feat: Support HiDream-O1-Image (CORE-187) (#13817)

* Initial HiDream01-image support

* Cleanup nodes

* Cleaner handling of empty placeholder models

* Remove snap_to_predefined, prefer tooltip for the trained resolutions

* Add model and block wrappers

* Fix shift tooltip

* Add node to work around the patch tile issue

Experimental, runs multiple passes with the patch grid offset and blends with various different methods.

* Qwen35 vision rotary_pos_emb cast fix

* Fix embedding layout type

* Some small optimizations

* Cleanup, don't need this fallback

* Prefix KV cache, cleanup

Bit of speed, reduce redundant code

* Get rid of redundant custom sampler, refactor noise scaling

Our existing lcm sampler is mathematically same, just added the missing options to it instead and a node to control them. Refactored the noise scaling and fix it for the stochastic samplers, add a generic node to control the initial noise scale.

* Update nodes_hidream_o1.py

* Fix some cache validation cases

* Keep existing sampling params

* Remove redundant video vision path

* Replace some numpy ops with torch

* Fx RoPE index for batch size > 1

* Prefer torch preprocessing

* Rename block_type to be compatible with existing patch nodes

* Fixes and tweaks

comfy/k_diffusion/sampling.py

@@ -242,6 +242,7 @@ def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -373,6 +374,7 @@ def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -686,6 +688,7 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
     lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
@@ -747,6 +750,7 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -832,6 +836,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     old_denoised = None
     h, h_last = None, None
@@ -889,6 +894,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     denoised_1, denoised_2 = None, None
     h, h_1, h_2 = None, None, None
@@ -1006,23 +1012,39 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None,
     return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
 
 @torch.no_grad()
-def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, s_noise=1.0, s_noise_end=None, noise_clip_std=0.0):
+
+    # s_noise / s_noise_end: per-step noise multiplier, linearly interpolated across steps
+    # noise_clip_std: clamp injected noise to +/- N stddevs (0 disables).
+
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
+    n_steps = max(1, len(sigmas) - 1)
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+
+    s_start = float(s_noise)
+    s_end = s_start if s_noise_end is None else float(s_noise_end)
+    for i in trange(n_steps, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
 
         x = denoised
         if sigmas[i + 1] > 0:
-            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
+            noise = noise_sampler(sigmas[i], sigmas[i + 1])
+            if noise_clip_std > 0:
+                clip_val = noise_clip_std * noise.std()
+                noise = noise.clamp(min=-clip_val, max=clip_val)
+            t = (i / (n_steps - 1)) if n_steps > 1 else 0.0
+            s_noise_i = s_start + (s_end - s_start) * t
+            if s_noise_i != 1.0:
+                noise = noise * s_noise_i
+            x = model_sampling.noise_scaling(sigmas[i + 1], noise, x)
     return x
 
 
-
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -1249,6 +1271,7 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     uncond_denoised = None
 
@@ -1296,6 +1319,7 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
 
     temp = [0]
     def post_cfg_function(args):
@@ -1371,6 +1395,7 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
     t_fn = lambda sigma: sigma.log().neg()
@@ -1504,6 +1529,7 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
 
     def default_er_sde_noise_scaler(x):
@@ -1574,9 +1600,10 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
+    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1645,9 +1672,10 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
+    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1713,6 +1741,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
     s_in = x.new_ones([x.shape[0]])
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
     lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
 

comfy/latent_formats.py

@@ -792,6 +792,13 @@ class ZImagePixelSpace(ChromaRadiance):
     """
     pass
 
+
+class HiDreamO1Pixel(ChromaRadiance):
+    """Pixel-space latent format for HiDream-O1.
+    No VAE — model patches/unpatches raw RGB internally with patch_size=32.
+    """
+    pass
+
 class CogVideoX(LatentFormat):
     """Latent format for CogVideoX-2b (THUDM/CogVideoX-2b).
 

comfy/ldm/hidream_o1/attention.py

@@ -0,0 +1,41 @@
+"""HiDream-O1 two-pass attention: tokens [0, ar_len) are causal, [ar_len, T)
+attend full K/V. Splitting Q at the boundary avoids the (B, 1, T, T) additive
+mask the general-purpose path would build (~500 MB at T~16K) and lets the
+gen half hit the user's preferred backend via optimized_attention.
+"""
+
+import torch
+
+import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def make_two_pass_attention(ar_len: int, transformer_options=None):
+    """Build a two-pass attention callable. AR pass uses SDPA-causal directly, gen pass routes through optimized_attention.
+    The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes.
+    """
+
+    def two_pass_attention(q, k, v, heads, **kwargs):
+        B, H, T, D = q.shape
+
+        if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+        elif ar_len >= T:
+            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
+        elif ar_len <= 0:
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+        else:
+            out_ar = comfy.ops.scaled_dot_product_attention(
+                q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len],
+                attn_mask=None, dropout_p=0.0, is_causal=True,
+            )
+            out_gen = optimized_attention(
+                q[:, :, ar_len:], k, v, heads,
+                mask=None, skip_reshape=True, skip_output_reshape=True,
+                transformer_options=transformer_options,
+            )
+            out = torch.cat([out_ar, out_gen], dim=2)
+
+        return out.transpose(1, 2).reshape(B, T, H * D)
+
+    return two_pass_attention

