# pip install diffusers, transformers, accelerate, safetensors, huggingface_hub import os os.system("pip install -U peft") import random import gradio as gr import numpy as np import PIL.Image import spaces import torch from diffusers import ( StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, ) from huggingface_hub import hf_hub_download from safetensors.torch import load_file DESCRIPTION = """ # Res-Adapter :Domain Consistent Resolution Adapter for Diffusion Models **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://huggingface.co/jiaxiangc/res-adapter ResAdapter by ByteDance. ByteDance provide a demo of [ResAdapter](https://huggingface.co/jiaxiangc/res-adapter) with [SDXL-Lightning-Step4](https://huggingface.co/ByteDance/SDXL-Lightning) to expand resolution range from 1024-only to 256~1024. """ if not torch.cuda.is_available(): DESCRIPTION += ( "\n

Running on CPU 🥶 This demo does not work on CPU. instead

" ) MAX_SEED = np.iinfo(np.int32).max CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1" MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting! unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda")) pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16") pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") pipe = pipe.to(device) # Load resadapter pipe.load_lora_weights( hf_hub_download( repo_id="jiaxiangc/res-adapter", subfolder="sdxl-i", filename="resolution_lora.safetensors", ), adapter_name="res_adapter", ) def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed @spaces.GPU(enable_queue=True) def generate( prompt: str, negative_prompt: str = "", prompt_2: str = "", negative_prompt_2: str = "", use_negative_prompt: bool = False, use_prompt_2: bool = False, use_negative_prompt_2: bool = False, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 0, num_inference_steps: int = 4, progress=gr.Progress(track_tqdm=True), ) -> PIL.Image.Image: print(f'** Generating image for: "{prompt}" **') generator = torch.Generator().manual_seed(seed) if not use_negative_prompt: negative_prompt = None # type: ignore if not use_prompt_2: prompt_2 = None # type: ignore if not use_negative_prompt_2: negative_prompt_2 = None # type: ignore pipe.set_adapters(["res_adapter"], adapter_weights=[0.0]) base_image = pipe( prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, width=width, height=height, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, output_type="pil", generator=generator, ).images[0] pipe.set_adapters(["res_adapter"], adapter_weights=[1.0]) res_adapt = pipe( prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, width=width, height=height, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, output_type="pil", generator=generator, ).images[0] return [res_adapt, base_image] examples = [ "A girl smiling", "A boy smiling", ] theme = gr.themes.Base( font=[ gr.themes.GoogleFont("Libre Franklin"), gr.themes.GoogleFont("Public Sans"), "system-ui", "sans-serif", ], ) with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", ) with gr.Group(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, container=False, placeholder="Enter your prompt", ) run_button = gr.Button("Generate") # result = gr.Gallery(label="Right is Res-Adapt-LORA and Left is Base"), with gr.Accordion("Advanced options", open=False): with gr.Row(): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False) use_negative_prompt_2 = gr.Checkbox( label="Use negative prompt 2", value=False ) negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter your prompt", visible=True, ) prompt_2 = gr.Text( label="Prompt 2", max_lines=1, placeholder="Enter your prompt", visible=False, ) negative_prompt_2 = gr.Text( label="Negative prompt 2", max_lines=1, placeholder="Enter a negative prompt", visible=False, ) 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=512, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0, maximum=20, step=0.1, value=0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=4, ) gr.Examples( examples=examples, inputs=prompt, outputs=None, fn=generate, cache_examples=CACHE_EXAMPLES, ) use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, queue=False, api_name=False, ) use_prompt_2.change( fn=lambda x: gr.update(visible=x), inputs=use_prompt_2, outputs=prompt_2, queue=False, api_name=False, ) use_negative_prompt_2.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt_2, outputs=negative_prompt_2, queue=False, api_name=False, ) gr.on( triggers=[ prompt.submit, negative_prompt.submit, prompt_2.submit, negative_prompt_2.submit, run_button.click, ], fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate, inputs=[ prompt, negative_prompt, prompt_2, negative_prompt_2, use_negative_prompt, use_prompt_2, use_negative_prompt_2, seed, width, height, guidance_scale, num_inference_steps, ], outputs=gr.Gallery(label="Left is ResAdapter and Right is Base"), api_name="run", ) if __name__ == "__main__": demo.queue(max_size=20, api_open=False).launch(show_api=False)