perflow-sd15-delta-weights

Github repo: https://github.com/magic-research/piecewise-rectified-flow <br>

Project page: https://piecewise-rectified-flow.github.io/

Demo:

import torch, torchvision
from diffusers import StableDiffusionPipeline, UNet2DConditionModel
from src.utils_perflow import merge_delta_weights_into_unet
from src.scheduler_perflow import PeRFlowScheduler
delta_weights = UNet2DConditionModel.from_pretrained("hansyan/perflow-sd15-delta-weights", torch_dtype=torch.float16, variant="v0-1",).state_dict()
pipe = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-8", torch_dtype=torch.float16,)
pipe = merge_delta_weights_into_unet(pipe, delta_weights)
pipe.scheduler = PeRFlowScheduler.from_config(pipe.scheduler.config, prediction_type="epsilon", num_time_windows=4)
pipe.to("cuda", torch.float16)

prompts_list = ["A man with brown skin, a beard, and dark eyes", "A colorful bird standing on the tree, open beak",]
for i, prompt in enumerate(prompts_list):
    generator = torch.Generator("cuda").manual_seed(1024)
    prompt = "RAW photo, 8k uhd, dslr, high quality, film grain, highly detailed, masterpiece; " + prompt
    neg_prompt = "distorted, blur, smooth, low-quality, warm, haze, over-saturated, high-contrast, out of focus, dark"
    samples = pipe(
        prompt              = [prompt] * 8, 
        negative_prompt     = [neg_prompt] * 8,
        height              = 512,
        width               = 512,
        num_inference_steps = 8, 
        guidance_scale      = 7.5,
        generator           = generator,
        output_type         = 'pt',
    ).images
    torchvision.utils.save_image(torchvision.utils.make_grid(samples, nrow=4), f"tmp_{i}.png")