comfy/ldm/hidream_o1/conditioning.py

@@ -0,0 +1,230 @@
+"""HiDream-O1 conditioning prep — ref-image dual path + extra_conds assembly.
+
+Each ref image goes through two paths: a 32x32 patchified stream concatenated
+to the noised target, and a Qwen3-VL ViT path producing tokens that scatter
+into input_ids at <|image_pad|> positions.
+"""
+
+from typing import List
+
+import torch
+
+import comfy.utils
+from comfy.text_encoders.qwen_vl import process_qwen2vl_images
+
+from .utils import (PATCH_SIZE, calculate_dimensions, cond_image_size, ref_max_size, resize_tensor)
+
+# Qwen3-VL ViT preprocessing constants (preprocessor_config.json).
+VIT_PATCH = 16
+VIT_MERGE = 2
+VIT_IMAGE_MEAN = [0.5, 0.5, 0.5]
+VIT_IMAGE_STD = [0.5, 0.5, 0.5]
+
+
+def prepare_ref_images(
+    ref_images: List[torch.Tensor],
+    target_h: int,
+    target_w: int,
+    device: torch.device,
+    dtype: torch.dtype,
+):
+    """Build the dual-path tensors for K reference images at (target_h, target_w).
+
+    Returns None for K=0, else a dict with ref_patches, ref_pixel_values,
+    ref_image_grid_thw, per_ref_vit_tokens, per_ref_patch_grids.
+    """
+    K = len(ref_images)
+    if K == 0:
+        return None
+    max_size = ref_max_size(max(target_h, target_w), K)
+    cis = cond_image_size(K)
+
+    refs_t = [img[0].clamp(0, 1).permute(2, 0, 1).unsqueeze(0).contiguous().float() for img in ref_images]
+    refs_t = [resize_tensor(t, max_size, PATCH_SIZE) for t in refs_t]
+
+    # 32-patch path.
+    ref_patches_per = []
+    per_ref_patch_grids = []
+    for t in refs_t:
+        t_norm = (t.squeeze(0) - 0.5) / 0.5  # (3, H, W) in [-1, 1]
+        h_p, w_p = t_norm.shape[-2] // PATCH_SIZE, t_norm.shape[-1] // PATCH_SIZE
+        per_ref_patch_grids.append((h_p, w_p))
+        patches = (
+            t_norm.reshape(3, h_p, PATCH_SIZE, w_p, PATCH_SIZE)
+            .permute(1, 3, 0, 2, 4)
+            .reshape(h_p * w_p, 3 * PATCH_SIZE * PATCH_SIZE)
+        )
+        ref_patches_per.append(patches)
+    ref_patches = torch.cat(ref_patches_per, dim=0).unsqueeze(0).to(device=device, dtype=dtype)
+
+    # ViT path.
+    refs_vlm_t = []
+    for t in refs_t:
+        _, _, h, w = t.shape
+        cond_w, cond_h = calculate_dimensions(cis, w / h)
+        cond_w = max(cond_w, VIT_PATCH * VIT_MERGE)
+        cond_h = max(cond_h, VIT_PATCH * VIT_MERGE)
+        refs_vlm_t.append(comfy.utils.common_upscale(t, cond_w, cond_h, "lanczos", "disabled"))
+
+    pv_list, grid_list, per_ref_vit_tokens = [], [], []
+    for t_v in refs_vlm_t:
+        pv, grid_thw = process_qwen2vl_images(
+            t_v.permute(0, 2, 3, 1),
+            min_pixels=0, max_pixels=10**12,
+            patch_size=VIT_PATCH, merge_size=VIT_MERGE,
+            image_mean=VIT_IMAGE_MEAN, image_std=VIT_IMAGE_STD,
+        )
+        grid_thw = grid_thw[0]
+        pv_list.append(pv.to(device=device, dtype=dtype))
+        grid_list.append(grid_thw.to(device=device))
+        # Post-merge token count = number of <|image_pad|> tokens this image expands to in input_ids.
+        gh, gw = int(grid_thw[1].item()), int(grid_thw[2].item())
+        per_ref_vit_tokens.append((gh // VIT_MERGE) * (gw // VIT_MERGE))
+
+    return {
+        "ref_patches": ref_patches,
+        "ref_pixel_values": torch.cat(pv_list, dim=0),
+        "ref_image_grid_thw": torch.stack(grid_list, dim=0),
+        "per_ref_vit_tokens": per_ref_vit_tokens,
+        "per_ref_patch_grids": per_ref_patch_grids,
+    }
+
+
+def build_ref_input_ids(
+    text_input_ids: torch.Tensor,
+    per_ref_vit_tokens: List[int],
+    image_token_id: int,
+    vision_start_id: int,
+    vision_end_id: int,
+):
+    """Splice [vision_start, image_pad*N, vision_end] blocks into input_ids
+    after the [im_start, user, \\n] prefix (matches original chat template).
+    """
+    ids = text_input_ids[0].tolist()
+    inserted = []
+    for n_pad in per_ref_vit_tokens:
+        inserted.extend([vision_start_id] + [image_token_id] * n_pad + [vision_end_id])
+    new_ids = ids[:3] + inserted + ids[3:]  # 3 = len([im_start, user, \n])
+    return torch.tensor([new_ids], dtype=text_input_ids.dtype, device=text_input_ids.device)
+
+
+def build_extra_conds(
+    text_input_ids: torch.Tensor,
+    noise: torch.Tensor,
+    ref_images: List[torch.Tensor] = None,
+    target_patch_size: int = 32,
+):
+    """Assemble all conditioning tensors for HiDreamO1Transformer.forward:
+    input_ids (with ref-vision tokens spliced in for the edit/IP path),
+    position_ids (MRoPE), token_types, vinput_mask, plus the ref
+    dual-path tensors when refs are provided.
+    """
+    from .utils import get_rope_index_fix_point
+    from comfy.text_encoders.hidream_o1 import (
+        IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID,
+    )
+
+    if text_input_ids.dim() == 1:
+        text_input_ids = text_input_ids.unsqueeze(0)
+    text_input_ids = text_input_ids.long().to(noise.device)
+    B = noise.shape[0]
+    if text_input_ids.shape[0] == 1 and B > 1:
+        text_input_ids = text_input_ids.expand(B, -1)
+
+    H, W = noise.shape[-2], noise.shape[-1]
+    h_p, w_p = H // target_patch_size, W // target_patch_size
+    image_len = h_p * w_p
+    image_grid_thw_tgt = torch.tensor(
+        [[1, h_p, w_p]], dtype=torch.long, device=text_input_ids.device,
+    )
+
+    out = {}
+    if ref_images:
+        ref = prepare_ref_images(ref_images, H, W, device=noise.device, dtype=noise.dtype)
+        text_input_ids = build_ref_input_ids(
+            text_input_ids, ref["per_ref_vit_tokens"],
+            IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID,
+        )
+        new_txt_len = text_input_ids.shape[1]
+
+        # Each ref's patchified stream gets a [vision_start, image_pad*N-1]
+        # block in the position-id stream after the noised target.
+        ref_grid_lengths = [hp * wp for (hp, wp) in ref["per_ref_patch_grids"]]
+        tgt_vision = torch.full((1, image_len), IMAGE_TOKEN_ID,
+                                dtype=text_input_ids.dtype, device=text_input_ids.device)
+        tgt_vision[:, 0] = VISION_START_ID
+        ref_vision_blocks = []
+        for rl in ref_grid_lengths:
+            blk = torch.full((1, rl), IMAGE_TOKEN_ID,
+                             dtype=text_input_ids.dtype, device=text_input_ids.device)
+            blk[:, 0] = VISION_START_ID
+            ref_vision_blocks.append(blk)
+        ref_vision_cat = torch.cat([tgt_vision] + ref_vision_blocks, dim=1)
+        input_ids_pad = torch.cat([text_input_ids, ref_vision_cat], dim=-1)
+        total_ref_patches_len = sum(ref_grid_lengths)
+        total_len = new_txt_len + image_len + total_ref_patches_len
+
+        # K (ViT, post-merge) + 1 (target) + K (ref-patches) image grids.
+        K = len(ref_images)
+        igthw_cond = ref["ref_image_grid_thw"].clone()
+        igthw_cond[:, 1] //= 2
+        igthw_cond[:, 2] //= 2
+        image_grid_thw_ref = torch.tensor(
+            [[1, hp, wp] for (hp, wp) in ref["per_ref_patch_grids"]],
+            dtype=torch.long, device=text_input_ids.device,
+        )
+        igthw_all = torch.cat([
+            igthw_cond.to(text_input_ids.device),
+            image_grid_thw_tgt,
+            image_grid_thw_ref,
+        ], dim=0)
+        position_ids, _ = get_rope_index_fix_point(
+            spatial_merge_size=1,
+            image_token_id=IMAGE_TOKEN_ID,
+            vision_start_token_id=VISION_START_ID,
+            input_ids=input_ids_pad, image_grid_thw=igthw_all,
+            attention_mask=None,
+            skip_vision_start_token=[0] * K + [1] + [1] * K,
+            fix_point=4096,
+        )
+
+        # tms + target_image + ref_patches are all gen.
+        tms_pos = new_txt_len - 1
+        ar_len = tms_pos
+        token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device)
+        token_types[:, tms_pos:] = 1
+        vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device)
+        vinput_mask[:, new_txt_len:] = True
+
+        # Leading batch dim sidesteps CONDRegular.process_cond's repeat_to_batch_size truncation
+        out["ref_pixel_values"] = ref["ref_pixel_values"].unsqueeze(0)
+        out["ref_image_grid_thw"] = ref["ref_image_grid_thw"].unsqueeze(0)
+        out["ref_patches"] = ref["ref_patches"]
+    else:
+        # T2I: text + noised target only, vision_start replaces the first image token
+        txt_len = text_input_ids.shape[1]
+        total_len = txt_len + image_len
+        vision_tokens = torch.full((B, image_len), IMAGE_TOKEN_ID,
+                                   dtype=text_input_ids.dtype, device=text_input_ids.device)
+        vision_tokens[:, 0] = VISION_START_ID
+        input_ids_pad = torch.cat([text_input_ids, vision_tokens], dim=-1)
+        position_ids, _ = get_rope_index_fix_point(
+            spatial_merge_size=1,
+            image_token_id=IMAGE_TOKEN_ID,
+            vision_start_token_id=VISION_START_ID,
+            input_ids=input_ids_pad, image_grid_thw=image_grid_thw_tgt,
+            attention_mask=None,
+            skip_vision_start_token=[1],
+        )
+        ar_len = txt_len - 1
+        token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device)
+        token_types[:, ar_len:] = 1
+        vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device)
+        vinput_mask[:, txt_len:] = True
+
+    out["input_ids"] = text_input_ids
+    out["position_ids"] = position_ids[:, 0].unsqueeze(0) # Collapse position_ids batch and add a leading dim so CONDRegular's batch-resize doesn't truncate the 3-axis MRoPE dim
+    out["token_types"] = token_types
+    out["vinput_mask"] = vinput_mask
+    out["ar_len"] = ar_len
+    return out

