GitHub - layerdiffusion/sd-forge-layerdiffusion: [WIP] Layer Diffusion for WebUI (via Forge) | bylayerdiffusion

sd-forge-layerdiffusion

Transparent Image Layer Diffusion using Latent Transparency

This is a WIP extension for SD WebUI (via Forge) to generate transparent images and layers.

The image generating and basic layer functionality is working now, but the transparent img2img is not finished yet (will finish in about one week).

This code base is highly dynamic and may change a lot in the next month. If you are from professional content creation studio and need all previous results to be strictly reproduced, you may consider backup files during each update.

Before You Start

Because many people may be curious about how the latent preview looks like during a transparent diffusion process, I recorded a video so that you can see it before you download the models and extensions:

screen_record.mp4

You can see that the native transparent diffusion can process transparent glass, semi-transparent glowing effects, etc, that are not possible with simple background removal methods. Native transparent diffusion also gives you detailed fur, hair, whiskers, and detailed structure like that skeleton.

Model Notes

Note that all currently released models are for SDXL. Models for SD1.5 may be provided later if demanded.

Note that in this extension, all model downloads/selections are fully automatic. In fact most users can just skip this section.

Below models are released:

layer_xl_transparent_attn.safetensors This is a rank-256 LoRA to turn a SDXL into a transparent image generator. It will change the latent distribution of the model to a "transparent latent space" that can be decoded by the special VAE pipeline.
layer_xl_transparent_conv.safetensors This is an alternative model to turn your SDXL into a transparent image generator. This safetensors file includes an offset of all conv layers (and actually, all layers that are not q,k,v of any attention layers). These offsets can be merged to any XL model to change the latent distribution to transparent images. Because we excluded the offset training of any q,k,v layers, the prompt understanding of SDXL should be perfectly preserved. However, in practice, I find the layer_xl_transparent_attn.safetensors will lead to better results. This layer_xl_transparent_conv.safetensors is still included for some special use cases that needs special prompt understanding. Also, this model may introduce a strong style influence to the base model.
layer_xl_fg2ble.safetensors This is a safetensors file includes offsets to turn a SDXL into a layer generating model, that is conditioned on foregrounds, and generates blended compositions.
layer_xl_fgble2bg.safetensors This is a safetensors file includes offsets to turn a SDXL into a layer generating model, that is conditioned on foregrounds and blended compositions, and generates backgrounds.
layer_xl_bg2ble.safetensors This is a safetensors file includes offsets to turn a SDXL into a layer generating model, that is conditioned on backgrounds, and generates blended compositions.
layer_xl_bgble2fg.safetensors This is a safetensors file includes offsets to turn a SDXL into a layer generating model, that is conditioned on backgrounds and blended compositions, and generates foregrounds.
vae_transparent_encoder.safetensors This is an image encoder to extract a latent offset from pixel space. The offset can be added to latent images to help the diffusion of transparency. Note that in the paper we used a relatively heavy model with exactly same amount of parameters as the SD VAE. The released model is more light weighted, requires much less vram, and does not influence result quality in my tests.
vae_transparent_decoder.safetensors This is an image decoder that takes SD VAE outputs and latent image as inputs, and outputs a real PNG image. The model architecture is also more lightweight than the paper version to reduce VRAM requirement. I have made sure that the reduced parameters does not influence result quality.

Below models may be released soon (if necessary):

A model that can generate foreground and background together (using attention sharing similar to AnimateDiff). I put this model on hold because of these reasons: (1) the other released models can already achieve all functionalities and this model does not bring more functionalities. (2) the inference speed of this model is 3x slower than others and requires 4x more VRAM than other released model, and I am working on reducing the VRAM of this model if necessary. (3) This model will involve more hyperparameters and if demanded, I will investigate the best practice for inference/training before release it.
The current background-conditioned foreground model may be a bit too lightweight. I will probably release a heavier one with more parameters and different behaviors (see also the discussions later).
Because the difference between diffusers training and k-diffusion inference, I can observe some mystical problems like sometimes DPM++ will give artifacts but Euler A will fix it. I am looking into it and may provide some revised model that works better with all A1111 samplers.

Sanity Check

We highly encourage you to go through the sanity check and get exactly same results (so that if any problem occurs, we will know if the problem is on our side).

