Upload app.ipynb

app.ipynb

@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c00cc11f-9150-4f82-85a4-08142e9ad14f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd /content/ComfyUI\n",
+    "\n",
+    "import random\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import nodes\n",
+    "from nodes import NODE_CLASS_MAPPINGS\n",
+    "from comfy_extras import nodes_custom_sampler\n",
+    "from comfy import model_management\n",
+    "\n",
+    "DualCLIPLoader = NODE_CLASS_MAPPINGS[\"DualCLIPLoader\"]()\n",
+    "UNETLoader = NODE_CLASS_MAPPINGS[\"UNETLoader\"]()\n",
+    "RandomNoise = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"RandomNoise\"]()\n",
+    "BasicGuider = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"BasicGuider\"]()\n",
+    "KSamplerSelect = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"KSamplerSelect\"]()\n",
+    "BasicScheduler = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"BasicScheduler\"]()\n",
+    "SamplerCustomAdvanced = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"SamplerCustomAdvanced\"]()\n",
+    "VAELoader = NODE_CLASS_MAPPINGS[\"VAELoader\"]()\n",
+    "VAEDecode = NODE_CLASS_MAPPINGS[\"VAEDecode\"]()\n",
+    "EmptyLatentImage = NODE_CLASS_MAPPINGS[\"EmptyLatentImage\"]()\n",
+    "\n",
+    "with torch.inference_mode():\n",
+    "    clip = DualCLIPLoader.load_clip(\"t5xxl_fp8_e4m3fn.safetensors\", \"clip_l.safetensors\", \"flux\")[0]\n",
+    "    unet = UNETLoader.load_unet(\"flux1-dev-fp8.safetensors\", \"fp8_e4m3fn\")[0]\n",
+    "    vae = VAELoader.load_vae(\"ae.sft\")[0]\n",
+    "\n",
+    "def closestNumber(n, m):\n",
+    "    q = int(n / m)\n",
+    "    n1 = m * q\n",
+    "    if (n * m) > 0:\n",
+    "        n2 = m * (q + 1)\n",
+    "    else:\n",
+    "        n2 = m * (q - 1)\n",
+    "    if abs(n - n1) < abs(n - n2):\n",
+    "        return n1\n",
+    "    return n2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "371fe26c-4994-47a0-a966-627c7b2b0c82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.inference_mode():\n",
+    "    positive_prompt = \"black forest toast spelling out the words 'FLUX DEV', tasty, food photography, dynamic shot\"\n",
+    "    width = 1024\n",
+    "    height = 1024\n",
+    "    seed = 0\n",
+    "    steps = 20\n",
+    "    sampler_name = \"euler\"\n",
+    "    scheduler = \"simple\"\n",
+    "\n",
+    "    if seed == 0:\n",
+    "        seed = random.randint(0, 18446744073709551615)\n",
+    "    print(seed)\n",
+    "\n",
+    "    cond, pooled = clip.encode_from_tokens(clip.tokenize(positive_prompt), return_pooled=True)\n",
+    "    cond = [[cond, {\"pooled_output\": pooled}]]\n",
+    "    noise = RandomNoise.get_noise(seed)[0] \n",
+    "    guider = BasicGuider.get_guider(unet, cond)[0]\n",
+    "    sampler = KSamplerSelect.get_sampler(sampler_name)[0]\n",
+    "    sigmas = BasicScheduler.get_sigmas(unet, scheduler, steps, 1.0)[0]\n",
+    "    latent_image = EmptyLatentImage.generate(closestNumber(width, 16), closestNumber(height, 16))[0]\n",
+    "    sample, sample_denoised = SamplerCustomAdvanced.sample(noise, guider, sampler, sigmas, latent_image)\n",
+    "    model_management.soft_empty_cache()\n",
+    "    decoded = VAEDecode.decode(vae, sample)[0].detach()\n",
+    "    Image.fromarray(np.array(decoded*255, dtype=np.uint8)[0]).save(\"/content/flux.png\")\n",
+    "\n",
+    "Image.fromarray(np.array(decoded*255, dtype=np.uint8)[0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}