Upload dc.py

dc.py

@@ -671,6 +671,7 @@ from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_pat
     normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en, get_t2i_model_info, get_civitai_tag, save_image_history)
 
 
+
 #@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,
@@ -708,6 +709,67 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     lora5 = get_valid_lora_path(lora5)
     progress(1, desc="Preparation completed. Starting inference...")
 
+    progress(0, desc="Loading model...")
+    for _ in sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0]):
+        pass
+    progress(1, desc="Model loaded.")
+    progress(0, desc="Starting Inference...")
+    images = None
+    for info_state, stream_images, info_images in 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
+    ):
+        images = stream_images
+    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,
+           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...")
     sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0])
     progress(1, desc="Model loaded.")