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wailovet
2024-04-30 12:41:16 +08:00
parent 8bc89d2118
commit f0122b52bf
5 changed files with 268 additions and 150 deletions
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__init__.py
+16 -2
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@@ -8,6 +8,7 @@ import comfy.utils
import shutil
import comfy.samplers
AUTHOR_NAME = u"[MinusZone]"
CATEGORY_NAME = f"{AUTHOR_NAME} Utils"
@@ -32,7 +33,6 @@ import mz_llava
class MZ_LLamaCPPOptions:
@classmethod
def INPUT_TYPES(s):
@@ -59,6 +59,9 @@ class MZ_LLamaCPPOptions:
RETURN_TYPES = ("LLamaCPPOptions",)
RETURN_NAMES = ("llama_cpp_options",)
FUNCTION = "create"
CATEGORY = CATEGORY_NAME
def create(self, **kwargs):
@@ -125,6 +128,7 @@ class MZ_LLama3CLIPTextEncode:
RETURN_TYPES = ("STRING", "CONDITIONING",)
RETURN_NAMES = ("text", "conditioning",)
FUNCTION = "encode"
CATEGORY = CATEGORY_NAME
def encode(self, **kwargs):
@@ -179,6 +183,7 @@ class MZ_Phi3CLIPTextEncode:
RETURN_TYPES = ("STRING", "CONDITIONING",)
RETURN_NAMES = ("text", "conditioning",)
FUNCTION = "encode"
CATEGORY = CATEGORY_NAME
def encode(self, **kwargs):
@@ -221,6 +226,7 @@ class MZ_BaseLLamaCPPCLIPTextEncode:
return result
RETURN_TYPES = ("STRING", "CONDITIONING",)
RETURN_NAMES = ("text", "conditioning",)
FUNCTION = "encode"
CATEGORY = CATEGORY_NAME
def encode(self, **kwargs):
@@ -279,6 +285,7 @@ class MZ_LLavaImageInterrogator:
"llama_cpp_options": ("LLamaCPPOptions", ),
}}
RETURN_TYPES = ("STRING", "CONDITIONING",)
RETURN_NAMES = ("text", "conditioning",)
FUNCTION = "interrogate"
CATEGORY = CATEGORY_NAME
def interrogate(self, **kwargs):
@@ -317,6 +324,7 @@ class MZ_BaseLLavaImageInterrogator:
"llama_cpp_options": ("LLamaCPPOptions", ),
}}
RETURN_TYPES = ("STRING", "CONDITIONING",)
RETURN_NAMES = ("text", "conditioning",)
FUNCTION = "interrogate"
CATEGORY = CATEGORY_NAME
def interrogate(self, **kwargs):
@@ -352,6 +360,7 @@ class MZ_LLamaCPPInterrogator:
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
FUNCTION = "simple_interrogator"
CATEGORY = CATEGORY_NAME
def simple_interrogator(self, model_file, prompt, use_system=True, system="You are a helpful assistant.", n_gpu_layers=-1):
@@ -367,4 +376,9 @@ class MZ_LLamaCPPInterrogator:
# NODE_CLASS_MAPPINGS["MZ_LLamaCPPInterrogator"] = MZ_LLamaCPPInterrogator
# NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPInterrogator"] = f"{AUTHOR_NAME} - LLamaCPP simple interrogator"
import mz_gen_translate
mz_gen_translate.gen_translate(NODE_DISPLAY_NAME_MAPPINGS, NODE_CLASS_MAPPINGS)
mz_gen_translate.py
+81
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@@ -0,0 +1,81 @@
import os
import json
ZH_Replace_Map = {
"llama_cpp_model": "llama.cpp模型",
"mmproj_model": "mmproj模型",
"resolution": "分辨率",
"sd_format": "SD格式化",
"image": "图像",
"download_source": "下载源",
"prompt_version": "提示词版本",
"style_presets": "风格预设",
"keep_device": "模型常驻显存",
"llama_cpp_options": "llama.cpp可选配置",
"Options": "可选配置",
"LLamaCPP": "llama.cpp",
"CLIPTextEncode": "CLIP文本编码器",
"clip": "CLIP",
"conditioning": "条件",
"ImageInterrogator": "图像反推",
}
def gen_translate(NODE_DISPLAY_NAME_MAPPINGS={}, NODE_CLASS_MAPPINGS={}):
translation_dir = os.path.join(os.path.dirname(
os.path.dirname(__file__)), "AIGODLIKE-COMFYUI-TRANSLATION", "zh-CN", "Nodes")
translation_config = os.path.join(
translation_dir, "ComfyUI_MinusZone.translate.json")
if os.path.exists(translation_dir):
if os.path.exists(translation_config):
translate_config = "{}"
with open(translation_config, "r") as f:
translate_config = f.read()
nodes = json.loads(translate_config)
for key in NODE_DISPLAY_NAME_MAPPINGS:
if key not in nodes:
