{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d89263a2-a275-4c06-83fd-7e7eee0cb8b8", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "034cfb966d4a483d992215a77c642389", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading pipeline components...: 0%| | 0/5 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prompts = [\n", "#'аниме девушка, waifu, يبتسم جنسيا , sur le fond de la tour Eiffel'\n", "\"A hauntingly ethereal figure with decaying flesh and glowing metallic enhancements, blending Art Nouveau and cybernetic futurism, stands in a twilight forest bathed in warm sunset hues, casting golden light on surreal, ghostly trees with autumn leaves. The atmosphere is melancholic, evoking the styles of Rockwell and Parrish.\"\n", "#,\"1girl, small breast, red dress, sea\"\n", "#,\"девушка в красном платье на фоне моря\"\n", ",\"cat girl in school uniform, brown hair, orange background, striped bow, white shirt, black jacket, blue bowtie, long sleeves, fangs, sharp teeth, facial mark, striped bowtie, upper body, hoodie, looking at viewer, sharp teeth, tongue.\"\n", "#,\"нарядная новогодняя елка, красивые игрушки, звезда сверху, огоньки, на тёмном фоне\"\n", ",\"In the dark, smoky background, a figure clad in a vibrant red bodysuit stands out, its intricate designs and armor plating giving it a formidable appearance. The helmet, matching the suit, features a glowing red visor with glowing red eyes, enhancing the character's mysterious aura.\"\n", ",\"A young woman with striking features, a mix of black and gold hair floating around her head, vibrant yellow eyes half-closed, and a blue sweater contrasting the black background, framed in a simple yet striking composition.\"\n", ",'A fluffy domestic cat with piercing green eyes sits attentively in a sunlit room filled with natural light, its soft fur reflecting warm hues of orange through golden windows.'\n", ",\"A fierce woman in black-and-white, wearing a spiked iron mask with sharp metallic spikes, sporting a high ponytail, her skin marked with battle-dirt, and eyes that reflect determination and strength. The mask's menacing aura contrasts with her intense expression, capturing her mysterious and intimidating nature.\"\n", ",\"A close-up of an astronaut's helmet with frosted, opaque visor, reflecting space's cold, frozen texture, resting on the visor a butterfly with vibrant, intricately patterned wings, and distant stars' faint glow.\"\n", ",\"A watercolor painting of a knight in a hazy blue field, his armor blending soft greys and silvers, holding a large red rose, with a serene yet commanding posture against distant mountains and wildflowers.\"\n", ",\"A warm glow of lanterns casts soft light over a snowy forest trail lined with tall, snow-covered trees, their branches casting a gentle glow. Soft falling snowflakes create a serene atmosphere, while a dark, mysterious background adds to the solitude and magic of the scene.\"\n", ",\"Ein junges Mädchen mit langen braunen Haaren und braunen Augen steht an einer Backsteinwand, trägt ein weißes Hemd mit einem schwarzen Matrosenkragen und einen schwarzen Faltenrock. Sie lächelt und schaut direkt zum Betrachter, während Sonnenlicht durch die grünen Ranken hinter ihr fällt.\"\n", ",\"air in flowing, ethereal lines with subtle gradients and lightness, capturing the intangible essence through wind, breath, and atmospheric effects.\"\n", ",\"A radiant voluptuous woman in Arizona's Grand Canyon at twilight, her fiery ginger hair cascading over a halter top with intricate lace, plunging neckline, black thongs, silver rings, and stacked bangles, surrounded by deep purples and burnt oranges in the sky, her light-blue eyes glowing with wonder as she gazes at the luminous beauty of the night.\"\n", ",\"A 3D render of a hyper-realistic digital illustration of a surreal, moonlit scene. A silhouette of a mannequin man is climbing a delicate ladder suspended in an infinite gray twilight. The ladder is attached to a cloud. The background is a vast, starry sky with a crescent moon. The malemannequin is reaching for a glowing star.