comfy/ldm/hidream_o1/model.py

@@ -0,0 +1,306 @@
+"""HiDream-O1-Image transformer.
+
+Pixel-space DiT built on Qwen3-VL: the vision tower (Qwen35VisionModel)
+encodes ref images, the Qwen3-VL-8B decoder (Llama2_ with interleaved MRoPE)
+processes a unified text+image sequence, and 32x32 patch embed/unembed
+shims map raw RGB in and out of LLM hidden space. The Qwen3-VL deepstack
+mergers go unused — their weights are dropped at load.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import einops
+import torch
+import torch.nn as nn
+
+import comfy.patcher_extension
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
+from comfy.text_encoders.llama import Llama2_
+from comfy.text_encoders.qwen35 import Qwen35VisionModel
+
+from .attention import make_two_pass_attention
+
+
+IMAGE_TOKEN_ID = 151655   # Qwen3-VL <|image_pad|>
+TMS_TOKEN_ID = 151673     # HiDream-O1 <|tms_token|>
+PATCH_SIZE = 32
+
+
+@dataclass
+class HiDreamO1TextConfig:
+    """Qwen3-VL-8B text-decoder dims (matches public Qwen3-VL-8B-Instruct)."""
+    vocab_size: int = 151936
+    hidden_size: int = 4096
+    intermediate_size: int = 12288
+    num_hidden_layers: int = 36
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 8
+    head_dim: int = 128
+    max_position_embeddings: int = 128000
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 5000000.0
+    rope_scale: Optional[float] = None
+    rope_dims: List[int] = field(default_factory=lambda: [24, 20, 20])
+    interleaved_mrope: bool = True
+    transformer_type: str = "llama"
+    rms_norm_add: bool = False
+    mlp_activation: str = "silu"
+    qkv_bias: bool = False
+    q_norm: str = "gemma3"
+    k_norm: str = "gemma3"
+    final_norm: bool = True
+    lm_head: bool = False
+    stop_tokens: List[int] = field(default_factory=lambda: [151643, 151645])
+
+
+QWEN3VL_VISION_DEFAULTS = dict(
+    hidden_size=1152,
+    num_heads=16,
+    intermediate_size=4304,
+    depth=27,
+    patch_size=16,
+    temporal_patch_size=2,
+    in_channels=3,
+    spatial_merge_size=2,
+    num_position_embeddings=2304,
+    deepstack_visual_indexes=(8, 16, 24),
+    out_hidden_size=4096,  # final merger projects directly into LLM hidden
+)
+
+
+class BottleneckPatchEmbed(nn.Module):
+    # 3072 -> 1024 -> 4096 (raw 32x32 RGB patch -> bottleneck -> LLM hidden).
+    def __init__(self, patch_size=32, in_chans=3, pca_dim=1024, embed_dim=4096, bias=True, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.proj1 = ops.Linear(patch_size * patch_size * in_chans, pca_dim, bias=False, device=device, dtype=dtype)
+        self.proj2 = ops.Linear(pca_dim, embed_dim, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.proj2(self.proj1(x))
+
+
+class FinalLayer(nn.Module):
+    # 4096 -> 3072 (LLM hidden -> flat pixel patch).
+    def __init__(self, hidden_size, patch_size=32, out_channels=3, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.linear = ops.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class HiDreamO1Transformer(nn.Module):
+    """HiDream-O1 unified pixel-level transformer."""
+
+    def __init__(self, image_model=None, dtype=None, device=None, operations=None,
+                 text_config_overrides=None, vision_config_overrides=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+
+        text_cfg = HiDreamO1TextConfig(**(text_config_overrides or {}))
+        vision_cfg = dict(QWEN3VL_VISION_DEFAULTS)
+        if vision_config_overrides:
+            vision_cfg.update(vision_config_overrides)
+        vision_cfg["out_hidden_size"] = text_cfg.hidden_size
+
+        self.text_config = text_cfg
+        self.vision_config = vision_cfg
+        self.hidden_size = text_cfg.hidden_size
+        self.patch_size = PATCH_SIZE
+        self.in_channels = 3
+        self.tms_token_id = TMS_TOKEN_ID
+
+        self.visual = Qwen35VisionModel(vision_cfg, device=device, dtype=dtype, ops=operations)
+        self.language_model = Llama2_(text_cfg, device=device, dtype=dtype, ops=operations)
+        self.t_embedder1 = TimestepEmbedder(
+            text_cfg.hidden_size, device=device, dtype=dtype, operations=operations,
+        )
+        self.x_embedder = BottleneckPatchEmbed(
+            patch_size=self.patch_size, in_chans=self.in_channels,
+            pca_dim=text_cfg.hidden_size // 4, embed_dim=text_cfg.hidden_size,
+            bias=True, device=device, dtype=dtype, ops=operations,
+        )
+        self.final_layer2 = FinalLayer(
+            text_cfg.hidden_size, patch_size=self.patch_size,
+            out_channels=self.in_channels, device=device, dtype=dtype, ops=operations,
+        )
+
+        self._visual_cache = None
+        self._kv_cache_entries = []
+
+    def clear_kv_cache(self):
+        self._kv_cache_entries = []
+        self._visual_cache = None
+
+    def forward(self, x, timesteps, context=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+        ).execute(x, timesteps, context, transformer_options, **kwargs)
+
+    def _forward(self, x, timesteps, context=None, transformer_options={}, input_ids=None, attention_mask=None, position_ids=None,
+                 vinput_mask=None, ar_len=None, ref_pixel_values=None, ref_image_grid_thw=None, ref_patches=None, **kwargs):
+        """Returns flow-match velocity (x - x_pred) / sigma"""
+
+        if input_ids is None or position_ids is None:
+            raise ValueError("HiDreamO1Transformer requires input_ids and position_ids in conditioning")
+
+        B, _, H, W = x.shape
+        h_p, w_p = H // self.patch_size, W // self.patch_size
+        tgt_image_len = h_p * w_p
+
+        z = einops.rearrange(
+            x, 'B C (H p1) (W p2) -> B (H W) (C p1 p2)',
+            p1=self.patch_size, p2=self.patch_size,
+        )
+        vinputs = torch.cat([z, ref_patches.to(z.dtype)], dim=1) if ref_patches is not None else z
+
+        inputs_embeds = self.language_model.embed_tokens(input_ids).to(x.dtype)
+
+        if ref_pixel_values is not None and ref_image_grid_thw is not None:
+            # ViT output is constant across sampling steps within a generation
+            # identity-key by the input tensor so refs don't recompute every step.
+            cached = self._visual_cache
+            if cached is not None and cached[0] is ref_pixel_values:
+                image_embeds = cached[1]
+            else:
+                ref_pv = ref_pixel_values.to(inputs_embeds.device)
+                ref_grid = ref_image_grid_thw.to(inputs_embeds.device).long()
+                # extra_conds wraps with a leading batch dim; refs are model-level so [0] always recovers them.
+                if ref_pv.dim() == 3:
+                    ref_pv = ref_pv[0]
+                if ref_grid.dim() == 3:
+                    ref_grid = ref_grid[0]
+                image_embeds = self.visual(ref_pv, ref_grid).to(inputs_embeds.dtype)
+                self._visual_cache = (ref_pixel_values, image_embeds)
+            # image_pad positions identical across batch (input_ids shared cond/uncond).
