Update app.py

app.py

@@ -7,16 +7,22 @@ import gradio as gr
 import numpy as np
 import random
 #from diffusers import DiffusionPipeline
-from diffusers import StableDiffusionXLPipeline
+from diffusers import StableDiffusionXLPipeline, DDIMScheduler
+from huggingface_hub import hf_hub_download
 
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1216
 
+base_model_id = "Laxhar/noobai-XL-1.0"
+repo_name = "ByteDance/Hyper-SD"
+# Take 2-steps lora as an example
+ckpt_name = "Hyper-SDXL-8steps-lora.safetensors"
+
+# Load model.
 #pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
 pipe = StableDiffusionXLPipeline.from_pretrained(
-    #"yodayo-ai/kivotos-xl-2.0", 
-    "Laxhar/noobai-XL-1.0", 
+    base_model_id, 
     torch_dtype=torch.float16, 
     use_safetensors=True,
     custom_pipeline="lpw_stable_diffusion_xl",
@@ -24,8 +30,15 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 )
 pipe.to('cuda')
 
+#pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
+pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
+pipe.fuse_lora()
+# Ensure ddim scheduler timestep spacing set as trailing !!!
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+# lower eta results in more detail
+
 prompt = "1girl, solo, upper body, v, smile, looking at viewer, outdoors, night, masterpiece, best quality, very aesthetic, absurdres"
-negative_prompt = "nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
+negative_prompt = "(worst quality),(low quality),lowres,(bad anatomy),(deformed anatomy),(deformed fingers),(blurry),(extra finger),(extra arms), (extra legs),(monochrome:1.4),(grayscale:1.4),((watermark)),(overweight female:1.6),((pointy ears)),mascot,stuffed human, stuffed animal,chibi,english text, chinese text, korean text
 
 @spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):