add unet from LCM SDXL

unet/README.md

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+---
+library_name: diffusers
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+- text-to-image
+license: openrail++
+inference: false
+---
+
+# Latent Consistency Model (LCM): SDXL
+
+Latent Consistency Model (LCM) was proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) 
+by *Simian Luo, Yiqin Tan et al.* and [Simian Luo](https://huggingface.co/SimianLuo), [Suraj Patil](https://huggingface.co/valhalla), and [Daniel Gu](https://huggingface.co/dg845)
+succesfully applied the same approach to create LCM for SDXL.
+
+This checkpoint is a LCM distilled version of [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows
+to reduce the number of inference steps to only between **2 - 8 steps**.
+
+
+## Usage
+
+LCM SDXL is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first 
+install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`.
+audio dataset from the Hugging Face Hub:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade diffusers transformers accelerate peft
+```
+
+### Text-to-Image
+
+The model can be loaded with it's base pipeline `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
+Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0.
+
+```python
+from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
+import torch
+
+unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-sdxl", torch_dtype=torch.float16, variant="fp16")
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16")
+
+pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
+pipe.to("cuda")
+
+prompt = "a close-up picture of an old man standing in the rain"
+
+image = pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0]
+```
+
+![](./image.png)
+
+### Image-to-Image
+
+Works as well! TODO docs
+
+### Inpainting
+
+Works as well! TODO docs
+
+### ControlNet
+
+Works as well! TODO docs
+
+### T2I Adapter
+
+Works as well! TODO docs
+
+## Speed Benchmark
+
+TODO
+
+## Training
+
+TODO

unet/config.json

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+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.23.0.dev0",
+  "_name_or_path": "/home/patrick/lcm-sdxl-base-1.0",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 2048,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 128,
+  "time_cond_proj_dim": 256,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": true
+}

unet/diffusion_pytorch_model.fp16.safetensors

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unet/diffusion_pytorch_model.safetensors

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