fix:the error of SDXLClipModel in ComfyUI revision 2016[c2cb8e88] and above

README.en.md

@@ -39,6 +39,12 @@ Usage：<br>
 
 ## Changelog
 
+
+**2024-02-26**
+
+- Fixed the error of SDXLClipModel in ComfyUI revision 2016[c2cb8e88] and above (the revision number was judged to be compatible with the old revision)
+- Fixed `easy detailerFix` generation error when batch size is greater than 1
+
 **v1.0.8(2024-02-25)**
 
 - `easy cascadeLoader` stage_c and stage_b support the checkpoint model (Download [checkpoints](https://huggingface.co/stabilityai/stable-cascade/tree/main/comfyui_checkpoints) models) 

README.md

@@ -47,6 +47,11 @@ stage_c 与 stage_b 可以使用[checkpoints](https://huggingface.co/stabilityai
 
 ## 更新日志
 
+**2024-02-26**
+
+- 修复 SDXLClipModel 在 ComfyUI 修订版本号 2016[c2cb8e88] 及以上的报错（判断了版本号可兼容老版本）
+- 修复 `easy detailerFix` 批次大小大于1时生成出错
+
 **v1.0.8(2024-02-25)**
 
 - `easy cascadeLoader` stage_c 与 stage_b 支持checkpoint模型 (需要下载[checkpoints](https://huggingface.co/stabilityai/stable-cascade/tree/main/comfyui_checkpoints)) 
@@ -59,7 +64,6 @@ stage_c 与 stage_b 可以使用[checkpoints](https://huggingface.co/stabilityai
 - 增加 `easy preSamplingCascade` - stabled cascade stage_c 预采样参数
 - 增加 `easy fullCascadeKSampler` - stable cascade stage_c 完整版采样器
 - 增加 `easy cascadeKSampler` - stable cascade stage-c ksampler simple
-- 优化图生图流程[示例](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#图生图)
 
 **v1.0.6 (2024-02-16)**
 

py/adv_encode.py

@@ -4,9 +4,9 @@ import itertools
 
 from comfy import model_management
 from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
-
 from nodes import NODE_CLASS_MAPPINGS
 
+from .libs.utils import compare_revision
 
 def _grouper(n, iterable):
     it = iter(iterable)
@@ -243,7 +243,11 @@ def encode_token_weights_l(model, token_weight_pairs):
 
 def encode_token_weights(model, token_weight_pairs, encode_func):
     if model.layer_idx is not None:
-        model.cond_stage_model.clip_layer(model.layer_idx)
+        # 2016 [c2cb8e88] 及以上版本去除了sdxl clip的clip_layer方法
+        if compare_revision(2016):
+            model.cond_stage_model.set_clip_options({'layer': model.layer_idx})
+        else:
+            model.cond_stage_model.clip_layer(model.layer_idx)
 
     model_management.load_model_gpu(model.patcher)
     return encode_func(model.cond_stage_model, token_weight_pairs)

py/easyNodes.py

@@ -3182,16 +3182,10 @@ class preDetailerFix:
         if vae is None:
             raise Exception(f"[ERROR] pipe['vae'] is missing")
         if optional_image is not None:
-            batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1
-            samples = {"samples": vae.encode(optional_image)}
-            samples = RepeatLatentBatch().repeat(samples, batch_size)[0]
-            image = optional_image
+            images = optional_image
         else:
-            samples = pipe["samples"] if "samples" in pipe else None
-            if samples is None:
-                raise Exception(f"[ERROR] pipe['samples'] is missing")
-            image = pipe["images"] if "images" in pipe else None
-            if image is None:
+            images = pipe["images"] if "images" in pipe else None
+            if images is None:
                 raise Exception(f"[ERROR] pipe['image'] is missing")
         positive = pipe["positive"] if "positive" in pipe else None
         if positive is None:
@@ -3209,8 +3203,7 @@ class preDetailerFix:
         loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {}
 
         new_pipe = {
-            "samples": samples,
-            "images": image,
+            "images": images,
             "model": model,
             "clip": clip,
             "vae": vae,
@@ -3325,21 +3318,22 @@ class detailerFix:
         start_time = int(time.time() * 1000)
 
         cls = ALL_NODE_CLASS_MAPPINGS["FaceDetailer"]
-        enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = cls().enhance_face(
+
+        result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, pipe, result_cnet_images = cls().doit(
             image, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
             scheduler,
             positive, negative, denoise, feather, noise_mask, force_inpaint,
             bbox_threshold, bbox_dilation, bbox_crop_factor,
             sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
-            sam_mask_hint_use_negative, drop_size, bbox_detector_opt, segm_detector_opt, sam_model_opt, wildcard,
-            detailer_hook=None, cycle=cycle)
+            sam_mask_hint_use_negative, drop_size, bbox_detector_opt, wildcard, cycle, sam_model_opt, segm_detector_opt,
+            detailer_hook=None)
 
         # 细节修复结束时间
         end_time = int(time.time() * 1000)
 
         spent_time = '细节修复:' + str((end_time - start_time) / 1000) + '秒'
 
-        results = easySave(enhanced_img, save_prefix, image_output, prompt, extra_pnginfo)
+        results = easySave(result_img, save_prefix, image_output, prompt, extra_pnginfo)
         sampler.update_value_by_id("results", my_unique_id, results)
 
         # Clean loaded_objects
@@ -3347,7 +3341,7 @@ class detailerFix:
 
         new_pipe = {
             "samples": None,
-            "images": enhanced_img,
+            "images": result_img,
             "model": model,
             "clip": clip,
             "vae": vae,
@@ -3372,12 +3366,12 @@ class detailerFix:
 
         if image_output in ("Hide", "Hide/Save"):
             return {"ui": {},
-                    "result": (new_pipe, enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list)}
+                    "result": (new_pipe, result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, result_cnet_images )}
 
         if image_output in ("Sender", "Sender/Save"):
             PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results})
 
-        return {"ui": {"images": results}, "result": (new_pipe, enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list)}
+        return {"ui": {"images": results}, "result": (new_pipe, result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, result_cnet_images )}
 
 def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
     for full_folder_path in full_folder_paths:

py/libs/utils.py

@@ -1,3 +1,20 @@
+comfy_ui_revision = None
+def get_comfyui_revision():
+    try:
+        import git
+        import os
+        import folder_paths
+        repo = git.Repo(os.path.dirname(folder_paths.__file__))
+        comfy_ui_revision = len(list(repo.iter_commits('HEAD')))
+    except:
+        comfy_ui_revision = "Unknown"
+    return comfy_ui_revision
+
+def compare_revision(num):
+    global comfy_ui_revision
+    if not comfy_ui_revision:
+        comfy_ui_revision = get_comfyui_revision()
+    return True if comfy_ui_revision == 'Unknown' or int(comfy_ui_revision) >= num else False
 
 def find_nearest_steps(clip_id, prompt):
     """Find the nearest KSampler or preSampling node that references the given id."""