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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 on demand | |
| 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 | |
| ).to("cuda") | |
| # 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 GPU for inference and clear unnecessary variables | |
| with torch.no_grad(): | |
| generator = torch.Generator(pipe.device).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() | |