make cfg 1.0 not do uncond, set steps by sigma schedule

mochi_preview/dit/joint_model/asymm_models_joint.py

@@ -237,7 +237,8 @@ class AsymmetricAttention(nn.Module):
             return out.view(total, local_dim)
         
     def sdpa_attention(self, qkv):
-        q, k, v = rearrange(qkv, '(b s) t h d -> t b h s d', b=1)
+        q, k, v = qkv.unbind(dim=1)
+        q, k, v = [x.permute(1, 0, 2).unsqueeze(0) for x in (q, k, v)]
         with torch.autocast(mm.get_autocast_device(self.device), enabled=False):
             with sdpa_kernel(backends):
                 out = F.scaled_dot_product_attention(
@@ -248,10 +249,13 @@ class AsymmetricAttention(nn.Module):
                     dropout_p=0.0, 
                     is_causal=False
                     )
-                return rearrange(out, 'b h s d -> s (b h d)')
+                return out.permute(2, 0, 1, 3).reshape(out.shape[2], -1)
         
     def sage_attention(self, qkv):
-        q, k, v = rearrange(qkv, '(b s) t h d -> t b h s d', b=1)
+        #q, k, v = rearrange(qkv, '(b s) t h d -> t b h s d', b=1)
+        q, k, v = qkv.unbind(dim=1)
+        q, k, v = [x.permute(1, 0, 2).unsqueeze(0) for x in (q, k, v)]
+
         with torch.autocast(mm.get_autocast_device(self.device), enabled=False):
             out = sageattn(
                 q, 
@@ -261,11 +265,14 @@ class AsymmetricAttention(nn.Module):
                 dropout_p=0.0, 
                 is_causal=False
                 )
-            return rearrange(out, 'b h s d -> s (b h d)')
+            #print(out.shape)
+            #out = rearrange(out, 'b h s d -> s (b h d)')
+            return out.permute(2, 0, 1, 3).reshape(out.shape[2], -1)
         
     def comfy_attention(self, qkv):
         from comfy.ldm.modules.attention import optimized_attention
-        q, k, v = rearrange(qkv, '(b s) t h d -> t b h s d', b=1)
+        q, k, v = qkv.unbind(dim=1)
+        q, k, v = [x.permute(1, 0, 2).unsqueeze(0) for x in (q, k, v)]
         with torch.autocast(mm.get_autocast_device(self.device), enabled=False):
             out = optimized_attention(
                 q, 

mochi_preview/t2v_synth_mochi.py

@@ -325,13 +325,15 @@ class T2VSynthMochiModel:
                 out_cond, out_uncond = torch.chunk(out, chunks=2, dim=0)
             else:
                 nonlocal sample, sample_null
-               
                 with torch.autocast(mm.get_autocast_device(self.device), dtype=autocast_dtype):
-                    out_cond = self.dit(z, sigma, **sample)
+                    if cfg_scale > 1.0:
-                    out_uncond = self.dit(z, sigma, **sample_null)
+                        out_cond = self.dit(z, sigma, **sample)
+                        out_uncond = self.dit(z, sigma, **sample_null)
+                    else:
+                        out_cond = self.dit(z, sigma, **sample)
+                        return out_cond
 
-            assert out_cond.shape == out_uncond.shape
+            return out_uncond + cfg_scale * (out_cond - out_uncond)
-            return out_uncond + cfg_scale * (out_cond - out_uncond), out_cond
         
         comfy_pbar = ProgressBar(sample_steps)
         for i in tqdm(range(0, sample_steps), desc="Processing Samples", total=sample_steps):
@@ -339,7 +341,7 @@ class T2VSynthMochiModel:
             dsigma = sigma - sigma_schedule[i + 1]
 
             # `pred` estimates `z_0 - eps`.
-            pred, output_cond = model_fn(
+            pred = model_fn(
                 z=z,
                 sigma=torch.full(
                     [B] if not batch_cfg else [B * 2], sigma, device=z.device
@@ -347,8 +349,6 @@ class T2VSynthMochiModel:
                 cfg_scale=cfg_schedule[i],
             )
             pred = pred.to(z)
-            output_cond = output_cond.to(z)
-
             z = z + dsigma * pred
             comfy_pbar.update(1)
 
@@ -356,7 +356,6 @@ class T2VSynthMochiModel:
             z = z[:B]
        
         self.dit.to(self.offload_device)
-    
         samples = unnormalize_latents(z.float(), self.vae_mean, self.vae_std)
         logging.info(f"samples shape: {samples.shape}")
         return samples

mz_gguf_loader.py

@@ -141,6 +141,7 @@ class WQLinear_GGUF(nn.Module):
             dequant = dequantize_blocks_Q8_0(self.Q8_0_qweight, x.dtype)
         else:
             raise ValueError(f"Unknown qtype: {self.qtype}")
+        
         return self.linear_ops(x, dequant, bias=self.bias.to(x.dtype) if self.bias is not None else None)
 
 

nodes.py

@@ -373,6 +373,8 @@ class MochiSampler:
 
         if opt_sigmas is not None:
             sigma_schedule = opt_sigmas.tolist()
+            sigma_schedule.extend([1.0])
+            steps = len(sigma_schedule)
             logging.info(f"Using sigma_schedule: {sigma_schedule}")
         else:
             sigma_schedule = linear_quadratic_schedule(steps, 0.025)