+            image_idx = (input_ids[0] == IMAGE_TOKEN_ID).nonzero(as_tuple=True)[0]
+            if image_idx.shape[0] != image_embeds.shape[0]:
+                raise ValueError(
+                    f"Image-token count {image_idx.shape[0]} != ViT output count "
+                    f"{image_embeds.shape[0]}; check tokenizer/processor alignment."
+                )
+            inputs_embeds[:, image_idx] = image_embeds.unsqueeze(0).expand(B, -1, -1)
+
+        sigma = timesteps.float() / 1000.0
+        t_pixeldit = 1.0 - sigma
+        t_emb = self.t_embedder1(t_pixeldit * 1000, inputs_embeds.dtype)
+        tms_mask_3d = (input_ids == self.tms_token_id).unsqueeze(-1).expand_as(inputs_embeds)
+        inputs_embeds = torch.where(tms_mask_3d, t_emb.unsqueeze(1).expand_as(inputs_embeds), inputs_embeds)
+
+        vinputs_embedded = self.x_embedder(vinputs.to(inputs_embeds.dtype))
+        inputs_embeds = torch.cat([inputs_embeds, vinputs_embedded], dim=1)
+
+        # extra_conds stores position_ids as (1, 3, T); process_cond repeats dim 0 to B. Take row 0.
+        freqs_cis = self.language_model.compute_freqs_cis(position_ids[0].to(x.device), x.device)
+        freqs_cis = tuple(t.to(x.dtype) for t in freqs_cis)
+
+        two_pass_attn = make_two_pass_attention(ar_len, transformer_options=transformer_options)
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.language_model.layers)
+        transformer_options["block_type"] = "double"
+
+        # Cache prefix K/V across steps. Key includes input_ids (prompt), ref_id
+        # (refs scatter into inputs_embeds), and position_ids (RoPE baked into cached K).
+        can_cache = not blocks_replace and ar_len > 0
+        cache_len = ar_len if can_cache else 0
+        ref_id = id(ref_pixel_values) if ref_pixel_values is not None else None
+        pos_ids_key = position_ids[..., :cache_len] if can_cache else position_ids
+        cache_entries = self._kv_cache_entries
+        # Drop stale entries from a previous device (model was unloaded and reloaded).
+        if cache_entries and cache_entries[0]["input_ids"].device != input_ids.device:
+            cache_entries = []
+            self._kv_cache_entries = []
+        kv_cache = None
+        if can_cache:
+            for entry in cache_entries:
+                ck = entry["input_ids"]
+                ep = entry["position_ids"]
+                if (entry["cache_len"] == cache_len
+                        and ck.shape == input_ids.shape and torch.equal(ck, input_ids)
+                        and entry["ref_id"] == ref_id
+                        and ep.shape == pos_ids_key.shape and torch.equal(ep, pos_ids_key)):
+                    kv_cache = entry
+                    break
+
+        if kv_cache is not None:
+            # Hot path: project Q/K/V only for fresh positions; past_key_value prepends cached AR K/V.
+            hidden_states = inputs_embeds[:, cache_len:]
+            sliced_freqs = tuple(t[..., cache_len:, :] for t in freqs_cis)
+            for i, layer in enumerate(self.language_model.layers):
+                transformer_options["block_index"] = i
+                K_i, V_i = kv_cache["kv"][i]
+                hidden_states, _ = layer(
+                    x=hidden_states, attention_mask=None, freqs_cis=sliced_freqs, optimized_attention=two_pass_attn,
+                    past_key_value=(K_i, V_i, cache_len),
+                )
+        else:
+            # Cold path: run full sequence; if cacheable, snapshot K/V at AR positions.
+            snapshots = [] if can_cache else None
+            past_kv_cold = () if can_cache else None
+            hidden_states = inputs_embeds
+            for i, layer in enumerate(self.language_model.layers):
+                transformer_options["block_index"] = i
+                if ("double_block", i) in blocks_replace:
+                    def block_wrap(args, _layer=layer):
+                        out = {}
+                        out["x"], _ = _layer(
+                            x=args["x"], attention_mask=args.get("attention_mask"),
+                            freqs_cis=args["freqs_cis"], optimized_attention=args["optimized_attention"],
+                            past_key_value=None,
+                        )
+                        return out
+                    out = blocks_replace[("double_block", i)](
+                        {"x": hidden_states, "attention_mask": None,
+                         "freqs_cis": freqs_cis, "optimized_attention": two_pass_attn,
+                         "transformer_options": transformer_options},
+                        {"original_block": block_wrap},
+                    )
+                    hidden_states = out["x"]
+                else:
+                    hidden_states, present_kv = layer(
+                        x=hidden_states, attention_mask=None,
+                        freqs_cis=freqs_cis, optimized_attention=two_pass_attn,
+                        past_key_value=past_kv_cold,
+                    )
+                    if snapshots is not None:
+                        K, V, _ = present_kv
+                        snapshots.append((K[:, :, :cache_len].contiguous(),
+                                          V[:, :, :cache_len].contiguous()))
+            if snapshots is not None:
+                # Cap at 2 entries (cond + uncond). Multi-cond workflows LRU-evict.
+                new_entry = {
+                    "input_ids": input_ids.clone(),
+                    "cache_len": cache_len,
+                    "kv": snapshots,
+                    "ref_id": ref_id,
+                    "position_ids": pos_ids_key.clone(),
+                }
+                self._kv_cache_entries = (cache_entries + [new_entry])[-2:]
+
+        if self.language_model.norm is not None:
+            hidden_states = self.language_model.norm(hidden_states)
+
+        # Slice target-image positions before the final projection so the Linear only runs on tgt_image_len tokens.
+        # In the hot path hidden_states starts at original position cache_len, so masks/indices shift by cache_len.
+        sliced_offset = cache_len if kv_cache is not None else 0
+        if vinput_mask is not None:
+            vmask = vinput_mask.to(x.device).bool()
+            if sliced_offset > 0:
+                vmask = vmask[:, sliced_offset:]
+            target_hidden = hidden_states[vmask].view(B, -1, hidden_states.shape[-1])[:, :tgt_image_len]
+        else:
+            txt_seq_len = input_ids.shape[1]
+            start = txt_seq_len - sliced_offset
+            target_hidden = hidden_states[:, start:start + tgt_image_len]
+        x_pred_tgt = self.final_layer2(target_hidden)
+
+        # fp32 final subtraction, bf16 here noticeably degrades samples.
+        x_pred_img = einops.rearrange(
+            x_pred_tgt, 'B (H W) (C p1 p2) -> B C (H p1) (W p2)',
+            H=h_p, W=w_p, p1=self.patch_size, p2=self.patch_size,
+        )
+        return (x.float() - x_pred_img.float()) / sigma.view(B, 1, 1, 1).clamp_min(1e-3)

