Comments (3)
Hi,
Prompt-to-prompt can be used with our diffusers-based training and models.
The original prompt-to-prompt might need to be changed for diffusers==0.14.0.
I have uploaded a working implementation here. The main changes involve the ldm_stable
model load and minor modifications in register_attention_control
.
Another thing that needs to be changed in prompt-to-prompt is here to the below line.
noise_pred = model.unet(latents_input, t, context)["sample"]
Let me know if you still face any issues. Thanks!
from custom-diffusion.
@nupurkmr9 Hi, thanks for you work, I'd like to ask if you have attention_map visualization codes which are not based on diffusers? Really thanks for your help!
from custom-diffusion.
Hi, I always used the diffusers based implementation for attention map visualization. Thanks.
from custom-diffusion.
Related Issues (20)
- Errors in using diffuser version HOT 3
- A question for the regularization captions HOT 1
- questions about crossattn in CustomDiffusionAttnProcessor
- Question about the cross attention HOT 1
- ImportError: cannot import name 'insecure_hashlib' from 'huggingface_hub.utils' 怎么解决? HOT 1
- Hi, How toMultiple-Concept Fine-tuning for DreamBooth?
- if it OK with no regularization? my results don't have any consistancy with reference images. HOT 3
- Ours (w/fine-tune all) ? HOT 2
- OOM while running sample.py HOT 4
- why my optimization based weights merging is not work? HOT 4
- Is it possible to train multi joint training with SDXL? diffusers_training_sdxl.py seems impossible for multi joint training. HOT 2
- Question about loss on valid image region during training HOT 1
- Missing text prompts? HOT 2
- Does the diffusers version now work properly? HOT 3
- why the results produced by the model training and verification are inconsistent with the results I test using the saved model weights? HOT 1
- Evaluation:where is the “prompts.json”file?
- Unable to retrieve the image used for regularization. HOT 1
- Is this version trained with lora? HOT 1
- Tensors not Updated after Training HOT 1
- retrieve.py question HOT 1
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