nodes[key] = {}
title = NODE_DISPLAY_NAME_MAPPINGS[key]
for k, v in ZH_Replace_Map.items():
title = title.replace(k, v)
nodes[key]["title"] = title
if key in NODE_CLASS_MAPPINGS:
node = NODE_CLASS_MAPPINGS[key]
node_INPUT_TYPES = node.INPUT_TYPES()
node_INPUT_TYPES_required = node_INPUT_TYPES.get(
"required", {})
nodes[key]["widgets"] = {}
for widget_name, _ in node_INPUT_TYPES_required.items():
widget_name_zh = widget_name
for k, v in ZH_Replace_Map.items():
widget_name_zh = widget_name_zh.replace(k, v)
nodes[key]["widgets"][widget_name] = widget_name_zh
node_INPUT_TYPES_optional = node_INPUT_TYPES.get(
"optional", {})
nodes[key]["inputs"] = {}
for widget_name, _ in node_INPUT_TYPES_optional.items():
widget_name_zh = widget_name
for k, v in ZH_Replace_Map.items():
widget_name_zh = widget_name_zh.replace(k, v)
nodes[key]["inputs"][widget_name] = widget_name_zh
try:
node_RETURN_NAMES = node.RETURN_NAMES
nodes[key]["outputs"] = {}
for widget_name in node_RETURN_NAMES:
widget_name_zh = widget_name
for k, v in ZH_Replace_Map.items():
widget_name_zh = widget_name_zh.replace(k, v)
nodes[key]["outputs"][widget_name] = widget_name_zh
except:
pass
with open(translation_config, "w") as f:
f.write(json.dumps(nodes, indent=4, ensure_ascii=False))
mz_llama_cpp.py
+1
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@@ -60,6 +60,7 @@ def check_llama_cpp_requirements():
cuda_version = "cu" + torch.version.cuda.replace(".", "")
if cuda_version not in ["cu121", "cu122", "cu123"]:
cuda_version = "cu121"
print(f"Warning: The current version of cuda is not supported. (警告: 当前cuda版本不支持) {torch.version.cuda} (默认使用cu121)")
else:
cuda_version = "cpu"
mz_llava.py
+5
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@@ -9,6 +9,8 @@ import mz_prompts
LLava_models = [
"llava-1.6-mistral-7b-gguf/llava-1.6-mistral-7b.Q5_K_M.gguf",
"llava-v1.6-vicuna-13b-gguf/llava-v1.6-vicuna-13b.Q5_K_M.gguf",
"ggml_llava-v1.5-7b/ggml-model-q4_k.gguf",
"ggml_llava-v1.5-7b/ggml-model-q5_k.gguf",
"ggml_llava-v1.5-7b/ggml-model-f16.gguf",
@@ -18,12 +20,15 @@ LLava_models = [
]
LLava_mmproj_models = [
"llava-1.6-mistral-7b-gguf/mmproj-model-f16.gguf",
"llava-v1.6-vicuna-13b-gguf/mmproj-model-f16.gguf",
"ggml_llava-v1.5-7b/mmproj-model-f16.gguf",
"ggml_bakllava-1/mmproj-model-f16.gguf",
]
huggingface_models_map = {
"llava-v1.6-vicuna-13b-gguf": "cjpais",
"llava-1.6-mistral-7b-gguf": "cjpais",
"ggml_llava-v1.5-7b": "mys",
"ggml_llava-v1.5-13b": "mys",
mz_prompt_utils.py
+165 -148
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@@ -1,16 +1,16 @@
import os
import os
import shutil
import subprocess
import sys
import threading
import time
import time
import numpy as np
import folder_paths
import base64
from PIL import Image,ImageFilter
from PIL import Image, ImageFilter
import io
import torch
import torch
import re
import hashlib
import cv2
@@ -19,25 +19,26 @@ temp_directory = folder_paths.get_temp_directory()
from tqdm import tqdm
import requests
import comfy.utils
CACHE_POOL = {}
class Utils:
def Md5(str):
return hashlib.md5(str.encode('utf-8')).hexdigest()
def check_frames_path(frames_path):
if frames_path == "" or frames_path.startswith(".") or frames_path.startswith("/") or frames_path.endswith("/") or frames_path.endswith("\\"):
return "frames_path不能为空"
frames_path = os.path.join(
folder_paths.get_output_directory(), frames_path)
if frames_path == folder_paths.get_output_directory():
return "frames_path不能为output目录"
return ""
def base64_to_pil_image(base64_str):
@@ -53,7 +54,6 @@ class Utils:
base64_str = base64.b64decode(base64_str)
return Image.open(io.BytesIO(base64_str))
def pil_image_to_base64(pil_image):