comfy/ldm/hidream_o1/utils.py

@@ -0,0 +1,173 @@
+"""HiDream-O1 input-prep helpers: image/resolution math and unified-sequence
+RoPE position-id assembly. The fix_point offset in get_rope_index_fix_point
+lets the target image and patchified ref images share spatial RoPE positions
+despite living at different sequence indices — same 2D image plane.
+"""
+
+import math
+from typing import Optional
+
+import torch
+
+
+PATCH_SIZE = 32
+CONDITION_IMAGE_SIZE = 384  # ViT-side base size for ref images
+
+
+def resize_tensor(img_t, image_size, patch_size=16):
+    """img_t: (1, 3, H, W) float [0, 1]. Fit to image_size**2 area, patch-aligned, center-cropped."""
+
+    while min(img_t.shape[-2], img_t.shape[-1]) >= 2 * image_size: # Pre-halves with 2x2 box averaging while the image is still very large
+        img_t = torch.nn.functional.avg_pool2d(img_t, kernel_size=2, stride=2)
+
+    _, _, height, width = img_t.shape
+    m = patch_size
+    s_max = image_size * image_size
+    scale = math.sqrt(s_max / (width * height))
+
+    candidates = [
+        (round(width * scale) // m * m, round(height * scale) // m * m),
+        (round(width * scale) // m * m, math.floor(height * scale) // m * m),
+        (math.floor(width * scale) // m * m, round(height * scale) // m * m),
+        (math.floor(width * scale) // m * m, math.floor(height * scale) // m * m),
+    ]
+    candidates = sorted(candidates, key=lambda x: x[0] * x[1], reverse=True)
+    new_size = candidates[-1]
+    for c in candidates:
+        if c[0] * c[1] <= s_max:
+            new_size = c
+            break
+
+    new_w, new_h = new_size
+    s1 = width / new_w
+    s2 = height / new_h
+    if s1 < s2:
+        resize_w, resize_h = new_w, round(height / s1)
+    else:
+        resize_w, resize_h = round(width / s2), new_h
+    img_t = torch.nn.functional.interpolate(img_t, size=(resize_h, resize_w), mode="bicubic")
+    top = (resize_h - new_h) // 2
+    left = (resize_w - new_w) // 2
+    return img_t[..., top:top + new_h, left:left + new_w]
+
+
+def calculate_dimensions(max_size, ratio):
+    """(W, H) for an aspect ratio fitting in max_size**2 area, 32-aligned."""
+    width = math.sqrt(max_size * max_size * ratio)
+    height = width / ratio
+    width = int(width / 32) * 32
+    height = int(height / 32) * 32
+    return width, height
+
+
+def ref_max_size(target_max_dim, k):
+    """K-dependent ref-image max dim before patchifying."""
+    if k == 1:
+        return target_max_dim
+    if k == 2:
+        return target_max_dim * 48 // 64
+    if k <= 4:
+        return target_max_dim // 2
+    if k <= 8:
+        return target_max_dim * 24 // 64
+    return target_max_dim // 4
+
+
+def cond_image_size(k):
+    """K-dependent ViT-side image size."""
+    if k <= 4:
+        return CONDITION_IMAGE_SIZE
+    if k <= 8:
+        return CONDITION_IMAGE_SIZE * 48 // 64
+    return CONDITION_IMAGE_SIZE // 2
+
+
+def get_rope_index_fix_point(
+    spatial_merge_size: int,
+    image_token_id: int,
+    vision_start_token_id: int,
+    input_ids: Optional[torch.LongTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    skip_vision_start_token=None,
+    fix_point: int = 4096,
+):
+    mrope_position_deltas = []
+    if input_ids is not None and image_grid_thw is not None:
+        total_input_ids = input_ids
+        if attention_mask is None:
+            attention_mask = torch.ones_like(total_input_ids)
+        position_ids = torch.ones(
+            3, input_ids.shape[0], input_ids.shape[1],
+            dtype=input_ids.dtype, device=input_ids.device,
+        )
+        attention_mask = attention_mask.to(total_input_ids.device)
+        for i, input_ids_b in enumerate(total_input_ids):
+            fp = fix_point
+            image_index = 0
+            input_ids_b = input_ids_b[attention_mask[i] == 1]
+            vision_start_indices = torch.argwhere(input_ids_b == vision_start_token_id).squeeze(1)
+            vision_tokens = input_ids_b[vision_start_indices + 1]
+            image_nums = (vision_tokens == image_token_id).sum()
+            input_tokens = input_ids_b.tolist()
+            llm_pos_ids_list = []
+            st = 0
+            remain_images = image_nums
+            for _ in range(image_nums):
+                if image_token_id in input_tokens and remain_images > 0:
+                    ed = input_tokens.index(image_token_id, st)
+                else:
+                    ed = len(input_tokens) + 1
+                t = image_grid_thw[image_index][0]
+                h = image_grid_thw[image_index][1]
+                w = image_grid_thw[image_index][2]
+                image_index += 1
+                remain_images -= 1
+                llm_grid_t = t.item()
+                llm_grid_h = h.item() // spatial_merge_size
+                llm_grid_w = w.item() // spatial_merge_size
+                text_len = ed - st
+                text_len -= skip_vision_start_token[image_index - 1]
+                text_len = max(0, text_len)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten()
+                h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
+                w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
+
+                if skip_vision_start_token[image_index - 1]:
+                    if fp > 0:
+                        fp = fp - st_idx
+                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + fp + st_idx)
+                    fp = 0
+                else:
+                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+                st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+            if st < len(input_tokens):
+                st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                text_len = len(input_tokens) - st
+                llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+            position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
+            mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
+        mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
+        return position_ids, mrope_position_deltas
+
+    if attention_mask is not None:
+        position_ids = attention_mask.long().cumsum(-1) - 1
+        position_ids.masked_fill_(attention_mask == 0, 1)
+        position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
+        max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
+        mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
+    else:
+        position_ids = (
+            torch.arange(input_ids.shape[1], device=input_ids.device)
+            .view(1, 1, -1).expand(3, input_ids.shape[0], -1)
+        )
+        mrope_position_deltas = torch.zeros(
+            [input_ids.shape[0], 1], device=input_ids.device, dtype=input_ids.dtype,
+        )
+    return position_ids, mrope_position_deltas

comfy/model_base.py

@@ -58,6 +58,8 @@ import comfy.ldm.cogvideo.model
 import comfy.ldm.rt_detr.rtdetr_v4
 import comfy.ldm.ernie.model
 import comfy.ldm.sam3.detector
+import comfy.ldm.hidream_o1.model
+from comfy.ldm.hidream_o1.conditioning import build_extra_conds
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -1674,6 +1676,32 @@ class HiDream(BaseModel):
             out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond))
         return out
 
+class HiDreamO1(BaseModel):
+    """HiDream-O1-Image: pixel-space DiT (no VAE). Refs from HiDreamO1ReferenceImages and tokens from the stub TE flow through
+    extra_conds; the heavy preprocessing lives in comfy.ldm.hidream_o1.conditioning."""
+    PATCH_SIZE = 32
+
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream_o1.model.HiDreamO1Transformer)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        text_input_ids = kwargs.get("text_input_ids", None)
+        noise = kwargs.get("noise", None)
+        if text_input_ids is None or noise is None:
+            return out
+
+        conds = build_extra_conds(
+            text_input_ids, noise,
+            ref_images=kwargs.get("reference_latents", None),
+            target_patch_size=self.PATCH_SIZE,
+        )
+        for k, v in conds.items():
+            # ar_len is a Python int (precomputed to avoid a GPU sync in forward).
+            cls = comfy.conds.CONDConstant if k == "ar_len" else comfy.conds.CONDRegular
+            out[k] = cls(v)
+        return out
+
 class Chroma(Flux):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma):
         super().__init__(model_config, model_type, device=device, unet_model=unet_model)

comfy/model_detection.py

@@ -620,6 +620,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys
         return dit_config
 
+    if '{}t_embedder1.mlp.0.weight'.format(key_prefix) in state_dict_keys and '{}x_embedder.proj1.weight'.format(key_prefix) in state_dict_keys:  # HiDream-O1
+        return {"image_model": "hidream_o1"}
+
     if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys:  # HiDream
         dit_config = {}
         dit_config["image_model"] = "hidream"

comfy/model_sampling.py

@@ -93,7 +93,8 @@ class CONST:
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
         sigma = reshape_sigma(sigma, noise.ndim)
-        return sigma * noise + (1.0 - sigma) * latent_image
+        s = getattr(self, "noise_scale", 1.0)
+        return sigma * (s * noise) + (1.0 - sigma) * latent_image
 
     def inverse_noise_scaling(self, sigma, latent):
         sigma = reshape_sigma(sigma, latent.ndim)
@@ -288,7 +289,11 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
         else:
             sampling_settings = {}
 
-        self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000))
+        self.set_noise_scale(sampling_settings.get("noise_scale", 1.0))
+        self.set_parameters(
+            shift=sampling_settings.get("shift", 1.0),
+            multiplier=sampling_settings.get("multiplier", 1000),
+        )
 
     def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
         self.shift = shift
@@ -296,6 +301,9 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
         ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier)
         self.register_buffer('sigmas', ts)
 
