Error in generating images: Conv2d object has no attribute delete_adapter #337
Description
LCM LoRA Error in generating images: Conv2d object has no attribute delete_adapter
{'clip_skip': 1,
'controlnet': None,
'diffusion_task': 'text_to_image',
'dirs': {'controlnet': '/app/controlnet_models', 'lora': '/app/lora_models'},
'gguf_model': {'clip_path': None,
'diffusion_path': None,
'gguf_models': '/app/models/gguf',
't5xxl_path': None,
'vae_path': None},
'guidance_scale': 1,
'image_height': 512,
'image_width': 512,
'inference_steps': 10,
'init_image': None,
'lcm_lora': {'base_model_id': 'Fictiverse/Stable_Diffusion_PaperCut_Model',
'lcm_lora_id': 'latent-consistency/lcm-lora-sdxl'},
'lcm_model_id': 'SimianLuo/LCM_Dreamshaper_v7',
'lora': {'enabled': False,
'fuse': True,
'models_dir': '/app/lora_models',
'path': '',
'weight': 0.5},
'negative_prompt': '',
'number_of_images': 1,
'openvino_lcm_model_id': 'rupeshs/sdxl-turbo-openvino-int8',
'prompt': 'A very cute, happy and playful Mythological symbol of the zodiac '
'sign Virgo, looking amazed at the starry sky. whimsical '
'illustration style, sharp focus, high detail, crisp image',
'rebuild_pipeline': False,
'seed': 123123,
'strength': 0.6,
'token_merging': 0.0,
'use_gguf_model': False,
'use_lcm_lora': True,
'use_offline_model': False,
'use_openvino': False,
'use_safety_checker': False,
'use_seed': False,
'use_tiny_auto_encoder': False}
***** Init LCM-LoRA pipeline - Fictiverse/Stable_Diffusion_PaperCut_Model *****
Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]
Loading pipeline components...: 14%|█▍ | 1/7 [00:00<00:01, 4.52it/s]
Loading pipeline components...: 57%|█████▋ | 4/7 [00:00<00:00, 12.38it/s]
Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 12.32it/s]
Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 11.48it/s]
Error in generating images: 'Conv2d' object has no attribute 'delete_adapter'
Traceback (most recent call last):
File "/app/env/lib/python3.11/site-packages/diffusers/loaders/peft.py", line 352, in load_lora_adapter
incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/env/lib/python3.11/site-packages/peft/utils/save_and_load.py", line 158, in set_peft_model_state_dict
load_result = model.load_state_dict(peft_model_state_dict, strict=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/env/lib/python3.11/site-packages/torch/nn/modules/module.py", line 2624, in load_state_dict
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel:
size mismatch for down_blocks.1.attentions.0.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 640, 1, 1]).
size mismatch for down_blocks.1.attentions.0.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.1.attentions.0.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 640, 1, 1]).
size mismatch for down_blocks.1.attentions.0.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for down_blocks.1.attentions.1.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 640, 1, 1]).
size mismatch for down_blocks.1.attentions.1.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.1.attentions.1.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 640, 1, 1]).
size mismatch for down_blocks.1.attentions.1.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for down_blocks.2.attentions.0.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for down_blocks.2.attentions.0.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([1280, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.2.attentions.0.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for down_blocks.2.attentions.0.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([1280, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for down_blocks.2.attentions.1.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for down_blocks.2.attentions.1.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([1280, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for down_blocks.2.attentions.1.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for down_blocks.2.attentions.1.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([1280, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.0.resnets.2.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 2560, 3, 3]).
size mismatch for up_blocks.0.resnets.2.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 2560, 1, 1]).
size mismatch for up_blocks.1.attentions.0.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.1.attentions.0.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.lora_B.lcm.weight: copying a param with shape torch.Size([5120, 64]) from checkpoint, the shape in current model is torch.Size([10240, 64]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2560]) from checkpoint, the shape in current model is torch.Size([64, 5120]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.0.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.1.attentions.0.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.attentions.1.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.1.attentions.1.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.lora_B.lcm.weight: copying a param with shape torch.Size([5120, 64]) from checkpoint, the shape in current model is torch.Size([10240, 64]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2560]) from checkpoint, the shape in current model is torch.Size([64, 5120]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.1.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.1.attentions.1.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.attentions.2.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.1.attentions.2.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_q.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_q.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_k.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_v.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.lora_B.lcm.weight: copying a param with shape torch.Size([5120, 64]) from checkpoint, the shape in current model is torch.Size([10240, 64]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2560]) from checkpoint, the shape in current model is torch.Size([64, 5120]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.attentions.2.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.1.attentions.2.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.0.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 2560, 3, 3]).
size mismatch for up_blocks.1.resnets.0.conv1.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.0.time_emb_proj.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.resnets.0.conv2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1280, 3, 3]).
