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_in
下一行的 scale_model_input 是Diffusers 的 API 要求,可以忽略。 # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_...
training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale wit...
=len(down_block_types):raiseValueError(f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`:{block_out_channels}. `down_block_types`:{down_block_types}.")# inputself.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=...
input_ids.to(torch_device))[0] # Create random noise latents = torch.randn( (batch_size, unet.in_channels, height // 8, width // 8), generator=generator, ) latents = latents.to(torch_device) # Decode the image # scale and decode the image latents with vae latents = 1 / ...
img2img_deepbooru.click(fn=interrogate_deepbooru, inputs=image_input,outputs=[prompt]) img2img_button.click(predit_img2img, inputs=[prompt, negative_prompt, image_input, model_selected, n_steps, high_noise_frac,cfg_scale,strength], outputs=image_output) ...
gpuscaleschedulertexttorch Stable Diffusion是一个文本到图像的潜在扩散模型,由CompVis、Stability AI和LAION的研究人员和工程师创建。它使用来自LAION-5B数据库子集的512x512图像进行训练。使用这个模型,可以生成包括人脸在内的任何图像,因为有开源的预训练模型,所以我们也可以在自己的机器上运行它,如下图所示。 deep...
LTX Video 0.9.5 is the updated version of the super-fast LTX Video model series. The latest model introduces additional conditioning options, such as keyframe-based animation and video extension (both forward and backward). To support these additional conditioning inputs, we’ve introduced the LT...
scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, ).sample # perform guidance if do_...
def load_model_hook(models, input_dir): ... 跳过上面的代码,还是日志配置。 # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", ...