v0.6.2 (bugfixes)
- Fix certain resolutions not working with xFormers - Fix specifying ToMe seed - Fix UI spacing (broken by SD UI v1.6.0) - Fix deprecation warningspull/38/head
dvruette
parent
9562bcb30c
commit
43aa3ce962
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@ -27,7 +27,7 @@ except ImportError:
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from modules.ui import create_refresh_button
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__version__ = "0.6.1"
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__version__ = "0.6.2"
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DEBUG = os.getenv("DEBUG", "false").lower() in ("true", "1")
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@ -179,15 +179,15 @@ class FabricScript(modules.scripts.Script):
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with gr.Tabs():
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with gr.Tab("Current batch"):
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# TODO: figure out why the display is shared between tabs
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self.img2img_selected_display = gr.Image(value=None, type="pil", label="Selected image", visible=is_img2img).style(height=256)
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self.txt2img_selected_display = gr.Image(value=None, type="pil", label="Selected image", visible=not is_img2img).style(height=256)
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self.img2img_selected_display = gr.Image(value=None, type="pil", label="Selected image", visible=is_img2img, height=256)
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self.txt2img_selected_display = gr.Image(value=None, type="pil", label="Selected image", visible=not is_img2img, height=256)
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with gr.Row():
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like_btn_selected = gr.Button("👍 Like")
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dislike_btn_selected = gr.Button("👎 Dislike")
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with gr.Tab("Upload image"):
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upload_img_input = gr.Image(type="pil", label="Upload image").style(height=256)
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upload_img_input = gr.Image(type="pil", label="Upload image", height=256)
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with gr.Row():
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like_btn_uploaded = gr.Button("👍 Like")
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@ -198,26 +198,25 @@ class FabricScript(modules.scripts.Script):
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with gr.Row():
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remove_selected_like_btn = gr.Button("Remove selected", interactive=False)
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clear_liked_btn = gr.Button("Clear")
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like_gallery = gr.Gallery(label="Liked images", elem_id="fabric_like_gallery").style(columns=4, height=128)
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like_gallery = gr.Gallery(label="Liked images", elem_id="fabric_like_gallery", columns=4, height=192)
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with gr.Tab("👎 Dislikes"):
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with gr.Row():
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remove_selected_dislike_btn = gr.Button("Remove selected", interactive=False)
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clear_disliked_btn = gr.Button("Clear")
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dislike_gallery = gr.Gallery(label="Disliked images", elem_id="fabric_dislike_gallery").style(columns=4, height=128)
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dislike_gallery = gr.Gallery(label="Disliked images", elem_id="fabric_dislike_gallery", columns=4, height=192)
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save_preset_btn = gr.Button("Save as preset")
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gr.HTML("<hr style='border-color: var(--block-border-color)'>")
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gr.HTML("<hr style='border-color: var(--block-border-color); margin-top: 1rem; margin-bottom: 1rem;'>")
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with gr.Column():
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with FormGroup():
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with FormRow():
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feedback_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.0, label="Feedback start")
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feedback_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.8, label="Feedback end")
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with FormRow():
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feedback_max_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.8, label="Feedback Strength", elem_id="fabric_max_weight")
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with FormRow():
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tome_enabled = gr.Checkbox(label="Enable Token Merging (faster, less VRAM, less accurate)", value=False)
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with gr.Accordion("Advanced options", open=DEBUG):
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@ -225,10 +224,9 @@ class FabricScript(modules.scripts.Script):
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feedback_min_weight = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.0, label="Min. strength", info="Minimum feedback strength at every diffusion step.", elem_id="fabric_min_weight")
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feedback_neg_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.5, label="Negative weight", info="Strength of negative feedback relative to positive feedback.", elem_id="fabric_neg_scale")
