framepack: patch solver for unsupported gpus

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -54,10 +54,11 @@
 - **Fixes**
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
+  - framepack: patch solver for unsupported gpus  
   - benchmark: remove forced sampler from system info benchmark  
   - xyz-grid: fix xyz grid with random seeds  
-  - fix download for sd15/sdxl reference models  
-
+  - reference: fix download for sd15/sdxl reference models  
+  
 ## Update for 2025-09-15
 
 ### Highlights for 2025-09-15

modules/framepack/framepack_worker.py

@@ -54,7 +54,7 @@ def worker(
 
     from modules.framepack.pipeline import hunyuan
     from modules.framepack.pipeline import utils
-    from modules.framepack.pipeline.k_diffusion_hunyuan import sample_hunyuan
+    from modules.framepack.pipeline import k_diffusion_hunyuan
 
     is_f1 = variant == 'forward-only'
     total_generated_frames = 0
@@ -244,7 +244,7 @@ def worker(
                 transformer.initialize_teacache(enable_teacache=use_teacache, num_steps=steps, rel_l1_thresh=shared.opts.teacache_thresh)
 
                 t_sample = time.time()
-                generated_latents = sample_hunyuan(
+                generated_latents = k_diffusion_hunyuan.sample_hunyuan(
                     transformer=transformer,
                     sampler='unipc',
                     width=width,

modules/framepack/pipeline/uni_pc_fm.py

@@ -5,7 +5,7 @@
 
 
 import torch
-
+import numpy as np
 from tqdm.auto import trange
 
 
@@ -13,6 +13,36 @@ def expand_dims(v, dims):
     return v[(...,) + (None,) * (dims - 1)]
 
 
+torch_linalg_solve = None
+
+
+def test_solver():
+    from modules import devices, shared
+    try:
+        a = torch.randn(50, 50).to(device=devices.device, dtype=torch.float32)
+        b = torch.randn(50, 2).to(device=devices.device, dtype=torch.float32)
+        _x = torch.linalg.solve(a, b)
+        return True
+    except Exception as e:
+        shared.log.debug(f'FramePack: solver=cpu {e}')
+        return False
+
+
+def linalg_solve(A, B, device):
+    global torch_linalg_solve # pylint: disable=global-statement
+    if torch_linalg_solve is None:
+        torch_linalg_solve = test_solver()
+    if torch_linalg_solve:
+        X = torch.linalg.solve(A, B)
+        return X
+    else:
+        A_np = A.float().cpu().numpy()
+        B_np = B.float().cpu().numpy()
+        X_np = np.linalg.solve(A_np, B_np)
+        X = torch.from_numpy(X_np).to(device=device, dtype=A.dtype)
+        return X
+
+
 class FlowMatchUniPC:
     def __init__(self, model, extra_args, variant='bh1'):
         self.model = model
@@ -78,7 +108,7 @@ class FlowMatchUniPC:
             if order == 2:
                 rhos_p = torch.tensor([0.5], device=b.device)
             else:
-                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+                rhos_p = linalg_solve(R[:-1, :-1], b[:-1], x.device)
         else:
             D1s = None
             rhos_p = None
@@ -86,7 +116,7 @@ class FlowMatchUniPC:
         if order == 1:
             rhos_c = torch.tensor([0.5], device=b.device)
         else:
-            rhos_c = torch.linalg.solve(R, b)
+            rhos_c = linalg_solve(R, b, x.device)
 
         x_t_ = expand_dims(t / t_prev_0, dims) * x - expand_dims(h_phi_1, dims) * model_prev_0