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Commit b916e230 authored by chenpangpang's avatar chenpangpang
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feat: 新版kolors初始提交

parent b3fae090
Pipeline #1579 canceled with stages
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Kolors/kolors/models/unet_2d_condition.py 0 → 100644
View file @ b916e230
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Kolors/kolors/pipelines/pipeline_stable_diffusion_xl_chatglm_256.py
View file @ b916e230
......@@ -331,7 +331,7 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoad
position_ids=text_inputs['position_ids'],
output_hidden_states=True)
prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
text_proj = output.hidden_states[-1][-1, :, :].clone() # [batch_size, 4096]
pooled_prompt_embeds = output.hidden_states[-1][-1, :, :].clone() # [batch_size, 4096]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
......@@ -387,7 +387,7 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoad
position_ids=uncond_input['position_ids'],
output_hidden_states=True)
negative_prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
negative_text_proj = output.hidden_states[-1][-1, :, :].clone() # [batch_size, 4096]
negative_pooled_prompt_embeds = output.hidden_states[-1][-1, :, :].clone() # [batch_size, 4096]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
......@@ -409,15 +409,16 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoad
# negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
negative_prompt_embeds = negative_prompt_embeds_list[0]
bs_embed = text_proj.shape[0]
text_proj = text_proj.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
negative_text_proj = negative_text_proj.repeat(1, num_images_per_prompt).view(
bs_embed = pooled_prompt_embeds.shape[0]
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
return prompt_embeds, negative_prompt_embeds, text_proj, negative_text_proj
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
......
Kolors/requirements.txt
View file @ b916e230
fire
triton
pydantic
accelerate==0.27.2
deepspeed==0.8.1
huggingface-hub==0.20.2
imageio==2.25.1
numpy==1.21.6
omegaconf==2.3.0
pandas==1.3.5
Pillow==9.4.0
tokenizers==0.13.2
torch==1.13.1
torchvision==0.14.1
transformers==4.26.1
xformers==0.0.16
safetensors==0.3.3
diffusers==0.28.2
sentencepiece==0.1.99
gradio==3.38.0
matplotlib-inline
ipython
accelerate
diffusers
invisible_watermark
torch
transformers
sentencepiece
gradio==3.40.0
\ No newline at end of file
Kolors/scripts/sample.py deleted 100644 → 0
View file @ b3fae090
import os, torch
# from PIL import Image
from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import StableDiffusionXLPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from diffusers import UNet2DConditionModel, AutoencoderKL
from diffusers import EulerDiscreteScheduler
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def infer(prompt):
ckpt_dir = f'{root_dir}/weights/Kolors'
text_encoder = ChatGLMModel.from_pretrained(
f'{ckpt_dir}/text_encoder',
torch_dtype=torch.float16).half()
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half()
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half()
pipe = StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
force_zeros_for_empty_prompt=False)
pipe = pipe.to("cuda")
pipe.enable_model_cpu_offload()
image = pipe(
prompt=prompt,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=5.0,
num_images_per_prompt=1,
generator= torch.Generator(pipe.device).manual_seed(66)).images[0]
image.save(f'{root_dir}/scripts/outputs/sample_test.jpg')
if __name__ == '__main__':
import fire
fire.Fire(infer)
Kolors/scripts/sampleui.py deleted 100644 → 0
View file @ b3fae090
import os
import torch
import gradio as gr
# from PIL import Image
from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import StableDiffusionXLPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from diffusers import UNet2DConditionModel, AutoencoderKL
from diffusers import EulerDiscreteScheduler
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Initialize global variables for models and pipeline
text_encoder = None
tokenizer = None
vae = None
scheduler = None
unet = None
pipe = None
def remove_folder(path):
if os.path.exists(path):
if os.path.isfile(path) or os.path.islink(path):
os.remove(path)
else:
for filename in os.listdir(path):
remove_folder(os.path.join(path, filename))
os.rmdir(path)
def load_models():
global text_encoder, tokenizer, vae, scheduler, unet, pipe
if text_encoder is None:
ckpt_dir = f'{root_dir}/weights/Kolors'
# Load the text encoder on CPU (this speeds stuff up 2x)
text_encoder = ChatGLMModel.from_pretrained(
f'{ckpt_dir}/text_encoder',
torch_dtype=torch.float16).to('cpu').half()
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
# Load the VAE and UNet on GPU
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to('cuda')
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to('cuda')
# Prepare the pipeline
pipe = StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
force_zeros_for_empty_prompt=False)
pipe = pipe.to("cuda")
pipe.enable_model_cpu_offload() # Enable offloading to balance CPU/GPU usage
def infer(prompt, use_random_seed, seed, height, width, num_inference_steps, guidance_scale, num_images_per_prompt):
load_models()
if use_random_seed:
seed = torch.randint(0, 2 ** 32 - 1, (1,)).item()
