Update app.py

app.py

@@ -40,54 +40,6 @@ RealVisXLv4_pipe = DiffusionPipeline.from_pretrained(
         variant="fp16"
     )
 RealVisXLv4_pipe.to("cuda")
-### SDXL Turbo #### 
-pipe_turbo = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo",
-                                                       vae=vae,
-                                                       torch_dtype=torch.float16,
-                                                       variant="fp16"
-                                                      )
-pipe_turbo.to("cuda")
-
-### SDXL Lightning ### 
-base = "stabilityai/stable-diffusion-xl-base-1.0"
-repo = "ByteDance/SDXL-Lightning"
-ckpt = "sdxl_lightning_1step_unet_x0.safetensors" 
-
-unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float16)
-unet.load_state_dict(load_file(hf_hub_download(repo, ckpt)))
-pipe_lightning = StableDiffusionXLPipeline.from_pretrained(base,
-                                                           unet=unet,
-                                                           vae=vae,
-                                                           text_encoder=pipe_turbo.text_encoder,
-                                                           text_encoder_2=pipe_turbo.text_encoder_2,
-                                                           tokenizer=pipe_turbo.tokenizer,
-                                                           tokenizer_2=pipe_turbo.tokenizer_2,
-                                                           torch_dtype=torch.float16,
-                                                           variant="fp16"
-                                                          )#.to("cuda")
-del unet
-pipe_lightning.scheduler = EulerDiscreteScheduler.from_config(pipe_lightning.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
-pipe_lightning.to("cuda")
-
-### Hyper SDXL ### 
-repo_name = "ByteDance/Hyper-SD"
-ckpt_name = "Hyper-SDXL-1step-Unet.safetensors"
-
-unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float16)
-unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name)))
-pipe_hyper = StableDiffusionXLPipeline.from_pretrained(base,
-                                                       unet=unet,
-                                                       vae=vae,
-                                                       text_encoder=pipe_turbo.text_encoder,
-                                                       text_encoder_2=pipe_turbo.text_encoder_2,
-                                                       tokenizer=pipe_turbo.tokenizer,
-                                                       tokenizer_2=pipe_turbo.tokenizer_2,
-                                                       torch_dtype=torch.float16,
-                                                       variant="fp16"
-                                                      )#.to("cuda")
-pipe_hyper.scheduler = LCMScheduler.from_config(pipe_hyper.scheduler.config)
-pipe_hyper.to("cuda")
-del unet
 
 @spaces.GPU
 def run_comparison(prompt: str,
@@ -105,45 +57,6 @@ def run_comparison(prompt: str,
     seed = int(randomize_seed_fn(seed, randomize_seed))
     if not use_negative_prompt:
         negative_prompt = ""
-    image_turbo=pipe_turbo(prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images_per_prompt,
-        cross_attention_kwargs={"scale": 0.65},
-        output_type="pil",
-        ).images
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-    yield image_turbo, None, None, None, None
-    image_lightning=pipe_lightning(prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images_per_prompt,
-        cross_attention_kwargs={"scale": 0.65},
-        output_type="pil",
-        ).images
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-    yield image_turbo, image_lightning, None, None, None
-    image_hyper=pipe_hyper(prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images_per_prompt,
-        cross_attention_kwargs={"scale": 0.65},
-        output_type="pil",
-        ).images
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-    yield image_turbo, image_lightning, image_hyper, None, None
     image_r3=RealVisXLv3_pipe(prompt=prompt,
         negative_prompt=negative_prompt,
         width=width,
@@ -156,7 +69,7 @@ def run_comparison(prompt: str,
         ).images
     image_paths = [save_image(img) for img in images]
     return image_paths, seed
-    yield image_turbo, image_lightning, image_hyper,image_r3, None
+    yield image_r3, None
     image_r4=RealVisXLv4_pipe(prompt=prompt,
         negative_prompt=negative_prompt,
         width=width,
@@ -169,7 +82,7 @@ def run_comparison(prompt: str,
         ).images
     image_paths = [save_image(img) for img in images]
     return image_paths, seed
-    yield image_turbo, image_lightning, image_hyper,image_r3, image_r4
+    yield image_r3, image_r4
 
 examples = ["A dignified beaver wearing glasses, a vest, and colorful neck tie.",
 "The spirit of a tamagotchi wandering in the city of Barcelona",
@@ -245,15 +158,6 @@ with gr.Blocks() as demo:
             )
 
     with gr.Row():
-        with gr.Column():
-            image_turbo = gr.Gallery(label="SDXL Turbo",columns=1, preview=True,)
-            gr.Markdown("## [SDXL Turbo](https://huggingface.co/stabilityai/sdxl-turbo)")
-        with gr.Column():
-            image_lightning = gr.Gallery(label="SDXL Lightning",columns=1, preview=True,)
-            gr.Markdown("## [SDXL Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)")
-        with gr.Column():
-            image_hyper = gr.Gallery(label="Hyper SDXL",columns=1, preview=True,)
-            gr.Markdown("## [Hyper SDXL](https://huggingface.co/ByteDance/Hyper-SD)")
         with gr.Column():
             image_r3 = gr.Gallery(label="RealVisXL V3",columns=1, preview=True,)
             gr.Markdown("## [RealVisXL V3](https://huggingface.co)")