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--- |
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license: cc-by-nc-4.0 |
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--- |
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**Github repo**: https://github.com/magic-research/piecewise-rectified-flow <br> |
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**PeRFlow accelerated SDXL-base**: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 |
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**Demo:** |
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```python |
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from pathlib import Path |
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import torch, torchvision |
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from diffusers import StableDiffusionXLPipeline |
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pipe = StableDiffusionXLPipeline.from_pretrained("hansyan/perflow-sdxl-base", torch_dtype=torch.float16, use_safetensors=True, variant="v0-fix") |
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from src.scheduler_perflow import PeRFlowScheduler |
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pipe.scheduler = PeRFlowScheduler.from_config(pipe.scheduler.config, prediction_type="ddim_eps", num_time_windows=4) |
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pipe.to("cuda", torch.float16) |
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prompts_list = [ |
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["photorealistic, uhd, high resolution, high quality, highly detailed; RAW photo, a handsome man, wearing a black coat, outside, closeup face", |
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"distorted, blur, low-quality, haze, out of focus",], |
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["photorealistic, uhd, high resolution, high quality, highly detailed; masterpiece, A closeup face photo of girl, wearing a rain coat, in the street, heavy rain, bokeh,", |
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"distorted, blur, low-quality, haze, out of focus",], |
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["photorealistic, uhd, high resolution, high quality, highly detailed; RAW photo, a red luxury car, studio light", |
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"distorted, blur, low-quality, haze, out of focus",], |
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["photorealistic, uhd, high resolution, high quality, highly detailed; masterpiece, A beautiful cat bask in the sun", |
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"distorted, blur, low-quality, haze, out of focus",], |
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] |
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num_inference_steps = 6 # suggest steps >= num_win=4 |
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cfg_scale_list = [2.0] # suggest values [1.5, 2.0, 2.5] |
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num_img = 2 |
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seed = 42 |
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for cfg_scale in cfg_scale_list: |
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for i, prompts in enumerate(prompts_list): |
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setup_seed(seed) |
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prompt, neg_prompt = prompts[0], prompts[1] |
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samples = pipe( |
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prompt = [prompt] * num_img, |
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negative_prompt = [neg_prompt] * num_img, |
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height = 1024, |
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width = 1024, |
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num_inference_steps = num_inference_steps, |
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guidance_scale = cfg_scale, |
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output_type = 'pt', |
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).images |
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cfg_int = int(cfg_scale); cfg_float = int(cfg_scale*10 - cfg_int*10) |
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save_name = f'step_{num_inference_steps}_txt{i+1}_cfg{cfg_int}-{cfg_float}.png' |
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torchvision.utils.save_image(torchvision.utils.make_grid(samples, nrow = num_img), os.path.join("demo", save_name)) |
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``` |