buffered = io.BytesIO()
pil_image.save(buffered, format="PNG")
@@ -61,7 +61,6 @@ class Utils:
img_str = str(img_str, encoding="utf-8")
return f"data:image/png;base64,{img_str}"
def listdir_png(path):
try:
files = os.listdir(path)
@@ -74,33 +73,30 @@ class Utils:
return files
except Exception as e:
return []
def tensor2pil(image):
return Image.fromarray(np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
# Convert PIL to Tensor
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)[0]
def pil2cv(image):
return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
def cv2pil(image):
return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
def list_tensor2tensor(data):
result_tensor = torch.stack(data)
return result_tensor
def loadImage(path):
img = Image.open(path)
img = img.convert("RGB")
return img
def vae_encode_crop_pixels(pixels):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
@@ -112,61 +108,58 @@ class Utils:
def native_vae_encode(vae, image):
pixels = Utils.vae_encode_crop_pixels(image)
t = vae.encode(pixels[:,:,:,:3])
return {"samples":t}
t = vae.encode(pixels[:, :, :, :3])
return {"samples": t}
def native_vae_encode_for_inpaint(vae, pixels, mask):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
mask = torch.nn.functional.interpolate(mask.reshape(
(-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
pixels = pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset]
# grow mask by a few pixels to keep things seamless in latent space
#grow mask by a few pixels to keep things seamless in latent space
mask_erosion = mask
m = (1.0 - mask.round()).squeeze(1)
for i in range(3):
pixels[:,:,:,i] -= 0.5
pixels[:,:,:,i] *= m
pixels[:,:,:,i] += 0.5
for i in range(3):
pixels[:, :, :, i] -= 0.5
pixels[:, :, :, i] *= m
pixels[:, :, :, i] += 0.5
t = vae.encode(pixels)
return {"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}
return {"samples": t, "noise_mask": (mask_erosion[:, :, :x, :y].round())}
def native_vae_decode(vae, samples):
return vae.decode(samples["samples"])
def native_clip_text_encode(clip,text):
def native_clip_text_encode(clip, text):
tokens = clip.tokenize(text)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return [[cond, {"pooled_output": pooled}]]
def cache_get(key):
def cache_get(key):
return CACHE_POOL.get(key, None)
def cache_set(key, value):
def cache_set(key, value):
global CACHE_POOL
CACHE_POOL[key] = value
return True
def get_models_path():
models_path = os.path.join(folder_paths.models_dir, "minus_zone_models")
models_path = os.path.join(
folder_paths.models_dir, "minus_zone_models")
os.makedirs(models_path, exist_ok=True)
return models_path
def translate_text(text, from_code, to_code):
def translate_text(text, from_code, to_code):
try:
import argostranslate
from argostranslate import translate
@@ -177,28 +170,30 @@ class Utils:
import argostranslate
from argostranslate import translate
try:
translation = translate.get_translation_from_codes(from_code, to_code)
try:
translation = translate.get_translation_from_codes(
from_code, to_code)
if translation is None:
raise Exception("Translation not found")
except Exception as e:
print(e)
print(e)
argostranslate.package.update_package_index()
available_packages = argostranslate.package.get_available_packages()
package_to_install = next(
filter(
lambda x: (x.from_code == from_code and x.to_code == to_code), available_packages,
)
lambda x: (x.from_code == from_code and x.to_code ==
to_code), available_packages,
)
)
download_path = package_to_install.download()
print("package_to_install.download():",download_path)
print("package_to_install.download():", download_path)