\"\n", ",\"A digitally painted detailed manhwa-style novel illustration of a woman in a corset-laced red romper, black tights with a fantasy pattern, seated on a window seat, her brown hair loose and framing her face, with a deep royal red accent on her silhouette, and a romantic, mystical atmosphere evoking adventure and wonder.\"\n", "#,\"тарелка с кибер едой будущего \"\n", "#,\"ариец в имперских доспехах будущего \"\n", "#,\"Эльфиечка Дня желает всем доброго утра и хорошего настроения!\"\n", ",\"general, holding, 1boy, hat, standing, male focus, black headwear, full body, solo, dark, parody, microphone, fake screenshot, teeth, blood, looking at viewer, gameplay mechanics, chibi, pokemon (creature)\"\n", ",\"a young anime girl with long, dark hair styled in two ponytails. She is wearing a white collared shirt and a green bow tie. In the background, there are two additional characters, one of whom is holding a hairbrush and the other is looking at the girl. The girl's expression appears to be one of sadness or disappointment. Pentagon Pixiv\"\n", "#,\"девушка в толстовке , без капюшона , длинные черные штаны , карие глаза, светлые волосы, anime, bloody theme \"\n", "#,\"ржавый краб, логотип, очень ржавый, очень краб\"\n", ",\"an anime-style illustration of a young girl with blue eyes and white hair, wearing a green dress and a black bow in her hair, sitting on a wooden bench, with a full moon in the background and a tree in the distance.\"\n", ",\"A hyper-realistic tattoo on a young woman's back features an alien creature with intricate body and facial features, rendered in hyper-detailed textures, revealing the alien's translucent skin beneath.\"\n", "]\n", "prompts2 = [ \"a cat\", \"a dog\", \"1girl\", \"man\"]\n", "\n", "generator = torch.Generator(device=\"cuda\").manual_seed(42)\n", "negative_prompt=\"bad quality, low resolution, oversaturated, sketch\"\n", "\n", "all_images = []\n", "# Обработка батчей с прогресс-баром\n", "for i, prompt in enumerate(prompts):\n", " image = pipeline(\n", " prompt = prompt,\n", " negative_prompt = negative_prompt,\n", " guidance_scale = 4,\n", " width = 1024,\n", " height = 1280,\n", " seed = 42,\n", " batch_size = 1,\n", " )[0]\n", " all_images.extend(image)\n", "\n", "import matplotlib.pyplot as plt\n", "import math\n", "\n", "def display_image_grid(images, prompts, cols=3, save_path=None):\n", " \"\"\"\n", " Отображает грид изображений с сохранением соотношения сторон и подписями.\n", " \"\"\"\n", " n = len(images)\n", " rows = math.ceil(n / cols)\n", "\n", " # Создаем фигуру с учетом реального соотношения сторон (640/576 ≈ 1.11)\n", " fig_width = cols * 4\n", " fig_height = rows * 4.5\n", " fig, axes = plt.subplots(rows, cols, figsize=(fig_width, fig_height))\n", "\n", " # Если только один ряд или один столбец, делаем axes списком\n", " if rows == 1:\n", " axes = [axes]\n", " if cols == 1:\n", " axes = [[ax] for ax in axes]\n", "\n", " axes = axes.flatten()\n", "\n", " for i, (img, prompt) in enumerate(zip(images, prompts)):\n", " ax = axes[i]\n", " ax.imshow(img)\n", " ax.axis(\"off\")\n", " ax.set_aspect(\"equal\") # сохраняем соотношение сторон\n", "\n", " # Урезаем и разбиваем подпись\n", " truncated_prompt = prompt[:60] + \"…\" if len(prompt) > 60 else prompt\n", " words = truncated_prompt.split(\" \")\n", " half = len(words) // 2\n", " line1 = \" \".join(words[:half])\n", " line2 = \" \".join(words[half:])\n", " ax.set_title(f\"{line1}\\n{line2}\", fontsize=10, wrap=True)\n", "\n", " # Прячем лишние subplot'ы, если их больше, чем изображений\n", " for j in range(len(images), len(axes)):\n", " axes[j].axis(\"off\")\n", "\n", " plt.tight_layout(pad=1.5)\n", "\n", " if save_path:\n", " plt.savefig(save_path, bbox_inches=\"tight\", dpi=400, format=\"jpeg\")\n", "\n", " plt.show()\n", "\n", "\n", "display_image_grid(all_images, prompts, save_path=\"media/result_grid.jpg\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "044a8b5e-f786-48ea-86a3-cb581b3ca1c9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Keyword arguments {'trust_remote_code': True} are not expected by SdxsPipeline and will be ignored.