changing stable cascade sampler

[saeedkhanehgir]

Hi

According to this link . I want to test other samplers in stable cascade. but I get the below error.

my inference code

""" ImageGenerator module"""
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline,EulerAncestralDiscreteScheduler
from diffusers.schedulers import DPMSolverMultistepScheduler


class ImageGenerator:
 """ImageGenerator class"""

 def __init__(self):

 self.prior_path = './models/image_generation/prior-bf16'
 self.decoder_path = './models/image_generation/decoder-bf16'
 self.img_width = 1024
 self.img_height = 1024
 self.prior_cfg = 4
 self.decoder_cfg = 1.1
 self.prior_num_steps = 20
 self.decoder_num_steps = 10
 self.num_images_per_prompt = 1
 self.prior = None
 self.decoder = None

 def load_model(self):

 self.prior = StableCascadePriorPipeline.from_pretrained(
 self.prior_path, torch_dtype=torch.bfloat16,
 ).to('cuda')
 self.decoder = StableCascadeDecoderPipeline.from_pretrained(
 self.decoder_path, torch_dtype=torch.bfloat16
 ).to('cuda')

 scheduler = EulerAncestralDiscreteScheduler()
 self.prior.scheduler = scheduler
 self.decoder.scheduler = scheduler

 # warmup
 prior_output = self.prior(
 prompt="a cat",
 height=1024,
 width=1024,
 negative_prompt="",
 guidance_scale=4,
 num_images_per_prompt=1,
 num_inference_steps=10,
 )

 _ = self.decoder(
 image_embeddings=prior_output.image_embeddings.to(torch.bfloat16),
 prompt="a cat",
 negative_prompt="",
 guidance_scale=1.1,
 output_type="pil",
 num_inference_steps=3,
 )

 def inference(self, prompt, negative_prompt):

 try :
 prior_output = self.prior(
 prompt=prompt,
 height=self.img_height,
 width=self.img_width,
 negative_prompt=negative_prompt,
 guidance_scale=self.prior_cfg,
 num_images_per_prompt=1,
 num_inference_steps=self.prior_num_steps,
 )

 result = self.decoder(
 image_embeddings=prior_output.image_embeddings.to(torch.bfloat16),
 prompt=prompt,
 negative_prompt=negative_prompt,
 guidance_scale=self.decoder_cfg,
 output_type="pil",
 num_inference_steps=self.decoder_num_steps,
 )
 except Exception as e:
 print('error : ',e)

 return result

if __name__ == '__main__':
 generator = ImageGenerator()
 generator.load_model()

 prompt = "a man"
 negative_prompt = ""

 result = generator.inference(prompt,negative_prompt)
 result.images[0].save('result.png')

when I don't change sampler everything is ok.

my diffusers package version is 0.27.2

Thanks

[tolgacangoz]

Hi @saeedkhanehgir . Try this:

pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)