argostranslate.package.install_from_path(download_path)
translation = translate.get_translation_from_codes(from_code, to_code)
translation = translate.get_translation_from_codes(
from_code, to_code)
if translation is None:
return text
return text
# Translate
translatedText = translation.translate(
@@ -206,16 +201,12 @@ class Utils:
return translatedText
def zh2en(text):
return Utils.translate_text(text, "zh", "en")
def en2zh(text):
return Utils.translate_text(text, "en", "zh")
def prompt_zh_to_en(prompt):
prompt = prompt.replace("，", ",")
prompt = prompt.replace("。", ",")
@@ -229,77 +220,78 @@ class Utils:
if tags[i][0].isupper():
tags[i] = tags[i].lower().replace(".", "")
return ",".join(tags)
def mask_resize(mask, width, height):
mask = mask.unsqueeze(0).unsqueeze(0)
mask = torch.nn.functional.interpolate(mask, size=(height, width), mode="bilinear")
mask = torch.nn.functional.interpolate(
mask, size=(height, width), mode="bilinear")
mask = mask.squeeze(0).squeeze(0)
return mask
def mask_threshold(interested_mask):
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
ret,thresh1 = cv2.threshold(mask_image_cv2,127,255,cv2.THRESH_BINARY)
ret, thresh1 = cv2.threshold(
mask_image_cv2, 127, 255, cv2.THRESH_BINARY)
thresh1 = Utils.cv2pil(thresh1)
thresh1 = np.array(thresh1)
thresh1 = thresh1[:,:,0]
thresh1 = thresh1[:, :, 0]
return Utils.pil2tensor(thresh1)
def mask_erode(interested_mask, value):
value = int(value)
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
kernel = np.ones((5,5),np.uint8)
erosion = cv2.erode(mask_image_cv2,kernel,iterations = value)
mask_image_cv2 = Utils.pil2cv(mask_image)
kernel = np.ones((5, 5), np.uint8)
erosion = cv2.erode(mask_image_cv2, kernel, iterations=value)
erosion = Utils.cv2pil(erosion)
erosion = np.array(erosion)
erosion = erosion[:,:,0]
return Utils.pil2tensor(erosion)
erosion = erosion[:, :, 0]
return Utils.pil2tensor(erosion)
def mask_dilate(interested_mask, value):
value = int(value)
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
kernel = np.ones((5,5),np.uint8)
dilation = cv2.dilate(mask_image_cv2,kernel,iterations = value)
mask_image_cv2 = Utils.pil2cv(mask_image)
kernel = np.ones((5, 5), np.uint8)
dilation = cv2.dilate(mask_image_cv2, kernel, iterations=value)
dilation = Utils.cv2pil(dilation)
dilation = np.array(dilation)
dilation = dilation[:,:,0]
dilation = dilation[:, :, 0]
return Utils.pil2tensor(dilation)
def mask_edge_opt(interested_mask, edge_feathering):
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
# 高斯模糊
dilation2 = Utils.cv2pil(mask_image_cv2)
dilation2 = mask_image.filter(ImageFilter.GaussianBlur(edge_feathering))
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
# 高斯模糊
dilation2 = Utils.cv2pil(mask_image_cv2)
dilation2 = mask_image.filter(
ImageFilter.GaussianBlur(edge_feathering))
# mask_image dilation2 图片蒙版叠加
dilation2 = Utils.pil2cv(dilation2)
# dilation2[mask_image_cv2 < 127] = 0
dilation2 = Utils.cv2pil(dilation2)
dilation2 = Utils.cv2pil(dilation2)
# to RGB
dilation2 = np.array(dilation2)
dilation2 = dilation2[:,:,0]
dilation2 = dilation2[:, :, 0]
return Utils.pil2tensor(dilation2)
def mask_composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
def mask_composite(destination, source, x, y, mask=None, multiplier=8, resize_source=False):
source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
source = torch.nn.functional.interpolate(source, size=(
destination.shape[2], destination.shape[3]), mode="bilinear")
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
x = max(-source.shape[3] * multiplier,
min(x, destination.shape[3] * multiplier))
y = max(-source.shape[2] * multiplier,
