiPrompt] Complete! Generated {len(all_images)} images.")
# 마지막 프리뷰 정보 반환 (UI에 유지)
ui_images = []
if enable_preview and unique_id is not None and len(lines) > 0:
last_preview = f"multiprompt_preview_{unique_id}_{len(lines)-1}.png"
ui_images = [{"filename": last_preview, "subfolder": "", "type": "temp"}]
return {"ui": {"images": ui_images}, "result": (all_images,)}
# 마지막 프리뷰는 이미 send_preview로 전송됨
return {"ui": {"images": []}, "result": (all_images,)}
def _get_next_counter(self, prefix):
"""다음 카운터 번호"""