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import gradio as gr |
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import numpy as np |
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import random |
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import torch |
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from PIL import Image |
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import os |
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from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor |
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from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter import StableDiffusionXLPipeline |
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from kolors.models.modeling_chatglm import ChatGLMModel |
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from kolors.models.tokenization_chatglm import ChatGLMTokenizer |
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from kolors.models.unet_2d_condition import UNet2DConditionModel |
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from diffusers import AutoencoderKL, EulerDiscreteScheduler |
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from huggingface_hub import snapshot_download |
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import spaces |
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device = "cuda" |
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root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
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ckpt_dir = f'{root_dir}/weights/Kolors' |
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snapshot_download(repo_id="Kwai-Kolors/Kolors", local_dir=ckpt_dir) |
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snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus", local_dir=f"{root_dir}/weights/Kolors-IP-Adapter-Plus") |
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text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device) |
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tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder') |
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vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device) |
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scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler") |
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unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device) |
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image_encoder = CLIPVisionModelWithProjection.from_pretrained( |
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f'{root_dir}/weights/Kolors-IP-Adapter-Plus/image_encoder', |
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ignore_mismatched_sizes=True |
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).to(dtype=torch.float16, device=device) |
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ip_img_size = 336 |
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clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size) |
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pipe = StableDiffusionXLPipeline( |
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vae=vae, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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unet=unet, |
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scheduler=scheduler, |
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image_encoder=image_encoder, |
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feature_extractor=clip_image_processor, |
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force_zeros_for_empty_prompt=False |
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).to(device) |
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if hasattr(pipe.unet, 'encoder_hid_proj'): |
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pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj |
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pipe.load_ip_adapter(f'{root_dir}/weights/Kolors-IP-Adapter-Plus', subfolder="", weight_name=["ip_adapter_plus_general.bin"]) |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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@spaces.GPU |
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def infer(prompt, ip_adapter_image, ip_adapter_scale=0.5, negative_prompt="", seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator(device="cuda").manual_seed(seed) |
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pipe.to("cuda") |
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image_encoder.to("cuda") |
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pipe.image_encoder = image_encoder |
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pipe.set_ip_adapter_scale([ip_adapter_scale]) |
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image = pipe( |
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prompt=prompt, |
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ip_adapter_image=[ip_adapter_image], |
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negative_prompt=negative_prompt, |
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height=height, |
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width=width, |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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num_images_per_prompt=1, |
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generator=generator, |
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).images[0] |
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return image, seed |
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examples = [ |
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["A dog", "minta.jpeg", 0.3], |
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["A cat", "blue_hair.png", 0.55], |
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[" ", "abaporu.jpg", 1.0], |
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] |
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css=""" |
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#col-container { |
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margin: 0 auto; |
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max-width: 720px; |
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} |
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#result img{ |
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object-position: top; |
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} |
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#result .image-container{ |
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height: 100% |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f""" |
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# Kolors IP-Adapter - image reference and variations |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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with gr.Row(): |
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with gr.Column(): |
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ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil") |
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ip_adapter_scale = gr.Slider( |
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label="Image influence scale", |
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info="Use 1 for creating variations", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.05, |
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value=0.5, |
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) |
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result = gr.Image(label="Result", elem_id="result") |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Text( |
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label="Negative prompt", |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.0, |
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maximum=10.0, |
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step=0.1, |
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value=5.0, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=50, |
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) |
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gr.Examples( |
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examples=examples, |
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fn=infer, |
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inputs=[prompt, ip_adapter_image, ip_adapter_scale], |
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outputs=[result, seed], |
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cache_examples="lazy" |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn=infer, |
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inputs=[prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], |
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outputs=[result, seed] |
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) |
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demo.queue().launch() |
