Upload folder using huggingface_hub

README.md

@@ -0,0 +1,42 @@
+---
+tags:
+- text-to-image
+- stable-diffusion
+- lora
+- diffusers
+base_model: runwayml/stable-diffusion-v1-5
+license: mit
+library_name: diffusers
+---
+# Model description 
+
+Official TCD LoRA for Stable Diffusion v1.5 of the paper [Trajectory Consistency Distillation](https://arxiv.org/abs/2402.19159). 
+
+For more usage please found at [Project Page](https://mhh0318.github.io/tcd/)
+
+Here is a simple example:
+`
+```python
+import torch
+from diffusers import  StableDiffusionPipeline, TCDScheduler
+device = "cuda"
+base_model_id = "runwayml/stable-diffusion-v1-5"
+tcd_lora_id = "h1t/TCD-SD15-LoRA"
+pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device)
+pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+pipe.load_lora_weights(tcd_lora_id)
+pipe.fuse_lora()
+prompt = "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor."
+image = pipe(
+    prompt=prompt,
+    num_inference_steps=4,
+    guidance_scale=0,
+    # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step.
+    # A value of 0.3 often yields good results.
+    # We recommend using a higher eta when increasing the number of inference steps.
+    eta=0.3, 
+    generator=torch.Generator(device=device).manual_seed(42),
+).images[0]
+```
+
+![](assets/result.png)

pytorch_lora_weights.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eaecb24a1cda4411eab67275b1d991071216ac93693e8fa0c9226c9df0386232
+size 134621556