import spaces import random import torch from huggingface_hub import snapshot_download from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor from kolors.pipelines import pipeline_stable_diffusion_xl_chatglm_256_ipadapter, pipeline_stable_diffusion_xl_chatglm_256 from kolors.models.modeling_chatglm import ChatGLMModel from kolors.models.tokenization_chatglm import ChatGLMTokenizer from kolors.models import unet_2d_condition from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel import gradio as gr import numpy as np device = "cuda" ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors") ckpt_IPA_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus") text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device) tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder') vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device) scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler") unet_t2i = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device) unet_i2i = unet_2d_condition.UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device) image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_IPA_dir}/image_encoder',ignore_mismatched_sizes=True).to(dtype=torch.float16, device=device) ip_img_size = 336 clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size) pipe_t2i = pipeline_stable_diffusion_xl_chatglm_256.StableDiffusionXLPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet_t2i, scheduler=scheduler, force_zeros_for_empty_prompt=False ).to(device) pipe_i2i = pipeline_stable_diffusion_xl_chatglm_256_ipadapter.StableDiffusionXLPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet_i2i, scheduler=scheduler, image_encoder=image_encoder, feature_extractor=clip_image_processor, force_zeros_for_empty_prompt=False ).to(device) if hasattr(pipe_i2i.unet, 'encoder_hid_proj'): pipe_i2i.unet.text_encoder_hid_proj = pipe_i2i.unet.encoder_hid_proj pipe_i2i.load_ip_adapter(f'{ckpt_IPA_dir}' , subfolder="", weight_name=["ip_adapter_plus_general.bin"]) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 @spaces.GPU def infer(prompt, ip_adapter_image = None, ip_adapter_scale = 0.5, negative_prompt = "", seed = 0, randomize_seed = False, width = 1024, height = 1024, guidance_scale = 5.0, num_inference_steps = 25 ): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) if ip_adapter_image is None: pipe_t2i.to(device) image = pipe_t2i( prompt = prompt, negative_prompt = negative_prompt, guidance_scale = guidance_scale, num_inference_steps = num_inference_steps, width = width, height = height, generator = generator ).images[0] return image else: pipe_i2i.to(device) image_encoder.to(device) pipe_i2i.image_encoder = image_encoder pipe_i2i.set_ip_adapter_scale([ip_adapter_scale]) image = pipe_i2i( prompt=prompt , ip_adapter_image=[ip_adapter_image], negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, num_images_per_prompt=1, generator=generator ).images[0] return image examples = [ ] css=""" #col-left { margin: 0 auto; max-width: 600px; } #col-right { margin: 0 auto; max-width: 750px; } """ def load_description(fp): with open(fp, 'r', encoding='utf-8') as f: content = f.read() return content with gr.Blocks(css=css) as Kolors: gr.HTML(load_description("assets/title.md")) with gr.Row(): with gr.Column(elem_id="col-left"): with gr.Row(): prompt = gr.Textbox( label="Prompt", placeholder="Enter your prompt", lines=2 ) with gr.Row(): ip_adapter_image = gr.Image(label="Image Prompt (optional)", type="pil") with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Textbox( label="Negative prompt", placeholder="Enter a negative prompt", visible=True, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=10, maximum=50, step=1, value=25, ) with gr.Row(): ip_adapter_scale = gr.Slider( label="Image influence scale", info="Use 1 for creating variations", minimum=0.0, maximum=1.0, step=0.05, value=0.5, ) with gr.Row(): run_button = gr.Button("Run") with gr.Column(elem_id="col-right"): result = gr.Image(label="Result", show_label=False) with gr.Row(): gr.Examples( fn = infer, examples = examples, inputs = [prompt, ip_adapter_image, ip_adapter_scale], outputs = [result] ) run_button.click( fn = infer, inputs = [prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs = [result] ) Kolors.queue().launch(debug=True)