Update app.py

app.py

@@ -14,40 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
-import os.path
-
 import torch
 from diffusers import AutoPipelineForText2Image, LCMScheduler
 from llama_cpp import Llama
 import gradio as gr
 
-parser = argparse.ArgumentParser(description='Japanese translation and hallucinations for SD')
-parser.add_argument('--gguf_path',
-                    type=str,
-                    default=None,
-                    help='load gguf filepath')
-parser.add_argument('--sd_model_name',
-                    type=str,
-                    default="segmind/SSD-1B",
-                    help='sd model HF name')
-parser.add_argument('--sd_adapter_name',
-                    type=str,
-                    default="latent-consistency/lcm-lora-ssd-1b",
-                    help='sd lora adaptor HF name')
-parser.add_argument('--cpu',
-                    action='store_true',
-                    help='force use cpu (intel).')
-parser.add_argument('--share',
-                    action='store_true',
-                    help='force use cpu.')
-parser.add_argument('--openvino_path',
-                    type=str,
-                    default=None,
-                    help='load openvio model filepath')
-
-args = parser.parse_args()
-
 llm_model_path = args.gguf_path
 sd_model_name = args.sd_model_name
 sd_adapter_name = args.sd_adapter_name
@@ -59,50 +30,15 @@ height = 512
 num_inference_steps = 4
 guidance_scale = 1.0
 
-if args.cpu:
-    if args.openvino_path is None:
-        if not os.path.exists('./sd-1.5-lcm-openvino'):
-            from huggingface_hub import snapshot_download
-            download_folder = snapshot_download(repo_id="Intel/sd-1.5-lcm-openvino")
-            import shutil
-            shutil.copytree(download_folder, "./sd-1.5-lcm-openvino'")
-            args.openvino_path = './sd-1.5-lcm-openvino'
-        else:
-            args.openvino_path = './sd-1.5-lcm-openvino'
-    from openvino_pipe import LatentConsistencyEngine
-    pipe = LatentConsistencyEngine(
-        args.openvino_path
-    )
-else:
-    pipe = AutoPipelineForText2Image.from_pretrained(
-        sd_model_name,
-        torch_dtype=torch.float16 if use_cuda else torch.float32,
-    )
-    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-
-    if use_cuda:
-        pipe.enable_xformers_memory_efficient_attention()
-        pipe.enable_model_cpu_offload()
-        pipe.to("cuda")
-
-    if sd_adapter_name is not None:
-        pipe.load_lora_weights(sd_adapter_name)
-        if use_cuda:
-            pipe.fuse_lora()
-
-if llm_model_path is None:
-    from huggingface_hub import hf_hub_download
-    llm_model_path = hf_hub_download(
-        repo_id="taoki/llm-jp-1.3b-v1.0-staircaptions-FT",
-        filename="llm-jp-1.3b-v1.0_staircaptions-FT_Q4_K_S.gguf",
-    )
+from openvino_pipe import LatentConsistencyEngine
+pipe = LatentConsistencyEngine(
+    "Intel/sd-1.5-lcm-openvino"
+)
 
 llm = Llama(
-    model_path=llm_model_path,
-    n_gpu_layers=25 if use_cuda else -1,
+    model_path="llm-jp-1.3b-v1.0_staircaptions-FT_Q4_K_S.gguf",
 )
 
-
 def ja2prompt(ja_prompt):
     response = llm(f"### Instruction:\n{ja_prompt}\n### Response:\n", max_tokens=128)
     return response['choices'][0]['text']