Update app.py

app.py

@@ -148,18 +148,22 @@ def infer(pm):
     
         p1 = pm["p"]
         name = generate_random_string(12)+".png"
+        neg = pm["n"]
 
+        if neg != "":
+            neg=,f' (((({neg}))))'
         _do = ['beautiful', 'playful', 'photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable coloring', 'rough texture', 'best quality', 'focused']
         if p1 != "":
             _do.append(f'{p1}')
-        posi = " ".join(_do)
+        posi = " ".join(_do)+neg
 
         return Piper(name,posi,pm["m"])
 
-def run(m,p1,*result):
+def run(m,p1,p2,*result):
     
         p1_en = translate(p1,"english")
-        pm = {"p":p1_en,"m":m}
+        p2_en = translate(p2,"english")
+        pm = {"p":p1_en,"n":p2_en,"m":m}
         ln = len(result)
         print("images: "+str(ln))
         rng = list(range(ln))
@@ -189,27 +193,29 @@ def main():
     fps=40
     time=5
     device = "cuda"
-    dtype = torch.float16
+    dtype = torch.bfloat16
     result=[]
     step = 2
 
     progress=gr.Progress()
     progress((0, step))
 
-    base = "emilianJR/epiCRealism"
-    repo = "ByteDance/SDXL-Lightning"
-    ckpt = f"sdxl_lightning_{step}step_unet.safetensors"
+    #base="SG161222/Realistic_Vision_V6.0_B1_noVAE"
+    #vae="stabilityai/sd-vae-ft-mse-original"
+    #repo = "ByteDance/SDXL-Lightning"
+    #ckpt = f"sdxl_lightning_{step}step_unet.safetensors"
 
-    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(device, dtype)
-    unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
+    #unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(device)
+    #unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
 
+    base = "emilianJR/epiCRealism"
     repo = "ByteDance/AnimateDiff-Lightning"
     ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
 
     adapter = MotionAdapter().to(device, dtype)
     adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device), strict=False)
 
-    pipe = AnimateDiffPipeline.from_pretrained(base, unet=unet, motion_adapter=adapter, torch_dtype=dtype, variant="fp16").to(dtype=dtype)
+    pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype, variant="fp16").to(dtype=dtype)
     pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
     
     mp.set_start_method("spawn", force=True)
@@ -222,7 +228,14 @@ def main():
             with gr.Row():
                 prompt = gr.Textbox(
                     elem_id="prompt",
-                    placeholder="DESCRIPTION",
+                    placeholder="INCLUDE",
+                    container=False,
+                    max_lines=1
+                )
+            with gr.Row():
+                prompt2 = gr.Textbox(
+                    elem_id="prompt",
+                    placeholder="EXCLUDE",
                     container=False,
                     max_lines=1
                 )
@@ -250,8 +263,8 @@ def main():
                 result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
                 
         gr.on(
-            triggers=[run_button.click, prompt.submit],
-            fn=run,inputs=[motion,prompt,*result],outputs=result
+            triggers=[run_button.click, prompt.submit, prompt2.submit],
+            fn=run,inputs=[motion,prompt,prompt2,*result],outputs=result
         )
         demo.queue().launch()