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "28c75e29f2b945f5a6773811b5384119", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading pipeline components...: 0%| | 0/5 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import torch\n", "from diffusers import DiffusionPipeline\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "dtype = torch.float16 if torch.cuda.is_available() else torch.float32\n", "\n", "pipe_id = \"/workspace/sdxs-08b\"\n", "pipe = DiffusionPipeline.from_pretrained(\n", " pipe_id,\n", " torch_dtype=dtype,\n", " trust_remote_code=True\n", ").to(device)\n", "\n", "prompt = \"girl, smiling, red eyes, blue hair, white shirt\"\n", "negative_prompt=\"low quality, bad quality\"\n", "image = pipe(\n", " prompt=prompt,\n", " negative_prompt = negative_prompt,\n", ").images[0]\n", "\n", "image.show(image)\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "76c7e40e-0326-42bc-b717-e46bcf790dfa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "UNet2DConditionModel(\n", " (conv_in): Conv2d(16, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_proj): Timesteps()\n", " (time_embedding): TimestepEmbedding(\n", " (linear_1): Linear(in_features=256, out_features=1024, bias=True)\n", " (act): SiLU()\n", " (linear_2): Linear(in_features=1024, out_features=1024, bias=True)\n", " )\n", " (down_blocks): ModuleList(\n", " (0): DownBlock2D(\n", " (resnets): ModuleList(\n", " (0-1): 2 x ResnetBlock2D(\n", " (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n", " (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=256, bias=True)\n", " (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " )\n", " )\n", " (downsamplers): ModuleList(\n", " (0): Downsample2D(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", " )\n", " )\n", " )\n", " (1): CrossAttnDownBlock2D(\n", " (attentions): ModuleList(\n", " (0-1): 2 x Transformer2DModel(\n", " (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n", " (proj_in): Linear(in_features=512, out_features=512, bias=True)\n", " (transformer_blocks): ModuleList(\n", " (0): BasicTransformerBlock(\n", " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (attn1): Attention(\n", " (to_q): Linear(in_features=512, out_features=512, bias=False)\n", " (to_k): Linear(in_features=512, out_features=512, bias=False)\n", " (to_v): Linear(in_features=512, out_features=512, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (attn2): Attention(\n", " (to_q): Linear(in_features=512, out_features=512, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=512, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=512, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (ff): FeedForward(\n", " (net): ModuleList(\n", " (0): GEGLU(\n", " (proj): Linear(in_features=512, out_features=4096, bias=True)\n", " )\n", " (1): Dropout(p=0.0, inplace=False)\n", " (2): Linear(in_features=2048, out_features=512, bias=True)\n", " )\n", " )\n", " )\n", " )\n", " (proj_out): Linear(in_features=512, out_features=512, bias=True)\n", " )\n", " )\n", " (resnets): 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ModuleList(\n", " (0): Linear(in_features=1024, out_features=1024, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (attn2): Attention(\n", " (to_q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=1024, out_features=1024, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm3): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (ff): FeedForward(\n", " (net): ModuleList(\n", " (0): GEGLU(\n", " (proj): Linear(in_features=1024, out_features=8192, bias=True)\n", " )\n", " (1): Dropout(p=0.0, inplace=False)\n", " (2): Linear(in_features=4096, out_features=1024, bias=True)\n", " )\n", " )\n", " )\n", " )\n", " (proj_out): Linear(in_features=1024, out_features=1024, bias=True)\n", " )\n", " )\n", " (resnets): ModuleList(\n", " (0-2): 3 x ResnetBlock2D(\n", " (norm1): GroupNorm(32, 2048, eps=1e-05, affine=True)\n", " (conv1): Conv2d(2048, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", " (norm2): GroupNorm(32, 