min(y, destination.shape[2] * multiplier))
left, top = (x // multiplier, y // multiplier)
right, bottom = (left + source.shape[3], top + source.shape[2],)
@@ -308,44 +300,46 @@ class Utils:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = torch.nn.functional.interpolate(mask.reshape(
(-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
visible_width, visible_height = (
destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
destination_portion = inverse_mask * \
destination[:, :, top:bottom, left:right]
destination[:, :, top:bottom, left:right] = source_portion + destination_portion
destination[:, :, top:bottom,
left:right] = source_portion + destination_portion
return destination
def latent_upscale_by(samples, scale_by):
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, "nearest-exact", "disabled")
s["samples"] = comfy.utils.common_upscale(
samples["samples"], width, height, "nearest-exact", "disabled")
return s
def resize_by(image, percent):
# 判断类型是否为PIL
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
width, height = image.size
new_width = int(width * percent)
new_height = int(height * percent)
return image.resize((new_width, new_height), Image.LANCZOS)
def resize_max(im, dst_w, dst_h):
def resize_max(im, dst_w, dst_h):
src_w, src_h = im.size
if src_h < src_w:
@@ -359,22 +353,20 @@ class Utils:
newWidth = newWidth // 8 * 8
return im.resize((newWidth, newHeight), Image.Resampling.LANCZOS)
def get_device():
return comfy.model_management.get_torch_device()
def download_file(url, filepath, threads=8):
def download_file(url, filepath, threads=8, retries=6):
get_size_tmp = requests.get(url, stream=True)
total_size = int(get_size_tmp.headers.get("content-length", 0))
print(f"Downloading {url} to {filepath} with size {total_size} bytes")
base_filename = os.path.basename(filepath)
cache_dir = os.path.join(os.path.dirname(filepath), f"{base_filename}.t_{threads}_cache")
cache_dir = os.path.join(os.path.dirname(
filepath), f"{base_filename}.t_{threads}_cache")
os.makedirs(cache_dir, exist_ok=True)
def get_total_existing_size():
@@ -382,21 +374,18 @@ class Utils:
existing_size = 0
for f in fs:
if f.startswith("block_"):
existing_size += os.path.getsize(os.path.join(cache_dir, f))
existing_size += os.path.getsize(
os.path.join(cache_dir, f))
return existing_size
total_existing_size = get_total_existing_size()
if total_size != 0 and total_existing_size != total_size:
with tqdm(total=total_size, initial=total_existing_size, unit="B", unit_scale=True) as progress_bar:
all_threads = []
for i in range(threads):
for i in range(threads):
cache_filepath = os.path.join(cache_dir, f"block_{i}")
start = total_size // threads * i
@@ -412,7 +401,6 @@ class Utils:
else:
existing_size = 0
headers = {"Range": f"bytes={start + existing_size}-{end}"}
if end == total_size:
headers = {"Range": f"bytes={start + existing_size}-"}
@@ -422,7 +410,7 @@ class Utils:
# Streaming, so we can iterate over the response.
response = requests.get(url, stream=True, headers=headers)
def download_file_thread(response, cache_filepath):
block_size = 1024
if end - (start + existing_size) < block_size:
@@ -434,15 +422,15 @@ class Utils:
len(data)
)
t = threading.Thread(target=download_file_thread, args=(response, cache_filepath))
t = threading.Thread(
target=download_file_thread, args=(response, cache_filepath))
all_threads.append(t)
t.start()
for t in all_threads:
t.join()
if total_size != 0 and get_total_existing_size() > total_size:
# 文件下载失败
@@ -450,12 +438,19 @@ class Utils:
raise RuntimeError("Download failed, file is incomplete")
if total_size != 0 and total_size != get_total_existing_size():
