update

app.py

@@ -21,19 +21,20 @@ text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dt
 tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
 vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half()
 scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
-unet_t2i = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half()
+# unet_t2i = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half()
 unet_i2i = unet_2d_condition.UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half()
 image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_IPA_dir}/image_encoder',ignore_mismatched_sizes=True).to(dtype=torch.float16, device=device)
 ip_img_size = 336
 clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
 
-pipe_t2i = pipeline_stable_diffusion_xl_chatglm_256.StableDiffusionXLPipeline(
-    vae=vae,text_encoder=text_encoder, 
-    tokenizer=tokenizer, 
-    unet=unet_t2i, 
-    scheduler=scheduler, 
-    force_zeros_for_empty_prompt=False
-).to(device)
+# pipe_t2i = pipeline_stable_diffusion_xl_chatglm_256.StableDiffusionXLPipeline(
+#     vae=vae,
+#     text_encoder=text_encoder, 
+#     tokenizer=tokenizer, 
+#     unet=unet_t2i, 
+#     scheduler=scheduler, 
+#     force_zeros_for_empty_prompt=False
+# ).to(device)
 
 pipe_i2i = pipeline_stable_diffusion_xl_chatglm_256_ipadapter.StableDiffusionXLPipeline(
     vae=vae,
@@ -49,7 +50,7 @@ pipe_i2i = pipeline_stable_diffusion_xl_chatglm_256_ipadapter.StableDiffusionXLP
 if hasattr(pipe_i2i.unet, 'encoder_hid_proj'):
     pipe_i2i.unet.text_encoder_hid_proj = pipe_i2i.unet.encoder_hid_proj
     
-pipe_i2i.load_ip_adapter( f'{ckpt_IPA_dir}' , subfolder="", weight_name=["ip_adapter_plus_general.bin"])
+pipe_i2i.load_ip_adapter(f'{ckpt_IPA_dir}' , subfolder="", weight_name=["ip_adapter_plus_general.bin"])
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
@@ -60,7 +61,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     generator = torch.Generator().manual_seed(seed)
 
     if ip_adapter_image is None:
-        image = pipe_t2i(
+        image = pipe_i2i(
             prompt = prompt, 
             negative_prompt = negative_prompt,
             guidance_scale = guidance_scale, 
@@ -118,10 +119,9 @@ with gr.Blocks(css=css) as demo:
     with gr.Row():
         with gr.Column(elem_id="col-left"):
             with gr.Row():
-                prompt = gr.Text(
+                prompt = gr.Textbox(
                     label="Prompt",
                     show_label=False,
-                    max_lines=1,
                     placeholder="Enter your prompt",
                     container=False,
                 )
@@ -129,9 +129,8 @@ with gr.Blocks(css=css) as demo:
             with gr.Row():
                 ip_adapter_image = gr.Image(label="IP-Adapter Image (optional)", type="pil")
             with gr.Accordion("Advanced Settings", open=False):
-                negative_prompt = gr.Text(
+                negative_prompt = gr.Textbox(
                     label="Negative prompt",
-                    max_lines=1,
                     placeholder="Enter a negative prompt",
                     visible=True,
                 )