1024, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " )\n", " (upsamplers): ModuleList(\n", " (0): Upsample2D(\n", " (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " )\n", " )\n", " )\n", " (1): CrossAttnUpBlock2D(\n", " (attentions): ModuleList(\n", " (0-2): 3 x Transformer2DModel(\n", " (norm): GroupNorm(32, 1024, eps=1e-06, affine=True)\n", " (proj_in): Linear(in_features=1024, out_features=1024, bias=True)\n", " (transformer_blocks): ModuleList(\n", " (0-3): 4 x BasicTransformerBlock(\n", " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (attn1): Attention(\n", " (to_q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=1024, out_features=1024, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (attn2): Attention(\n", " (to_q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=1024, out_features=1024, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm3): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (ff): FeedForward(\n", " (net): ModuleList(\n", " (0): GEGLU(\n", " (proj): Linear(in_features=1024, out_features=8192, bias=True)\n", " )\n", " (1): Dropout(p=0.0, inplace=False)\n", " (2): Linear(in_features=4096, out_features=1024, bias=True)\n", " )\n", " )\n", " )\n", " )\n", " (proj_out): Linear(in_features=1024, out_features=1024, bias=True)\n", " )\n", " )\n", " (resnets): ModuleList(\n", " (0-1): 2 x ResnetBlock2D(\n", " (norm1): GroupNorm(32, 2048, eps=1e-05, affine=True)\n", " (conv1): Conv2d(2048, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", " (norm2): GroupNorm(32, 1024, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (2): ResnetBlock2D(\n", " (norm1): GroupNorm(32, 1536, eps=1e-05, affine=True)\n", " (conv1): Conv2d(1536, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", " (norm2): GroupNorm(32, 1024, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(1536, 1024, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " )\n", " (upsamplers): ModuleList(\n", " (0): Upsample2D(\n", " (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " )\n", " )\n", " )\n", " (2): CrossAttnUpBlock2D(\n", " (attentions): ModuleList(\n", " (0-2): 3 x Transformer2DModel(\n", " (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n", " (proj_in): Linear(in_features=512, out_features=512, bias=True)\n", " (transformer_blocks): ModuleList(\n", " (0): BasicTransformerBlock(\n", " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (attn1): Attention(\n", " (to_q): Linear(in_features=512, out_features=512, bias=False)\n", " (to_k): Linear(in_features=512, out_features=512, bias=False)\n", " (to_v): Linear(in_features=512, out_features=512, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (attn2): Attention(\n", " (to_q): Linear(in_features=512, out_features=512, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=512, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=512, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (ff): FeedForward(\n", " (net): ModuleList(\n", " (0): GEGLU(\n", " (proj): Linear(in_features=512, out_features=4096, bias=True)\n", " )\n", " (1): Dropout(p=0.0, inplace=False)\n", " (2): Linear(in_features=2048, out_features=512, bias=True)\n", " )\n", " )\n", " )\n", " )\n", " (proj_out): Linear(in_features=512, out_features=512, bias=True)\n", " )\n", " )\n", " (resnets): ModuleList(\n", " (0): ResnetBlock2D(\n", " (norm1): GroupNorm(32, 1536, eps=1e-05, affine=True)\n", " (conv1): Conv2d(1536, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=512, bias=True)\n", " (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(1536, 512, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (1): ResnetBlock2D(\n", " (norm1): GroupNorm(32, 1024, eps=1e-05, affine=True)\n", " (conv1): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=512, bias=True)\n", " (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (2): ResnetBlock2D(\n", " (norm1): GroupNorm(32, 