generator = torch.Generator(pipe.device).manual_seed(seed)
images = pipe(
prompt=prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
generator=generator
).images
saved_images = []
output_dir = f'{root_dir}/scripts/outputs'
remove_folder(output_dir)
os.makedirs(output_dir, exist_ok=True)
for i, image in enumerate(images):
file_path = os.path.join(output_dir, 'sample_test.jpg')
base_name, ext = os.path.splitext(file_path)
counter = 1
while os.path.exists(file_path):
file_path = f"{base_name}_{counter}{ext}"
counter += 1
image.save(file_path)
saved_images.append(file_path)
return saved_images
def gradio_interface():
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.Markdown("## Kolors: Diffusion Model Gradio Interface")
prompt = gr.Textbox(label="Prompt")
use_random_seed = gr.Checkbox(label="Use Random Seed", value=True)
seed = gr.Slider(minimum=0, maximum=2 ** 32 - 1, step=1, label="Seed", randomize=True, visible=False)
use_random_seed.change(lambda x: gr.update(visible=not x), use_random_seed, seed)
height = gr.Slider(minimum=128, maximum=2048, step=64, label="Height", value=1024)
width = gr.Slider(minimum=128, maximum=2048, step=64, label="Width", value=1024)
num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, label="Inference Steps", value=50)
guidance_scale = gr.Slider(minimum=1.0, maximum=20.0, step=0.1, label="Guidance Scale", value=5.0)
num_images_per_prompt = gr.Slider(minimum=1, maximum=10, step=1, label="Images per Prompt", value=1)
btn = gr.Button("Generate Image")
with gr.Column():
output_images = gr.Gallery(label="Output Images", elem_id="output_gallery")
btn.click(
fn=infer,
inputs=[prompt, use_random_seed, seed, height, width, num_inference_steps, guidance_scale,
num_images_per_prompt],
outputs=output_images
)
return demo
if __name__ == '__main__':
load_models()
gradio_interface().launch(server_name='0.0.0.0')
Kolors/setup.py deleted 100644 → 0
View file @ b3fae090
from setuptools import setup, find_packages
setup(
name="kolors",
version="0.1",
author="Kolors",
description="The training and inference code for Kolors models.",
packages=find_packages(),
install_requires=[],
dependency_links=[],
)
hf_down.py 0 → 100644
View file @ b916e230
# pip install huggingface-cli
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
model_list = [
"Kwai-Kolors/Kolors",
"Kwai-Kolors/Kolors-IP-Adapter-Plus"
]
os.system("pip install -U huggingface-hub")
for model_path in model_list:
os.system(
f"huggingface-cli download --resume-download {model_path} --local-dir ./{model_path} --local-dir-use-symlinks False")
run.sh
View file @ b916e230
#!/bin/bash
cd /root/Kolors
source activate kolors
python3 scripts/sampleui.py
python app.py
启动器.ipynb
View file @ b916e230
......@@ -7,14 +7,16 @@
"tags": []
},
"source": [
"## 说明\n",
"\n",
"- 启动和重启 Notebook 点上方工具栏中的「重启并运行所有单元格」。出现 `http://0.0.0.0:7860` 这个字样就算成功了。可以去控制台打开「自定义服务」了\n",
"- 访问自定义服务端口号设置为7860\n",
"\n",
"## 功能介绍\n",
"## 项目介绍\n",
"- 原项目地址：https://github.com/Kwai-Kolors/Kolors\n",
"- 可图大模型是由快手可图团队开发的基于潜在扩散的大规模文本到图像生成模型。Kolors 在数十亿图文对下进行训练，在视觉质量、复杂语义理解、文字生成（中英文字符）等方面，相比于开源/闭源模型，都展示出了巨大的优势。同时，Kolors 支持中英双语，在中文特色内容理解方面更具竞争力。"
"- 可图大模型是由快手可图团队开发的基于潜在扩散的大规模文本到图像生成模型。Kolors 在数十亿图文对下进行训练，在视觉质量、复杂语义理解、文字生成（中英文字符）等方面，相比于开源/闭源模型，都展示出了巨大的优势。同时，Kolors 支持中英双语，在中文特色内容理解方面更具竞争力。\n",
"## 使用说明\n",
"- 启动和重启 Notebook 点上方工具栏中的「重启并运行所有单元格」。出现如下内容就算成功了：\n",
" - `Running on local URL: http://0.0.0.0:7860`\n",
" - `Running on public URL: https://xxxxxxxxxxxxxxx.gradio.live`\n",
"- 通过以下方式开启页面：\n",
" - 控制台打开「自定义服务」了，访问自定义服务端口号设置为7860\n",
" - 直接打开显示的公开链接`public URL`\n"
]
},
{
......@@ -24,39 +26,34 @@
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/opt/conda/envs/kolors/lib/python3.8/site-packages/gradio_client/documentation.py:105: UserWarning: Could not get documentation group for <class 'gradio.mix.Parallel'>: No known documentation group for module 'gradio.mix'\n",
" warnings.warn(f\"Could not get documentation group for {cls}: {exc}\")\n",
"/opt/conda/envs/kolors/lib/python3.8/site-packages/gradio_client/documentation.py:105: UserWarning: Could not get documentation group for <class 'gradio.mix.Series'>: No known documentation group for module 'gradio.mix'\n",
" warnings.warn(f\"Could not get documentation group for {cls}: {exc}\")\n",
"/opt/conda/envs/kolors/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.\n",
" deprecate(\"Transformer2DModelOutput\", \"1.0.0\", deprecation_message)\n",
"Loading checkpoint shards: 100%|██████████████████| 7/7 [00:16<00:00, 2.35s/it]\n",
"Running on local URL: http://0.0.0.0:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n",
"IMPORTANT: You are using gradio version 3.38.0, however version 4.29.0 is available, please upgrade.\n",
"--------\n",
"100%|███████████████████████████████████████████| 50/50 [00:26<00:00, 1.90it/s]\n"
]
}
],
"outputs": [],
"source": [
"# 启动\n",
"!bash run.sh"
]
},
{
"cell_type": "markdown",
"source": [
"---\n",
"**扫码关注公众号，获取更多资讯**<br>\n",
"<div align=center>\n",
"<img src=\"assets/二维码.jpeg\" width = 20% />\n",
"</div>\n"
],
"metadata": {
"collapsed": false
},
"id": "2f54158c2967bc25"
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e81ae9d-3a34-43a0-943a-ff5e9d6ce961",
"metadata": {},
"outputs": [],
"source": []
"source": [],
"metadata": {
"collapsed": false
},
"id": "6dc59fbbcf222b6b"
}
],
"metadata": {
......
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