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| # 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 AutoPipelineForText2Image, DPMSolverMultistepScheduler | |
| from huggingface_hub import hf_hub_download | |
| from diffusers.models.attention_processor import AttnProcessor2_0 | |
| 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 LORAs by ByteDance | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<h1>Running on CPU 🥶 This demo does not work on CPU.</a> instead</h1>" | |
| 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") | |
| pipe = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',use_safetensors=True)# torch_dtype=torch.float16, variant="safetensors") | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++") | |
| pipe.load_lora_weights( | |
| hf_hub_download( | |
| repo_id="jiaxiangc/res-adapter", | |
| subfolder="sdxl-i", | |
| filename="resolution_lora.safetensors", | |
| ), | |
| adapter_name="res_adapter", | |
| ) | |
| pipe.set_adapters(["res_adapter"], adapter_weights=[1.0]) | |
| pipe = pipe.to(device) | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| 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_base: float = 5.0, | |
| num_inference_steps_base: int = 20, | |
| 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 | |
| res_adapt=pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| prompt_2=prompt_2, | |
| negative_prompt_2=negative_prompt_2, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale_base, | |
| num_inference_steps=num_inference_steps_base, | |
| generator=generator, | |
| output_type="pil", | |
| ).images[0] | |
| pipe.unet.set_attn_processor(AttnProcessor2_0()) | |
| base_image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| prompt_2=prompt_2, | |
| negative_prompt_2=negative_prompt_2, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale_base, | |
| num_inference_steps=num_inference_steps_base, | |
| generator=generator, | |
| output_type="pil").images[0] | |
| return [res_adapt,base_image] | |
| examples = [ | |
| "A realistic photograph of an astronaut in a jungle, cold color palette, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| "cinematic film still, photo of a girl, cyberpunk, neonpunk, headset, city at night, sony fe 12-24mm f/2.8 gm, close up, 32k uhd, wallpaper, analog film grain, SONY headset" | |
| ] | |
| 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=True) | |
| 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="ugly, deformed, noisy, blurry, nsfw, low contrast, text, BadDream, 3d, cgi, render, fake, anime, open mouth, big forehead, long neck", | |
| 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_base = gr.Slider( | |
| label="Guidance scale for base", | |
| minimum=1, | |
| maximum=20, | |
| step=0.1, | |
| value=9.5, | |
| ) | |
| num_inference_steps_base = gr.Slider( | |
| label="Number of inference steps for base", | |
| minimum=10, | |
| maximum=100, | |
| step=1, | |
| value=25, | |
| ) | |
| 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_base, | |
| num_inference_steps_base, | |
| ], | |
| outputs=gr.Gallery(label="Right is Res-Adapt-LORA and Left is Base"), | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20, api_open=False).launch(show_api=False) |