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Running
on
Zero
| import os | |
| donwload_repo_loc= "./models/image_encoder/" | |
| os.system("pip install -U peft") | |
| # os.system(f"wget -O {donwload_repo_loc}config.json https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/config.json?download=true") | |
| # os.system(f"wget -O {donwload_repo_loc}model.safetensors https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors?download=true") | |
| # os.system(f"wget -O {donwload_repo_loc}pytorch_model.bin https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/pytorch_model.bin?download=true") | |
| import spaces | |
| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionXLPipeline | |
| from PIL import Image | |
| from ip_adapter import IPAdapterXL | |
| base_model_path = "stabilityai/stable-diffusion-xl-base-1.0" | |
| device = "cuda" | |
| image_encoder_path = donwload_repo_loc #"sdxl_models/image_encoder" | |
| ip_ckpt = "./models/ip-adapter_sdxl.bin" | |
| # load SDXL pipeline | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| base_model_path, | |
| torch_dtype=torch.float16, | |
| add_watermarker=False, | |
| ) | |
| # generate image variations with only image prompt | |
| def create_image(image_pil,target,prompt,n_prompt,scale, guidance_scale,num_samples,num_inference_steps,seed): | |
| # load ip-adapter | |
| if target =="Load original IP-Adapter": | |
| # target_blocks=["blocks"] for original IP-Adapter | |
| ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["blocks"]) | |
| elif target=="Load only style blocks": | |
| # target_blocks=["up_blocks.0.attentions.1"] for style blocks only | |
| ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["up_blocks.0.attentions.1"]) | |
| elif target == "Load style+layout block": | |
| # target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks | |
| ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"]) | |
| image_pil=image_pil.resize((512, 512)) | |
| images = ip_model.generate(pil_image=image_pil, | |
| prompt=prompt, | |
| negative_prompt=n_prompt, | |
| scale=scale, | |
| guidance_scale=guidance_scale, | |
| num_samples=num_samples, | |
| num_inference_steps=num_inference_steps, | |
| seed=seed, | |
| #neg_content_prompt="a rabbit", | |
| #neg_content_scale=0.5, | |
| ) | |
| # images[0].save("result.png") | |
| del ip_model | |
| return images | |
| DESCRIPTION = """ | |
| # InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation | |
| **Demo by [ameer azam] - [Twitter](https://twitter.com/Ameerazam18) - [GitHub](https://github.com/AMEERAZAM08)) - [Hugging Face](https://huggingface.co/ameerazam08)** | |
| This is a demo of https://github.com/InstantStyle/InstantStyle. | |
| """ | |
| block = gr.Blocks(css="footer {visibility: hidden}").queue(max_size=10) | |
| with block: | |
| with gr.Row(): | |
| with gr.Column(): | |
| # gr.Markdown("## <h1 align='center'>InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation </h1>") | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Tabs(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_pil = gr.Image(label="Style Image", type='pil') | |
| target = gr.Dropdown(["Load original IP-Adapter","Load only style blocks","Load style+layout block"], label="Load Style", info="IP-Adapter Layers") | |
| prompt = gr.Textbox(label="Prompt",value="a cat, masterpiece, best quality, high quality") | |
| n_prompt = gr.Textbox(label="Neg Prompt",value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry") | |
| scale = gr.Slider(minimum=0,maximum=2.0, step=0.01,value=1.0, label="scale") | |
| guidance_scale = gr.Slider(minimum=1,maximum=15.0, step=0.01,value=5.0, label="guidance_scale") | |
| num_samples= gr.Slider(minimum=1,maximum=3.0, step=1.0,value=1.0, label="num_samples") | |
| num_inference_steps = gr.Slider(minimum=5,maximum=50.0, step=1.0,value=30, label="num_inference_steps") | |
| seed = gr.Slider(minimum=-1000000,maximum=1000000,value=1, step=1, label="Seed Value") | |
| generate_button = gr.Button("Generate Image") | |
| with gr.Column(): | |
| generated_image = gr.Gallery(label="Generated Image") | |
| generate_button.click(fn=create_image, inputs=[image_pil,target,prompt,n_prompt,scale, guidance_scale,num_samples,num_inference_steps,seed], | |
| outputs=[generated_image]) | |
| block.launch() |