add ernie-image

Signed-off-by: Vladimir Mandic <mandic00@live.com>

.gitignore

@@ -37,6 +37,7 @@ package-lock.json
 *.rar
 *.7z
 *.pyc
+*.out
 /*.bat
 /*.sh
 /*.txt

CHANGELOG.md

@@ -3,6 +3,9 @@
 ## Update for 2026-04-15
 
 - **Models**
+  - [Baidu ERNIE-Image](https://huggingface.co/baidu/ERNIE-Image) text-to-image FlowMatch diffusion transformer model with Mistral3 text encoding
+    includes *ERNIE-Image* (base) and *ERNIE-Image-Turbo* (distilled) variants
+    uses *ErnieImageTransformer2DModel* with *AutoencoderKLFlux2* latent decoding at 1024px
   - [Zeta-Chroma](https://huggingface.co/lodestones/Zeta-Chroma) pixel-space diffusion transformer image model  
     generates images directly in RGB space using NextDiT-style architecture  
     *note*: requires large number of steps to achieve sane results  
@@ -72,10 +75,10 @@
   - UI CSS fixes, thanks @awsr
   - detect/warn if space present in system path
   - add `ftfy` to requirements
-  - fix upscaler init error should not block server
+  - upscaler init error should not block server
   - improve torch nvidia arch detection
   - add torch amd arch detection
-  - fix prompt weighted lists and internal wildcards
+  - prompt weighted lists and internal wildcards
   - improve `path_to_repo` handling for custom paths
   - eliminate `api` auth security bypass
   - multiple `schedulers` signature corrections
@@ -89,6 +92,7 @@
   - patch `unipc` for timesteps device placement, thanks @resonantsky
   - `civitai` search and base-model discovery improvements
   - validate all `reference` jsons
+  - ui log formatting  
 
 ## Update for 2026-04-01
 

data/reference-distilled.json

@@ -56,6 +56,16 @@
     "tags": "distilled",
     "date": "2025 August"
   },
+  "Baidu ERNIE-Image-Turbo": {
+    "path": "baidu/ERNIE-Image-Turbo",
+    "preview": "baidu--ERNIE-Image-Turbo.jpg",
+    "desc": "ERNIE-Image-Turbo is a distilled ERNIE-Image variant optimized for fast generation with fewer denoising steps.",
+    "skip": true,
+    "extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
+    "size": 0,
+    "tags": "distilled",
+    "date": "2026 April"
+  },
   "Qwen-Image-Lightning-Edit": {
     "path": "vladmandic/Qwen-Lightning-Edit",
     "preview": "vladmandic--Qwen-Lightning-Edit.jpg",

data/reference.json

@@ -162,6 +162,16 @@
     "date": "2025 November"
   },
 
+  "Baidu ERNIE-Image": {
+    "path": "baidu/ERNIE-Image",
+    "preview": "baidu--ERNIE-Image.jpg",
+    "desc": "ERNIE-Image is a text-to-image diffusion transformer model that combines a Mistral3 text encoder with a FlowMatch transformer and Flux2-style VAE for 1024px image generation.",
+    "skip": true,
+    "extras": "sampler: Default, cfg_scale: 4.0, steps: 50",
+    "size": 0,
+    "date": "2026 April"
+  },
+
   "Qwen-Image": {
     "path": "Qwen/Qwen-Image",
     "preview": "Qwen--Qwen-Image.jpg",

javascript/logMonitor.js

@@ -30,8 +30,9 @@ async function logMonitor() {
       const level = `<td style="color: var(--color-${l.level.toLowerCase()})">${l.level}</td>`;
       if (l.level === 'WARNING') logWarnings++;
       if (l.level === 'ERROR') logErrors++;
-      const module = `<td style="color: var(--var(--neutral-400))">${l.module}</td>`;
-      row.innerHTML = `<td>${dateToStr(l.created)}</td>${level}<td>${l.facility}</td>${module}<td>${htmlEscape(l.msg)}</td>`;
+      const module = `<td style="color: var(--neutral-400)">${l.module}</td>`;
+      const facility = l.facility !== 'sd' ? `<td>${l.facility}</td>` : '<td></td>';
+      row.innerHTML = `<td>${dateToStr(l.created)}</td>${level}${facility}${module}<td>${htmlEscape(l.msg)}</td>`;
       logMonitorEl.appendChild(row);
     } catch (e) {
       error(`logMonitor: ${e}\n${line}`);

modules/modeldata.py

@@ -88,6 +88,8 @@ def get_model_type(pipe):
         model_type = 'meissonic'
     elif 'Qwen' in name:
         model_type = 'qwen'
+    elif 'ErnieImage' in name or 'ERNIE-Image' in name:
+        model_type = 'ernieimage'
     elif 'NextStep' in name:
         model_type = 'nextstep'
     elif 'XOmni' in name or 'X-Omni' in name:

modules/sd_detect.py

@@ -142,6 +142,8 @@ def guess_by_name(fn, current_guess):
         new_guess = 'PRX'
     elif 'gemini-' in fn.lower() and 'image' in fn.lower():
         new_guess = 'NanoBanana'
+    elif 'ernie-image' in fn.lower():
+        new_guess = 'ERNIE-Image'
     elif 'z-image' in fn.lower() or 'z_image' in fn.lower():
         new_guess = 'Z-Image'
     elif 'longcat-image' in fn.lower():

modules/sd_hijack_te.py

@@ -4,6 +4,10 @@ from modules import shared, errors, timer, sd_models
 from modules.logger import log
 