# 文件损坏
raise RuntimeError(f"Download failed: {total_size} != {get_total_existing_size()}")
if retries > 0:
retries -= 1
print(
f"Download failed: {total_size} != {get_total_existing_size()}, retrying... {retries} retries left")
return Utils.download_file(url, filepath, threads, retries)
# 文件损坏
raise RuntimeError(
f"Download failed: {total_size} != {get_total_existing_size()}")
if os.path.exists(filepath):
shutil.move(filepath, filepath + ".old." + time.strftime("%Y%m%d%H%M%S"))
shutil.move(filepath, filepath + ".old." +
time.strftime("%Y%m%d%H%M%S"))
# merge the files
with open(filepath, "wb") as f:
@@ -463,16 +458,16 @@ class Utils:
cache_filepath = os.path.join(cache_dir, f"block_{i}")
with open(cache_filepath, "rb") as cf:
f.write(cf.read())
shutil.rmtree(cache_dir)
return filepath
def hf_download_model(url, only_get_path=False):
if not url.startswith("https://"):
raise ValueError("URL must start with https://")
if url.startswith("https://huggingface.co/") or url.startswith("https://hf-mirror.com/"):
base_model_path = os.path.abspath(os.path.join(Utils.get_models_path(), "transformers_models"))
base_model_path = os.path.abspath(os.path.join(
Utils.get_models_path(), "transformers_models"))
# https://huggingface.co/FaradayDotDev/llama-3-8b-Instruct-GGUF/resolve/main/llama-3-8b-Instruct.Q2_K.gguf?download=true
texts = url.split("?")[0].split("/")
file_name = texts[-1]
@@ -483,10 +478,12 @@ class Utils:
if os.path.exists(save_path) is False:
if only_get_path:
return None
os.makedirs(os.path.join(base_model_path, zone_path), exist_ok=True)
os.makedirs(os.path.join(
base_model_path, zone_path), exist_ok=True)
Utils.download_file(url, save_path)
Utils.print_log(f"File {save_path} => {os.path.getsize(save_path)} ")
Utils.print_log(
f"File {save_path} => {os.path.getsize(save_path)} ")
# 获取大小
if os.path.getsize(save_path) == 0:
@@ -495,17 +492,19 @@ class Utils:
os.remove(save_path)
raise ValueError(f"Download failed: {url}")
return save_path
else:
else:
texts = url.split("?")[0].split("/")
host = texts[2].replace(".", "_")
base_model_path = os.path.abspath(os.path.join(Utils.get_models_path(), f"{host}_models"))
base_model_path = os.path.abspath(os.path.join(
Utils.get_models_path(), f"{host}_models"))
file_name = texts[-1]
file_name_no_ext = os.path.splitext(file_name)[0]
file_ext = os.path.splitext(file_name)[1]
md5_hash = Utils.Md5(url)
save_path = os.path.join(base_model_path, f"{file_name_no_ext}.{md5_hash}{file_ext}")
save_path = os.path.join(
base_model_path, f"{file_name_no_ext}.{md5_hash}{file_ext}")
if os.path.exists(save_path) is False:
if only_get_path:
@@ -515,18 +514,16 @@ class Utils:
return save_path
def print_log(*args):
if os.environ.get("MZ_DEV", None) is not None:
print(*args)
def modelscope_download_model(model_type, model_name, only_get_path=False):
if model_type not in modelscope_models_map:
if only_get_path:
return None
raise ValueError(f"模型类型 {model_type} 不支持")
if model_name not in modelscope_models_map[model_type]:
if only_get_path:
return None
@@ -538,15 +535,17 @@ class Utils:
model_info = modelscope_models_map[model_type][model_name]
url = model_info["url"]
output = model_info["output"]
save_path = os.path.abspath(os.path.join(Utils.get_models_path(), output))
save_path = os.path.abspath(
os.path.join(Utils.get_models_path(), output))
if not os.path.exists(save_path):
if only_get_path:
return None
save_path = Utils.download_file(url, save_path)
return save_path
modelscope_models_map = {
"llama3":{
"llama3": {
"Meta-Llama-3-8B-Instruct.Q4_K_M.gguf": {
"url": "https://modelscope.cn/api/v1/models/LLM-Research/Meta-Llama-3-8B-Instruct-GGUF/repo?Revision=master&FilePath=Meta-Llama-3-8B-Instruct-Q4_K_M.gguf",
