Update app.py

app.py

@@ -3,24 +3,30 @@ import gradio as gr
 import numpy as np
 import spaces
 import torch
-from diffusers import AutoPipelineForText2Image, AutoencoderKL #,EulerDiscreteScheduler
+from diffusers import AutoPipelineForText2Image, AutoencoderKL  # , EulerDiscreteScheduler
+
+# 添加导入语句
+from sd_embed.embedding_funcs import get_weighted_text_embeddings_sdxl
 
 if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>你现在运行在CPU上 但是只支持GPU.</p>"
+    DESCRIPTION += "\n<p>你现在运行在CPU上，但是该程序仅支持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)
+    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.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+    pipe.tokenizer.model_max_length = 512
     pipe.to("cuda")
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
@@ -43,10 +49,40 @@ def infer(
     progress=gr.Progress(track_tqdm=True),
 ):
     seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator("cuda").manual_seed(seed)
+
+    # 使用 get_weighted_text_embeddings_sdxl 获取文本嵌入
+    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,
+            device=pipe.device,
+        )
+    else:
+        (
+            prompt_embeds,
+            _,
+            pooled_prompt_embeds,
+            _,
+        ) = get_weighted_text_embeddings_sdxl(
+            pipe,
+            prompt=prompt,
+            device=pipe.device,
+        )
+        prompt_neg_embeds = None
+        negative_pooled_prompt_embeds = None
+
     image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=prompt_neg_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
         width=width,
         height=height,
         guidance_scale=guidance_scale,
@@ -62,13 +98,13 @@ 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.""")
@@ -140,7 +176,7 @@ 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,