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| import gradio as gr | |
| import torch | |
| from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler | |
| from huggingface_hub import hf_hub_download | |
| import spaces | |
| from PIL import Image | |
| # Constants | |
| base = "stabilityai/stable-diffusion-xl-base-1.0" | |
| repo = "tianweiy/DMD2" | |
| checkpoints = { | |
| "1-Step" : ["dmd2_sdxl_1step_unet_fp16.bin", 1], | |
| "4-Step" : ["dmd2_sdxl_4step_unet_fp16.bin", 4], | |
| } | |
| loaded = None | |
| CSS = """ | |
| .gradio-container { | |
| max-width: 690px !important; | |
| } | |
| """ | |
| # Ensure model and scheduler are initialized in GPU-enabled function | |
| if torch.cuda.is_available(): | |
| unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) | |
| pipe = DiffusionPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda") | |
| # Function | |
| def generate_image(prompt, ckpt="4-steps"): | |
| global loaded | |
| print(prompt, ckpt) | |
| checkpoint = checkpoints[ckpt][0] | |
| num_inference_steps = checkpoints[ckpt][1] | |
| if loaded != num_inference_steps: | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
| pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoint), map_location="cuda")) | |
| loaded = num_inference_steps | |
| if loaded == 1: | |
| results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0, timesteps=[399]) | |
| else: | |
| results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0, timesteps=[999, 749, 499, 249]) | |
| return results.images[0] | |
| examples = [ | |
| "a cat eating a piece of cheese", | |
| "a ROBOT riding a BLUE horse on Mars, photorealistic", | |
| "Ironman VS Hulk, ultrarealistic", | |
| "a CUTE robot artist painting on an easel", | |
| "Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k", | |
| "An alien holding sign board contain word 'Flash', futuristic, neonpunk", | |
| "Kids going to school, Anime style" | |
| ] | |
| # Gradio Interface | |
| with gr.Blocks(css=CSS, theme="soft") as demo: | |
| gr.HTML("<h1><center>Adobe DMD2🦖</center></h1>") | |
| gr.HTML("<p><center><a href='https://huggingface.co/tianweiy/DMD2'>DMD2</a> text-to-image generation</center></p>") | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Textbox(label='Enter your prompt (English)', scale=8) | |
| ckpt = gr.Dropdown(label='Steps',choices=['1-Step', '4-Step'], value='4-Step', interactive=True) | |
| submit = gr.Button(scale=1, variant='primary') | |
| img = gr.Image(label='DMD2 Generated Image') | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=img, | |
| fn=generate_image, | |
| cache_examples="lazy", | |
| ) | |
| prompt.submit(fn=generate_image, | |
| inputs=[prompt, ckpt], | |
| outputs=img, | |
| ) | |
| submit.click(fn=generate_image, | |
| inputs=[prompt, ckpt], | |
| outputs=img, | |
| ) | |
| demo.queue().launch() |