From 75830e217960a30e6771c137f1318925af9f5fa3 Mon Sep 17 00:00:00 2001 From: hugojarkoff Date: Fri, 2 Feb 2024 16:54:46 +0000 Subject: [PATCH] Add T2I-Adapter subsection to SDXL Adaptation guide --- docs/guides/adapting_sdxl/index.md | 157 +++++++++++++++++- .../scifi_pixel_IP_T2I_sdxl.webp | Bin 0 -> 135358 bytes .../zoe-depth-map-german-castle.png | Bin 0 -> 271336 bytes 3 files changed, 151 insertions(+), 6 deletions(-) create mode 100644 docs/guides/adapting_sdxl/scifi_pixel_IP_T2I_sdxl.webp create mode 100644 docs/guides/adapting_sdxl/zoe-depth-map-german-castle.png diff --git a/docs/guides/adapting_sdxl/index.md b/docs/guides/adapting_sdxl/index.md index 8d27ea7..585d324 100644 --- a/docs/guides/adapting_sdxl/index.md +++ b/docs/guides/adapting_sdxl/index.md @@ -155,7 +155,8 @@ predicted_image.save("vanilla_sdxl.png") It's time to execute your code. The resulting image should look like this:
- Image title + Generated image of a castle using default SDXL weights +
Generated image of a castle using default SDXL weights.
It is not really what we prompted the model for, unfortunately. To get a more futuristic-looking castle, you can either go for tedious prompt engineering, or use a pretrainered LoRA tailored to our use case, like the [Sci-fi Environments](https://civitai.com/models/105945?modelVersionId=140624) LoRA available on Civitai. We'll now show you how the LoRA option works with Refiners. @@ -235,7 +236,8 @@ manager.add_loras("scifi-lora", tensors=scifi_lora_weights) You should get something like this - pretty neat, isn't it?
- Image title + Sci-fi castle +
Generated image of a castle in sci-fi style.
## Multiple LoRAs @@ -256,7 +258,7 @@ manager.add_multiple_loras( ) ``` -Adapters such as LoRAs also have a [scale](https://github.com/finegrain-ai/refiners/blob/fd01ba910efb764b4521254cded2530b6c31cbd4/src/refiners/fluxion/adapters/lora.py#L17) (roughly) quantifying the effect of this Adapter. +Adapters such as LoRAs also have a [scale][refiners.fluxion.adapters.Lora.scale] (roughly) quantifying the effect of this Adapter. Refiners allows setting different scales for each Adapter, allowing the user to balance the effect of each Adapter: ```py @@ -334,7 +336,8 @@ manager.add_multiple_loras( The results are looking great:
- Image title + Sci-fi Pixel Art castle +
Generated image of a castle in sci-fi, pixel art style.
## Multiple LoRAs + IP-Adapter @@ -346,7 +349,7 @@ For instance, IP-Adapter (covered in [a previous blog post](https://blog.finegra In our example, consider this image of the [Neuschwanstein Castle](https://en.wikipedia.org/wiki/Neuschwanstein_Castle):
- Image title + Castle Image
Credits: Bayerische Schlösserverwaltung, Anton Brandl
@@ -470,10 +473,152 @@ with torch.no_grad(): The result looks convincing: we do get a *pixel-art, futuristic-looking Neuschwanstein castle*!
- Image title + Generated image in sci-fi, pixel art style, using IP-Adapter. +
Generated image in sci-fi, pixel art style, using IP-Adapter.
+## Everything else + T2I-Adapter + +T2I-Adapters[^1] are a powerful class of Adapters aiming at controlling the Text-to-Image (T2I) diffusion process with external control signals, such as canny edges or pose estimations inputs. +In this section, we will compose our previous example with the [Depth-Zoe Adapter](https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0), providing a depth condition to the diffusion process using the following depth map as input signal: + +
+ Input depth map of the initial castle image +
Input depth map of the initial castle image.
