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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.chdir(\"..\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from diffusers.pipelines import FluxPipeline\n",
"from src.condition import Condition\n",
"from PIL import Image\n",
"\n",
"from src.generate import generate, seed_everything"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pipe = FluxPipeline.from_pretrained(\n",
" \"black-forest-labs/FLUX.1-schnell\", torch_dtype=torch.bfloat16\n",
")\n",
"pipe = pipe.to(\"cuda\")\n",
"pipe.load_lora_weights(\n",
" \"Yuanshi/OminiControl\",\n",
" weight_name=f\"omini/subject_512.safetensors\",\n",
" adapter_name=\"subject\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(\"assets/penguin.jpg\").convert(\"RGB\").resize((512, 512))\n",
"\n",
"condition = Condition(\"subject\", image)\n",
"\n",
"prompt = \"On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.\"\n",
"\n",
"\n",
"seed_everything(0)\n",
"\n",
"result_img = generate(\n",
" pipe,\n",
" prompt=prompt,\n",
" conditions=[condition],\n",
" num_inference_steps=8,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"\n",
"concat_image = Image.new(\"RGB\", (1024, 512))\n",
"concat_image.paste(image, (0, 0))\n",
"concat_image.paste(result_img, (512, 0))\n",
"concat_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(\"assets/tshirt.jpg\").convert(\"RGB\").resize((512, 512))\n",
"\n",
"condition = Condition(\"subject\", image)\n",
"\n",
"prompt = \"On the beach, a lady sits under a beach umbrella. She's wearing this shirt and has a big smile on her face, with her surfboard hehind her. The sun is setting in the background. The sky is a beautiful shade of orange and purple.\"\n",
"\n",
"\n",
"seed_everything()\n",
"\n",
"result_img = generate(\n",
" pipe,\n",
" prompt=prompt,\n",
" conditions=[condition],\n",
" num_inference_steps=8,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"\n",
"concat_image = Image.new(\"RGB\", (1024, 512))\n",
"concat_image.paste(condition.condition, (0, 0))\n",
"concat_image.paste(result_img, (512, 0))\n",
"concat_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(\"assets/rc_car.jpg\").convert(\"RGB\").resize((512, 512))\n",
"\n",
"condition = Condition(\"subject\", image)\n",
"\n",
"prompt = \"A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.\"\n",
"\n",
"seed_everything()\n",
"\n",
"result_img = generate(\n",
" pipe,\n",
" prompt=prompt,\n",
" conditions=[condition],\n",
" num_inference_steps=8,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"\n",
"concat_image = Image.new(\"RGB\", (1024, 512))\n",
"concat_image.paste(condition.condition, (0, 0))\n",
"concat_image.paste(result_img, (512, 0))\n",
"concat_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(\"assets/clock.jpg\").convert(\"RGB\").resize((512, 512))\n",
"\n",
"condition = Condition(\"subject\", image)\n",
"\n",
"prompt = \"In a Bauhaus style room, this item is placed on a shiny glass table, with a vase of flowers next to it. In the afternoon sun, the shadows of the blinds are cast on the wall.\"\n",
"\n",
"seed_everything()\n",
"\n",
"result_img = generate(\n",
" pipe,\n",
" prompt=prompt,\n",
" conditions=[condition],\n",
" num_inference_steps=8,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"\n",
"concat_image = Image.new(\"RGB\", (1024, 512))\n",
"concat_image.paste(condition.condition, (0, 0))\n",
"concat_image.paste(result_img, (512, 0))\n",
"concat_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = Image.open(\"assets/oranges.jpg\").convert(\"RGB\").resize((512, 512))\n",
"\n",
"condition = Condition(\"subject\", image)\n",
"\n",
"prompt = \"A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.\"\n",
"\n",
"seed_everything()\n",
"\n",
"result_img = generate(\n",
" pipe,\n",
" prompt=prompt,\n",
" conditions=[condition],\n",
" num_inference_steps=8,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"\n",
"concat_image = Image.new(\"RGB\", (1024, 512))\n",
"concat_image.paste(condition.condition, (0, 0))\n",
"concat_image.paste(result_img, (512, 0))\n",
"concat_image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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