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with FormGroup():
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tome_ratio = gr.Slider(minimum=0.0, maximum=0.75, step=0.125, value=0.5, label="ToMe merge ratio", info="Percentage of tokens to be merged (higher improves speed)", elem_id="fabric_tome_ratio")
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tome_max_tokens = gr.Slider(minimum=4096, maximum=16*4096, step=4096, value=2*4096, label="ToMe max. tokens", info="Maximum number of tokens after merging (lower improves VRAM usage)", elem_id="fabric_tome_max_tokens")
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tome_seed = gr.Number(label="ToMe seed", value=-1, info="Random seed for ToMe partition", elem_id="fabric_tome_seed")
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tome_seed = gr.Number(label="ToMe seed", value=-1, step=1, info="Random seed for ToMe partition", elem_id="fabric_tome_seed")
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@ -455,7 +453,7 @@ class FabricScript(modules.scripts.Script):
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tome_enabled=tome_enabled,
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tome_ratio=(round(tome_ratio * 16) / 16),
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tome_max_tokens=tome_max_tokens,
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tome_seed=get_fixed_seed(tome_seed),
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tome_seed=get_fixed_seed(int(tome_seed)),
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)
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@ -468,6 +466,11 @@ class FabricScript(modules.scripts.Script):
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log_params["neg_images"] = json.dumps(disliked_paths)
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del log_params["enabled"]
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if not params.tome_enabled:
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del log_params["tome_ratio"]
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del log_params["tome_max_tokens"]
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del log_params["tome_seed"]
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log_params = {f"fabric_{k}": v for k, v in log_params.items()}
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p.extra_generation_params.update(log_params)
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@ -202,10 +202,11 @@ def patch_unet_forward_pass(p, unet, params):
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merge, _ = compute_merge(feedback_ctx, args=tome_args, size=(tome_h_latent * num_fb, w_latent), max_tokens=params.tome_max_tokens)
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feedback_ctx = merge(feedback_ctx)
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ctx = torch.cat([context, feedback_ctx], dim=1) # (n_cond, seq + seq*n_pos, dim)
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weights = torch.ones(ctx.shape[1], device=ctx.device, dtype=ctx.dtype) # (seq + seq*n_pos,)
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weights[_x.shape[1]:] = w
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else:
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ctx = context
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weights = torch.ones_like(ctx[0, :, 0]) # (seq + seq*n_pos,)
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weights[_x.shape[1]:] = w
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weights = None
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return weighted_attention(attn1, attn1._fabric_old_forward, _x, ctx, weights, **kwargs) # (n_cond, seq, dim)
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outs = []
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@ -63,12 +63,20 @@ def weighted_attention(self, attn_fn, x, context=None, weights=None, **kwargs):
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def _get_attn_bias(weights, shape=None, dtype=torch.float32):
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# shape of weights needs to be divisible by 8 in order for xformers attn bias to work
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last_dim = ((weights.shape[-1] - 1) // 8 + 1) * 8
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w_bias = torch.zeros(weights.shape[:-1] + (last_dim,), device=weights.device, dtype=weights.dtype)
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min_val = torch.finfo(dtype).min
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w_bias = weights.log().clamp(min=min_val)
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w_bias[..., :weights.shape[-1]] = weights.log().clamp(min=min_val)
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if shape is not None:
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assert shape[-1] == w_bias.shape[-1], "Last dimension of shape must match last dimension of weights (number of keys)"
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w_bias = w_bias.view([1] * (len(shape) - 1) + [-1]).expand(shape)
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assert shape[-1] == weights.shape[-1], "Last dimension of shape must match last dimension of weights (number of keys)"
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w_bias = w_bias.view([1] * (len(shape) - 1) + [-1]).expand(shape[:-1] + (last_dim,))
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# cast first in order to preserve multiple-of-8 stride
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w_bias = w_bias.to(dtype=dtype)
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w_bias = w_bias[..., :weights.shape[-1]]
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return w_bias
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### The following attn functions are copied and adapted from modules.sd_hijack_optimizations
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