The two used models are:

https://civitai.com/models/133005?modelVersionId=198530 Juggernaut XL V6 (note that the used one is V6, not v7 or v8 or V9)
https://civitai.com/models/261336?modelVersionId=295158 anima_pencil-XL 1.0.0 (note that the used one is 1.0.0, not 1.5.0)

We will first test transparent image generating. Set your extension to this:

an apple, high quality

Negative prompt: bad, ugly

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 5, Seed: 12345, Size: 1024x1024, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: Only Generate Transparent Image (Attention Injection), layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: False, layerdiffusion_bg_image: False, layerdiffusion_blend_image: False, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

Make sure that you get this apple

woman, messy hair, high quality

Negative prompt: bad, ugly

Make sure that you get the woman with hair as messy as this

a cup made of glass, high quality

Negative prompt: bad, ugly

Make sure that you get this cup

glowing effect, book of magic, high quality

Negative prompt: bad, ugly

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 7, Seed: 12345, Size: 1024x1024, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: Only Generate Transparent Image (Attention Injection), layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: True, layerdiffusion_bg_image: False, layerdiffusion_blend_image: True, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

make sure that you get this glowing book

OK then lets move on to a bit longer prompt:

(this prompt is from https://civitai.com/images/3160575)

photograph close up portrait of Female boxer training, serious, stoic cinematic 4k epic detailed 4k epic detailed photograph shot on kodak detailed bokeh cinematic hbo dark moody

Negative prompt: (worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 7, Seed: 12345, Size: 896x1152, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: Only Generate Transparent Image (Attention Injection), layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: False, layerdiffusion_bg_image: False, layerdiffusion_blend_image: False, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

Anime model test:

girl in dress, high quality

Negative prompt: nsfw, bad, ugly, text, watermark

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 7, Seed: 12345, Size: 896x1152, Model hash: 7ed8da12d9, Model: animaPencilXL_v100, layerdiffusion_enabled: True, layerdiffusion_method: Only Generate Transparent Image (Attention Injection), layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: False, layerdiffusion_bg_image: False, layerdiffusion_blend_image: False, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

(I am not very good at writing prompts in the AnimagineXL format, and perhaps you can get better results with better prompts)

Background Condition

First download this image:

then set the interface with

then set the parameters with

old man sitting, high quality

Negative prompt: bad, ugly

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 7, Seed: 12345, Size: 896x1152, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: From Background to Blending, layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: False, layerdiffusion_bg_image: True, layerdiffusion_blend_image: False, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

Then set the interface with (you first change the mode and then drag the image from result to interface)

Then change the sampler to Euler A or UniPC or some other sampler that is not dpm (This is probably because of some difference between diffusers training script and webui's k-diffusion. I am still looking into this and may revise my training script and model very soon so that this step will be removed.)

FAQ:

OK. But how can I get a background image like this?

You can use the Foreground Condition to get a background like this. We will describe it in the next section.

Or you can use old inpainting tech to perform foreground removal on any image to get a background like this.

Wait. Why you generate it with two steps? Can I generate it with one pass?

Two steps allows for more flexible editing. We will release the one-step model soon if necessary, but that model is 2x larger and requires 4x larger VRAM, and we are still working on reducing the computation requirement of that model. (But in my tests, the current solution is better than that model in most cases.)

Also you can see that the current model is about 680MB and in particular I think it is a bit too lightweight and will soon release a relatively heavier model for potential stronger structure understanding (but that is still under experiments).

Foreground Condition

First we generate a dog

a dog sitting, high quality

Negative prompt: bad, ugly

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 7, Seed: 12345, Size: 896x1152, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: Only Generate Transparent Image (Attention Injection), layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: True, layerdiffusion_bg_image: False, layerdiffusion_blend_image: False, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

then change to From Foreground to Blending and drag the transparent image to foreground input.

Note that you drag the real transparent image, not the visualization with checkboard background. Make sure tou see this

then do this

a dog sitting in room, high quality

Negative prompt: bad, ugly

Steps: 20, Sampler: DPM++ 2M SDE Karras, CFG scale: 7, Seed: 12345, Size: 896x1152, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: From Foreground to Blending, layerdiffusion_weight: 1, layerdiffusion_ending_step: 1, layerdiffusion_fg_image: True, layerdiffusion_bg_image: False, layerdiffusion_blend_image: False, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b

Then change mode, drag your image, so that

(Note that here I set stop at as 0.5 to get better results since I do not need the bg to be exactly same)

then do this

room, high quality

Negative prompt: bad, ugly

Steps: 20, Sampler: UniPC, CFG scale: 7, Seed: 12345, Size: 896x1152, Model hash: 1fe6c7ec54, Model: juggernautXL_version6Rundiffusion, layerdiffusion_enabled: True, layerdiffusion_method: From Foreground and Blending to Background, layerdiffusion_weight: 1, layerdiffusion_ending_step: 0.5, layerdiffusion_fg_image: True, layerdiffusion_bg_image: False, layerdiffusion_blend_image: True, layerdiffusion_resize_mode: Crop and Resize, Version: f0.0.17v1.8.0rc-latest-269-gef35383b