File size: 13,944 Bytes
668ebc0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Aniimage-2 Image generator\n",
"\n",
"This notebook runs [`8BitStudio/Aniimage-2`](https://huggingface.co/8BitStudio/Aniimage-2) with a Gradio interface. It manually assembles the repository's UNet with the matching VAE and CLIP text encoder because the repository is not packaged as a complete Diffusers pipeline.\n",
"\n",
"## Before running\n",
"\n",
"1. In Colab choose **Runtime → Change runtime type → T4 GPU** (or a better GPU).\n",
"2. Run every cell in order. The first model load downloads several gigabytes and can take a few minutes.\n",
"3. Open the Gradio link printed by the final cell. Generated images are also saved under `/content/aniimage2_outputs`.\n",
"\n",
"The defaults follow the model card: 512×512, DPM++ SDE Karras, 50 steps, v-prediction, zero-terminal-SNR, CLIP penultimate layer, and CFG rescale 0.7. The default negative prompt includes `NSFW` because omitting it may produce NSFW images.\n",
"\n",
"If you want to generate NSFW images, remove the `NSFW` text in the negative prompt. \n",
"\n",
"If your results look \"glitched\", try lowering your CFG to around 7 or 6.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip -q install -U \"diffusers>=0.37.1\" \"transformers>=4.46,<5\" accelerate safetensors huggingface_hub \"gradio>=5,<7\" \"click>=8.2\" \"Pillow<11.0.0\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import random\n",
"from pathlib import Path\n",
"\n",
"import gradio as gr\n",
"import numpy as np\n",
"import torch\n",
"from PIL import Image\n",
"from diffusers import (\n",
" AutoencoderKL,\n",
" DDIMScheduler,\n",
" DPMSolverMultistepScheduler,\n",
" EulerAncestralDiscreteScheduler,\n",
" EulerDiscreteScheduler,\n",
" UNet2DConditionModel,\n",
")\n",
"from huggingface_hub import hf_hub_download\n",
"from transformers import CLIPTextModel, CLIPTokenizer\n",
"\n",
"REPO_ID = \"8BitStudio/Aniimage-2\"\n",
"CLIP_ID = \"openai/clip-vit-large-patch14\"\n",
"OUTPUT_DIR = Path(\"/content/aniimage2_outputs\")\n",
"OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
"\n",
"if not torch.cuda.is_available():\n",
" raise RuntimeError(\"No GPU detected. In Colab select Runtime > Change runtime type > T4 GPU, then run again.\")\n",
"\n",
"DEVICE = \"cuda\"\n",
"DTYPE = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16\n",
"torch.backends.cuda.matmul.allow_tf32 = True\n",
"torch.backends.cudnn.allow_tf32 = True\n",
"print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
"print(f\"Model dtype: {DTYPE}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Download the small model metadata first so every component matches training.\n",
"config_path = hf_hub_download(REPO_ID, \"Aniimage-2/model_config.json\")\n",
"with open(config_path, \"r\", encoding=\"utf-8\") as f:\n",
" MODEL_CONFIG = json.load(f)\n",
"\n",
"VAE_ID = MODEL_CONFIG[\"vae\"]\n",
"GUIDANCE_RESCALE = float(MODEL_CONFIG.get(\"guidance_rescale\", 0.7))\n",
"\n",
"print(\"Loading Aniimage-2 UNet...\")\n",
"unet = UNet2DConditionModel.from_pretrained(\n",
" REPO_ID,\n",
" subfolder=\"Aniimage-2/unet\",\n",
" torch_dtype=DTYPE,\n",
" low_cpu_mem_usage=True,\n",
").to(DEVICE).eval()\n",
"unet.requires_grad_(False)\n",
"\n",
"print(f\"Loading VAE: {VAE_ID}...\")\n",
"vae = AutoencoderKL.from_pretrained(VAE_ID, torch_dtype=DTYPE).to(DEVICE).eval()\n",
"vae.requires_grad_(False)\n",
"vae.enable_slicing()\n",
"\n",
"print(f\"Loading text encoder: {CLIP_ID}...\")\n",
"tokenizer = CLIPTokenizer.from_pretrained(CLIP_ID)\n",