+    def set_noise_scale(self, noise_scale):
+        self.noise_scale = float(noise_scale)
+
     @property
     def sigma_min(self):
         return self.sigmas[0]

comfy/ops.py

@@ -1285,7 +1285,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 if quant_format in ["float8_e4m3fn", "float8_e5m2"] and weight_key in state_dict:
                     self.quant_format = quant_format
                     qconfig = QUANT_ALGOS[quant_format]
-                    layout_cls = get_layout_class(qconfig["comfy_tensor_layout"])
+                    self.layout_type = qconfig["comfy_tensor_layout"]
+                    layout_cls = get_layout_class(self.layout_type)
                     weight = state_dict.pop(weight_key)
                     manually_loaded_keys = [weight_key]
 

comfy/sd.py

@@ -239,7 +239,8 @@ class CLIP:
         model_management.archive_model_dtypes(self.cond_stage_model)
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
-        ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher
+        te_disable_dynamic = disable_dynamic or getattr(self.cond_stage_model, "disable_offload", False)
+        ModelPatcher = comfy.model_patcher.ModelPatcher if te_disable_dynamic else comfy.model_patcher.CoreModelPatcher
         self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         #Match torch.float32 hardcode upcast in TE implemention
         self.patcher.set_model_compute_dtype(torch.float32)
@@ -776,6 +777,7 @@ class VAE:
                 self.latent_channels = 3
                 self.latent_dim = 2
                 self.output_channels = 3
+                self.disable_offload = True
             elif "vocoder.activation_post.downsample.lowpass.filter" in sd: #MMAudio VAE
                 sample_rate = 16000
                 if sample_rate == 16000:

comfy/supported_models.py

@@ -28,6 +28,7 @@ import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
 import comfy.text_encoders.ernie
 import comfy.text_encoders.cogvideo
+import comfy.text_encoders.hidream_o1
 
 from . import supported_models_base
 from . import latent_formats
@@ -1431,6 +1432,50 @@ class HiDream(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return None #  TODO
 
+class HiDreamO1(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "hidream_o1",
+    }
+
+    sampling_settings = {
+        "shift": 3.0,
+        "noise_scale": 8.0,
+    }
+
+    latent_format = latent_formats.HiDreamO1Pixel
+    memory_usage_factor = 0.6
+    # fp16 not supported: LM MLP down_proj activations fp16 overflow, causing NaNs
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    optimizations = {"fp8": False}
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.HiDreamO1(self, device=device)
+
+    def process_unet_state_dict(self, state_dict):
+        # Drop unused Qwen3-VL deepstack merger weights; upstream discards them at inference.
+        for key in list(state_dict.keys()):
+            if "visual.deepstack_merger_list" in key:
+                del state_dict[key]
+        return state_dict
+
+    def process_vae_state_dict(self, state_dict):
+        # Pixel-space model: inject sentinel so VAE construction picks PixelspaceConversionVAE.
+        return {"pixel_space_vae": torch.tensor(1.0)}
+
+    def process_clip_state_dict(self, state_dict):
+        # Tokenizer-only TE: inject sentinel so load_state_dict_guess_config triggers CLIP init.
+        return {"_hidream_o1_te_sentinel": torch.zeros(1)}
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(
+            comfy.text_encoders.hidream_o1.HiDreamO1Tokenizer,
+            comfy.text_encoders.hidream_o1.HiDreamO1TE,
+        )
+
 class Chroma(supported_models_base.BASE):
     unet_config = {
         "image_model": "chroma",
@@ -2018,6 +2063,7 @@ models = [
     Hunyuan3Dv2,
     Hunyuan3Dv2_1,
     HiDream,
+    HiDreamO1,
     Chroma,
     ChromaRadiance,
     ACEStep,

comfy/text_encoders/hidream_o1.py

@@ -0,0 +1,119 @@
+"""HiDream-O1-Image tokenizer-only text encoder.
+
+The real Qwen3-VL backbone runs inside diffusion_model.* every step, so this
+module just tokenizes the prompt into text_input_ids and emits them as
+conditioning. Position ids / token_types / vinput_mask depend on target H/W
+and are built later in model_base.HiDreamO1.extra_conds.
+"""
+
+import os
+
+import torch
+from transformers import Qwen2Tokenizer
+
+from comfy import sd1_clip
+
+
+# Qwen3-VL special tokens
+IM_START_ID = 151644
+IM_END_ID = 151645
+ASSISTANT_ID = 77091
+USER_ID = 872
+NEWLINE_ID = 198
+VISION_START_ID = 151652
+VISION_END_ID = 151653
+IMAGE_TOKEN_ID = 151655
+VIDEO_TOKEN_ID = 151656
+# HiDream-O1-specific tokens
+BOI_TOKEN_ID = 151669
+BOR_TOKEN_ID = 151670
+EOR_TOKEN_ID = 151671
+BOT_TOKEN_ID = 151672
+TMS_TOKEN_ID = 151673
+
+
+class HiDreamO1QwenTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer"
+        )
+        super().__init__(
+            tokenizer_path,
+            pad_with_end=False,
+            embedding_size=4096,
+            embedding_key="hidream_o1",
+            tokenizer_class=Qwen2Tokenizer,
+            has_start_token=False,
+            has_end_token=False,
+            pad_to_max_length=False,
+            max_length=99999999,
+            min_length=1,
+            pad_token=151643,
+            tokenizer_data=tokenizer_data,
+        )
+
+
+class HiDreamO1Tokenizer(sd1_clip.SD1Tokenizer):
+    """Wraps prompt in the upstream chat template ending with boi/tms markers.
+    Image tokens get spliced in at sample time once target H/W is known.
+    """
+
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(
+            embedding_directory=embedding_directory,
+            tokenizer_data=tokenizer_data,
+            name="hidream_o1",
+            tokenizer=HiDreamO1QwenTokenizer,
+        )
+
+    def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
+        text_tokens_dict = super().tokenize_with_weights(
+            text, return_word_ids=return_word_ids, disable_weights=True, **kwargs
+        )
+        text_tuples = text_tokens_dict["hidream_o1"][0]
+        text_tuples = [t for t in text_tuples if int(t[0]) != 151643]  # strip pad
+
+        # <|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant\n<|boi|><|tms|>
+        def tok(tid):
+            return (tid, 1.0) if not return_word_ids else (tid, 1.0, 0)
+
+        prefix = [tok(IM_START_ID), tok(USER_ID), tok(NEWLINE_ID)]
+        suffix = [
+            tok(IM_END_ID), tok(NEWLINE_ID),
+            tok(IM_START_ID), tok(ASSISTANT_ID), tok(NEWLINE_ID),
+            tok(BOI_TOKEN_ID), tok(TMS_TOKEN_ID),
+        ]
+        full = prefix + list(text_tuples) + suffix
+        return {"hidream_o1": [full]}
+
+
+class HiDreamO1TE(torch.nn.Module):
+    """Passthrough TE: emits int token ids; the Qwen3-VL backbone in diffusion_model does the actual encoding."""
+
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        self.dtypes = {torch.float32}
+        self.disable_offload = True # skips dynamic VRAM management for this zero-parameter module
+        self.device = torch.device("cpu") if device is None else torch.device(device)
+
+    def encode_token_weights(self, token_weight_pairs):
+        tok_pairs = token_weight_pairs["hidream_o1"][0]
+        ids = [int(t[0]) for t in tok_pairs]
+        input_ids = torch.tensor([ids], dtype=torch.long)
+        # Surrogate keeps the cross_attn slot non-empty for CONDITIONING
+        # plumbing; the model reads text_input_ids out of `extra` instead.
+        cross_attn = input_ids.unsqueeze(-1).to(torch.float32)
+        extra = {"text_input_ids": input_ids}
+        return cross_attn, None, extra
+
+    def load_sd(self, sd):
+        return []
+
+    def get_sd(self):
+        return {}
+
+    def reset_clip_options(self):
+        pass
+
+    def set_clip_options(self, options):
+        pass

comfy/text_encoders/llama.py

@@ -397,7 +397,7 @@ class RMSNorm(nn.Module):
 
 
 
-def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None):
+def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None, interleaved_mrope=False):
     if not isinstance(theta, list):
         theta = [theta]
 
