diffusers
diffusers copied to clipboard
huggingface
Come on, come on, let's adapt the conversion script to SD 2.0
Is your feature request related to a problem? Please describe. It would be great if we could run SD 2 with cpu_offload, attention slicing, xformers, etc...
Describe the solution you'd like Adapt the conversion script to SD 2.0
Describe alternatives you've considered Stability AI's repo is not as flexible.
🤗 Diffusers with Stable Diffusion 2 is live!
anton-l commented (https://github.com/huggingface/diffusers/issues/1388#issuecomment-1327731012)
diffusers==0.9.0 with Stable Diffusion 2 are live!
Installation
pip install diffusers[torch]==0.9 transformers
Release Information https://github.com/huggingface/diffusers/releases/tag/v0.9.0
Contributors @kashif @pcuenca @patrickvonplaten @anton-l @patil-suraj
📰 News
-
Weights for the 768x768 model is up! - Averad
- https://huggingface.co/stabilityai/stable-diffusion-2
-
#1386 - by patil-suraj and patrickvonplaten has been merged into --main by patil-suraj
-
Weights for the 512x512 models are up! - patrickvonplaten
- https://huggingface.co/stabilityai/stable-diffusion-2-base
- https://huggingface.co/stabilityai/stable-diffusion-2-inpainting
✏️ Notes & Information
Related huggingface/diffusers Pull Requests:
- #1424
- #1421
- #1420
- #1419
- #1416
- #1415
- #1414
- #1412
- #1411
- #1410
- #1402
- #1400
- #1397
- #1396
- #1386 - by patil-suraj and patrickvonplaten
👇 Quick Links:
- https://github.com/Stability-AI/stablediffusion
- https://huggingface.co/stabilityai/stable-diffusion-2
- https://huggingface.co/stabilityai/stable-diffusion-2-base
- https://huggingface.co/stabilityai/stable-diffusion-2-depth
- https://huggingface.co/stabilityai/stable-diffusion-2-inpainting
- https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler
👁️ User Submitted Resources:
- Test Version of convert_original_stable_diffusion_to_diffusers.py by hafriedlander
- Example of accessing the penultimate text embedding layer by hafriedlander
- NovelAI Improvements on Stable Diffusion
💭 User Story (Prior to Huggingface Diffusers 0.9.0 Release)
Stability-AI has released Stable Diffusion 2.0 models/workflow. When you run convert_original_stable_diffusion_to_diffusers.py on the new Stability-AI/stablediffusion models the following errors occur.
convert_original_stable_diffusion_to_diffusers.py --checkpoint_path="./512-inpainting-ema.ckpt" --dump_path="./512-inpainting-ema_diffusers"
Output:
Traceback (most recent call last):
File "convert_original_stable_diffusion_to_diffusers.py", line 720, in <module>
unet.load_state_dict(converted_unet_checkpoint)
File "lib\site-packages\torch\nn\modules\module.py", line 1667, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel:
size mismatch for down_blocks.0.attentions.0.proj_in.weight: copying a param with shape torch.Size([320, 320]) from checkpoint, the shape in current model is torch.Size([320, 320, 1, 1]).
size mismatch for down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([320, 768]).
size mismatch for down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([320, 768]).
size mismatch for down_blocks.0.attentions.0.proj_out.weight: copying a param with shape torch.Size([320, 320]) from checkpoint, the shape in current model is torch.Size([320, 320, 1, 1]).
.... blocks.1.attentions blocks.2.attentions etc. etc.
Semi-Related:
- https://github.com/Stability-AI/stablediffusion/issues/4
- https://github.com/Stability-AI/stablediffusion/issues/9
- https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5009 [dupe]
- https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5011
- https://github.com/huggingface/diffusers/issues/1392
- https://github.com/huggingface/diffusers/issues/1398
- https://github.com/huggingface/diffusers/issues/1404
- https://github.com/db0/AI-Horde/issues/86
- https://github.com/JoePenna/Dreambooth-Stable-Diffusion/issues/112
- https://github.com/TheLastBen/fast-stable-diffusion/issues/599
- https://github.com/ShivamShrirao/diffusers/issues/143
- https://github.com/Sygil-Dev/nataili/issues/67
- https://github.com/Sygil-Dev/sygil-webui/issues/1686
after looking at it I'm not sure it has anything to do with the script, seems like the u2net on diffusers needs to have 4 dimensions on the tensor size.
needs to have 4 dimensions
So I guess this will take time...
maybe not, I'm not that knowledgeable on the subject but I assume a unet2D needs to be 4D, or maybe you can just artificially add it idk
rudimentary support for stable diffusion 2.0
https://github.com/MrCheeze/stable-diffusion-webui/commit/069591b06bbbdb21624d489f3723b5f19468888d
Originally posted by @152334H in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5011#issuecomment-1325971596
https://github.com/hafriedlander/diffusers/blob/stable_diffusion_2/scripts/convert_original_stable_diffusion_to_diffusers.py
Notes:
- Only tested on the two txt2img models, not inpaint / depth2img / upscaling
- You will need to change your text embedding to use the penultimate layer too
- It spits out a bunch of warnings about vision_model, but that's fine
- I have no idea if this is right or not. It generates images, no guarantee beyond that. (Hence no PR - if you're patient, I'm sure the Diffusers team will do a better job than I have)
Here's an example of accessing the penultimate text embedding layer https://github.com/hafriedlander/stable-diffusion-grpcserver/blob/b34bb27cf30940f6a6a41f4b77c5b77bea11fd76/sdgrpcserver/pipeline/text_embedding/basic_text_embedding.py#L33
https://github.com/hafriedlander/diffusers/blob/stable_diffusion_2/scripts/convert_original_stable_diffusion_to_diffusers.py
Notes:
- Only tested on the two txt2img models, not inpaint / depth2img / upscaling
- You will need to change your text embedding to use the penultimate layer too
- It spits out a bunch of warnings about vision_model, but that's fine
- I have no idea if this is right or not. It generates images, no guarantee beyond that. (Hence no PR - if you're patient, I'm sure the Diffusers team will do a better job than I have)
doesn't seem to work for me on the 768-v model using the v2 config for v
TypeError: EulerDiscreteScheduler.init() got an unexpected keyword argument 'prediction_type'
Appears I'm also having unexpected argument error, but of a different arg:
Command:
python convert.py --checkpoint_path="models/512-base-ema.ckpt" --dump_path="outputs/" --original_config_file="v2-inference.yaml"
Result:
│ 736 │ unet = UNet2DConditionModel(**unet_config) │ │ 737 │ unet.load_state_dict(converted_unet_checkpoint)
TypeError: init() got an unexpected keyword argument 'use_linear_projection'
I can't seem to find a resolution to this one.