"text_encoder = CLIPTextModel.from_pretrained(CLIP_ID, torch_dtype=DTYPE).to(DEVICE).eval()\n",
"text_encoder.requires_grad_(False)\n",
"\n",
"# Aniimage-2 was trained using CLIP's penultimate transformer layer.\n",
"clip_inner = getattr(text_encoder, \"text_model\", text_encoder)\n",
"clip_inner.encoder.layers = torch.nn.ModuleList(list(clip_inner.encoder.layers[:-1]))\n",
"\n",
"print(\"Aniimage-2 is loaded and ready.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def make_scheduler(name):\n",
" base = dict(\n",
" num_train_timesteps=int(MODEL_CONFIG.get(\"num_train_timesteps\", 1000)),\n",
" beta_schedule=MODEL_CONFIG.get(\"beta_schedule\", \"scaled_linear\"),\n",
" prediction_type=MODEL_CONFIG.get(\"prediction_type\", \"v_prediction\"),\n",
" rescale_betas_zero_snr=bool(MODEL_CONFIG.get(\"zero_terminal_snr\", True)),\n",
" timestep_spacing=MODEL_CONFIG.get(\"timestep_spacing\", \"trailing\"),\n",
" )\n",
" if name == \"DPM++ SDE Karras\":\n",
" return DPMSolverMultistepScheduler(\n",
" **base, algorithm_type=\"sde-dpmsolver++\", solver_order=2, use_karras_sigmas=True\n",
" )\n",
" if name == \"DPM++ 2M Karras\":\n",
" return DPMSolverMultistepScheduler(\n",
" **base, algorithm_type=\"dpmsolver++\", solver_order=2, use_karras_sigmas=True\n",
" )\n",
" if name == \"Euler a\":\n",
" return EulerAncestralDiscreteScheduler(**base)\n",
" if name == \"Euler\":\n",
" return EulerDiscreteScheduler(**base)\n",
" if name == \"DDIM\":\n",
" return DDIMScheduler(**base, clip_sample=False, set_alpha_to_one=False)\n",
" raise ValueError(f\"Unknown scheduler: {name}\")\n",
"\n",
"\n",
"@torch.inference_mode()\n",
"def encode_prompts(prompt, negative_prompt):\n",
" tokens = tokenizer(\n",
" [negative_prompt or \"\", prompt],\n",
" padding=\"max_length\",\n",
" max_length=tokenizer.model_max_length,\n",
" truncation=True,\n",
" return_tensors=\"pt\",\n",
" )\n",
" return text_encoder(tokens.input_ids.to(DEVICE))[0]\n",
"\n",
"\n",
"def rescale_cfg(noise_cfg, noise_text, amount):\n",
" dims = tuple(range(1, noise_cfg.ndim))\n",
" std_text = noise_text.std(dim=dims, keepdim=True)\n",
" std_cfg = noise_cfg.std(dim=dims, keepdim=True).clamp_min(1e-6)\n",
" noise_rescaled = noise_cfg * (std_text / std_cfg)\n",
" return amount * noise_rescaled + (1.0 - amount) * noise_cfg\n",
"\n",
"\n",
"@torch.inference_mode()\n",
"def generate_one(prompt, negative_prompt, scheduler_name, steps, cfg_scale, seed):\n",
" scheduler = make_scheduler(scheduler_name)\n",
" scheduler.set_timesteps(int(steps), device=DEVICE)\n",
" embeddings = encode_prompts(prompt, negative_prompt)\n",
"\n",
" generator = torch.Generator(device=DEVICE).manual_seed(int(seed))\n",
" latent_size = int(MODEL_CONFIG.get(\"image_size\", 512)) // 8\n",
" latents = torch.randn(\n",
" (1, int(unet.config.in_channels), latent_size, latent_size),\n",
" generator=generator, device=DEVICE, dtype=torch.float32,\n",
" ) * scheduler.init_noise_sigma\n",
"\n",
" for timestep in scheduler.timesteps:\n",
" latent_input = torch.cat([latents, latents], dim=0)\n",
" latent_input = scheduler.scale_model_input(latent_input, timestep)\n",
" with torch.autocast(\"cuda\", dtype=DTYPE):\n",
" prediction = unet(latent_input, timestep, encoder_hidden_states=embeddings).sample\n",
" pred_negative, pred_text = prediction.chunk(2)\n",
" prediction = pred_negative + float(cfg_scale) * (pred_text - pred_negative)\n",
" prediction = rescale_cfg(prediction, pred_text, GUIDANCE_RESCALE)\n",
" latents = scheduler.step(prediction, timestep, latents).prev_sample\n",
"\n",
