mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'development' of github.com:invoke-ai/InvokeAI into development
This commit is contained in:
Lincoln Stein
commit
2d84e28d32
@ -640,9 +640,11 @@ class InvokeAIWebServer:
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if generation_parameters['progress_latents']:
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image = self.generate.sample_to_lowres_estimated_image(sample)
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(width, height) = image.size
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width *= 8
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height *= 8
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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img_base64 = "data:image/jpeg;base64," + base64.b64encode(buffered.getvalue()).decode('UTF-8')
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img_base64 = "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode('UTF-8')
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self.socketio.emit(
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"intermediateResult",
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{
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@ -60,6 +60,7 @@ We thank them for all of their time and hard work.
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- [Dmitry T.](https://github.com/ArDiouscuros)
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- [Kent Keirsey](https://github.com/hipsterusername)
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- [psychedelicious](https://github.com/psychedelicious)
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- [damian0815](https://github.com/damian0815)
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## **Original CompVis Authors:**
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@ -119,19 +119,19 @@ class Generator():
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# write an approximate RGB image from latent samples for a single step to PNG
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def sample_to_lowres_estimated_image(self,samples):
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# adapted from code by @erucipe and @keturn here:
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# origingally adapted from code by @erucipe and @keturn here:
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# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
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# these numbers were determined empirically by @keturn
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v1_4_latent_rgb_factors = torch.tensor([
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# these updated numbers for v1.5 are from @torridgristle
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v1_5_latent_rgb_factors = torch.tensor([
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# R G B
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[ 0.298, 0.207, 0.208], # L1
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[ 0.187, 0.286, 0.173], # L2
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[-0.158, 0.189, 0.264], # L3
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[-0.184, -0.271, -0.473], # L4
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[ 0.3444, 0.1385, 0.0670], # L1
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[ 0.1247, 0.4027, 0.1494], # L2
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[-0.3192, 0.2513, 0.2103], # L3
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[-0.1307, -0.1874, -0.7445] # L4
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], dtype=samples.dtype, device=samples.device)
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latent_image = samples[0].permute(1, 2, 0) @ v1_4_latent_rgb_factors
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latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
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latents_ubyte = (((latent_image + 1) / 2)
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.clamp(0, 1) # change scale from -1..1 to 0..1
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.mul(0xFF) # to 0..255
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@ -68,6 +68,8 @@ class CrossAttentionControl:
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indices[b0:b1] = indices_target[a0:a1]
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mask[b0:b1] = 1
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cls.inject_attention_function(model)
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for m in cls.get_attention_modules(model, cls.CrossAttentionType.SELF):
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m.last_attn_slice_mask = None
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m.last_attn_slice_indices = None
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@ -76,8 +78,6 @@ class CrossAttentionControl:
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m.last_attn_slice_mask = mask.to(device)
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m.last_attn_slice_indices = indices.to(device)
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cls.inject_attention_function(model)
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class CrossAttentionType(Enum):
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SELF = 1
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@ -151,72 +151,33 @@ class CrossAttentionControl:
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#else:
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# print(f"in wrangler, whole, use_last_attn_slice is {self.use_last_attn_slice}, save_last_attn_slice is {self.save_last_attn_slice}")
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if self.use_last_attn_slice:
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this_attn_slice = attn_slice
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if self.last_attn_slice_mask is not None:
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# indices and mask operate on dim=2, no need to slice
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base_attn_slice_full = torch.index_select(self.last_attn_slice, -1, self.last_attn_slice_indices)
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base_attn_slice_mask = self.last_attn_slice_mask
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if dim is None:
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base_attn_slice = base_attn_slice_full
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#print("using whole base slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
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elif dim == 0:
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base_attn_slice = base_attn_slice_full[start:end]
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#print("using base dim 0 slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
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elif dim == 1:
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base_attn_slice = base_attn_slice_full[:, start:end]
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#print("using base dim 1 slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
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attn_slice = this_attn_slice * (1 - base_attn_slice_mask) + \
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base_attn_slice * base_attn_slice_mask
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last_attn_slice = self.last_attn_slice
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# print("took whole slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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else:
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if dim is None:
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attn_slice = self.last_attn_slice
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#print("took whole slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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elif dim == 0:
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attn_slice = self.last_attn_slice[start:end]
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#print("took dim 0 slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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elif dim == 1:
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attn_slice = self.last_attn_slice[:, start:end]
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#print("took dim 1 slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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last_attn_slice = self.last_attn_slice[offset]
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if self.last_attn_slice_mask is None:
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# just use everything
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attn_slice = last_attn_slice
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else:
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last_attn_slice_mask = self.last_attn_slice_mask
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remapped_last_attn_slice = torch.index_select(last_attn_slice, -1, self.last_attn_slice_indices)
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this_attn_slice = attn_slice
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this_attn_slice_mask = 1 - last_attn_slice_mask
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attn_slice = this_attn_slice * this_attn_slice_mask + \
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remapped_last_attn_slice * last_attn_slice_mask
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if self.save_last_attn_slice:
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if dim is None:
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self.last_attn_slice = attn_slice
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elif dim == 0:
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# dynamically grow last_attn_slice if needed
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if self.last_attn_slice is None:
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self.last_attn_slice = attn_slice
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#print("no last_attn_slice: shape now", self.last_attn_slice.shape)
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elif self.last_attn_slice.shape[0] == start:
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self.last_attn_slice = torch.cat([self.last_attn_slice, attn_slice], dim=0)
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assert(self.last_attn_slice.shape[0] == end)
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#print("last_attn_slice too small, appended dim 0 shape", attn_slice.shape, ", shape now", self.last_attn_slice.shape)
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else:
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# no need to grow
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self.last_attn_slice[start:end] = attn_slice
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#print("last_attn_slice shape is fine, setting dim 0 shape", attn_slice.shape, ", shape now", self.last_attn_slice.shape)
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elif dim == 1:
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# dynamically grow last_attn_slice if needed
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if self.last_attn_slice is None:
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self.last_attn_slice = attn_slice
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elif self.last_attn_slice.shape[1] == start:
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self.last_attn_slice = torch.cat([self.last_attn_slice, attn_slice], dim=1)
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assert(self.last_attn_slice.shape[1] == end)
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self.last_attn_slice = { offset: attn_slice }
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else:
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# no need to grow
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self.last_attn_slice[:, start:end] = attn_slice
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if self.use_last_attn_weights and self.last_attn_slice_weights is not None:
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if dim is None:
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weights = self.last_attn_slice_weights
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elif dim == 0:
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weights = self.last_attn_slice_weights[start:end]
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elif dim == 1:
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weights = self.last_attn_slice_weights[:, start:end]
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attn_slice = attn_slice * weights
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self.last_attn_slice[offset] = attn_slice
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return attn_slice
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