import spaces 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") # Load the 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() pipe = StableDiffusionXLPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, force_zeros_for_empty_prompt=False) pipe = pipe.to("cuda") @spaces.GPU(duration=200) 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 image = 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=torch.Generator(pipe.device).manual_seed(seed) ).images return image, seed description = """

Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis

[Official Website] [Tech Report] [Model Page] [Github]

""" # 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", description=description, theme='bethecloud/storj_theme', ) iface.launch(debug=True)