Upload dc.py

dc.py

@@ -672,7 +672,7 @@ from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_pat
 
 
 #@spaces.GPU
-def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
            model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
            lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
            sampler = "Euler a", vae = None, translate=True, progress=gr.Progress(track_tqdm=True)):
@@ -730,64 +730,6 @@ def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidanc
     return output_image
 
 
-async def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
-           model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
-           lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
-           sampler = "Euler a", vae = None, translate=True, progress=gr.Progress(track_tqdm=True)):
-    import PIL
-    import numpy as np
-    MAX_SEED = np.iinfo(np.int32).max
-
-    load_lora_cpu = False
-    verbose_info = False
-    gpu_duration = 59
-
-    images: list[tuple[PIL.Image.Image, str | None]] = []
-    info_state = info_images = ""
-    progress(0, desc="Preparing...")
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed).seed()
-
-    if translate:
-        prompt = translate_to_en(prompt)
-        negative_prompt = translate_to_en(prompt)
-
-    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
-    progress(0.5, desc="Preparing...")
-    lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
-        set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
-    lora1 = get_valid_lora_path(lora1)
-    lora2 = get_valid_lora_path(lora2)
-    lora3 = get_valid_lora_path(lora3)
-    lora4 = get_valid_lora_path(lora4)
-    lora5 = get_valid_lora_path(lora5)
-    progress(1, desc="Preparation completed. Starting inference...")
-
-    progress(0, desc="Loading model...")
-    await sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0])
-    progress(1, desc="Model loaded.")
-    progress(0, desc="Starting Inference...")
-    info_state, images, info_images = await sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
-        guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
-        lora4, lora4_wt, lora5, lora5_wt, sampler,
-        height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024,
-        None, None, None, 0.35, 100, 200, 0.1, 0.1, 1.0, 0., 1., False, "Classic", None,
-        1.0, 100, 10, 30, 0.55, "Use same sampler", "", "",
-        False, True, 1, True, False, False, False, False, "./images", False, False, False, True, 1, 0.55,
-        False, False, False, True, False, "Use same sampler", False, "", "", 0.35, True, True, False, 4, 4, 32,
-        False, "", "", 0.35, True, True, False, 4, 4, 32,
-        True, None, None, "plus_face", "original", 0.7, None, None, "base", "style", 0.7, 0.0,
-        load_lora_cpu, verbose_info, gpu_duration
-    )
-    progress(1, desc="Inference completed.")
-    output_image = images[0][0] if images else None
-
-    return output_image
-
-
 #@spaces.GPU
 def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
             model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,