768, eps=1e-05, affine=True)\n", " (conv1): Conv2d(768, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=512, bias=True)\n", " (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " )\n", " (upsamplers): ModuleList(\n", " (0): Upsample2D(\n", " (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " )\n", " )\n", " )\n", " (3): UpBlock2D(\n", " (resnets): ModuleList(\n", " (0): ResnetBlock2D(\n", " (norm1): GroupNorm(32, 768, eps=1e-05, affine=True)\n", " (conv1): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=256, bias=True)\n", " (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (1-2): 2 x ResnetBlock2D(\n", " (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n", " (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=256, bias=True)\n", " (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " )\n", " )\n", " )\n", " (mid_block): UNetMidBlock2DCrossAttn(\n", " (attentions): ModuleList(\n", " (0): Transformer2DModel(\n", " (norm): GroupNorm(32, 1024, eps=1e-06, affine=True)\n", " (proj_in): Linear(in_features=1024, out_features=1024, bias=True)\n", " (transformer_blocks): ModuleList(\n", " (0-7): 8 x BasicTransformerBlock(\n", " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (attn1): Attention(\n", " (to_q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=1024, out_features=1024, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (attn2): Attention(\n", " (to_q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (to_out): ModuleList(\n", " (0): Linear(in_features=1024, out_features=1024, bias=True)\n", " (1): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (norm3): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (ff): FeedForward(\n", " (net): ModuleList(\n", " (0): GEGLU(\n", " (proj): Linear(in_features=1024, out_features=8192, bias=True)\n", " )\n", " (1): Dropout(p=0.0, inplace=False)\n", " (2): Linear(in_features=4096, out_features=1024, bias=True)\n", " )\n", " )\n", " )\n", " )\n", " (proj_out): Linear(in_features=1024, out_features=1024, bias=True)\n", " )\n", " )\n", " (resnets): ModuleList(\n", " (0-1): 2 x ResnetBlock2D(\n", " (norm1): GroupNorm(32, 1024, eps=1e-05, affine=True)\n", " (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (time_emb_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", " (norm2): GroupNorm(32, 1024, eps=1e-05, affine=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (nonlinearity): SiLU()\n", " )\n", " )\n", " )\n", " (conv_norm_out): GroupNorm(32, 256, eps=1e-05, affine=True)\n", " (conv_act): SiLU()\n", " (conv_out): Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", ")\n" ] } ], "source": [ "import torch\n", "from diffusers import UNet2DConditionModel\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "dtype = torch.float16 if torch.cuda.is_available() else torch.float32\n", "\n", "pipe_id = \"/workspace/sdxs/unet\"\n", "unet = UNet2DConditionModel.from_pretrained(\n", " pipe_id,\n", " torch_dtype=dtype,\n", ").to(device)\n", "\n", "unet.save_pretrained(pipe_id)\n", "print(unet)" ] }, { "cell_type": "code", "execution_count": 1, "id": "188279e8-f420-425a-9a29-01d5b60f8383", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n", "None\n", "Модель успешно проапгрейжена! Можно дообучать.\n" ] } ], "source": [ "import torch\n", "from diffusers import UNet2DConditionModel\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "dtype = torch.float16 if torch.cuda.is_available() else torch.float32\n", "\n", "pipe_id = \"unet_simple\"\n", "old_unet = UNet2DConditionModel.from_pretrained(\n", " pipe_id,\n", " torch_dtype=dtype,\n", ").to(device)\n", "print(old_unet.config.addition_embed_type)\n", "\n", "# 2. Создаем новую конфигурацию (добавляем text_time)\n", "new_config = old_unet.config\n", "new_config.addition_embed_type = \"text_time\"\n", "new_config.addition_time_embed_dim = 1024 # Размер вашего пулинга\n", "\n", "# 3. Инициализируем новую (пустую) модель\n", "new_unet = UNet2DConditionModel.from_config(new_config)\n", "\n", "# 4. Переносим веса\n", "new_state_dict = new_unet.state_dict()\n", "old_state_dict = old_unet.state_dict()\n", "\n", "for name, param in old_state_dict.items():\n", " if \"time_embedding.linear_1.weight\" in name:\n", " # МАГИЯ ХИРУРГИИ\n", " # param (старый вес) имеет форму [Out, 256]\n", " # new_unet...weight имеет форму [Out, 256 + 1024]\n", " \n", " # 1. Берем веса новой модели (там сейчас мусор/рандом)\n", " new_w = new_state_dict[name]\n", " \n", " # 2. Зануляем ту часть, которая отвечает за новый пулинг (это важно!)