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#!/usr/bin/env python3
#from diffusers import DiffusionPipeline, StableDiffusionPipeline, KDPM2DiscreteScheduler, KDPM2AncestralDiscreteScheduler, HeunDiscreteScheduler
from diffusers import DiffusionPipeline, StableDiffusionPipeline, HeunDiscreteScheduler
import torch
import os
seed = 33
inference_steps = 25
old_pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
old_pipe = old_pipe.to("cuda")
for prompt in ["astronaut riding horse", "whale falling from sky", "magical forest", "highly photorealistic picture of johnny depp"]:
# for sampler in ["sample_dpm_2_ancestral", "sample_dpm_2", "sample_heun"]:
# for sampler in ["heun", "sample_dpm_2_ancestral", "sample_dpm_2"]:
for sampler in ["sample_heun"]:
old_pipe.set_sampler(sampler)
torch.manual_seed(0)
image = old_pipe(prompt, num_inference_steps=inference_steps).images[0]
folder = f"a_{'_'.join(prompt.split())}"
os.makedirs(f"/home/patrick_huggingface_co/images/{folder}", exist_ok=True)
image.save(f"/home/patrick_huggingface_co/images/{folder}/{sampler}.png")
pipe = StableDiffusionPipeline(**old_pipe.components)
pipe = pipe.to("cuda")
# if sampler == "sample_dpm_2":
# pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
# elif sampler == "sample_dpm_2_ancestral":
# pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
if sampler == "sample_heun":
pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=inference_steps).images[0]
image.save(f"/home/patrick_huggingface_co/images/{folder}/hf_{sampler}.png")
break
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