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
其中guidance_scale参数表示图片生成过程中考虑 prompt 的权重。 创建text embedding 接下来,我们来对条件 prompt 进行 tokenize,并通过 text encoder 模型产生文本 embedding: text_input=tokenizer(prompt,padding="max_length",max_length=tokenizer.model_max_length,truncation=True,return_tensors="pt")withtorch.no...
no_grad(): text_embeddings = text_encoder(text_input.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 ...
可选和联合类型importtorch# 导入 PyTorch 库from...modelsimportUNet2DConditionModel, VQModel# 从模型模块导入 UNet2DConditionModel 和 VQModel 类from...schedulersimportDDPMScheduler# 从调度器模块导入 DDPMScheduler 类from...utilsimport(# 从 utils 模块导入 logging 工具logging,...
img2img_button.click(predit_img2img, inputs=[prompt, negative_prompt, image_input, model_selected, n_steps, high_noise_frac,cfg_scale,strength], outputs=image_output) clear_button.click(clear_img2img, inputs=[prompt, negative_prompt, image_input], outputs=[prompt, negative_prompt, im...
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...
你可以根据你的实际网络情况,来选择到底是从HuggingFace 下载模型[14]还是从ModelScope 来下载模型[15],如果你选择的是 Model Scope,别忘记在你下载完模型之后,再从 HuggingFace 进行下仓库内容除两个大尺寸模型文件之外的内容更新。 关于模型的快速下载,我在之前的文章里多次提到过,如果你选择使用 HuggingFace 来下载...
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_...
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) text_embed_input = text_embeddings Collaborator yiyixuxu Feb 15, 2024 Suggested change text_embed_input = text_embeddings src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py ...
The parameters of this model were fitted using a hybrid method that assembles Differential Evolution and Flexible Polyhedron (Nelder-Mead). A full-scale moving-bed diffuser located in a Brazilian mill was used as a case study to evaluate the simulator performance. The simulator test reached its ...