Update app.py

app.py

@@ -17,10 +17,35 @@ dtype = torch.float16
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 
 repo = "fluently/Fluently-XL-Final"
-pipe = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
-pipe.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
-pipe.set_adapters("lora")
-pipe.to("cuda")
+
+pipe_best = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_best.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle2")
+pipe_best.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
+pipe_best.set_adapters(["lora","0.5"], adapter_weights=[1.5, 0.7])
+pipe_best.to("cuda")
+
+pipe_ori = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_ori.load_lora_weights("RalFinger/origami-style-sdxl-lora", weight_name="ral-orgmi-sdxl.safetensors", adapter_name="origami")
+pipe_ori.set_adapters(["origami"], adapter_weights=[2])
+pipe_ori.to("cuda")
+
+pipe_3D = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_3D.load_lora_weights("artificialguybr/3DRedmond-V1", weight_name="3DRedmond-3DRenderStyle-3DRenderAF.safetensors", adapter_name="dalle2")
+pipe_3D.load_lora_weights("goofyai/3d_render_style_xl", weight_name="3d_render_style_xl.safetensors", adapter_name="dalle1")
+pipe_3D.set_adapters(["dalle2","dalle1"], adapter_weights=[1.1, 0.8])
+pipe_3D.to("cuda")
+
+pipe_pixel = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_pixel.load_lora_weights("artificialguybr/PixelArtRedmond", weight_name="PixelArtRedmond-Lite64.safetensors", adapter_name="lora")
+pipe_pixel.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+pipe_pixel.set_adapters(["lora", "pixel"], adapter_weights=[1.0, 1.2])
+pipe_pixel.to("cuda")
+
+pipe_logo = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_logo.load_lora_weights("artificialguybr/StickersRedmond", weight_name="StickersRedmond.safetensors", adapter_name="lora")
+pipe_logo.load_lora_weights("artificialguybr/LogoRedmond-LogoLoraForSDXL", weight_name="LogoRedmond_LogoRedAF.safetensors", adapter_name="pixel")
+pipe_logo.set_adapters(["lora", "pixel"], adapter_weights=[0.5, 1.2])
+pipe_logo.to("cuda")
 
 help_text = """
 To optimize image results:
@@ -88,14 +113,7 @@ def king(type ,
         if randomize_seed:
             seed = random.randint(0, 99999)
         generator = torch.Generator().manual_seed(seed)
-        image = pipe(
-            prompt = instruction,
-            guidance_scale = 5, 
-            num_inference_steps = steps, 
-            width = width, 
-            height = height,
-            generator = generator
-        ).images[0]         
+        image = pipe_best( prompt = instruction, guidance_scale = 5, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
         return seed, image
 
 client = InferenceClient()
@@ -169,6 +187,10 @@ with gr.Blocks(css=css) as demo:
     with gr.Row():
         input_image = gr.Image(label="Image", type="pil", interactive=True)
 
+    with gr.Row():
+        width = gr.Number(value=1024, step=16,label="Width", interactive=True)
+        height = gr.Number(value=1024, step=16,label="Height", interactive=True)
+    
     with gr.Row():
         text_cfg_scale = gr.Number(value=7.3, step=0.1, label="Text CFG", interactive=True)
         image_cfg_scale = gr.Number(value=1.7, step=0.1,label="Image CFG", interactive=True)
@@ -191,9 +213,7 @@ with gr.Blocks(css=css) as demo:
     )
 
     gr.Markdown(help_text)
-
     instruction.change(fn=response, inputs=[instruction,input_image], outputs=type, queue=False)
-
     input_image.upload(fn=response, inputs=[instruction,input_image], outputs=type, queue=False)
     
     gr.on(triggers=[
@@ -209,6 +229,8 @@ with gr.Blocks(css=css) as demo:
                 seed,
                 text_cfg_scale,
                 image_cfg_scale,
+                width,
+                height
             ],
             outputs=[seed, input_image],
         )