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synced 2025-12-20 19:42:59 +08:00
restored whitespace and fixed logging
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nolan4
parent
2d550102fc
commit
16adfe2153
@ -12,8 +12,6 @@ from comfy.ldm.flux.layers import EmbedND
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import comfy.ldm.common_dit
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import comfy.patcher_extension
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logger = logging.getLogger(__name__)
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class GELU(nn.Module):
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def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None):
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@ -401,7 +399,7 @@ class QwenImageTransformer2DModel(nn.Module):
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has_negative = cond_or_uncond and 1 in cond_or_uncond
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is_cfg_batched = has_positive and has_negative
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logger.debug(
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logging.debug(
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f"[EliGen Model] Processing {num_entities} entities for {height}x{width}px, "
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f"batch_size={actual_batch_size}, CFG_batched={is_cfg_batched}"
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)
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@ -457,7 +455,7 @@ class QwenImageTransformer2DModel(nn.Module):
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img_rope = self.pe_embedder(img_ids).squeeze(1).squeeze(0)
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logger.debug(f"[EliGen Model] RoPE shapes - img: {img_rope.shape}, txt: {txt_rotary_emb.shape}")
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logging.debug(f"[EliGen Model] RoPE shapes - img: {img_rope.shape}, txt: {txt_rotary_emb.shape}")
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# Concatenate text and image RoPE embeddings
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# Convert to latent dtype to match queries/keys
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@ -473,7 +471,7 @@ class QwenImageTransformer2DModel(nn.Module):
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if entity_masks.shape[3] != padded_h or entity_masks.shape[4] != padded_w:
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pad_h = padded_h - entity_masks.shape[3]
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pad_w = padded_w - entity_masks.shape[4]
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logger.debug(f"[EliGen Model] Padding masks by ({pad_h}, {pad_w})")
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logging.debug(f"[EliGen Model] Padding masks by ({pad_h}, {pad_w})")
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entity_masks = torch.nn.functional.pad(entity_masks, (0, pad_w, 0, pad_h), mode='constant', value=0)
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entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
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@ -488,7 +486,7 @@ class QwenImageTransformer2DModel(nn.Module):
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seq_lens = entity_seq_lens + [global_seq_len]
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total_seq_len = int(sum(seq_lens) + image.shape[1])
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logger.debug(f"[EliGen Model] total_seq={total_seq_len}")
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logging.debug(f"[EliGen Model] total_seq={total_seq_len}")
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patched_masks = []
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for i in range(N):
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@ -557,7 +555,7 @@ class QwenImageTransformer2DModel(nn.Module):
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# CFG batch: [positive, negative] - need different masks for each
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# Positive gets entity constraints, negative gets standard attention (all zeros)
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logger.debug(
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logging.debug(
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"[EliGen Model] CFG batched detected - creating separate masks. "
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"Positive (index 0) gets entity mask, Negative (index 1) gets standard mask"
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)
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@ -576,7 +574,7 @@ class QwenImageTransformer2DModel(nn.Module):
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# Concatenate masks to match batch
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attention_mask = torch.cat(mask_list, dim=0)
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logger.debug(
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logging.debug(
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f"[EliGen Model] Created {len(mask_list)} masks for CFG batch. "
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f"Final shape: {attention_mask.shape}"
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)
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@ -584,7 +582,7 @@ class QwenImageTransformer2DModel(nn.Module):
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# Add head dimension: [B, 1, seq, seq]
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attention_mask = attention_mask.unsqueeze(1)
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logger.debug(
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logging.debug(
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f"[EliGen Model] Attention mask created: shape={attention_mask.shape}, "
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f"valid_connections={num_valid_connections}/{total_seq_len * total_seq_len}"
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)
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@ -654,7 +652,7 @@ class QwenImageTransformer2DModel(nn.Module):
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batch_size = x.shape[0]
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if entity_prompt_emb is not None:
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logger.debug(
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logging.debug(
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f"[EliGen Forward] batch_size={batch_size}, cond_or_uncond={cond_or_uncond}, "
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f"has_positive={is_positive_cond}, has_negative={is_negative_cond}"
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)
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@ -119,6 +119,7 @@ def convert_tensor(extra, dtype, device):
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extra = comfy.model_management.cast_to_device(extra, device, None)
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return extra
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class BaseModel(torch.nn.Module):
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def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
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super().__init__()
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@ -380,6 +381,7 @@ class BaseModel(torch.nn.Module):
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def extra_conds_shapes(self, **kwargs):
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return {}
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def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
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adm_inputs = []
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weights = []
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@ -475,6 +477,7 @@ class SDXL(BaseModel):
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flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
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return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
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class SVD_img2vid(BaseModel):
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def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
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super().__init__(model_config, model_type, device=device)
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@ -551,6 +554,7 @@ class SV3D_p(SVD_img2vid):
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out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out))
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return torch.cat(out, dim=1)
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class Stable_Zero123(BaseModel):
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def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
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super().__init__(model_config, model_type, device=device)
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@ -634,11 +638,13 @@ class IP2P:
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image = utils.resize_to_batch_size(image, noise.shape[0])
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return self.process_ip2p_image_in(image)
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class SD15_instructpix2pix(IP2P, BaseModel):
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def __init__(self, model_config, model_type=ModelType.EPS, device=None):
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super().__init__(model_config, model_type, device=device)
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self.process_ip2p_image_in = lambda image: image
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class SDXL_instructpix2pix(IP2P, SDXL):
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def __init__(self, model_config, model_type=ModelType.EPS, device=None):
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super().__init__(model_config, model_type, device=device)
