一.初識ComfyUI
1.ComfyUI 是GUI的一種，是基於節點工作的使用者介面，主要用於操作影像的生成技術，ComfyUI 的特別之處在於它採用了一種模組化的設計，把影像生成的過程分解成了許多小的步驟，每個步驟都是一個節點。這些節點可以連線起來形成一個工作流程，這樣使用者就可以根據需要定製自己的影像生成過程
2.詳細教程見魔搭教程https://www.yuque.com/office/yuque/0/2024/pptx/1169882/1720432237458-e4b10804-b9cb-401d-aa2c-6de04b5276e0.pptx?from=https%3A%2F%2Fwww.yuque.com%2F2ai%2Fmodel%2Fgutsk9ezeymuebq9
二.開始實踐
1.下載安裝ComfyUI的執行檔案和task1中(見Datawhale X 魔搭 AI夏令營(一))微調完成Lora檔案
git lfs install git clone https://www.modelscope.cn/datasets/maochase/kolors_test_comfyui.git mv kolors_test_comfyui/* ./ rm -rf kolors_test_comfyui/ mkdir -p /mnt/workspace/models/lightning_logs/version_0/checkpoints/ mv epoch=0-step=500.ckpt /mnt/workspace/models/lightning_logs/version_0/checkpoints/
2.一鍵執行安裝程式
3.當執行到最後一個節點的內容輸出了一個訪問的連結的時候，複製連結到瀏覽器中訪問
https://internal-api-drive-stream.feishu.cn/space/api/box/stream/download/preview/GRrbbu8DXo3XrhxYzHwcvbvRnpf/?preview_type=16
三.淺嘗ComfyUI工作流
1.不帶Lora的工作流樣例(先下載工作流指令碼kolors_example.json)
點選檢視程式碼
{
"last_node_id": 15,
"last_link_id": 18,
"nodes": [
{
"id": 11,
"type": "VAELoader",
"pos": [
1323,
240
],
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "VAE",
"type": "VAE",
"links": [
12
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"sdxl.vae.safetensors"
]
},
{
"id": 10,
"type": "VAEDecode",
"pos": [
1368,
369
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 18
},
{
"name": "vae",
"type": "VAE",
"link": 12,
"slot_index": 1
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
13
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEDecode"
}
},
{
"id": 14,
"type": "KolorsSampler",
"pos": [
1011,
371
],
"size": {
"0": 315,
"1": 222
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "kolors_model",
"type": "KOLORSMODEL",
"link": 16
},
{
"name": "kolors_embeds",
"type": "KOLORS_EMBEDS",
"link": 17
}
],
"outputs": [
{
"name": "latent",
"type": "LATENT",
"links": [
18
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KolorsSampler"
},
"widgets_values": [
1024,
1024,
1000102404233412,
"fixed",
25,
5,
"EulerDiscreteScheduler"
]
},
{
"id": 6,
"type": "DownloadAndLoadKolorsModel",
"pos": [
201,
368
],
"size": {
"0": 315,
"1": 82
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "kolors_model",
"type": "KOLORSMODEL",
"links": [
16
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "DownloadAndLoadKolorsModel"
},
"widgets_values": [
"Kwai-Kolors/Kolors",
"fp16"
]
},
{
"id": 3,
"type": "PreviewImage",
"pos": [
1366,
468
],
"size": [
535.4001724243165,
562.2001106262207
],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 13
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 12,
"type": "KolorsTextEncode",
"pos": [
519,
529
],
"size": [
457.2893696934723,
225.28656056301645
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "chatglm3_model",
"type": "CHATGLM3MODEL",
"link": 14,
"slot_index": 0
}
],
"outputs": [
{
"name": "kolors_embeds",
"type": "KOLORS_EMBEDS",
"links": [
17
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KolorsTextEncode"
},
"widgets_values": [
"cinematic photograph of an astronaut riding a horse in space |\nillustration of a cat wearing a top hat and a scarf |\nphotograph of a goldfish in a bowl |\nanime screencap of a red haired girl",
"",
1
]
},
{
"id": 15,
"type": "Note",
"pos": [
200,
636
],
"size": [
273.5273818969726,
149.55464588512064
],
"flags": {},
"order": 2,
"mode": 0,
"properties": {
"text": ""
},
"widgets_values": [
"Text encoding takes the most VRAM, quantization can reduce that a lot.\n\nApproximate values I have observed:\nfp16 - 12 GB\nquant8 - 8-9 GB\nquant4 - 4-5 GB\n\nquant4 reduces the quality quite a bit, 8 seems fine"
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 13,
"type": "DownloadAndLoadChatGLM3",
"pos": [
206,
522
],
"size": [
274.5334274291992,
58
],
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "chatglm3_model",
"type": "CHATGLM3MODEL",
"links": [
14
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "DownloadAndLoadChatGLM3"
},
"widgets_values": [
"fp16"
]
}
],
"links": [
[
12,
11,
0,
10,
1,
"VAE"
],
[
13,
10,
0,
3,
0,
"IMAGE"
],
[
14,
13,
0,
12,
0,
"CHATGLM3MODEL"
],
[
16,
6,
0,
14,
0,
"KOLORSMODEL"
],
[
17,
12,
0,
14,
1,
"KOLORS_EMBEDS"
],
[
18,
14,
0,
10,
0,
"LATENT"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 1.1,
"offset": {
"0": -114.73954010009766,
"1": -139.79705810546875
}
}
},
"version": 0.4
}
2.完成第一次生圖
https://internal-api-drive-stream.feishu.cn/space/api/box/stream/download/preview/DIr4bvsLQoCzexxEnIUc2xAmneb/?preview_type=16