\n", " # Предположим, что время идет первым, а пулинг вторым (зависит от реализации concat)\n", " # Обычно concat([time, pool]), значит time занимает первые индексы\n", " time_dim = param.shape[1] \n", " \n", " new_w[:, :time_dim] = param # Копируем старое знание\n", " new_w[:, time_dim:] = 0 # Новые связи делаем \"неактивными\" на старте\n", " \n", " new_state_dict[name] = new_w\n", " \n", " elif \"time_embedding.linear_1.bias\" in name:\n", " # Биас просто копируем, так как выходной размер слоя не меняется\n", " new_state_dict[name] = param\n", " \n", " elif name in new_state_dict:\n", " # Все остальные слои просто копируем\n", " if new_state_dict[name].shape == param.shape:\n", " new_state_dict[name] = param\n", " else:\n", " print(f\"Внимание: несовпадение форм для {name}, пропускаем\")\n", "\n", "# 5. Загружаем собранный словарь\n", "new_unet.load_state_dict(new_state_dict)\n", "print(new_unet.config.addition_embed_type)\n", "new_unet.save_pretrained(\"unet\")\n", "print(\"Модель успешно проапгрейжена! Можно дообучать.\")" ] }, { "cell_type": "code", "execution_count": 1, "id": "7c3461ac-8544-4df4-8ab6-e54b239ca4e5", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "239e51a54c2343a4ba2f73a6686f725c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0bcbbdde09184b5c8b7b0b31d5899565", "version_major": 2, "version_minor": 0 }, "text/plain": [ "vocab.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d4ef94499174412b8a1e1a6b0884a03", "version_major": 2, "version_minor": 0 }, "text/plain": [ "merges.txt: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c98c9a3b005443209f2839682001028b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer.json: 0%| | 0.00/11.4M [00:00', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n", "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151657: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151658: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151665: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151666: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151667: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151668: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "}\n", ")\n", "saved\n" ] } ], "source": [ "from transformers import AutoTokenizer, AutoModel\n", "import torch\n", "\n", "device=\"cuda\"\n", "dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32\n", "\n", "\n", "model=\"Qwen/Qwen3-VL-2B-Instruct\"\n", "tokenizer = AutoTokenizer.from_pretrained(model)\n", "text_model = AutoModel.from_pretrained(model,torch_dtype=dtype).to(device).eval()\n", "\n", "print(text_model)\n", "print(tokenizer)\n", "tokenizer.save_pretrained(\"tokenizer\")\n", "text_model.save_pretrained(\"text_encoder\")\n", "print('saved')\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "638c946a-fd68-4bde-ae87-263ef5ea8679", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Загружаем токенизатор и базовый конфиг...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "85ccbdedb64049719784bdb1c35a5ab7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cc43a6865576408eb99676f101cc754f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "vocab.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d83333a8404c4c82af5b55ed9500d232", "version_major": 2, "version_minor": 0 }, "text/plain": [ "merges.txt: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f744e32d288f41bbb243ee90fef66000", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a4cf7d322ec45ada41bf2984081ea95", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "2. Собираем текстовый конфиг Qwen3...\n", "3. Загружаем веса VL модели (через базовый AutoModel)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a848fd2acf984f1da854b3833ea1429e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/4.26G [00:00