Merge branch 'main' into patch-1

environments-and-requirements/requirements-lin-amd.txt

@@ -1,6 +1,6 @@
 -r environments-and-requirements/requirements-base.txt
 # Get hardware-appropriate torch/torchvision
---extra-index-url https://download.pytorch.org/whl/rocm5.1.1 --trusted-host https://download.pytorch.org
+--extra-index-url https://download.pytorch.org/whl/rocm5.2 --trusted-host https://download.pytorch.org
 torch>=1.13.1
 torchvision>=0.14.1
 -e .

environments-and-requirements/requirements-lin-cuda.txt

@@ -1,4 +1,4 @@
---extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
+--trusted-host https://download.pytorch.org
 -r environments-and-requirements/requirements-base.txt
 torch>=1.13.1
 torchvision>=0.14.1

environments-and-requirements/requirements-win-colab-cuda.txt

@@ -1,6 +1,6 @@
 -r environments-and-requirements/requirements-base.txt
 # Get hardware-appropriate torch/torchvision
---extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
+--extra-index-url https://download.pytorch.org/whl/cu117 --trusted-host https://download.pytorch.org
 torch==1.13.1
 torchvision==0.14.1
 -e .

ldm/invoke/generator/diffusers_pipeline.py

@@ -391,7 +391,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, conditioning_data,
-                                    i, additional_guidance=additional_guidance)
+                                    step_index=i,
+                                    total_step_count=len(timesteps),
+                                    additional_guidance=additional_guidance)
             latents = step_output.prev_sample
             predicted_original = getattr(step_output, 'pred_original_sample', None)
 
@@ -410,7 +412,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor,
              conditioning_data: ConditioningData,
-             step_index:int | None = None, additional_guidance: List[Callable] = None):
+             step_index:int, total_step_count:int,
+             additional_guidance: List[Callable] = None):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
 
@@ -427,6 +430,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
             conditioning_data.guidance_scale,
             step_index=step_index,
+            total_step_count=total_step_count,
             threshold=conditioning_data.threshold
         )
 

ldm/models/diffusion/shared_invokeai_diffusion.py

@@ -89,6 +89,7 @@ class InvokeAIDiffuserComponent:
                                 conditioning: Union[torch.Tensor,dict],
                                 unconditional_guidance_scale: float,
                                 step_index: Optional[int]=None,
+                                total_step_count: Optional[int]=None,
                                 threshold: Optional[ThresholdSettings]=None,
                           ):
         """
@@ -106,6 +107,14 @@ class InvokeAIDiffuserComponent:
         cross_attention_control_types_to_do = []
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
+            if step_index is not None and total_step_count is not None:
+                # 🧨diffusers codepath
+                percent_through = step_index / total_step_count  # will never reach 1.0 - this is deliberate
+            else:
+                # legacy compvis codepath
+                # TODO remove when compvis codepath support is dropped
+                if step_index is None and sigma is None:
+                    raise ValueError(f"Either step_index or sigma is required when doing cross attention control, but both are None.")
                 percent_through = self.estimate_percent_through(step_index, sigma)
             cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)