in__call__ self.scheduler.set_timesteps(num_inference_steps, device=device) File"...\venv\Lib\site-packages\diffusers\schedulers\scheduling_euler_discrete.py", line 283,inset_timesteps sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) ^^^ TypeError: expected np.nda...
C. 在每个时间步长都用Unet模型预测一个噪声残差,并将它传递到调度器,以计算之前的噪声采样。 fromtqdm.autoimporttqdmscheduler.set_timesteps(num_inference_steps)fortintqdm(scheduler.timesteps):# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.latent_model_...
def default_valid_timestep(self): kwargs = dict(self.forward_default_kwargs) num_inference_steps = kwargs.pop("num_inference_steps", None) scheduler_config = self.get_scheduler_config() scheduler = self.scheduler_classes[0](**scheduler_config) scheduler.set_timesteps(num_inference_steps) ...
fromtqdm.autoimporttqdmscheduler.set_timesteps(num_inference_steps)fortintqdm(scheduler.timesteps):# 我们要做 classifier-guidance generation,所以先扩一下 latent,方便并行推理latent_model_input=torch.cat([latents]*2)latent_model_input=scheduler.scale_model_input(latent_model_input,timestep=t)# 预测噪...
lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))) return LambdaLR(optimizer, lr_lambda, last_...
by default prompt: `cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting` pipeline params: ```cpp ov::Tensor image = pipe.generate(prompt, ov::genai::width(512), ov::genai::height(512), ov::genai::num_inference_steps(20)); ``` output: ...
第一组: finished() && check_finished() 用于判断是否可以结束,以及finished_reason 是哪种情况 第二组: init_next_round_input #初始化本请求下一轮inference 需要的参数,比如计算需要用多长的kvcache (主要是计算fill_ids和extend_input_len) 第三组: init_incremental_detokenize 与 get_next_inc_detokeniza...
num_inference_steps=50, max_sequence_length=512, generator=torch.Generator("cpu").manual_seed(0) ).images[0] image.save("flux-dev.png") Logs ValueError: The current scheduler class<class'diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler'>'s `set_timesteps` does not suppor...
generator=torch.Generator("cuda").manual_seed(49136503742430)image=pipeline(prompt="enormous kirby. space background",num_inference_steps=20,guidance_scale=5.0,generator=generator, ).images[0]image and with the same settings on Forge we get the same result: ...
if a customtimestepsvalue is passed, we check to make sure it have the same length asnum_inference_steps, and the scheduler will decide the last sigma value based onfinal_sigmas_type if a customsigmasvalue is passed, it has to have the same length asnum_inference_steps + 1- i.e. ...