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Cleanup chroma PR.

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comfyanonymous
2025-04-30 20:57:30 -04:00
parent 4ca3d84277
commit 08ff5fa08a
9 changed files with 25 additions and 181 deletions
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63
comfy/model_base.py
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@@ -787,8 +787,8 @@ class PixArt(BaseModel):
return out
class Flux(BaseModel):
def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux):
super().__init__(model_config, model_type, device=device, unet_model=unet_model)
def concat_cond(self, **kwargs):
try:
@@ -1110,63 +1110,14 @@ class HiDream(BaseModel):
out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond))
return out
class Chroma(BaseModel):
class Chroma(Flux):
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma)
def concat_cond(self, **kwargs):
try:
#Handle Flux control loras dynamically changing the img_in weight.
num_channels = self.diffusion_model.img_in.weight.shape[1]
except:
#Some cases like tensorrt might not have the weights accessible
num_channels = self.model_config.unet_config["in_channels"]
out_channels = self.model_config.unet_config["out_channels"]
if num_channels <= out_channels:
return None
image = kwargs.get("concat_latent_image", None)
noise = kwargs.get("noise", None)
device = kwargs["device"]
if image is None:
image = torch.zeros_like(noise)
image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
image = utils.resize_to_batch_size(image, noise.shape[0])
image = self.process_latent_in(image)
if num_channels <= out_channels * 2:
return image
#inpaint model
mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
if mask is None:
mask = torch.ones_like(noise)[:, :1]
mask = torch.mean(mask, dim=1, keepdim=True)
mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center")
mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)
mask = utils.resize_to_batch_size(mask, noise.shape[0])
return torch.cat((image, mask), dim=1)
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
# upscale the attention mask, since now we
attention_mask = kwargs.get("attention_mask", None)
if attention_mask is not None:
shape = kwargs["noise"].shape
mask_ref_size = kwargs["attention_mask_img_shape"]
# the model will pad to the patch size, and then divide
# essentially dividing and rounding up
(h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
guidance = 0.0
out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor((guidance,)))
guidance = kwargs.get("guidance", 0)
if guidance is not None:
out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
return out