@@ -415,16 +415,27 @@ def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_di
         inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
         position_ids_expanded = position_ids[:, None, :].float()
         freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-        emb = torch.cat((freqs, freqs), dim=-1)
-        cos = emb.cos()
-        sin = emb.sin()
-        if rope_dims is not None and position_ids.shape[0] > 1:
-            mrope_section = rope_dims * 2
-            cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
-            sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
+        if rope_dims is not None and position_ids.shape[0] > 1 and interleaved_mrope:
+            # Qwen3-VL interleaved MRoPE: T-freqs by default, H/W replace every 3rd dim.
+            freqs_inter = freqs[0].clone()
+            for axis_idx, offset in ((1, 1), (2, 2)):
+                length = rope_dims[axis_idx] * 3
+                idx = slice(offset, length, 3)
+                freqs_inter[..., idx] = freqs[axis_idx, ..., idx]
+            emb = torch.cat((freqs_inter, freqs_inter), dim=-1)
+            cos = emb.cos().unsqueeze(0)
+            sin = emb.sin().unsqueeze(0)
         else:
-            cos = cos.unsqueeze(1)
-            sin = sin.unsqueeze(1)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+            if rope_dims is not None and position_ids.shape[0] > 1:
+                mrope_section = rope_dims * 2
+                cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
+                sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
+            else:
+                cos = cos.unsqueeze(1)
+                sin = sin.unsqueeze(1)
         sin_split = sin.shape[-1] // 2
         out.append((cos, sin[..., : sin_split], -sin[..., sin_split :]))
 
@@ -689,6 +700,7 @@ class Llama2_(nn.Module):
                                     self.config.rope_theta,
                                     self.config.rope_scale,
                                     self.config.rope_dims,
+                                    interleaved_mrope=getattr(self.config, "interleaved_mrope", False),
                                     device=device)
 
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None):

comfy/text_encoders/qwen35.py

@@ -651,7 +651,7 @@ class Qwen35VisionModel(nn.Module):
         x = self.patch_embed(x)
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw).to(x.device)
         x = x + pos_embeds
-        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        rotary_pos_emb = self.rot_pos_emb(grid_thw).to(x.device)
         seq_len = x.shape[0]
         x = x.reshape(seq_len, -1)
         rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)

comfy_extras/nodes_advanced_samplers.py

@@ -86,6 +86,37 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No
     return x
 
 
+class SamplerLCM(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="SamplerLCM",
+            category="sampling/samplers",
+            description=("LCM sampler with tunable per-step noise. s_noise is a multiplier on the model's training noise scale"),
+            inputs=[
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=64.0, step=0.01,
+                               tooltip="Per-step noise multiplier at the first step (1.0 = match training)."),
+                io.Float.Input("s_noise_end", default=1.0, min=0.0, max=64.0, step=0.01,
+                               tooltip="Per-step noise multiplier at the last step. Set equal to s_noise for a constant schedule."),
+                io.Float.Input("noise_clip_std", default=0.0, min=0.0, max=10.0, step=0.01,
+                               tooltip="Clamp per-step noise to +/- N*std. 0 disables."),
+            ],
+            outputs=[io.Sampler.Output()],
+        )
+
+    @classmethod
+    def execute(cls, s_noise, s_noise_end, noise_clip_std) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler(
+            "lcm",
+            {
+                "s_noise": float(s_noise),
+                "s_noise_end": float(s_noise_end),
+                "noise_clip_std": float(noise_clip_std),
+            },
+        )
+        return io.NodeOutput(sampler)
+
+
 class SamplerEulerCFGpp(io.ComfyNode):
     @classmethod
     def define_schema(cls) -> io.Schema:
@@ -114,6 +145,7 @@ class AdvancedSamplersExtension(ComfyExtension):
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             SamplerLCMUpscale,
+            SamplerLCM,
             SamplerEulerCFGpp,
         ]
 

