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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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Project page: https://piecewise-rectified-flow.github.io/ |
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```python |
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import torch, torchvision |
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from diffusers import StableDiffusionPipeline, UNet2DConditionModel |
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from src.utils_perflow import merge_delta_weights_into_unet |
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from src.scheduler_perflow import PeRFlowScheduler |
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delta_weights = UNet2DConditionModel.from_pretrained("hansyan/piecewise-rectified-flow-delta-weights", torch_dtype=torch.float16, variant="v0-1",).state_dict() |
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pipe = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-8", torch_dtype=torch.float16,) |
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pipe = merge_delta_weights_into_unet(pipe, delta_weights) |
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pipe.scheduler = PeRFlowScheduler.from_config(pipe.scheduler.config, prediction_type="epsilon", num_time_windows=4) |
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pipe.to("cuda", torch.float16) |
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prompts_list = ["A man with brown skin, a beard, and dark eyes", "A colorful bird standing on the tree, open beak",] |
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for i, prompt in enumerate(prompts_list): |
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generator = torch.Generator("cuda").manual_seed(1024) |
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prompt = "RAW photo, 8k uhd, dslr, high quality, film grain, highly detailed, masterpiece; " + prompt |
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neg_prompt = "distorted, blur, smooth, low-quality, warm, haze, over-saturated, high-contrast, out of focus, dark" |
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samples = pipe( |
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prompt = [prompt] * 8, |
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negative_prompt = [neg_prompt] * 8, |
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height = 512, |
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width = 512, |
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num_inference_steps = 8, |
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guidance_scale = 7.5, |
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generator = generator, |
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output_type = 'pt', |
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).images |
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torchvision.utils.save_image(torchvision.utils.make_grid(samples, nrow=4), f"tmp_{i}.png") |
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``` |
