Views
No views yet
/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats4-rank32-snrNone.safetensors here 💾.
models/Lora folder.<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats4-rank32-snrNone:1> to your prompt. On ComfyUI just load it as a regular LoRA./home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats4-rank32-snrNone_emb.safetensors here 💾.
embeddings folder/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats4-rank32-snrNone_emb to your prompt. For example, A photo of /home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats4-rank32-snrNone_emb
(you need both the LoRA and the embeddings as they were trained together for this LoRA)1from diffusers import AutoPipelineForText2Image
2import torch
3from huggingface_hub import hf_hub_download
4from safetensors.torch import load_file
5
6pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
7pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout-batch3-repeats4-rank32-snrNone', weight_name='pytorch_lora_weights.safetensors')
8embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout-batch3-repeats4-rank32-snrNone', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats4-rank32-snrNone_emb.safetensors', repo_type="model")
9state_dict = load_file(embedding_path)
10pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
11pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
12
13image = pipeline('A photo of <s0><s1> giving a speech').images[0]TOK → use <s0><s1> in your prompt