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| import torch | |
| import gradio as gr | |
| from functools import partial | |
| from diffusers_patch import OMSPipeline | |
| def create_sdxl_lcm_lora_pipe(sd_pipe_name_or_path, oms_name_or_path, lora_name_or_path): | |
| from diffusers import StableDiffusionXLPipeline, LCMScheduler | |
| sd_pipe = StableDiffusionXLPipeline.from_pretrained(sd_pipe_name_or_path, torch_dtype=torch.float16, variant="fp16", add_watermarker=False).to('cuda') | |
| print('successfully load pipe') | |
| sd_scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config) | |
| sd_pipe.load_lora_weights(lora_name_or_path, variant="fp16") | |
| pipe = OMSPipeline.from_pretrained(oms_name_or_path, sd_pipeline = sd_pipe, torch_dtype=torch.float16, variant="fp16", trust_remote_code=True, sd_scheduler=sd_scheduler) | |
| pipe.to('cuda') | |
| return pipe | |
| class GradioDemo: | |
| def __init__( | |
| self, | |
| sd_pipe_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0", | |
| oms_name_or_path = 'h1t/oms_b_openclip_xl', | |
| lora_name_or_path = 'latent-consistency/lcm-lora-sdxl' | |
| ): | |
| self.pipe = create_sdxl_lcm_lora_pipe(sd_pipe_name_or_path, oms_name_or_path, lora_name_or_path) | |
| def _inference( | |
| self, | |
| prompt = None, | |
| oms_prompt = None, | |
| oms_guidance_scale = 1.0, | |
| num_inference_steps = 4, | |
| sd_pipe_guidance_scale = 1.0, | |
| seed = 1024, | |
| oms_prompt_flag=True, | |
| ): | |
| pipe_kwargs = dict( | |
| prompt = prompt, | |
| num_inference_steps = num_inference_steps, | |
| guidance_scale = sd_pipe_guidance_scale, | |
| ) | |
| generator = torch.Generator(device=self.pipe.device).manual_seed(seed) | |
| pipe_kwargs.update(oms_flag=False) | |
| print(f'raw kwargs: {pipe_kwargs}') | |
| image_raw = self.pipe( | |
| **pipe_kwargs, | |
| generator=generator | |
| )['images'][0] | |
| generator = torch.Generator(device=self.pipe.device).manual_seed(seed) | |
| pipe_kwargs.update(oms_flag=True, oms_prompt=prompt, oms_guidance_scale=1.0) | |
| print(f'w/ oms wo/ cfg (consistent) kwargs: {pipe_kwargs}') | |
| image_oms_cp = self.pipe( | |
| **pipe_kwargs, | |
| generator=generator | |
| )['images'][0] | |
| if oms_prompt_flag: | |
| generator = torch.Generator(device=self.pipe.device).manual_seed(seed) | |
| pipe_kwargs.update(oms_prompt=oms_prompt) | |
| print(f'w/ oms wo/ cfg (inconsistent) kwargs: {pipe_kwargs}') | |
| image_oms_icp = self.pipe( | |
| **pipe_kwargs, | |
| generator=generator | |
| )['images'][0] | |
| else: | |
| image_oms_icp = None | |
| oms_guidance_flag = oms_guidance_scale != 1.0 | |
| if oms_guidance_flag: | |
| generator = torch.Generator(device=self.pipe.device).manual_seed(seed) | |
| pipe_kwargs.update(oms_guidance_scale=oms_guidance_scale) | |
| print(f'w/ oms +cfg (inconsistent) kwargs: {pipe_kwargs}') | |
| image_oms_cfg = self.pipe( | |
| **pipe_kwargs, | |
| generator=generator | |
| )['images'][0] | |
| else: | |
| image_oms_cfg = None | |
| return image_raw, image_oms_cp, image_oms_icp, image_oms_cfg, gr.update(visible=oms_prompt_flag), gr.update(visible=oms_guidance_flag) | |
| def mainloop(self): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# One More Step for SDXL w/ LCM-LoRA") | |
| with gr.Group() as inputs: | |
| prompt = gr.Textbox(label="Prompt", value="a cat against orange ground, studio") | |
| with gr.Accordion('OMS Prompt'): | |
| oms_prompt_checkbox = gr.Checkbox(info="Inconsistent OMS prompt allows the additional control of low freq info, default is the same as Prompt.", label="Adding OMS Prompt", value=True) | |
| oms_prompt = gr.Textbox(label="OMS Prompt", value="a black cat", info='try "a black cat" and "a black room" for diverse control.') | |
| with gr.Accordion('OMS Guidance'): | |
| oms_cfg_scale_checkbox = gr.Checkbox(info="OMS Guidance will enhance the OMS prompt, specially focus on color and brightness. ", label="Adding OMS Guidance", value=True) | |
| oms_guidance_scale = gr.Slider(label="OMS Guidance Scale", minimum=1.0, maximum=5.0, value=2., step=0.1) | |
| run_button = gr.Button(value="Generate images") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=4, step=1) | |
| sd_guidance_scale = gr.Slider(label="SD Pipe Guidance Scale", minimum=1, maximum=3, value=1.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=False, value=1024) | |
| with gr.Row(): | |
| output_raw = gr.Image(label="SDXL w/ LCM-LoRA ") | |
| output_oms_cp = gr.Image(label="w/ OMS (consistent) w/o OMS CFG") | |
| output_oms_icp = gr.Image(label="w/ OMS (inconsistent) w/o OMS CFG") | |
| output_oms_cfg = gr.Image(label="w/ OMS w/ OMS CFG") | |
| oms_prompt_checkbox.input( | |
| fn=lambda oms_prompt_flag, prompt, oms_prompt: (oms_prompt if oms_prompt_flag else prompt, gr.update(interactive=oms_prompt_flag)), | |
| inputs=[oms_prompt_checkbox, prompt, oms_prompt], | |
| outputs=[oms_prompt, oms_prompt] | |
| ) | |
| oms_cfg_scale_checkbox.input( | |
| fn=lambda oms_cfg_scale_flag: (1.5 if oms_cfg_scale_flag else 1.0, gr.update(interactive=oms_cfg_scale_flag)), | |
| inputs=[oms_cfg_scale_checkbox], | |
| outputs=[oms_guidance_scale, oms_guidance_scale] | |
| ) | |
| ips = [prompt, oms_prompt, oms_guidance_scale, num_steps, sd_guidance_scale, seed, oms_prompt_checkbox] | |
| run_button.click(fn=self._inference, inputs=ips, outputs=[output_raw, output_oms_cp, output_oms_icp, output_oms_cfg, output_oms_icp, output_oms_cfg]) | |
| demo.queue(max_size=20) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| gradio_demo = GradioDemo() | |
| gradio_demo.mainloop() | |