Create app.py

app.py

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+import gradio as gr
+import torch
+import os
+import gc
+import random
+from huggingface_hub import snapshot_download
+from diffusers import StableDiffusionXLPipeline, LCMScheduler
+from PIL import Image
+
+os.environ["XDG_CACHE_HOME"] = "/home/user/.cache"
+os.environ["TRANSFORMERS_CACHE"] = "/home/user/.cache/huggingface/transformers"
+os.environ["HF_HOME"] = "/home/user/.cache/huggingface"
+models = [
+    "Niggendar/fastPhotoPony_v80MixB",
+    "Niggendar/realisticPonyPhoto_v10",
+    "Niggendar/realmix_v10",
+    "Niggendar/realmixpony_v01",
+    "Niggendar/realmixpony_v02",
+    "Niggendar/recondiff_v10",
+    "Niggendar/Regro",
+    "Niggendar/relhCheckpoint_v20",
+]
+loras = ["openskyml/lcm-lora-sdxl-turbo"]
+pipe = None
+cached = {}
+cached_loras = {}
+def get_lora(lora_id):
+    if lora_id in cached_loras:
+        return cached_loras[lora_id]
+    lora_dir = snapshot_download(repo_id=lora_id, allow_patterns=["*.safetensors", "*.bin"])
+    lora_files = [f for f in os.listdir(lora_dir) if f.endswith((".safetensors", ".bin"))]
+    lora_path = os.path.join(lora_dir, lora_files[0])
+    cached_loras[lora_id] = lora_path
+    return lora_path
+def load_pipe(model_id, lora_id):
+    global pipe
+    if (model_id, lora_id) in cached:
+        pipe = cached[(model_id, lora_id)]
+        return
+    if pipe is not None:
+        pipe.to("meta")
+        pipe.unet = None
+        pipe.vae = None
+        pipe.text_encoder = None
+        del pipe
+        gc.collect()
+    cached.clear()
+    pipe = StableDiffusionXLPipeline.from_pretrained(model_id,torch_dtype=torch.float32,low_cpu_mem_usage=True    )
+    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+    pipe.load_lora_weights(get_lora(lora_id))
+    pipe.to("cpu", dtype=torch.float32)
+    pipe.enable_attention_slicing()
+    cached[(model_id, lora_id)] = pipe
+    return gr.update(value='-')
+def infer(model_id, lora_id, prompt, seed=None, steps=4, guid=0.1):
+    if seed is None or seed == "":
+        seed = random.randint(0, 2**32 - 1)
+    yield Image.new("RGB", (512, 512), color="gray"), gr.update(value='-')
+    image = pipe( prompt, generator=torch.manual_seed(int(seed)), num_inference_steps=steps,
+        guidance_scale=guid,width=128+256, height=128+256, added_cond_kwargs={}    ).images[0]
+    yield image, gr.update(value='-')
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=2):    
+            text2=gr.Textbox(label="Time",placeholder="timer",container=False,value='-')
+            mbtn=gr.Button(value="Load Pair")
+            modeldrop=gr.Dropdown(models, label="Model") 
+            loradrop=gr.Dropdown(loras, label="LCM LoRA")
+            with gr.Accordion(label="Settings", open=False):    
+                seed=gr.Textbox(label="Seed",visible=False)
+                steps=gr.Slider(1, 15, value=4, step=1, label="Steps") 
+                guidance=gr.Slider(0.0, 2.0, value=0.1, step=0.1, label="Guidance Scale")  
+        with gr.Column(scale=3):    
+            text= gr.Textbox(label="Prompt",container=False,placeholder="Prompt",value='')
+            gbtn=gr.Button(value="Generate")
+            imageout=gr.Image()
+    mbtn.click(fn=load_pipe, inputs=[ modeldrop, loradrop ], outputs=[text2])
+    gbtn.click(fn=infer, inputs=[ modeldrop, loradrop, text, seed,steps, guidance ], outputs=[imageout,text2])
+
+demo.queue()
+demo.launch(server_name="0.0.0.0", server_port=7860)
+