You need to use the absolute latest Diffusers and merge this PR (or use my branch which has it in it) https://github.com/huggingface/diffusers/pull/1386
(My branch is at https://github.com/hafriedlander/diffusers/tree/stable_diffusion_2)
Amazing to see the excitement here! We'll merge #1386 in a bit :-)
@patrickvonplaten the problems I've run into so far:
- attention_slicing doesn't work when attention_head_dim is a list (maybe you have a more elegant solution than that)
- tokenizer.model_max_length is max_long when using my converter above, so I use text_encoder.config.max_position_embeddings instead
That's super helpful @hafriedlander - thanks!
BTW, weights for the 512x512 are up:
- https://huggingface.co/stabilityai/stable-diffusion-2-base
- https://huggingface.co/stabilityai/stable-diffusion-2-inpainting
Looking into the 768x768 model now
Nice. Do you have a solution in mind for how to flag to the pipeline to use the penultimate layer in the CLIP model? (I just pass it in as an option at the moment)
Can you send me a link? Does the pipeline not work out of the box? cc @anton-l @patil-suraj
It works but I don't think it's correct. The Stability configuration files explicitly say to use the penultimate CLIP layer https://github.com/Stability-AI/stablediffusion/blob/33910c386eaba78b7247ce84f313de0f2c314f61/configs/stable-diffusion/v2-inference-v.yaml#L68
It's relatively easy to get access to the penultimate layer. I do it in my custom pipeline like this:
https://github.com/hafriedlander/stable-diffusion-grpcserver/blob/b34bb27cf30940f6a6a41f4b77c5b77bea11fd76/sdgrpcserver/pipeline/text_embedding/basic_text_embedding.py#L33
The problem is knowing when to do it and when not to.
I see! Thanks for the links - so they do this for both the 512x512 SD 2 and 768x768 SD 2 model?
It's a technique NovelAI discovered FYI (https://blog.novelai.net/novelai-improvements-on-stable-diffusion-e10d38db82ac)
Actually @patil-suraj solved it pretty cleanly by just removing the last layer: https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/text_encoder/config.json#L19
So this works out of the box
Notice the difference between: https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/text_encoder/config.json#L19 and https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/blob/main/config.json#L54
768x768 weights released:
- https://huggingface.co/stabilityai/stable-diffusion-2/tree/main
fp16 and other versions of the models appear to being being worked on and uploaded.
testing in progress on the horde https://github.com/Sygil-Dev/nataili/tree/v2 try it out Stable Diffusion 2.0 on our UI's
https://tinybots.net/artbot https://aqualxx.github.io/stable-ui/ https://dbzer0.itch.io/lucid-creations
https://sigmoid.social/@stablehorde/109398715339480426
SD 2.0
- [x] Initial implementation ready for testing
- [ ] img2img
- [ ] inpainting
- [ ] k_diffusers support
Originally posted by @AlRlC in https://github.com/Sygil-Dev/nataili/issues/67#issuecomment-1326385645
- https://github.com/TheLastBen/fast-stable-diffusion/commit/11fd38bfbd2f1ed42449b37ba88ba324ff42ba43
Create pathsV2.py
- https://github.com/TheLastBen/fast-stable-diffusion/commit/fe445d986f08a1134f26f5efcd1c0829f34bc481
Support for SD V.2
- https://github.com/TheLastBen/fast-stable-diffusion/commit/da9b38010c2edc8fcccf2b0b70f321af30c0ecb8
fix
- https://github.com/TheLastBen/fast-stable-diffusion/commit/6c84728c72bd9735b0a5be4c62a292554c3b41d1
fix
- https://github.com/TheLastBen/fast-stable-diffusion/commit/04ba92b1931ab6aa0269a0516640f8874b004885
fix
- https://github.com/TheLastBen/fast-stable-diffusion/commit/ebea13401da873b3420fdf6f0fa02df567534a55
Create sd_hijackV2.py
- https://github.com/TheLastBen/fast-stable-diffusion/commit/88496f5199c82e9c5ee2ae40bc980140d8cd4ce5
Create sd_samplersV2.py
- https://github.com/TheLastBen/fast-stable-diffusion/commit/f324b3d85473d308ebeefb03de58ae6eb9070f42
fix V2
Originally posted by @0xdevalias in https://github.com/TheLastBen/fast-stable-diffusion/issues/599#issuecomment-1326446674
Should work now, make sure you check the box "redownload original model" when choosing V2
https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb
Requires more than 12GB of RAM for now, so free colab probably won't suffice.
Originally posted by @TheLastBen in https://github.com/TheLastBen/fast-stable-diffusion/issues/599#issuecomment-1326461962