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@ -688,6 +694,7 @@ class StableCascade_C(BaseModel):
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out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn)
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return out
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class StableCascade_B(BaseModel):
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def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=StageB)
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@ -707,6 +714,7 @@ class StableCascade_B(BaseModel):
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out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
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return out
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class SD3(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper)
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@ -721,6 +729,7 @@ class SD3(BaseModel):
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out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
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return out
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class AuraFlow(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)
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@ -732,6 +741,7 @@ class AuraFlow(BaseModel):
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out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
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return out
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class StableAudio1(BaseModel):
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def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
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@ -770,6 +780,7 @@ class StableAudio1(BaseModel):
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sd["{}{}".format(k, l)] = s[l]
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return sd
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class HunyuanDiT(BaseModel):
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def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT)
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@ -903,6 +914,7 @@ class Flux(BaseModel):
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out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
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return out
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class GenmoMochi(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)
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@ -1154,6 +1166,7 @@ class WAN21(BaseModel):
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return out
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class WAN21_Vace(WAN21):
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def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
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super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel)
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@ -8,8 +8,6 @@ from typing import Optional
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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logger = logging.getLogger(__name__)
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class TextEncodeQwenImageEdit(io.ComfyNode):
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@classmethod
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@ -186,8 +184,8 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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width = latent_width * 8
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if pad_h > 0 or pad_w > 0:
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logger.debug(f"[EliGen] Latent padding detected: {unpadded_latent_height}x{unpadded_latent_width} → {latent_height}x{latent_width}")
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logger.debug(f"[EliGen] Target generation dimensions: {height}x{width} pixels ({latent_height}x{latent_width} latent)")
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logging.debug(f"[EliGen] Latent padding detected: {unpadded_latent_height}x{unpadded_latent_width} → {latent_height}x{latent_width}")
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logging.debug(f"[EliGen] Target generation dimensions: {height}x{width} pixels ({latent_height}x{latent_width} latent)")
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# Collect entity prompts and masks
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entity_prompts = [entity_prompt_1, entity_prompt_2, entity_prompt_3]
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@ -202,7 +200,7 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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# Log warning if some entities were skipped
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total_prompts_provided = len([p for p in entity_prompts if p.strip()])
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if len(valid_entities) < total_prompts_provided:
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logger.warning(f"[EliGen] Only {len(valid_entities)} of {total_prompts_provided} entity prompts have valid masks")
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logging.warning(f"[EliGen] Only {len(valid_entities)} of {total_prompts_provided} entity prompts have valid masks")
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# If no valid entities, return standard conditioning
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if len(valid_entities) == 0:
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@ -244,7 +242,7 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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)
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# Log original mask statistics
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logger.debug(
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logging.debug(
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f"[EliGen] Entity {i+1} input mask: shape={mask_tensor.shape}, "
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f"dtype={mask_tensor.dtype}, min={mask_tensor.min():.4f}, max={mask_tensor.max():.4f}"
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)
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@ -260,7 +258,7 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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# Check for constant masks (no variation)
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if mask_tensor.min() == mask_tensor.max() and mask_tensor.max() > 0:
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logger.warning(
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logging.warning(
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f"[EliGen] Entity {i+1} mask has no variation (all pixels = {mask_tensor.min():.4f}). "
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f"This entity will affect the entire image."
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)
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@ -284,12 +282,12 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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# Log size mismatch if mask doesn't match expected latent dimensions
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expected_h, expected_w = latent_height * 8, latent_width * 8
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if orig_h != expected_h or orig_w != expected_w:
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logger.info(
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logging.info(
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f"[EliGen] Entity {i+1} mask size mismatch: {orig_h}x{orig_w} vs expected {expected_h}x{expected_w}. "
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f"Will resize to {latent_height}x{latent_width} latent space."
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)
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else:
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logger.debug(f"[EliGen] Entity {i+1} mask: {orig_h}x{orig_w} → will resize to {latent_height}x{latent_width} latent")
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logging.debug(f"[EliGen] Entity {i+1} mask: {orig_h}x{orig_w} → will resize to {latent_height}x{latent_width} latent")
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# Convert MASK format [batch, height, width] to [batch, 1, height, width] for common_upscale
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# common_upscale expects [batch, channels, height, width]
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@ -319,7 +317,7 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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f"Original mask may have been too small or all black."
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)
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logger.debug(
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logging.debug(
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f"[EliGen] Entity {i+1} mask coverage: {active_pixels}/{total_pixels} pixels ({coverage_pct:.1f}%)"
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)
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@ -332,7 +330,7 @@ class TextEncodeQwenImageEliGen(io.ComfyNode):
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entity_masks_tensor = torch.stack(processed_entity_masks_no_batch, dim=0) # [num_entities, 1, H, W]
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entity_masks_tensor = entity_masks_tensor.unsqueeze(0) # [1, num_entities, 1, H, W]
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logger.debug(
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logging.debug(
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f"[EliGen] Stacked {len(valid_entities)} entity masks into tensor: "
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f"shape={entity_masks_tensor.shape} (expected: [1, {len(valid_entities)}, 1, {latent_height}, {latent_width}])"
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)
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