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1 Parent(s): 1d777bd

Delete app.ipynb

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  1. app.ipynb +0 -106
app.ipynb DELETED
@@ -1,106 +0,0 @@
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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "id": "c00cc11f-9150-4f82-85a4-08142e9ad14f",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "%cd /content/ComfyUI\n",
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- "\n",
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- "import random\n",
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- "import torch\n",
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- "import numpy as np\n",
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- "from PIL import Image\n",
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- "import nodes\n",
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- "from nodes import NODE_CLASS_MAPPINGS\n",
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- "from comfy_extras import nodes_custom_sampler\n",
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- "from comfy import model_management\n",
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- "\n",
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- "DualCLIPLoader = NODE_CLASS_MAPPINGS[\"DualCLIPLoader\"]()\n",
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- "UNETLoader = NODE_CLASS_MAPPINGS[\"UNETLoader\"]()\n",
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- "RandomNoise = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"RandomNoise\"]()\n",
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- "BasicGuider = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"BasicGuider\"]()\n",
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- "KSamplerSelect = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"KSamplerSelect\"]()\n",
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- "BasicScheduler = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"BasicScheduler\"]()\n",
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- "SamplerCustomAdvanced = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"SamplerCustomAdvanced\"]()\n",
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- "VAELoader = NODE_CLASS_MAPPINGS[\"VAELoader\"]()\n",
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- "VAEDecode = NODE_CLASS_MAPPINGS[\"VAEDecode\"]()\n",
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- "EmptyLatentImage = NODE_CLASS_MAPPINGS[\"EmptyLatentImage\"]()\n",
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- "\n",
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- "with torch.inference_mode():\n",
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- " clip = DualCLIPLoader.load_clip(\"t5xxl_fp8_e4m3fn.safetensors\", \"clip_l.safetensors\", \"flux\")[0]\n",
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- " unet = UNETLoader.load_unet(\"flux1-dev-fp8.safetensors\", \"fp8_e4m3fn\")[0]\n",
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- " vae = VAELoader.load_vae(\"ae.sft\")[0]\n",
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- "\n",
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- "def closestNumber(n, m):\n",
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- " q = int(n / m)\n",
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- " n1 = m * q\n",
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- " if (n * m) > 0:\n",
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- " n2 = m * (q + 1)\n",
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- " else:\n",
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- " n2 = m * (q - 1)\n",
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- " if abs(n - n1) < abs(n - n2):\n",
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- " return n1\n",
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- " return n2"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "id": "371fe26c-4994-47a0-a966-627c7b2b0c82",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "with torch.inference_mode():\n",
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- " positive_prompt = \"black forest toast spelling out the words 'FLUX DEV', tasty, food photography, dynamic shot\"\n",
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- " width = 1024\n",
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- " height = 1024\n",
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- " seed = 0\n",
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- " steps = 20\n",
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- " sampler_name = \"euler\"\n",
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- " scheduler = \"simple\"\n",
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- "\n",
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- " if seed == 0:\n",
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- " seed = random.randint(0, 18446744073709551615)\n",
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- " print(seed)\n",
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- "\n",
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- " cond, pooled = clip.encode_from_tokens(clip.tokenize(positive_prompt), return_pooled=True)\n",
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- " cond = [[cond, {\"pooled_output\": pooled}]]\n",
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- " noise = RandomNoise.get_noise(seed)[0] \n",
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- " guider = BasicGuider.get_guider(unet, cond)[0]\n",
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- " sampler = KSamplerSelect.get_sampler(sampler_name)[0]\n",
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- " sigmas = BasicScheduler.get_sigmas(unet, scheduler, steps, 1.0)[0]\n",
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- " latent_image = EmptyLatentImage.generate(closestNumber(width, 16), closestNumber(height, 16))[0]\n",
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- " sample, sample_denoised = SamplerCustomAdvanced.sample(noise, guider, sampler, sigmas, latent_image)\n",
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- " model_management.soft_empty_cache()\n",
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- " decoded = VAEDecode.decode(vae, sample)[0].detach()\n",
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- " Image.fromarray(np.array(decoded*255, dtype=np.uint8)[0]).save(\"/content/flux.png\")\n",
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- "\n",
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- "Image.fromarray(np.array(decoded*255, dtype=np.uint8)[0])"
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- ]
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- }
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- ],
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- "metadata": {
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- "kernelspec": {
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- "display_name": "Python 3 (ipykernel)",
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- "language": "python",
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- "name": "python3"
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- },
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- "language_info": {
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- "codemirror_mode": {
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- "name": "ipython",
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- "version": 3
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- },
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- "file_extension": ".py",
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- "mimetype": "text/x-python",
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- "name": "python",
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- "nbconvert_exporter": "python",
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- "pygments_lexer": "ipython3",
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- "version": "3.10.12"
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- }
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- },
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- "nbformat": 4,
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- "nbformat_minor": 5
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- }