3868 lines
138 KiB
Plaintext
3868 lines
138 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {
|
||
"id": "VUQ-pg04BB8L"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"!pip install -U diffusers transformers accelerate safetensors opencv-python pillow --quiet"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"id": "8EtwuCbBB7Rl"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from PIL import ImageEnhance, Image\n",
|
||
"\n",
|
||
"image = Image.open(\"input.jpg\").convert(\"RGB\").resize((512, 512))\n",
|
||
"\n",
|
||
"enhancer = ImageEnhance.Contrast(image)\n",
|
||
"image = enhancer.enhance(1.5)\n",
|
||
"\n",
|
||
"enhancer = ImageEnhance.Sharpness(image)\n",
|
||
"image = enhancer.enhance(2.0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 868,
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]
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"id": "mkrSLn0xg7Og",
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||
"outputId": "998d552a-c223-47af-da1f-c4895a25cd20"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n",
|
||
"Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Using device: cuda\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"model_index.json: 0%| | 0.00/609 [00:00<?, ?B/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "9e53c029478045c2b7594a66c7a6ab61"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "c7c4f29bdf134dd2823dbb3e69c7afbf"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Fetching 12 files: 0%| | 0/12 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "26302fcbacef4a969d6e3a48fac55cb9"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
|
||
"WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
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"text/plain": [
|
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"Loading pipeline components...: 0%| | 0/5 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "89f98e2cd0cb4c929432f900d29e3528"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Loading weights: 0%| | 0/196 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "849929921323404bb56bc9967ae44bde"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"\u001b[1mCLIPTextModel LOAD REPORT\u001b[0m from: /root/.cache/huggingface/hub/models--SG161222--Realistic_Vision_V5.1_noVAE/snapshots/1e9f017a7b1eaefb63a1900ea6c5953d2739fd21/text_encoder\n",
|
||
"Key | Status | | \n",
|
||
"-----------------------------------+------------+--+-\n",
|
||
"text_model.embeddings.position_ids | UNEXPECTED | | \n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
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||
"text/plain": [
|
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"models/ip-adapter-plus-face_sd15.bin: 0%| | 0.00/98.2M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "da557105e4674285bb840682f2037b71"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
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"data": {
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"text/plain": [
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"config.json: 0%| | 0.00/560 [00:00<?, ?B/s]"
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "27e59ba37ac94d1fa75e45405e3fc47f"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"models/image_encoder/model.safetensors: 0%| | 0.00/2.53G [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "1ecacb2e640845739171d68e0ad16442"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
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"Loading weights: 0%| | 0/520 [00:00<?, ?it/s]"
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "d8a62057cd6f46c0aff4249699970e8a"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"\u001b[1mCLIPVisionModelWithProjection LOAD REPORT\u001b[0m from: h94/IP-Adapter\n",
|
||
"Key | Status | | \n",
|
||
"-------------------------------------+------------+--+-\n",
|
||
"vision_model.embeddings.position_ids | UNEXPECTED | | \n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Image loaded at = 2026-04-06 07:21:37.664132\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
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"data": {
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" 0%| | 0/34 [00:00<?, ?it/s]"
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],
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"application/vnd.jupyter.widget-view+json": {
|
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "4da31fb724bc4b11a2a139d84f39caf7"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Image generated at = 2026-04-06 07:21:44.684451\n",
|
||
"Image saved at = 2026-04-06 07:21:45.105586\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from diffusers import StableDiffusionImg2ImgPipeline\n",
|
||
"from PIL import Image\n",
|
||
"from datetime import datetime\n",
|
||
"import torch\n",
|
||
"\n",
|
||
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"print(\"Using device:\", device)\n",
|
||
"\n",
|
||
"pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n",
|
||
" \"SG161222/Realistic_Vision_V5.1_noVAE\",\n",
|
||
" torch_dtype=torch.float16 if device == \"cuda\" else torch.float32\n",
|
||
").to(device)\n",
|
||
"\n",
|
||
"pipe.load_ip_adapter(\n",
|
||
" \"h94/IP-Adapter\",\n",
|
||
" subfolder=\"models\",\n",
|
||
" weight_name=\"ip-adapter-plus-face_sd15.bin\"\n",
|
||
")\n",
|
||
"\n",
|
||
"pipe.set_ip_adapter_scale(0.9)\n",
|
||
"\n",
|
||
"image = Image.open(\"input.jpg\").convert(\"RGB\").resize((512, 512))\n",
|
||
"print(\"Image loaded at =\", datetime.now())\n",
|
||
"\n",
|
||
"prompt = (\n",
|
||
" \"same exact person as input image, highly recognizable identity, \"\n",
|
||
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