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End of training

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  1. README.md +1 -1
  2. image_1.png +0 -0
  3. image_2.png +0 -0
  4. pytorch_lora_weights.bin +1 -1
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README.md CHANGED
@@ -12,7 +12,7 @@ inference: true
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  ---
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  # LoRA text2image fine-tuning - deepgoyal19/lora5_tb
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- These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
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  ![img_0](./image_0.png)
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  ![img_1](./image_1.png)
 
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  ---
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  # LoRA text2image fine-tuning - deepgoyal19/lora5_tb
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+ These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
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  ![img_0](./image_0.png)
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  ![img_1](./image_1.png)
image_1.png CHANGED
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+
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+ `text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["id2label"]` will be overriden. | 11/24 [03:44<04:47, 22.09s/it]
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+ {'prediction_type'} was not found in config. Values will be initialized to default values.
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+ {'scaling_factor'} was not found in config. Values will be initialized to default values.
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+ 0%| | 0/20 [00:00<?, ?it/s]
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+ Traceback (most recent call last): | 0/20 [00:00<?, ?it/s]
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+ File "/home/deepanshu/LMFlow-diffusion-main/examples/finetune.py", line 74, in <module>
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+ tuned_model = finetuner.finetune(model=model, dataset=dataset)
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+ File "/home/deepanshu/LMFlow-diffusion-main/src/lmflow_aigen/pipeline/diffusion_finetuner.py", line 444, in finetune
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+ model.log_validation(self.finetuner_args,self.accelerator,global_step)
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+ File "/home/deepanshu/LMFlow-diffusion-main/src/lmflow_aigen/models/diffusion_model.py", line 295, in log_validation
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+ self.images.append(pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0])
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
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+ return func(*args, **kwargs)
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py", line 684, in __call__
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+ noise_pred = self.unet(
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
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+ return forward_call(*args, **kwargs)
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/diffusers/models/unet_2d_condition.py", line 718, in forward
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+ sample = self.conv_in(sample)
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
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+ return forward_call(*args, **kwargs)
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 463, in forward
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+ return self._conv_forward(input, self.weight, self.bias)
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+ File "/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
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+ return F.conv2d(input, weight, bias, self.stride,
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+ RuntimeError: Input type (c10::Half) and bias type (float) should be the same
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+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - ***** Running training *****
2
+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - Num examples = 50
3
+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - Num Epochs = 2
4
+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - Instantaneous batch size per device = 1
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+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - Total train batch size (w. parallel, distributed & accumulation) = 1
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+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - Gradient Accumulation steps = 1
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+ 06/14/2023 16:31:01 - INFO - lmflow_aigen.pipeline.diffusion_finetuner - Total optimization steps = 60
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+ Steps: 0%| | 0/60 [00:00<?, ?it/s]
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+ Steps: 0%| | 0/60 [00:00<?, ?it/s]/home/deepanshu/anaconda3/envs/deepanshu/lib/python3.9/site-packages/xformers/ops/fmha/flash.py:339: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
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+ Potential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed. | 19/20 [00:02<00:00, 10.08it/s]
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