Update app.py

app.py

@@ -3,30 +3,24 @@ import gradio as gr
 import numpy as np
 import spaces
 import torch
-from diffusers import StableDiffusionXLPipeline, AutoencoderKL  # , EulerDiscreteScheduler
-
-# 添加导入语句
-from sd_embed.embedding_funcs import get_weighted_text_embeddings_sdxl
+from diffusers import AutoPipelineForText2Image, AutoencoderKL #,EulerDiscreteScheduler
 
 if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>你现在运行在CPU上，但是该程序仅支持GPU。</p>"
+    DESCRIPTION += "\n<p>你现在运行在CPU上 但是只支持GPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 4096
 
 if torch.cuda.is_available():
-    vae = AutoencoderKL.from_pretrained(
-        "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
-    )
-    pipe = StableDiffusionXLPipeline.from_pretrained(
+    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+    pipe = AutoPipelineForText2Image.from_pretrained(
         "John6666/noobai-xl-nai-xl-epsilonpred075version-sdxl",
         vae=vae,
         torch_dtype=torch.float16,
         use_safetensors=True,
-        add_watermarker=False,
+        add_watermarker=False
     )
-    # pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-    # pipe.tokenizer.model_max_length = 512
+    #pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
     pipe.to("cuda")
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
@@ -38,7 +32,7 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
 def infer(
     prompt: str,
     negative_prompt: str = "",
-    use_negative_prompt: bool = True,
+    use_negative_prompt: bool = False,
     seed: int = 1,
     width: int = 512,
     height: int = 768,
@@ -50,40 +44,15 @@ def infer(
 ):
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
-
-    # 使用 get_weighted_text_embeddings_sdxl 获取文本嵌入，不传递 device 参数
-    if use_negative_prompt and negative_prompt:
-        (
-            prompt_embeds,
-            prompt_neg_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = get_weighted_text_embeddings_sdxl(
-            pipe,
-            prompt=prompt,
-            neg_prompt=negative_prompt,
-        )
-    else:
-        (
-            prompt_embeds,
-            _,
-            pooled_prompt_embeds,
-            _,
-        ) = get_weighted_text_embeddings_sdxl(
-            pipe,
-            prompt=prompt,
-        )
-        prompt_neg_embeds = None
-        negative_pooled_prompt_embeds = None
-    
     image = pipe(
-        prompt_embeds=prompt_embeds,
-        negative_prompt_embeds=prompt_neg_embeds,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
         width=width,
         height=height,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         generator=generator,
+        use_resolution_binning=use_resolution_binning,
     ).images[0]
     return image, seed
 
@@ -93,13 +62,13 @@ examples = [
 ]
 
 css = '''
-    .gradio-container{max-width: 560px !important}
-    h1{text-align:center}
-    footer {
-        visibility: hidden
-    }
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
 '''
-
+    
 with gr.Blocks(css=css) as demo:
     gr.Markdown("""# 梦羽的模型生成器
         ### 快速生成NoobXL的模型图片.""")
@@ -171,7 +140,7 @@ with gr.Blocks(css=css) as demo:
     )
 
     gr.on(
-        triggers=[prompt.submit, run_button.click],
+        triggers=[prompt.submit,run_button.click],
         fn=infer,
         inputs=[
             prompt,