comfy_extras/nodes_hidream_o1.py

@@ -0,0 +1,256 @@
+from typing_extensions import override
+
+import torch
+
+import comfy.model_management
+import comfy.patcher_extension
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+
+class EmptyHiDreamO1LatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyHiDreamO1LatentImage",
+            display_name="Empty HiDream-O1 Latent Image",
+            category="latent/image",
+            description=(
+                "Empty pixel-space latent for HiDream-O1-Image. The model was "
+                "trained at ~4 megapixels; lower resolutions go off-distribution "
+                "and quality regresses noticeably. Trained resolutions: "
+                "2048x2048, 2304x1728, 1728x2304, 2560x1440, 1440x2560, "
+                "2496x1664, 1664x2496, 3104x1312, 1312x3104, 2304x1792, 1792x2304."
+            ),
+            inputs=[
+                io.Int.Input(id="width", default=2048, min=64, max=4096, step=32),
+                io.Int.Input(id="height", default=2048, min=64, max=4096, step=32),
+                io.Int.Input(id="batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[io.Latent().Output()],
+        )
+
+    @classmethod
+    def execute(cls, *, width: int, height: int, batch_size: int = 1) -> io.NodeOutput:
+        latent = torch.zeros(
+            (batch_size, 3, height, width),
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput({"samples": latent})
+
+
+class HiDreamO1ReferenceImages(io.ComfyNode):
+    """Attach reference images to both positive and negative conditioning."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="HiDreamO1ReferenceImages",
+            display_name="HiDream-O1 Reference Images",
+            category="conditioning/image",
+            description=(
+                "Attach 1-10 reference images to conditioning, one for edit instruction"
+                "or multiple for subject-driven personalization."
+            ),
+            inputs=[
+                io.Conditioning.Input(id="positive"),
+                io.Conditioning.Input(id="negative"),
+                io.Autogrow.Input(
+                    "images",
+                    template=io.Autogrow.TemplateNames(
+                        io.Image.Input("image"),
+                        names=[f"image_{i}" for i in range(1, 11)],
+                        min=1,
+                    ),
+                    tooltip=("Reference images. 1 image = instruction edit; 2-10 images = multi reference."
+                    ),
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, *, positive, negative, images: io.Autogrow.Type) -> io.NodeOutput:
+        refs = [images[f"image_{i}"] for i in range(1, 11) if f"image_{i}" in images]
+        positive = node_helpers.conditioning_set_values(positive, {"reference_latents": refs}, append=True)
+        negative = node_helpers.conditioning_set_values(negative, {"reference_latents": refs}, append=True)
+        return io.NodeOutput(positive, negative)
+
+
+class HiDreamO1PatchSeamSmoothing(io.ComfyNode):
+    PATCH_SIZE = 32
+    EDGE_FEATHER = 4
+
+    # Shift presets per (pattern, N). 8-pass = 4-quadrant + 4 quarter-patch offsets.
+    SHIFTS_BY_PATTERN = {
+        ("single_shift", 2): [(0, 0), (16, 16)],
+        ("single_shift", 4): [(0, 0), (16, 0), (0, 16), (16, 16)],
+        ("single_shift", 8): [(0, 0), (16, 0), (0, 16), (16, 16),
+                              (8, 8), (24, 8), (8, 24), (24, 24)],
+        ("symmetric", 2):    [(-8, -8), (8, 8)],
+        ("symmetric", 4):    [(-8, -8), (8, -8), (-8, 8), (8, 8)],
+        ("symmetric", 8):    [(-12, -12), (4, -12), (-12, 4), (4, 4),
+                              (-4, -4), (12, -4), (-4, 12), (12, 12)],
+    }
+    RAMP_LEVELS = {
+        "2":          [2],
+        "4":          [4],
+        "ramp_2_4":   [2, 4],
+        "ramp_2_4_8": [2, 4, 8],
+    }
+
+    @staticmethod
+    def _hann_tile(cy: int, cx: int, size: int = 32) -> torch.Tensor:
+        """size x size Hann tile peaking at (cy, cx) within a patch."""
+        half = size // 2
+        yy = torch.arange(size).view(size, 1)
+        xx = torch.arange(size).view(1, size)
+        dy = ((yy - cy + half) % size) - half
+        dx = ((xx - cx + half) % size) - half
+        return 0.25 * (1 + torch.cos(torch.pi * dy / half)) * (1 + torch.cos(torch.pi * dx / half))
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="HiDreamO1PatchSeamSmoothing",
+            display_name="HiDream-O1 Patch Seam Smoothing",
+            category="advanced/model",
+            is_experimental=True,
+            description=(
+                "Average the model output across multiple shifted patch-grid "
+                "positions during the late portion of sampling. Cancels seams."
+            ),
+            inputs=[
+                io.Model.Input(id="model"),
+                io.Float.Input(id="start_percent", default=0.8, min=0.0, max=1.0, step=0.01,
+                    tooltip="Sampling progress (0=start, 1=end) at which the blend turns ON.",
+                ),
+                io.Float.Input(id="end_percent", default=1.0, min=0.0, max=1.0, step=0.01,
+                    tooltip="Sampling progress at which the blend turns OFF.",
+                ),
+                io.Combo.Input(
+                    id="pattern",
+                    options=["single_shift", "symmetric"],
+                    default="single_shift",
+                    tooltip="Shift layout. single_shift: one pass at the natural patch grid + others offset. symmetric: all passes off-grid, shifts split around origin.",
+                ),
+                io.Combo.Input(
+                    id="passes",
+                    options=["2", "4", "ramp_2_4", "ramp_2_4_8"],
+                    default="2",
+                    tooltip="Number of passes per gated step. 2/4 = fixed. ramp_*: pass count increases as sampling approaches end (more smoothing where seams are most visible).",
+                ),
+                io.Combo.Input(
+                    id="blend",
+                    options=["average", "window", "median"],
+                    default="average",
+                    tooltip="average: equal-weight mean. window: Hann-windowed weighting favoring each pass away from its patch boundaries. median: per-pixel median, rejects wraparound-outlier passes.",
+                ),
+                io.Float.Input(id="strength", default=1.0, min=0.0, max=1.0, step=0.01,
+                    tooltip="Interpolation between the natural-grid pred (0) and the averaged result (1).",
+                ),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, *, model, start_percent: float, end_percent: float, pattern: str, passes: str, blend: str, strength: float) -> io.NodeOutput:
+        if strength <= 0.0 or end_percent <= start_percent:
+            return io.NodeOutput(model)
+
+        P = cls.PATCH_SIZE
+        half = P // 2
+        shift_levels = [cls.SHIFTS_BY_PATTERN[(pattern, n)] for n in cls.RAMP_LEVELS[passes]]
+
+        if blend == "window":
+            window_tile_levels = [
+                torch.stack([cls._hann_tile((half - sy) % P, (half - sx) % P, P) for sy, sx in lst], dim=0)
+                for lst in shift_levels
+            ]
+        else:
+            window_tile_levels = [None] * len(shift_levels)
+
+        m = model.clone()
+        model_sampling = m.get_model_object("model_sampling")
+        multiplier = float(model_sampling.multiplier)
+        start_t = float(model_sampling.percent_to_sigma(start_percent)) * multiplier
+        end_t = float(model_sampling.percent_to_sigma(end_percent)) * multiplier
+
+        edge_ramp_cache: dict = {}
+
+        def get_edge_ramp(H: int, W: int, device, dtype) -> torch.Tensor:
+            key = (H, W, device, dtype)
+            cached = edge_ramp_cache.get(key)
+            if cached is not None:
+                return cached
+            feather = cls.EDGE_FEATHER
+            ys = torch.minimum(torch.arange(H, device=device, dtype=torch.float32),
+                               (H - 1) - torch.arange(H, device=device, dtype=torch.float32))
+            xs = torch.minimum(torch.arange(W, device=device, dtype=torch.float32),
+                               (W - 1) - torch.arange(W, device=device, dtype=torch.float32))
+            y_mask = ((ys - P) / feather).clamp(0, 1)
+            x_mask = ((xs - P) / feather).clamp(0, 1)
+            ramp = (y_mask[:, None] * x_mask[None, :]).to(dtype)
+            edge_ramp_cache[key] = ramp
+            return ramp
+
+        def smoothing_wrapper(executor, *args, **kwargs):
+            x = args[0]
+            t = float(args[1][0])
+            pred = executor(*args, **kwargs)
+            if not (end_t <= t <= start_t):
+                return pred
+            # Pick shift-level by sigma phase across the gated range.
+            if len(shift_levels) == 1:
+                level_idx = 0
+            else:
+                phase = (start_t - t) / max(start_t - end_t, 1e-8)
+                level_idx = min(int(phase * len(shift_levels)), len(shift_levels) - 1)
+            shifts = shift_levels[level_idx]
+            window_tiles = window_tile_levels[level_idx]
+
+            preds = []
+            for sy, sx in shifts:
+                if sy == 0 and sx == 0:
+                    preds.append(pred)
+                    continue
+                x_rolled = torch.roll(x, shifts=(sy, sx), dims=(-2, -1))
+                pred_rolled = executor(x_rolled, *args[1:], **kwargs)
+                preds.append(torch.roll(pred_rolled, shifts=(-sy, -sx), dims=(-2, -1)))
+            stacked = torch.stack(preds, dim=0)  # (N, B, C, H, W)
+            _, _, _, H, W = stacked.shape
+            if blend == "window":
+                N = stacked.shape[0]
+                tiles = window_tiles.to(device=stacked.device, dtype=stacked.dtype)
+                w = tiles.repeat(1, H // P, W // P)[:, :H, :W]
+                sum_w = w.sum(dim=0, keepdim=True)
+                w = torch.where(sum_w < 1e-3, torch.full_like(w, 1.0 / N), w / sum_w.clamp(min=1e-8))
+                avg = (stacked * w[:, None, None, :, :]).sum(dim=0)
+            elif blend == "median":
+                avg = torch.median(stacked, dim=0).values
+            else:
+                avg = stacked.mean(dim=0)
+
+            # Mask out the P-px wraparound contamination strip at each edge.
+            mask = get_edge_ramp(H, W, pred.device, pred.dtype)
+            return pred * (1.0 - mask * strength) + avg * (mask * strength)
+
+        m.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "hidream_o1_patch_seam_smoothing", smoothing_wrapper)
+        return io.NodeOutput(m)
+
+
+class HiDreamO1Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyHiDreamO1LatentImage,
+            HiDreamO1ReferenceImages,
+            HiDreamO1PatchSeamSmoothing,
+        ]
+
+
+async def comfy_entrypoint() -> HiDreamO1Extension:
+    return HiDreamO1Extension()

comfy_extras/nodes_model_advanced.py

@@ -300,6 +300,29 @@ class RescaleCFG:
         m.set_model_sampler_cfg_function(rescale_cfg)
         return (m, )
 
+class ModelNoiseScale:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "noise_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 64.0, "step": 0.01,
+                                                       "tooltip": "Absolute training noise scale. For example HiDream-O1 base: 8.0, dev: 7.5."}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, noise_scale):
+        m = model.clone()
+        original = m.model.model_sampling
+        ms = type(original)(m.model.model_config)
+        ms.set_parameters(shift=original.shift, multiplier=original.multiplier)
+        ms.set_noise_scale(noise_scale)
+        m.add_object_patch("model_sampling", ms)
+        return (m, )
+
+
 class ModelComputeDtype:
     SEARCH_ALIASES = ["model precision", "change dtype"]
     @classmethod
@@ -327,6 +350,7 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingSD3": ModelSamplingSD3,
     "ModelSamplingAuraFlow": ModelSamplingAuraFlow,
     "ModelSamplingFlux": ModelSamplingFlux,
+    "ModelNoiseScale": ModelNoiseScale,
     "RescaleCFG": RescaleCFG,
     "ModelComputeDtype": ModelComputeDtype,
 }

nodes.py

@@ -2435,6 +2435,7 @@ async def init_builtin_extra_nodes():
         "nodes_sam3.py",
         "nodes_void.py",
         "nodes_wandancer.py",
+        "nodes_hidream_o1.py",
     ]
 
     import_failed = []