" scaled = (latents / vae.config.scaling_factor).to(dtype=DTYPE)\n",
" with torch.autocast(\"cuda\", dtype=DTYPE):\n",
" image = vae.decode(scaled).sample\n",
" image = (image.float() / 2 + 0.5).clamp(0, 1)\n",
" array = (image[0].permute(1, 2, 0).cpu().numpy() * 255).round().astype(np.uint8)\n",
" return Image.fromarray(array)\n",
"\n",
"\n",
"def generate_gallery(prompt, negative_prompt, scheduler_name, steps, cfg_scale, seed, randomize_seed, image_count, progress=gr.Progress()):\n",
" prompt = (prompt or \"\").strip()\n",
" if not prompt:\n",
" raise gr.Error(\"Enter a prompt first.\")\n",
"\n",
" count = int(image_count)\n",
" base_seed = random.randint(0, 2**31 - 1) if randomize_seed or int(seed) < 0 else int(seed)\n",
" images, records = [], []\n",
"\n",
" for index in range(count):\n",
" used_seed = (base_seed + index) % (2**31)\n",
" progress(index / count, desc=f\"Generating image {index + 1} of {count}\")\n",
" image = generate_one(prompt, negative_prompt, scheduler_name, steps, cfg_scale, used_seed)\n",
" path = OUTPUT_DIR / f\"aniimage2_{used_seed}.png\"\n",
" image.save(path)\n",
" images.append(image)\n",
" records.append(f\"- Seed `{used_seed}` — `{path}`\")\n",
"\n",
" progress(1.0, desc=\"Done\")\n",
" details = \"### Results\\n\" + \"\\n\".join(records)\n",
" return images, details, base_seed\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DEFAULT_NEGATIVE = (\n",
" \"NSFW, low quality, ugly, blurry, distorted, deformed, bad anatomy, bad proportions, \"\n",
" \"extra limbs, missing limbs, watermark, text, signature, washed out, flat colors, \"\n",
" \"manga panel, disfigured, poorly drawn, jpeg artifacts, cropped, out of frame\"\n",
")\n",
"\n",
"with gr.Blocks(title=\"Aniimage-2 Generator\", theme=gr.themes.Soft()) as demo:\n",
" gr.Markdown(\n",
" \"# Aniimage-2 Interactive Generator\\n\"\n",
" \"Use a short plain-English prompt for best results. Images are 512×512.\"\n",
" )\n",
" with gr.Row():\n",
" with gr.Column(scale=2):\n",
" prompt = gr.Textbox(\n",
" label=\"Prompt\",\n",
" value=\"A smiling anime girl with red hair and a school uniform\",\n",
" lines=3,\n",
" )\n",
" negative = gr.Textbox(label=\"Negative prompt\", value=DEFAULT_NEGATIVE, lines=4)\n",
" generate_button = gr.Button(\"Generate\", variant=\"primary\")\n",
" with gr.Column(scale=1):\n",
" scheduler = gr.Dropdown(\n",
" [\"DPM++ SDE Karras\", \"DPM++ 2M Karras\", \"Euler a\", \"Euler\", \"DDIM\"],\n",
" value=\"DPM++ SDE Karras\", label=\"Scheduler\",\n",
" )\n",
" steps = gr.Slider(10, 80, value=50, step=1, label=\"Steps\")\n",
" cfg = gr.Slider(1.0, 15.0, value=7.5, step=0.1, label=\"CFG scale\")\n",
" seed = gr.Number(value=-1, precision=0, label=\"Seed (-1 = random)\")\n",
" randomize = gr.Checkbox(value=True, label=\"Randomize seed\")\n",
" count = gr.Slider(1, 4, value=1, step=1, label=\"Number of images\")\n",
"\n",
" gallery = gr.Gallery(label=\"Generated images\", columns=2, object_fit=\"contain\", height=620)\n",
" result_info = gr.Markdown()\n",
"\n",
" generate_button.click(\n",
" fn=generate_gallery,\n",
" inputs=[prompt, negative, scheduler, steps, cfg, seed, randomize, count],\n",
" outputs=[gallery, result_info, seed],\n",
" concurrency_limit=1,\n",
" )\n",
"\n",
"demo.queue(max_size=8)\n",
"print(\"Interface created. Run the final cell to launch it.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"demo.launch(share=True, show_error=True)\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "Aniimage_2_Interactive_Colab.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|