size mismatch for up_blocks.1.resnets.0.conv2.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.0.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 2560, 1, 1]).
size mismatch for up_blocks.1.resnets.0.conv_shortcut.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.1.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 2560, 3, 3]).
size mismatch for up_blocks.1.resnets.1.conv1.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.1.time_emb_proj.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.resnets.1.conv2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1280, 3, 3]).
size mismatch for up_blocks.1.resnets.1.conv2.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.1.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 2560, 1, 1]).
size mismatch for up_blocks.1.resnets.1.conv_shortcut.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.2.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 960, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1920, 3, 3]).
size mismatch for up_blocks.1.resnets.2.conv1.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.2.time_emb_proj.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64]).
size mismatch for up_blocks.1.resnets.2.conv2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1280, 3, 3]).
size mismatch for up_blocks.1.resnets.2.conv2.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.resnets.2.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 960, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 1920, 1, 1]).
size mismatch for up_blocks.1.resnets.2.conv_shortcut.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.1.upsamplers.0.conv.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1280, 3, 3]).
size mismatch for up_blocks.1.upsamplers.0.conv.lora_B.lcm.weight: copying a param with shape torch.Size([640, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.0.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 960, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1920, 3, 3]).
size mismatch for up_blocks.2.resnets.0.conv1.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.0.time_emb_proj.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64]) from checkpoint, the shape in current model is torch.Size([640, 64]).
size mismatch for up_blocks.2.resnets.0.conv2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 640, 3, 3]).
size mismatch for up_blocks.2.resnets.0.conv2.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.0.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 960, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 1920, 1, 1]).
size mismatch for up_blocks.2.resnets.0.conv_shortcut.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.1.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 1280, 3, 3]).
size mismatch for up_blocks.2.resnets.1.conv1.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.1.time_emb_proj.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64]) from checkpoint, the shape in current model is torch.Size([640, 64]).
size mismatch for up_blocks.2.resnets.1.conv2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 640, 3, 3]).
size mismatch for up_blocks.2.resnets.1.conv2.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.1.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for up_blocks.2.resnets.1.conv_shortcut.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.2.conv1.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 960, 3, 3]).
size mismatch for up_blocks.2.resnets.2.conv1.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.2.time_emb_proj.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64]) from checkpoint, the shape in current model is torch.Size([640, 64]).
size mismatch for up_blocks.2.resnets.2.conv2.lora_A.lcm.weight: copying a param with shape torch.Size([64, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 640, 3, 3]).
size mismatch for up_blocks.2.resnets.2.conv2.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for up_blocks.2.resnets.2.conv_shortcut.lora_A.lcm.weight: copying a param with shape torch.Size([64, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 960, 1, 1]).
size mismatch for up_blocks.2.resnets.2.conv_shortcut.lora_B.lcm.weight: copying a param with shape torch.Size([320, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 64, 1, 1]).
size mismatch for mid_block.attentions.0.proj_in.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for mid_block.attentions.0.proj_in.lora_B.lcm.weight: copying a param with shape torch.Size([1280, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_v.lora_A.lcm.weight: copying a param with shape torch.Size([64, 2048]) from checkpoint, the shape in current model is torch.Size([64, 768]).
size mismatch for mid_block.attentions.0.proj_out.lora_A.lcm.weight: copying a param with shape torch.Size([64, 1280]) from checkpoint, the shape in current model is torch.Size([64, 1280, 1, 1]).
size mismatch for mid_block.attentions.0.proj_out.lora_B.lcm.weight: copying a param with shape torch.Size([1280, 64]) from checkpoint, the shape in current model is torch.Size([1280, 64, 1, 1]).
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/src/context.py", line 57, in generate_text_to_image
self.lcm_text_to_image.init(
File "/app/src/backend/lcm_text_to_image.py", line 283, in init
self.pipeline = get_lcm_lora_pipeline(
^^^^^^^^^^^^^^^^^^^^^^
File "/app/src/backend/pipelines/lcm_lora.py", line 89, in get_lcm_lora_pipeline
load_lcm_weights(
File "/app/src/backend/pipelines/lcm_lora.py", line 33, in load_lcm_weights
pipeline.load_lora_weights(
File "/app/env/lib/python3.11/site-packages/diffusers/loaders/lora_pipeline.py", line 202, in load_lora_weights
self.load_lora_into_unet(
File "/app/env/lib/python3.11/site-packages/diffusers/loaders/lora_pipeline.py", line 406, in load_lora_into_unet
unet.load_lora_adapter(
File "/app/env/lib/python3.11/site-packages/diffusers/loaders/peft.py", line 377, in load_lora_adapter
module.delete_adapter(adapter_name)
^^^^^^^^^^^^^^^^^^^^^
File "/app/env/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1962, in __getattr__
raise AttributeError(
AttributeError: 'Conv2d' object has no attribute 'delete_adapter'