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import os
import torch
import random
from huggingface_hub import snapshot_download
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
import gradio as gr
# Download the model files
ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
# Function to load models
def load_models():
text_encoder = ChatGLMModel.from_pretrained(
os.path.join(ckpt_dir, 'text_encoder'),
torch_dtype=torch.float16).half()
tokenizer = ChatGLMTokenizer.from_pretrained(os.path.join(ckpt_dir, 'text_encoder'))
vae = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir, "vae"), revision=None).half()
scheduler = EulerDiscreteScheduler.from_pretrained(os.path.join(ckpt_dir, "scheduler"))
unet = UNet2DConditionModel.from_pretrained(os.path.join(ckpt_dir, "unet"), revision=None).half()
return StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
force_zeros_for_empty_prompt=False
)
# Create a global variable to hold the pipeline
pipe = load_models()
def generate_image(prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, num_images_per_prompt, use_random_seed, seed, progress=gr.Progress(track_tqdm=True)):
if use_random_seed:
seed = random.randint(0, 2**32 - 1)
else:
seed = int(seed) # Ensure seed is an integer
# Move the model to the CPU for inference and clear unnecessary variables
with torch.no_grad():
generator = torch.Generator().manual_seed(seed)
result = pipe(
prompt=prompt,
negative_prompt=negative_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
)
image = result.images
return image, seed
# Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt"),
gr.Textbox(label="Negative Prompt")
],
additional_inputs=[
gr.Slider(512, 2048, 1024, step=64, label="Height"),
gr.Slider(512, 2048, 1024, step=64, label="Width"),
gr.Slider(20, 50, 20, step=1, label="Number of Inference Steps"),
gr.Slider(1, 20, 5, step=0.5, label="Guidance Scale"),
gr.Slider(1, 4, 1, step=1, label="Number of images per prompt"),
gr.Checkbox(label="Use Random Seed", value=True),
gr.Number(label="Seed", value=0, precision=0)
],
additional_inputs_accordion=gr.Accordion(label="Advanced settings", open=False),
outputs=[
gr.Gallery(label="Result", elem_id="gallery", show_label=False),
gr.Number(label="Seed Used")
],
title="Kolors",
theme='bethecloud/storj_theme',
)
iface.launch()
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