 
+debug_output = os.environ.get('SD_PROMPT_DEBUG', None)
+debug = log.trace if debug_output is not None else lambda *args, **kwargs: None
+
+
 def hijack_encode_prompt(*args, **kwargs):
     jobid = shared.state.begin('TE Encode')
     t0 = time.time()
@@ -18,8 +22,7 @@ def hijack_encode_prompt(*args, **kwargs):
             prompt = args_copy[0]
             patch_prompt = True
         res = prompt
-        if prompt is not None:
-            log.debug(f'Encode: prompt="{prompt}" hijack=True')
+        debug(f'Encode: prompt="{prompt}" hijack=True')
 
         if hasattr(shared.sd_model, 'before_prompt_encode'):
             log.debug(f'Encode: prompt="{prompt}" op=before')

modules/sd_models.py

@@ -509,6 +509,10 @@ def load_diffuser_force(detected_model_type, checkpoint_info, diffusers_load_con
             from pipelines.model_prx import load_prx
             sd_model = load_prx(checkpoint_info, diffusers_load_config)
             allow_post_quant = False
+        elif model_type in ['ERNIE-Image']:
+            from pipelines.model_ernie import load_ernie_image
+            sd_model = load_ernie_image(checkpoint_info, diffusers_load_config)
+            allow_post_quant = False
         elif model_type in ['Z-Image']:
             from pipelines.model_z_image import load_z_image
             sd_model = load_z_image(checkpoint_info, diffusers_load_config)
@@ -1293,10 +1297,14 @@ def set_diffuser_pipe(pipe, new_pipe_type):
 
 
 def add_noise_pred_to_diffusers_callback(pipe):
+    print('HERE1', hasattr(pipe, "_callback_tensor_inputs"))
     if not hasattr(pipe, "_callback_tensor_inputs"):
         return pipe
     if pipe.__class__.__name__.startswith("Anima"):
         return pipe
+    if pipe.__class__.__name__.startswith("ErnieImage"):
+        print('HERE2')
+        return pipe
     if pipe.__class__.__name__.startswith("StableCascade") and ("predicted_image_embedding" not in pipe._callback_tensor_inputs): # pylint: disable=protected-access
         pipe.prior_pipe._callback_tensor_inputs.append("predicted_image_embedding") # pylint: disable=protected-access
     elif "noise_pred" not in pipe._callback_tensor_inputs: # pylint: disable=protected-access

modules/shared_items.py

@@ -48,6 +48,7 @@ pipelines = {
     'WanAI': getattr(diffusers, 'WanPipeline', None),
     'Qwen': getattr(diffusers, 'QwenImagePipeline', None),
     'HunyuanImage': getattr(diffusers, 'HunyuanImagePipeline', None),
+    'ERNIE-Image': getattr(diffusers, 'ErnieImagePipeline', None),
     'Z-Image': getattr(diffusers, 'ZImagePipeline', None),
     'FLUX2': getattr(diffusers, 'Flux2Pipeline', None),
     'FLUX2 Klein': getattr(diffusers, 'Flux2KleinPipeline', None),

pipelines/model_ernie.py

@@ -0,0 +1,45 @@
+import diffusers
+import transformers
+from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
+from modules.logger import log
+from pipelines import generic
+
+
+def load_ernie_image(checkpoint_info, diffusers_load_config=None):
+    if diffusers_load_config is None:
+        diffusers_load_config = {}
+    repo_id = sd_models.path_to_repo(checkpoint_info)
+    sd_models.hf_auth_check(checkpoint_info)
+
+    load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
+    log.debug(f'Load model: type=ERNIE-Image repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
+
+    transformer = generic.load_transformer(
+        repo_id,
+        cls_name=diffusers.ErnieImageTransformer2DModel,
+        load_config=diffusers_load_config,
+    )
+    text_encoder = generic.load_text_encoder(
+        repo_id,
+        cls_name=transformers.Mistral3Model,
+        load_config=diffusers_load_config,
+    )
+
+    pipe = diffusers.ErnieImagePipeline.from_pretrained(
+        repo_id,
+        cache_dir=shared.opts.diffusers_dir,
+        transformer=transformer,
+        text_encoder=text_encoder,
+        **load_args,
+    )
+    pipe.task_args = {
+        'output_type': 'np',
+    }
+
+    del transformer
+    del text_encoder
+    sd_hijack_te.init_hijack(pipe)
+    sd_hijack_vae.init_hijack(pipe)
+
+    devices.torch_gc(force=True, reason='load')
+    return pipe