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Author SHA1 Message Date
Cédric Deltheil d02be0d10e tests: update ref image for SDXL IP-Adapter plus 
Note: https://pytorch.org/docs/stable/notes/randomness.html

> Completely reproducible results are not guaranteed across PyTorch
> releases [...]
 2023-10-10 14:19:47 +02:00
Cédric Deltheil b80769939d add support for self-attention guidance 
See https://arxiv.org/abs/2210.00939
 2023-10-09 17:33:15 +02:00
Cédric Deltheil 5fc6767a4a add IP-Adapter plus (aka fine-grained features) 2023-09-29 15:23:43 +02:00
Cédric Deltheil f37f25a2e4 add e2e test for T2I-Adapter XL canny 2023-09-25 13:54:26 +02:00
Cédric Deltheil 4301e81eb3 add e2e test for T2I-Adapter depth 
Expected output generated with diffusers' StableDiffusionAdapterPipeline
 2023-09-25 13:54:26 +02:00
Benjamin Trom 01aeaf3e36 add unit test for multi_diffusion 2023-09-19 15:30:50 +02:00
Pierre Chapuis c421cfd56c add a test for IP-Adapter + ControlNet 2023-09-13 14:24:53 +02:00
Cédric Deltheil eea340c6c4 add support for SDXL IP-Adapter 
This only supports the latest SDXL IP-Adapter release (2023.9.8) which
builds upon the ViT-H/14 CLIP image encoder.
 2023-09-12 18:00:39 +02:00
Cédric Deltheil f4e9707297 sdxl test: refreshed reference image 
The former one was generated using SDXL 0.9 vs 1.0. The new one has been
generated with diffusers:

    import torch
    from diffusers import StableDiffusionXLPipeline, DDIMScheduler

    noise_scheduler = DDIMScheduler(
        num_train_timesteps=1000,
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        clip_sample=False,
        set_alpha_to_one=False,
        steps_offset=1,
    )

    base_model_path = "/path/to/stabilityai/stable-diffusion-xl-base-1.0"

    device = "cuda"
    prompt = "a cute cat, detailed high-quality professional image"
    negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
    seed = 2

    pipe = StableDiffusionXLPipeline.from_pretrained(base_model_path, scheduler=noise_scheduler, torch_dtype=torch.float16, add_watermarker=False)
    pipe = pipe.to(device)
    generator = torch.Generator(device).manual_seed(seed)
    images = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=30, generator=generator).images
 2023-09-12 10:59:26 +02:00
limiteinductive 2786117469 implement SDXL + e2e test on random init 2023-09-07 18:34:42 +02:00
Cédric Deltheil c55917e293 add IP-Adapter support for SD 1.5 
Official repo: https://github.com/tencent-ailab/IP-Adapter
 2023-09-06 15:12:48 +02:00
Pierre Chapuis 0f476ea18b make high-level adapters Adapters 
This generalizes the Adapter abstraction to higher-level
constructs such as high-level LoRA (targeting e.g. the
SD UNet), ControlNet and Reference-Only Control.

Some adapters now work by adapting child models with
"sub-adapters" that they inject / eject when needed.
 2023-08-31 10:57:18 +02:00
Doryan Kaced 3680f9d196 Add support for learned concepts e.g. via textual inversion 2023-08-28 10:37:39 +02:00
Cédric Deltheil 48f674c433 initial commit 2023-08-04 15:28:41 +02:00