+
+ +First, download the image as well as the weights of T2I-Depth-Zoe-Adapter by calling the following commands: + +```bash +curl -O https://refine.rs/guides/adapting_sdxl/zoe-depth-map-german-castle.png +python scripts/conversion/convert_diffusers_t2i_adapter.py --from "TencentARC/t2i-adapter-depth-zoe-sdxl-1.0" --to t2i_depth_zoe_xl.safetensors --half +``` + +Then, just inject it as usual: + +```py +# Load T2I-Adapter +t2i_adapter = SDXLT2IAdapter( + target=sdxl.unet, + name="zoe-depth", + weights=load_from_safetensors("t2i_depth_zoe_xl.safetensors"), + scale=0.72, +).inject() +``` + +Finally, at runtime, compute the embedding of the input condition through the `t2i_adapter` object, and set its embedding calling `.set_condition_features()`: + +```py +from refiners.fluxion.utils import image_to_tensor, interpolate + +image_depth_condition = Image.open("zoe-depth-map-german-castle.png") + +with torch.no_grad(): + condition = image_to_tensor(image_depth_condition.convert("RGB"), device=sdxl.device, dtype=sdxl.dtype) + # Spatial dimensions should be divisible by default downscale factor (=16 for T2IAdapter ConditionEncoder) + condition = interpolate(condition, torch.Size((1024, 1024))) + t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition)) + +``` + +??? example "Expand to see the entire end-to-end code" + + ```py + import torch + from PIL import Image + + from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad, image_to_tensor, interpolate + from refiners.foundationals.latent_diffusion.lora import SDLoraManager + from refiners.foundationals.latent_diffusion.stable_diffusion_xl import StableDiffusion_XL, SDXLT2IAdapter + from refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt import SDXLIPAdapter + + # Load SDXL + sdxl = StableDiffusion_XL(device="cuda", dtype=torch.float16) + sdxl.clip_text_encoder.load_from_safetensors("DoubleCLIPTextEncoder.safetensors") + sdxl.unet.load_from_safetensors("sdxl-unet.safetensors") + sdxl.lda.load_from_safetensors("sdxl-lda.safetensors") + + # Load LoRAs weights from disk and inject them into target + manager = SDLoraManager(sdxl) + scifi_lora_weights = load_from_safetensors("Sci-fi_Environments_sdxl.safetensors") + pixel_art_lora_weights = load_from_safetensors("pixel-art-xl-v1.1.safetensors") + manager.add_multiple_loras( + tensors={"scifi-lora": scifi_lora_weights, "pixel-art-lora": pixel_art_lora_weights}, + scale={"scifi-lora": 1.5, "pixel-art-lora": 1.55}, + ) + + # Load IP-Adapter + ip_adapter = SDXLIPAdapter( + target=sdxl.unet, + weights=load_from_safetensors("ip-adapter-plus_sdxl_vit-h.safetensors"), + scale=1.0, + fine_grained=True, # Use fine-grained IP-Adapter (IP-Adapter Plus) + ) + ip_adapter.clip_image_encoder.load_from_safetensors("CLIPImageEncoderH.safetensors") + ip_adapter.inject() + + # Load T2I-Adapter + t2i_adapter = SDXLT2IAdapter( + target=sdxl.unet, + name="zoe-depth", + weights=load_from_safetensors("t2i_depth_zoe_xl.safetensors"), + scale=0.72, + ).inject() + + # Hyperparameters + prompt = "a futuristic castle surrounded by a forest, mountains in the background" + image_prompt = Image.open("german-castle.jpg") + image_depth_condition = Image.open("zoe-depth-map-german-castle.png") + seed = 42 + sdxl.set_inference_steps(50, first_step=0) + sdxl.set_self_attention_guidance( + enable=True, scale=0.75 + ) # Enable self-attention guidance to enhance the quality of the generated images + + with no_grad(): + clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding( + text=prompt + ", best quality, high quality", + negative_text="monochrome, lowres, bad anatomy, worst quality, low quality", + ) + time_ids = sdxl.default_time_ids + + clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(image_prompt)) + ip_adapter.set_clip_image_embedding(clip_image_embedding) + + condition = image_to_tensor(image_depth_condition.convert("RGB"), device=sdxl.device, dtype=sdxl.dtype) + # Spatial dimensions should be divisible by default downscale factor (=16 for T2IAdapter ConditionEncoder) + condition = interpolate(condition, torch.Size((1024, 1024))) + t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition)) + + manual_seed(seed=seed) + x = torch.randn(size=(1, 4, 128, 128), device=sdxl.device, dtype=sdxl.dtype) + + # Diffusion process + for step in sdxl.steps: + if step % 10 == 0: + print(f"Step {step}") + x = sdxl( + x, + step=step, + clip_text_embedding=clip_text_embedding, + pooled_text_embedding=pooled_text_embedding, + time_ids=time_ids, + ) + predicted_image = sdxl.lda.decode_latents(x) + + predicted_image.save("scifi_pixel_IP_T2I_sdxl.png") + + ``` + +The results look convincing: the depth and proportions of the initial castle are more faithful, while preserving our *futuristic, pixel-art style*! +
+ Generated image in sci-fi, pixel art style, using IP and T2I Adapters +
Generated image in sci-fi, pixel art style, using IP and T2I Adapters.
+
+ ## Wrap up As you can see in this guide, composing Adapters on top of foundation models is pretty seamless in Refiners, allowing practitioners to quickly test out different combinations of Adapters for their needs. We encourage you to try out different ones, and even train some yourselves! + +[^1]: Mou, C., Wang, X., Xie, L., Zhang, J., Qi, Z., Shan, Y., & Qie, X. (2023). 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