"output": "modelscope_models/Meta-Llama-3-8B-Instruct-GGUF/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf",
@@ -558,35 +557,53 @@ modelscope_models_map = {
"SHA256": "1ea28c7c5f2a1ada991c40940a59bd42668939feeafb125d0764c38ae815b2a3",
},
},
"llava":{
"ggml_bakllava-1/ggml-model-q5_k.gguf":{
"llava": {
"ggml_bakllava-1/ggml-model-q5_k.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=ggml_bakllava-1%2Fggml-model-q5_k.gguf",
"output": "modelscope_models/ggml_bakllava-1/ggml-model-q5_k.gguf",
"SHA256": "c93de1376be9b6977cc94d252a3d165d6059e07b528de0fa762534d9599b27d6",
},
"ggml_llava-v1.5-7b/ggml-model-q4_k.gguf":{
"ggml_llava-v1.5-7b/ggml-model-q4_k.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=ggml_llava-v1.5-7b%2Fggml-model-q4_k.gguf",
"output": "modelscope_models/ggml_llava-v1.5-7b/ggml-model-q4_k.gguf",
"SHA256": "7ac9c2f7b8d76cc7f3118cdf0953ebab7a7a9b12bad5dbe237219d2ab61765ea",
},
"ggml_bakllava-1/mmproj-model-f16.gguf":{
"ggml_bakllava-1/mmproj-model-f16.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=ggml_bakllava-1%2Fmmproj-model-f16.gguf",
"output": "modelscope_models/ggml_bakllava-1/mmproj-model-f16.gguf",
"SHA256": "2e467eba710002839e0966d5e329942bb836eabd4e787bc713b07eff1d8ea13b"
},
"ggml_llava-v1.5-7b/mmproj-model-f16.gguf":{
"ggml_llava-v1.5-7b/mmproj-model-f16.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=ggml_llava-v1.5-7b%2Fmmproj-model-f16.gguf",
"output": "modelscope_models/ggml_llava-v1.5-7b/mmproj-model-f16.gguf",
"SHA256": "b7c8ff0f58fca47d28ba92c4443adf8653f3349282cb8d9e6911f22d9b3814fe"
},
"llava-v1.6-vicuna-13b-gguf/llava-v1.6-vicuna-13b.Q5_K_M.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=llava-v1.6-vicuna-13b-gguf%2Fllava-v1.6-vicuna-13b.Q5_K_M.gguf",
"output": "modelscope_models/llava-v1.6-vicuna-13b-gguf/llava-v1.6-vicuna-13b.Q5_K_M.gguf",
"SHA256": "fac8ecd642ac1c2dd8ef8fb6d6daac349293051b468f5fdc21921b94dfb2916a",
},
"llava-v1.6-vicuna-13b-gguf/mmproj-model-f16.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=llava-v1.6-vicuna-13b-gguf%2Fmmproj-model-f16.gguf",
"output": "modelscope_models/llava-v1.6-vicuna-13b-gguf/mmproj-model-f16.gguf",
"SHA256": "9860fbc162d6df40d12f32211aed11ab4826e16e473e6fff595496b9943872e2"
},
"llava-1.6-mistral-7b-gguf/llava-1.6-mistral-7b.Q5_K_M.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=llava-1.6-mistral-7b-gguf%2Fllava-v1.6-mistral-7b.Q5_K_M.gguf",
"output": "modelscope_models/llava-1.6-mistral-7b-gguf/llava-v1.6-mistral-7b.Q5_K_M.gguf",
"SHA256": "b1d37fc65ecb80aa8f1ce185bf4d7605bc3c5cc5bcc77a160c3a1b0377631112",
},
"llava-1.6-mistral-7b-gguf/mmproj-model-f16.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=llava-1.6-mistral-7b-gguf%2Fmmproj-model-f16.gguf",
"output": "modelscope_models/llava-1.6-mistral-7b-gguf/mmproj-model-f16.gguf",
"SHA256": "00205ee8a0d7a381900cd031e43105f86aa0d8c07bf329851e85c71a26632d16",
},
},
"phi3":{
"Phi-3-mini-4k-instruct-q4.gguf":{
"url":"https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=Phi-3-mini-4k-instruct-gguf%2FPhi-3-mini-4k-instruct-q4.gguf",
"output":"modelscope_models/Phi-3-mini-4k-instruct-gguf/Phi-3-mini-4k-instruct-q4.gguf",
"SHA256":"1cd9a9df07350196623f93bf4829cf228959e07ad32f787b8fdd7f5956f5b9de"
"phi3": {
"Phi-3-mini-4k-instruct-q4.gguf": {
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=Phi-3-mini-4k-instruct-gguf%2FPhi-3-mini-4k-instruct-q4.gguf",
"output": "modelscope_models/Phi-3-mini-4k-instruct-gguf/Phi-3-mini-4k-instruct-q4.gguf",
"SHA256": "1cd9a9df07350196623f93bf4829cf228959e07ad32f787b8fdd7f5956f5b9de"
}
},
}