Update app.py

app.py

@@ -64,7 +64,7 @@ class HFEmbedder(nn.Module):
 
 device = "cuda"
 t5 = HFEmbedder("silveroxides/t5xxl_flan_enc", max_length=512, torch_dtype=torch.bfloat16).to(device)
-clip = HFEmbedder("openai/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to(device)
+clip = HFEmbedder("silveroxides/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to(device)
 ae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device)
 # quantize(t5, weights=qfloat8)
 # freeze(t5)
@@ -660,8 +660,8 @@ def get_lin_function(
 def get_schedule(
     num_steps: int,
     image_seq_len: int,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
+    base_shift: float = 0.6,
+    max_shift: float = 1.25,
     shift: bool = True,
 ) -> list[float]:
     # extra step for zero
@@ -832,20 +832,20 @@ def create_demo():
             with gr.Column():
                 prompt = gr.Textbox(label="Prompt(한글 가능)", value="A cute and fluffy golden retriever puppy sitting upright, holding a neatly designed white sign with bold, colorful lettering that reads 'Have a Happy Day!' in cheerful fonts. The puppy has expressive, sparkling eyes, a happy smile, and fluffy ears slightly flopped. The background is a vibrant and sunny meadow with soft-focus flowers, glowing sunlight filtering through the trees, and a warm golden glow that enhances the joyful atmosphere. The sign is framed with small decorative flowers, adding a charming and wholesome touch. Ensure the text on the sign is clear and legible.")
                 
-                width = gr.Slider(minimum=128, maximum=2048, step=64, label="Width", value=768)
-                height = gr.Slider(minimum=128, maximum=2048, step=64, label="Height", value=768)
-                guidance = gr.Slider(minimum=1.0, maximum=5.0, step=0.1, label="Guidance", value=3.5)
+                width = gr.Slider(minimum=128, maximum=3072, step=64, label="Width", value=768)
+                height = gr.Slider(minimum=128, maximum=3072, step=64, label="Height", value=768)
+                guidance = gr.Slider(minimum=1.0, maximum=20.0, step=0.1, label="Guidance", value=3.5)
                 inference_steps = gr.Slider(
                     label="Inference steps",
                     minimum=1,
-                    maximum=30,
+                    maximum=360,
                     step=1,
                     value=30,
                 )
                 seed = gr.Number(label="Seed", precision=-1)
                 do_img2img = gr.Checkbox(label="Image to Image", value=False)
                 init_image = gr.Image(label="Input Image", visible=False)
-                image2image_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Noising strength", value=0.8, visible=False)
+                image2image_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Noising strength", value=0.4, visible=False)
                 resize_img = gr.Checkbox(label="Resize image", value=True, visible=False)
                 generate_button = gr.Button("Generate")