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Revert "Not sure if this actually changes anything but it can't hurt."

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This reverts commit 34608de2e9.
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comfyanonymous 2024-08-14 01:05:17 -04:00
parent 3e52e0364c
commit a5af64d3ce
3 changed files with 14 additions and 15 deletions
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4
comfy/ldm/flux/controlnet_xlabs.py
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@ -64,8 +64,8 @@ class ControlNetFlux(Flux):
img = img + controlnet_cond img = img + controlnet_cond
vec = self.time_in(timestep_embedding(timesteps, 256)) vec = self.time_in(timestep_embedding(timesteps, 256))
if self.params.guidance_embed: if self.params.guidance_embed:
vec.add_(self.guidance_in(timestep_embedding(guidance, 256))) vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
vec.add_(self.vector_in(y)) vec = vec + self.vector_in(y)
txt = self.txt_in(txt) txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1) ids = torch.cat((txt_ids, img_ids), dim=1)

21
comfy/ldm/flux/layers.py
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@ -150,16 +150,14 @@ class DoubleStreamBlock(nn.Module):
# prepare image for attention # prepare image for attention
img_modulated = self.img_norm1(img) img_modulated = self.img_norm1(img)
img_mod1.scale += 1 img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
img_modulated = img_mod1.scale * img_modulated + img_mod1.shift
img_qkv = self.img_attn.qkv(img_modulated) img_qkv = self.img_attn.qkv(img_modulated)
img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
# prepare txt for attention # prepare txt for attention
txt_modulated = self.txt_norm1(txt) txt_modulated = self.txt_norm1(txt)
txt_mod1.scale += 1 txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
txt_modulated = txt_mod1.scale * txt_modulated + txt_mod1.shift
txt_qkv = self.txt_attn.qkv(txt_modulated) txt_qkv = self.txt_attn.qkv(txt_modulated)
txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
@ -172,12 +170,12 @@ class DoubleStreamBlock(nn.Module):
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
# calculate the img bloks # calculate the img bloks
img = img.addcmul(img_mod1.gate, self.img_attn.proj(img_attn)) img = img + img_mod1.gate * self.img_attn.proj(img_attn)
img.addcmul_(img_mod2.gate, self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)) img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
# calculate the txt bloks # calculate the txt bloks
txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn)) txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
txt.addcmul_(txt_mod2.gate, self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)) txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
if txt.dtype == torch.float16: if txt.dtype == torch.float16:
txt = txt.clip(-65504, 65504) txt = txt.clip(-65504, 65504)
@ -223,8 +221,8 @@ class SingleStreamBlock(nn.Module):
def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor: def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
mod, _ = self.modulation(vec) mod, _ = self.modulation(vec)
mod.scale += 1 x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
qkv, mlp = torch.split(self.linear1(mod.scale * self.pre_norm(x) + mod.shift), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k = self.norm(q, k, v) q, k = self.norm(q, k, v)
@ -232,7 +230,8 @@ class SingleStreamBlock(nn.Module):
# compute attention # compute attention
attn = attention(q, k, v, pe=pe) attn = attention(q, k, v, pe=pe)
# compute activation in mlp stream, cat again and run second linear layer # compute activation in mlp stream, cat again and run second linear layer
x.addcmul_(mod.gate, self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))) output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
x += mod.gate * output
if x.dtype == torch.float16: if x.dtype == torch.float16:
x = x.clip(-65504, 65504) x = x.clip(-65504, 65504)
return x return x

4
comfy/ldm/flux/model.py
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@ -106,9 +106,9 @@ class Flux(nn.Module):
if self.params.guidance_embed: if self.params.guidance_embed:
if guidance is None: if guidance is None:
raise ValueError("Didn't get guidance strength for guidance distilled model.") raise ValueError("Didn't get guidance strength for guidance distilled model.")
vec.add_(self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))) vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
vec.add_(self.vector_in(y)) vec = vec + self.vector_in(y)
txt = self.txt_in(txt) txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1) ids = torch.cat((txt_ids, img_ids), dim=1)