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Add process_img function from comfy/ldm/flux/model.py to enable the use of ScaleROPE

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silveroxides 2025-11-11 16:32:31 +01:00
parent 5ebcab3c7d
commit 9d0107ee90
1 changed files with 71 additions and 17 deletions
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88
comfy/ldm/chroma/model.py
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@ -257,29 +257,83 @@ class Chroma(nn.Module):
img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels)
return img
def forward(self, x, timestep, context, guidance, control=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, timestep, context, guidance, control, transformer_options, **kwargs)
def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
bs, c, h, w = x.shape
x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
if img.ndim != 3 or context.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
h_len = ((h + (self.patch_size // 2)) // self.patch_size)
w_len = ((w + (self.patch_size // 2)) // self.patch_size)
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
h_offset = ((h_offset + (self.patch_size // 2)) // self.patch_size)
w_offset = ((w_offset + (self.patch_size // 2)) // self.patch_size)
steps_h = h_len
steps_w = w_len
rope_options = transformer_options.get("rope_options", None)
if rope_options is not None:
h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
index += rope_options.get("shift_t", 0.0)
h_offset += rope_options.get("shift_y", 0.0)
w_offset += rope_options.get("shift_x", 0.0)
img_ids = torch.zeros((steps_h, steps_w, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 0] = img_ids[:, :, 1] + index
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=x.dtype).unsqueeze(0)
return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
def forward(self, x, timestep, context, guidance=None, ref_latents=None, control=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, timestep, context, guidance, ref_latents, control, transformer_options, **kwargs)
def _forward(self, x, timestep, context, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
bs, c, h_orig, w_orig = x.shape
h_len = ((h_orig + (self.patch_size // 2)) // self.patch_size)
w_len = ((w_orig + (self.patch_size // 2)) // self.patch_size)
img, img_ids = self.process_img(x, transformer_options=transformer_options)
if img.ndim != 3 or context.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
img_tokens = img.shape[1]
if ref_latents is not None:
h = 0
w = 0
index = 0
ref_latents_method = kwargs.get("ref_latents_method", "offset")
for ref in ref_latents:
if ref_latents_method == "index":
index += 1
h_offset = 0
w_offset = 0
elif ref_latents_method == "uxo":
index = 0
h_offset = h_len * self.patch_size + h
w_offset = w_len * self.patch_size + w
h += ref.shape[-2]
w += ref.shape[-1]
else:
index = 1
h_offset = 0
w_offset = 0
if ref.shape[-2] + h > ref.shape[-1] + w:
w_offset = w
else:
h_offset = h
h = max(h, ref.shape[-2] + h_offset)
w = max(w, ref.shape[-1] + w_offset)
kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
img = torch.cat([img, kontext], dim=1)
img_ids = torch.cat([img_ids, kontext_ids], dim=1)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w]
out = out[:, :img_tokens]
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]