https://internal-api-drive-stream.feishu.cn/space/api/box/stream/download/preview/G4XjbLn9YoXhkuxHvNJcZyKinmd/?preview_type=16
3. 帶Lora的工作流樣例(工作流指令碼kolors_with_lora_example.json)
點選檢視程式碼
{
"last_node_id": 16,
"last_link_id": 20,
"nodes": [
{
"id": 11,
"type": "VAELoader",
"pos": [
1323,
240
],
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "VAE",
"type": "VAE",
"links": [
12
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"sdxl.vae.safetensors"
]
},
{
"id": 10,
"type": "VAEDecode",
"pos": [
1368,
369
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 18
},
{
"name": "vae",
"type": "VAE",
"link": 12,
"slot_index": 1
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
13
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEDecode"
}
},
{
"id": 15,
"type": "Note",
"pos": [
200,
636
],
"size": {
"0": 273.5273742675781,
"1": 149.5546417236328
},
"flags": {},
"order": 1,
"mode": 0,
"properties": {
"text": ""
},
"widgets_values": [
"Text encoding takes the most VRAM, quantization can reduce that a lot.\n\nApproximate values I have observed:\nfp16 - 12 GB\nquant8 - 8-9 GB\nquant4 - 4-5 GB\n\nquant4 reduces the quality quite a bit, 8 seems fine"
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 13,
"type": "DownloadAndLoadChatGLM3",
"pos": [
206,
522
],
"size": {
"0": 274.5334167480469,
"1": 58
},
"flags": {},
"order": 2,
"mode": 0,
"outputs": [
{
"name": "chatglm3_model",
"type": "CHATGLM3MODEL",
"links": [
14
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "DownloadAndLoadChatGLM3"
},
"widgets_values": [
"fp16"
]
},
{
"id": 6,
"type": "DownloadAndLoadKolorsModel",
"pos": [
201,
368
],
"size": {
"0": 315,
"1": 82
},
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "kolors_model",
"type": "KOLORSMODEL",
"links": [
19
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "DownloadAndLoadKolorsModel"
},
"widgets_values": [
"Kwai-Kolors/Kolors",
"fp16"
]
},
{
"id": 12,
"type": "KolorsTextEncode",
"pos": [
519,
529
],
"size": {
"0": 457.28936767578125,
"1": 225.28656005859375
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "chatglm3_model",
"type": "CHATGLM3MODEL",
"link": 14,
"slot_index": 0
}
],
"outputs": [
{
"name": "kolors_embeds",
"type": "KOLORS_EMBEDS",
"links": [
17
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KolorsTextEncode"
},
"widgets_values": [
"二次元，長髮，少女，白色背景",
"",
1
]
},
{
"id": 3,
"type": "PreviewImage",
"pos": [
1366,
469
],
"size": {
"0": 535.400146484375,
"1": 562.2001342773438
},
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 13
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 16,
"type": "LoadKolorsLoRA",
"pos": [
606,
368
],
"size": {
"0": 317.4000244140625,
"1": 82
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "kolors_model",
"type": "KOLORSMODEL",
"link": 19
}
],
"outputs": [
{
"name": "kolors_model",
"type": "KOLORSMODEL",
"links": [
20
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "LoadKolorsLoRA"
},
"widgets_values": [
"/mnt/workspace/models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt",
2
]
},
{
"id": 14,
"type": "KolorsSampler",
"pos": [
1011,
371
],
"size": {
"0": 315,
"1": 266
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "kolors_model",
"type": "KOLORSMODEL",
"link": 20
},
{
"name": "kolors_embeds",
"type": "KOLORS_EMBEDS",
"link": 17
},
{
"name": "latent",
"type": "LATENT",
"link": null
}
],
"outputs": [
{
"name": "latent",
"type": "LATENT",
"links": [
18
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KolorsSampler"
},
"widgets_values": [
1024,
1024,
0,
"fixed",
25,
5,
"EulerDiscreteScheduler",
1
]
}
],
"links": [
[
12,
11,
0,
10,
1,
"VAE"
],
[
13,
10,
0,
3,
0,
"IMAGE"
],
[
14,
13,
0,
12,
0,
"CHATGLM3MODEL"
],
[
17,
12,
0,
14,
1,
"KOLORS_EMBEDS"
],
[
18,
14,
0,
10,
0,
"LATENT"
],
[
19,
6,
0,
16,
0,
"KOLORSMODEL"
],
[
20,
16,
0,
14,
0,
"KOLORSMODEL"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 1.2100000000000002,
"offset": {
"0": -183.91309381910426,
"1": -202.11110769225016
}
}
},
"version": 0.4
}
4.生圖步驟同上
四.Lora詳解https://www.bilibili.com/video/BV1nT421k7Fa/?t=28.133192&spm_id_from=333.1350.jump_directly&vd_source=87de892b60ecaebe6b8575e21f4aa997
五.準備一個高質量的資料集
當我們進行圖片生成相關的工作時，選擇合適的資料集是非常重要的。如何找到適合自己的資料集呢，這裡給大家整理了一些重要的參考維度，希望可以幫助你快速找到適合的資料集：
1.明確你的需求和目標
a.關注應用場景**：確定你的模型將被應用到什麼樣的場景中（例如，藝術風格轉換、產品影像生成、醫療影像合成等）。
b.關注資料型別**：你需要什麼樣的圖片？比如是真實世界的照片還是合成影像？是黑白的還是彩色的？是高解析度還是低解析度？
c.關注資料量**：考慮你的任務應該需要多少圖片來支援訓練和驗證。
2.資料集來源整理
以下渠道來源均需要考慮合規性問題，請大家在使用資料集過程中謹慎選擇
